Topic:"Demand Side Management" in M12932

Summary of Year Four Evaluation Results The Year 4 Report is the first EM&V conducted internally by NS Power. Overall, the findings indicate that the TVP Pilot continues to achieve peak load reductions in response to time‑varying price sig...

AI summary The Year 4 Evaluation of the Time-Varying Pricing (TVP) Pilot shows continued peak load reductions, especially in residential and commercial sectors. Participation has grown, and the report highlights statistically significant load shifting for commercial tariffs. NS Power faced delays due to a cyber incident and has requested further extensions for filing the report. The evaluation was conducted internally with advisory support from Econoler and includes stakeholder input and appendices with detailed findings.

Conclusion The Year Four Report ( Appendix A ) provides evaluated results for the 2024/25 Season, including sustained residential load shifting, statistically significant commercial load shifting, and initial results for the MURB TOU Tarif...

AI summary The Year Four Report evaluates the 2024/25 Season, highlighting residential and commercial load shifting results and initial MURB TOU Tariff outcomes. Econoler's review supports NS Power's methodology. The report includes survey data, marketing activities, and stakeholder session materials. NS Power plans to resume TVP Pilot rates for the 2026/27 Season, per the Board-approved framework.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to Review of accessibility as applicable to vulnerable customers (i.e. low-income) Examine the results of the Year 3 report as it rela...

AI summary The text outlines several deliverables and commitments related to accessibility for vulnerable customers, metering and billing system capabilities, and the evaluation of demand charge impacts on participation in time-varying pricing (TVP) programs. NS Power is tasked with addressing gaps in income analysis, assessing impacts on low-income customers, and evaluating the demand charge as a barrier to participation in TVP for General Service customers.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to Evaluate the level of costs that are truly driven by an individual's non-coincident demands Synapse recommends that NS Power evalua...

AI summary The document outlines several commitments and recommendations related to demand charges, time-varying pricing (TVP), and tariff structures. Key points include evaluating non-coincident demand costs, modeling demand charge changes for commercial customers, assessing TVP's alignment with avoided costs, and proposing a weekend-inclusive TOU tariff for stakeholder review.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to Demand-related costs v. demand-related revenues, by class Provided in response to Synapse's comments on Technical Session 2 Technic...

AI summary The document outlines deliverables related to demand-related costs and revenues by class, segmentation model data, and standard load shapes for segments. These responses were provided in reaction to comments from Synapse, the Consumer Advocate, and the SBA during Technical Session 2 and are referenced in appendices.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to File YR4 Report Report on the Year Four findings no later than 31 Jul 2025 File the annual Evaluation, Measurement and Verification...

AI summary The document outlines deliverables for the Year 4 report, including the submission of findings and an EM&V report by July 31, 2025. It also includes commitments by NS Power to analyze system margin forecasts and CPP events and to report on stakeholder engagement related to TVP options for small businesses.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to Report on the review of marketing and communication efforts and its planned changes in the Year 4 Report Synapse recommended NS Pow...

AI summary The document outlines two key deliverables for NS Power's Year 4 Report: a review of marketing and communication efforts and an analysis of commercial class customer characteristics in the TVP pilot. The Board emphasizes the need for NS Power to communicate effectively with customers about bill savings and improve enrollment in peak load reduction programs.

Summary of Deliverable / Commitment Detail & Context Source 1 Initial Due by / Completed on Status Refer to Continue to file annual Evaluation Reports Maintain the established TVP Report and EM&V processes File an annual EM&V repor...

AI summary The document outlines commitments related to the continuation of annual Evaluation Reports and the planning of a MURB Industry Group Session. These efforts aim to support the adoption of TVP rates by MURB customers through incentives and rebates, with a focus on maintaining established processes and engaging stakeholders.

Abbreviations AMI Advanced Metering Infrastructure ANL Adjusted Net Load CPP Critical Peak Pricing DD Difference-in-difference DDD Triple difference-in-differences (Eco Shift) DOM Domestic Rate Class DR Demand Response EM&V Evaluation Meas...

AI summary The text provides a list of abbreviations and their full forms used in the regulatory proceeding. It includes terms related to energy, metering, pricing, and evaluation methodologies.

Definitions The hourly net system requirement (MW) less all wind generation (MW) Cohorts are numbered based on the Phase they enrolled in the TVP program. For example, Cohort 4 refers to participants recruited in Phase 4 (i.e. the fourth W...

AI summary The text provides definitions related to a TVP program, rate classes, and energy consumption metrics. It includes details about cohorts, customer classifications, and income brackets. These definitions are used for methodology and results analysis.

Since the first report, modifications to the evaluation metrics and the rates themselves have been made with the support of expert consultants and stakeholders. These modifications have refined the evaluation to provide accurate load and b...

AI summary The TVP Pilot has evolved with modifications to evaluation metrics and rates, leading to more accurate load and billing effects. Residential and commercial participants have seen increased enrollment, with statistically significant load reductions observed. The MURB TOU Tariff evaluation shows small but significant load shifts during evening peaks, while no demand charge changes were noted despite its removal.

Residential Findings In total, 5,616 TOU and 2,642 CPP residential customers participated in the TVP program in Phase 4. Of these, 4,938 TOU and 2,310 CPP customers were included in the regression when counting only those who meet the crit...

AI summary In Phase 4 of the TVP program, 5,616 TOU and 2,642 CPP residential customers participated, with 4,938 TOU and 2,310 CPP customers included in the regression analysis. Participants achieved significant electricity load reductions during peak periods and events, consistent with Phase 3 findings.

nergy consumption (27.7 ± 37 kWh per year) while still saving on their annual electricity bill ($153 per year) in addition to any fuel savings from non-electric heating sources they may have reduced. Residential CPP participants reduced lo...

AI summary Residential CPP participants reduced load during peak periods and achieved energy savings, with those using electric heating showing the highest load reduction. Electrified participants had significant load reduction during peak hours but no significant change in electricity bills, though they may have saved on other fuels. Load reduction was lower for those with non-electric heating.

Commercial Findings The commercial TVP Tariffs attracted more businesses, with Phase 4 enrolment (108 businesses) higher compared to Phase 3 (65 businesses). While "small sample size and the wide variety of energy consumption profiles prev...

AI summary The commercial TVP Tariffs attracted more businesses, with Phase 4 enrolment higher than Phase 3. Improved regression analysis in Phase 4 enabled a more robust assessment, showing significant electricity load reductions during peak periods for TOU and events for CPP.

Introduction Time-Varying Pricing (TVP) is a scalable customer-focused demand response (DR) program. In Nova Scotia, the TVP pilot has grown significantly since it began in November 2021. Since its inception, the TVP pilot has offered two...

AI summary Time-Varying Pricing (TVP) is a demand response program in Nova Scotia that offers customers rate choices to shift electricity usage from peak periods. The TVP pilot, launched in 2021, includes multiple tariffs and rate classes. Phase 4 introduced a new multi-unit residential building (MURB) TOU Pilot Tariff, which is being evaluated as part of the TVP Phase 4 EM&V report.

Table 4: Impact Evaluation Metric Summary Category Metric TOU, CPP, MURB TOU (all rate classes unless specified) • Change in load (kW) during peak, and overall impact during Winter and non-Winter. Mid-peak hours included for MURB TOU. • Ch...

AI summary Table 4 outlines impact evaluation metrics for time-varying pricing (TVP) programs, including changes in load during peak periods, economic impacts on electricity bills, and price elasticity. Metrics are categorized by load and usage impact, and economic impact, with specific considerations for different rate classes and regions.

2 Control Group Selection The control group selection methodology uses 1-hour average and 15-minute peak load data to determine the best 1:1 control match by four neighbourhoods. If treatment customers are distributed throughout the provin...

AI summary The document discusses the methodology for selecting control groups in a regulatory proceeding, using geographic distribution data to ensure that treatment and control customers are similarly distributed across Nova Scotia. This helps in making fair comparisons between programs like Time-Varying Pricing (TVP) and Commercial and Industrial Program (CPP).

2.1.2 E1 Program Balancing As discussed in methodology, E1 program participation is accounted for in control group selection, refer to Attachment III: Methodology. The results indicate that E1 program participation across all sub-groups (e...

AI summary The E1 Program Balancing section discusses how program participation is accounted for in control group selection, citing Attachment III: Methodology. The results show that participation across sub-groups has been balanced with an overall accuracy of 99.1%.

2.2 Commercial TOU & CPP The same methodological approach as used with residential control group (with exception to Neighbourhood Criteria, Heating Classification, and Eco Shift) was used in attempting to select a control group for Small G...

AI summary The analysis of control group selection for Commercial Time-of-Use (TOU) and Commercial and Industrial Program (CPP) rates shows poor matching accuracy for Small General and General TOU/CPP, making them unsuitable. However, MURB TOU shows good match accuracy, allowing for a control group. The methodology used was similar to the residential control group, with some exceptions.

3.1.1 Change in Load during Peak Periods [Table 10](#page-49-0) summarizes the results of the analysis for morning and evening TOU peak hours for each cohort and for all cohorts combined. The average load reductions are presented along wit...

AI summary The text discusses load reductions during peak periods for residential Time-Varying Pricing (TOU) participants. It highlights statistically significant load reductions in both morning and evening peak hours, with higher reductions during evenings, particularly in colder months like January and February. The analysis includes average load reductions, margin of error, and relative reductions for different cohorts.

Change in Load by Space Heating Classes during TOU Peak Hours [Table 11](#page-52-0) summarizes the average load reductions during morning and evening TOU peak hours for space heating classes of all cohorts combined in Phase 4. The load re...

AI summary Table 11 shows average load reductions during TOU peak hours for different space heating classes in Phase 4. Participants with steady electric systems had the highest load reductions, while those with non-electric systems had the lowest. Electrification of heating systems also resulted in notable load reductions.

Change in Load with Eco Shift E1's Eco Shift is a DR initiative offered separately from TVP and was implemented within subset of the TVP treatment participants who opted into both programs. It is designed to encourage load reduction during...

AI summary The analysis evaluates the impact of Eco Shift, a demand response (DR) initiative, on load reduction in conjunction with Time-of-Use (TOU) pricing. Results show that residential TOU participants enrolled in Eco Shift achieved significantly higher load reductions, particularly during Eco Shift DR events, compared to those without Eco Shift participation.

3.1.3 Change in Load during Highest ANL Hours [Figure 20](#page-57-0) depicts the distribution of Top 20, 50, and 88 highest ANL hours for residential TOU participants during morning peak, evening peak, and off-peak hours as well as weeken...

AI summary The document analyzes load changes during the highest Adjusted Net Load (ANL) hours for residential Time-of-Use (TOU) participants. Results show statistically significant load reductions, with a 6% relative reduction during TOU peak periods, particularly during morning and evening peak hours on weekdays.

3.2.2 Price Elasticity Residential TOU participants' responsivity to electricity prices ($/kWh) was evaluated by estimating daily price elasticity during non-holiday weekdays in Winter season, as well as the Inter-period Substitution Price...

AI summary The analysis of residential TOU participants' price elasticity during the winter season shows a daily price elasticity of -1.05 and an inter-period substitution elasticity of -0.14. These values indicate that increases in electricity prices lead to decreased electricity usage and a shift of load from peak to off-peak hours.

4 Residential CPP Tariff Impacts This section presents the analysis results for load and economic impacts of the Domestic CPP Tariff for all cohorts in Phase 4. The goal of the residential CPP pilot is to encourage customers to shift their...

AI summary This section analyzes the load and economic impacts of the Domestic CPP Tariff during Phase 4, which ran from April 1, 2024, to March 31, 2025. The goal is to encourage customers to shift electricity usage during CPP events to non-event periods, reducing system utilization and providing demand response capacity.

4.1 Load & Usage Impacts This section presents and discusses the electrical load impact from the Domestic CPP Tariff.

AI summary This section presents and discusses the electrical load impact from the Domestic CPP Tariff, focusing on how it affects load and usage patterns within the Nova Scotia regulatory proceeding.

4.1.1 Change in Load during Peak Periods [Table 22](#page-66-0) summarizes the results of the analysis for morning and evening CPP events for each cohort and all participants combined. The average load reduction metrics are presented along...

AI summary The analysis of load reduction during morning and evening CPP events shows statistically significant results across all cohorts, with cohort 4 having lower margin of error due to a higher number of participants. The overall average load reduction for residential CPP participants was 0.69 kW.

Morning Events Evening Events Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Load Reductiona (kW) 0.68 ± 0.10 1.22 ± 0.16 0.62 ± 0.06 0.60 ± 0.03 0.61± 0.03 0.85 ± 0.10 1.60 ± 0.60 0.88 ± 0....

AI summary Table 22 presents data on load reduction during Critical Peak Pricing (CPP) events for residential participants across different cohorts. The table includes average load reduction in kW, percentage reduction relative to pre-pilot average load, and significance levels, indicating statistically significant results across all cohorts.

Change in Load by Space Heating Classes During Peak Events [Table 23](#page-67-0) summarizes the average load reductions during morning and evening events for space heating classes of all cohorts combined in Phase 4. It is important to not...

AI summary Table 23 summarizes average load reductions during morning and evening peak events for space heating classes in Phase 4. The analysis compares event and reference days during the 2024/25 Winter, noting that changes in space heating type between baseline and pilot periods do not directly affect load reduction results in the DiD analysis.

Morning Evening Parameters De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic Avg. Load Reductiona 0.57 ± 0.66 ± 0.74 ±...

AI summary The table presents load reduction data for different parameters and time periods, showing average load reductions and their significance. It indicates that residential CPP participants experienced varying degrees of load reduction during morning and evening hours, with statistical significance across all categories.

Change in Load per Critical Peak Event [Figure 23](#page-68-0)a illustrates the average load reduction (kW) per CPP event date for all Phase 4 cohorts, along the corresponding event-average outdoor temperature (°C). The residential CPP par...

AI summary The document discusses load reduction data from Critical Peak Pricing (CPP) participants in Nova Scotia, showing significant reductions during both morning and evening events, with higher reductions during evenings and in the Halifax Area. Higher-income participants demonstrated greater load reduction percentages compared to lower-income participants.

Low-Income Medium-Income High-Income Parameters AM PM Overall AM PM Overall AM PM Overall Event Event Event Event Event Event Avg. Load 0.43 0.61 0.52 0.55 0.69 0.62 0.78 1.01 0.88 Reductiona (kW) ± 0.06 ± 0.06 ± 0.04 ± 0.04 ± 0.04 ± 0.03...

AI summary The table presents data on load reduction during critical peak events for residential participants in the Commercial and Industrial Program (CPP), categorized by income level. It shows average load, reduction, and relative load reduction percentages across different income groups, with all values statistically significant.

4.1.2 Snapback Effect The CPP participants exhibit relatively small but statistically significant snapback effects during morning, evening and double-event snapback periods, indicating significant increase in electricity usage during the f...

AI summary The CPP participants show a statistically significant snapback effect, with a 0.15 kW increase in electricity usage during the four hours following peak events, particularly during double-snapback periods, which is higher than during morning or evening snapbacks.

Table 25: Snapback Effect for Residential CPP Participants Parameters Average Load Reduction a (kW) Significance (p_Value≤0.05) Morning Snapback -0.04 ± 0.02 ✓ Evening Snapback -0.08 ± 0.03 ✓ Double-Event Snapbackb -0.15 ± 0.05 ✓ Overall S...

AI summary Table 25 presents the snapback effect for residential Critical Peak Pricing (CPP) participants, showing average load reductions during morning, evening, and double-event snapback periods, all statistically significant. The overall snapback effect indicates a reduction in electricity usage of -0.06 kW.

4.1.3 Change in Load during Highest ANL Hours [Figure 29](#page-73-0) illustrates the distribution of Top 20, 50 and 88 highest ANL hours for residential CPP participants during weekdays and weekends as well as across CPP events. The major...

AI summary The text discusses the distribution of highest ANL (Adjusted Net Load) hours for residential CPP (Critical Peak Pricing) participants during weekdays and weekends, showing that most high load hours occur on weekdays. Load reductions during these periods are statistically significant, with slightly higher reductions during CPP peak periods.

Parameters Highest ANL Hours ANL Hours Coincide with CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load Reductiona (kW) 0.75 ± 0.63 ± 0.55 ± 0.85 ± 0.84 ± 0.80 ± 0.07 0.06 0.04 0.07 0.06 0.05 Avg. Load – CPP Participants, Pre-P...

AI summary Table 26 presents data on the change in load during the highest winter ANL hours for residential CPP participants, showing average load reductions and their significance. The table indicates that load reductions are statistically significant across all categories.

The change in daily usage levels was evaluated through two analyses. For the first analysis, the daily usage reduction during event days was estimated, when at least one CPP event occurred, compared to reference days. For the second analys...

AI summary The document evaluates the impact of Critical Peak Pricing (CPP) on daily electricity usage through two analyses. The first analysis found a statistically significant overall reduction of 1.7 kWh in daily usage during CPP events, though not all cohorts showed significant results. The second analysis compared usage during winter and non-winter days during the treatment period to the pre-pilot period, with no significant differences observed by heating class.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekdays Avg. Usage Reduction a (kWh/day) 3.0 ± 1.4 3.1 ± 2.0 1.0 ± 0.9 1.1 ± 0.5 1.2 ± 0.4 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 50.7 60.5 44....

AI summary The table presents data on average usage reduction across different cohorts during winter weekdays, weekends/holidays, and non-winter days. It shows varying levels of reduction, with significance markers indicating statistical significance for most comparisons, except for some non-winter days. The data relates to residential Critical Peak Pricing (CPP) participants before the pilot program.

Parameters De- Electrified Electrified Steady Electric Steady Non- Electric Winter, Non-Holiday Weekdays Avg. Usage Reduction (kWh/day) a 1.31 ± 1.08 -3.84 ± 0.81 $2.02 \pm 0.45$ $0.15 \pm 0.33$ Avg. Usage – Residential CPP 42.0 29.6 55.6...

AI summary The table presents data on average usage reduction and significance levels for different electrification scenarios across various time periods. It shows mixed results, with some categories showing significant reductions and others not, indicating varying impacts of electrification on energy usage.

4.1.5 Effect of Weather (Temperature) on Load Reduction The DiD analysis reveals that the average load reduction is increased by $0.019 \, \text{kW}$ ( $\pm 0.003 \, \text{kW}$ ) per 1 °C decrease in outdoor temperature, implying that CPP...

AI summary The DiD analysis shows that CPP customers increase load reduction by 0.019 kW per 1 °C decrease in outdoor temperature, indicating a stronger response to colder weather. Figure 30 visually represents this correlation, showing an upward trend in load reduction as temperatures drop.

4.1.6 Effect of Consecutive CPP Events on Load Reduction During Phase 4, like Phase 3, there was only one instance of two consecutive CPP events, which occurred when events 5 and 6 took place on December 23, 2024. The second of these conse...

AI summary During Phase 4, a single instance of two consecutive Commercial and Industrial Program (CPP) events occurred on December 23, 2024. The second event resulted in a slightly higher, but not statistically significant, load reduction compared to the first. However, due to the limited data, no broader conclusions can be drawn about the impact of consecutive CPP events.

4.2.2 Price Elasticity Price elasticity was evaluated to measure residential CPP participants' responsivity to electricity prices during Phase 4. Specifically, NS Power evaluated Daily Price Elasticity during Winter season and annual overa...

AI summary The analysis evaluates residential CPP participants' price elasticity during winter, revealing a Daily Price Elasticity of -0.2 and an Inter-period Substitution Price Elasticity of -0.19, both statistically significant, indicating reduced electricity usage in response to price changes.

5 Commercial TOU Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial TOU program. The goal of the commercial TOU pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial TOU program, aiming to encourage load shifting during peak periods. The pilot uses price differentials based on system savings and infrastructure cost avoidance. Phase 4 has 50 participants, with most enrolled in earlier cohorts.

6 Commercial CPP Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial CPP program. The goal of the commercial CPP pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial CPP program, which aims to encourage load shifting during peak events to reduce energy costs and infrastructure expenses. The analysis is based on a Mixed-Effects modeling semi-DiD approach and covers CPP events from November 01, 2024, to March 31, 2025.

6.1.1 Change in Load during Peak Events [Table 43](#page-91-3) summarizes the results of the analysis for morning and evening CPP events for all cohorts of commercial CPP participants in Phase 4, where the average load reduction values are...

AI summary The analysis in Table 43 shows that commercial CPP participants achieved a 10.4% average load reduction during morning and evening peak events in Phase 4, with results presented at a 90% confidence level.

Change in Load during Peak Events by Region [Table 44](#page-94-0) presents the average load reduction (kW) by region (Halifax area versus the rest of Nova Scotia) for commercial CPP participants during morning, evening, and all CPP events...

AI summary Table 44 shows that commercial CPP participants in the Halifax area experience significant load reductions during peak events, while participants in the rest of Nova Scotia show no significant load changes. The average load reduction in the Halifax area is 8.6 kW, or 10.7% relative reduction, whereas the rest of Nova Scotia shows an average load increase of 0.7 kW.

Halifax Area Rest of Nova Scotia Parameters Morning Events Evening Events All Events Morning Events Evening Events All Events Avg. Load Reduction (kW)a 10.6 ± 3.8 7.0 ± 3.4 8.6 ± 3.0 -0.1 ± 0.6 -1.0 ± 1.0 -0.7 ± 0.7 Avg. Load – Commercial...

AI summary The table compares average load reduction during morning and evening events in the Halifax Area and Rest of Nova Scotia, showing significant reductions in the Halifax Area but minimal or negative changes in the Rest of Nova Scotia. The data includes statistical significance indicators and notes on load reduction percentages.

6.1.2 Snapback Effect The snapback effect refers to commercial CPP customers that use more electricity than the baseline during the hours immediately following CPP event hours, compared to the pre-pilot period. This effect was evaluated ba...

AI summary The snapback effect refers to increased electricity usage by commercial CPP customers immediately after CPP event hours. The analysis found statistically insignificant morning and evening snapback effects, but a significant 6.9 kW load increase during event days with both morning and evening CPP events.

Table 45: Snapback Effect for Commercial CPP Participants Parameters Load Reduction a (kW) Avg. Significance (p_Value≤0.05) Morning Snapback 0.6 ± 0.9 X Evening Snapback -0.1 ± 1.3 X b Morning & Evening Snapback -6.9 ± 3.4 ✓ Overall Snapba...

AI summary Table 45 presents the snapback effect for commercial CPP participants, showing load reduction and increase values with their significance levels. The data indicates mixed results, with some periods showing significant load reduction and others not.

6.1.3 Change in Load during Highest ANL Hours [Figure](#page-97-0) 39 depicts the distribution of Top 20, 50 and 88 highest ANL hours for commercial CPP participants during weekdays and weekends as well as across CPP events. As seen in [Fi...

AI summary The text discusses the distribution of the highest ANL hours for commercial CPP participants during weekdays and weekends, highlighting that the majority of these hours occur on weekdays. It also notes significant load reductions during these peak periods, with slightly higher reductions during ANL hours that coincide with CPP peak periods.

Parameters Highest ANL Hours ANL Hours Coincide CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load Reduction (kW)a 6.8 ± 3.3 6.6 ± 2.8 5.0 ± 2.1 7.4 ± 3.4 8.2 ± 3.1 7.4 ± 2.6 Avg. Load Participants, Pre Pilot (kW) 64.4 61.4 61....

AI summary The table presents data on load reduction and participation in a pilot program, showing average load reductions and percentages for different tiers of ANL hours. The data indicates statistically significant results across all categories, with load reduction percentages ranging from 8.1% to 13.3%.

6.1.4 Change in Usage Changes in electricity usage were estimated by applying the same Mixed-Effect modeling approach that was used for estimating hourly load impact, where the change in daily usage levels was evaluated through two analyse...

AI summary The analysis estimates electricity usage changes using a Mixed-Effect modeling approach, revealing that commercial CPP participants reduced their daily electricity usage by an average of 65 kWh/day during event days compared to reference days, with significant reductions observed during winter and non-winter periods.

Parameters Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non Winter Annual Average Avg. Usage Reduction (kWh/day)a 54.0 ± 10.9 51.2 ± 11.6 62.3 ± 16.6 -21.4 ± 3.7 -12.2 ± 6.7 Avg. Usage – Commercial CPP Participant...

AI summary Table 47 presents data on the average usage reduction for commercial CPP participants across different time periods, showing reductions during winter and non-winter periods. The data highlights the impact of weather on load reduction, with significant statistical significance across all categories.

6.2.2 Price Elasticity NS Power evaluated price elasticity to measure commercial CPP participants' responsivity to electricity prices during Phase 4 of the pilot. Specifically, NS Power evaluated Daily Price Elasticity during Winter season...

AI summary NS Power evaluated price elasticity for commercial Critical Peak Pricing (CPP) participants during Phase 4 of a pilot. The analysis found a Daily Price Elasticity of -0.38 during winter and -0.48 annually, both statistically significant. The Inter-period Substitution Price Elasticity was -0.14, but not statistically significant.

7.1.1 Change in Load during Peak Periods [Figure](#page-103-0) 41a illustrates the load profiles for MURB TOU participants and the control group during the pre-pilot and pilot periods with the corresponding variation in outdoor temperature...

AI summary The document discusses the impact of MURB TOU (Time-of-Use) on load reduction during peak periods. Load profiles show statistically significant reductions in electrical load, particularly during morning and evening peaks, with an overall 1.9% reduction in electrical load as revealed by the DiD analysis.

7.1.2 Snapback Effect The snapback effect refers to premises that use more electricity than the baseline during the hours immediately following peak periods, compared to the pre-pilot period. This effect is evaluated based on the same DiD...

AI summary The snapback effect refers to increased electricity usage following peak periods, evaluated using a Difference-in-Differences (DiD) approach. MURB TOU participants showed average load reductions during morning and evening snapback periods, with only the morning reduction being statistically significant.

7.1.3 Change in Load during Highest ANL Hours [Figure](#page-106-0) 43 depicts the distribution of Top 20, 50 and 88 highest ANL hours for MURB TOU participants during morning peak, evening peak, mid-peak, and off-peak hours. As seen in [F...

AI summary The text discusses the change in load during the highest Apparent Net Load (ANL) hours for MURB TOU participants. It indicates that load reductions occur during these hours, with statistically significant reductions during the top 50 and 88 highest ANL hours, particularly during TOU peak periods.

Parameters Highest ANL Hours ANL Hours Coincide TOU Peak Periods Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load (kW)a Reduction 3.3 ± 4.0 4.2 ± 2.9 3.2 ± 2.1 2.9 ± 4.2 3.7 ± 3.0 2.8 ± 2.5 Avg. Load – MURB TOU Participants, Pre-Pilot (...

AI summary Table 52 presents data on the change in load (kW) during highest winter ANL hours for MURB TOU participants, including average load reduction, relative load reduction percentages, and significance levels. The data compares top 20, top 50, and top 88 participants.

Changes in daily electricity usage were estimated by applying the same DiD approach that was used for estimating hourly load impact. [Table 53](#page-107-3) presents the estimated changes in daily electricity usage (kWh/day) for MURB TOU p...

AI summary The document discusses the estimation of changes in daily electricity usage for MURB TOU participants using a DiD approach, with results presented in Table 53.

Table 53: Change in Daily Electricity Usage (kWh/day) for MURB TOU Participants Winter Months Winter Winter Months Parameters Overall January February March November December Avg. Usage Reduction 16.5 ± 30 22 ± 45 33 ± 97 -34 ± 35 66 ± 68...

AI summary Table 53 presents the change in daily electricity usage (kWh/day) for MURB TOU participants during winter months, showing varying levels of usage reduction and increase across different months, with significance marked for all parameters.

7.1.5 Change in Demand Demand for this evaluation is defined as the maximum 15-minute electricity consumption in terms of power (kW) calculated at AMI interval data frequency (i.e. every 15 minutes)[. Figure](#page-108-0) 44 illustrates a...

AI summary The evaluation examines changes in demand among MURB TOU participants during the Winter period. While some months showed indications of demand reductions or increases, these changes were not statistically significant. Overall, no significant changes in monthly maximum demand were observed during the Winter months or across the Winter period.

Winter Winter Months Parameters Overall January February March November December Avg. Monthly Demand Reduction (kW)a -0.12 ± 1.62 3.28 ± 6.13 -1.66 ± 6.01 0.87 ± 1.38 -1.65 ± 2.76 -0.84 ± 2.64 Avg. Monthly Demand – MURB TOU Participants, P...

AI summary The table presents demand reduction data for winter months, showing mixed results with some months indicating demand reduction and others showing demand increase. The data includes average monthly demand reduction, average monthly demand for MURB TOU participants before the pilot, and relative monthly demand change percentages. All values are statistically significant.

Table 55: Changes in Demand by MURB TOU Participants during Winter Parameters Pre- Morning Peak b Morning Peak c Morning Snapback Pre- Evening Peak e Evening Peak f Evening Snapback g Overall Daily Avg. Demand Reduction (kW) a -0.2 ± 0.2 0...

AI summary Table 55 presents changes in demand by MURB TOU participants during winter, showing average demand reductions and increases across different peak and snapback periods, along with statistical significance and relative demand changes.

7.2.1 Change in Electricity Bills For the assessment of the economic impacts of the MURB TOU program, the same DiD approach used to estimate load and demand impacts was applied to evaluate changes in electricity bills for MURB TOU particip...

AI summary The economic impact of the MURB TOU program was assessed using a DiD approach, applying 2025 tariffs to evaluate changes in electricity bills for participants based on customer type and pilot period.

7.2.2 Price Elasticity NS Power evaluated participants' responsiveness to energy price under MURB TOU pricing program, focusing on estimating daily price elasticity and inter-period Substitution Price elasticity. While Daily Price Elastici...

AI summary NS Power evaluated the price elasticity of MURB participants under a TOU pricing program. The daily price elasticity was estimated at -1.19, and the inter-period substitution price elasticity at -0.032, but neither was statistically significant, indicating limited responsiveness to price changes.

Residential TOU & CPP - Participants continue to demonstrate statistically significant reduced load during peak periods with minimal snapback. - For the first time, TVP combined with technology (e.g. smart thermostats) and DR (i.e. Eco Shi...

AI summary The analysis shows that residential Time-of-Use (TOU) and Critical Peak Pricing (CPP) customers experienced reduced load during peak periods, especially when combined with smart technology like Eco Shift and smart thermostats. Electrification of space heating also contributed to load reduction without significantly increasing annual energy consumption.

Commercial TOU & CPP - Participants for the first time have demonstrated statistically significant reduced load during peak periods. This new finding is largely attributable to improvement in methodology and increased sample sizes. - The l...

AI summary The document discusses findings from commercial Time-of-Use (TOU) and Critical Peak Pricing (CPP) programs, noting statistically significant load reductions during peak periods, especially in cold weather. However, results vary by rate class, with Small General participants showing less responsiveness.

Table 60: Top 88 ANL Hours 2020 - 2021 Load - Wind % of Halifax Temp. TOU Period 49 26-Feb-23 5:00 AM 1,844.6 85.3% -15.5 Off-Peak 50 1-Feb-23 6:00 PM 1,841.6 85.1% -11.1 PM Yes 51 1-Feb-23 8:00 PM 1,835.6 84.9% -13 PM Yes 52 25-Feb-23 5:0...

AI summary Table 60 lists the top 88 ANL hours from 2020 to 2021, showing load data related to wind, temperature, and time-of-use periods. It includes metrics such as load, percentage of load, temperature, and time-of-use classifications.

Energy Savings Programs E1 program participation[15](#page-127-1) and evaluation reports[16](#page-127-2) are used for control group selection balancing. Refer to [Attachment III: Methodology](#page-126-0) (section III.2.4 E1 [Program Bala...

AI summary The document discusses the E1 program's participation and evaluation reports used for control group selection balancing. It highlights the Eco Shift program, which uses behind-the-meter technologies like smart thermostats and EV charging to manage load during events. The program's impact is analyzed in sections related to time-varying pricing (TVP) and critical peak pricing (CPP).

Table 66: Summary of Changes to Control Group Methodology[18](#page-128-3) Previous Method New Method Projected Benefit Neighbourhood criteria based on feeder and postal code combination Neighbourhood criteria to be based on region (i.e. C...

AI summary The document outlines changes to the control group methodology, including the use of region-based neighborhood criteria, inclusion of heating classification and Eco Shift participation in the selection process, and improved load similarity metrics. These changes aim to enhance the accuracy and effectiveness of the control group selection for TVP programs.

Where: i are datapoints corresponding the 24 hours in both seasons (Winter/non-Winter) and day types (weekend/weekday) for a total of 96 different combinations. $Tavg_i$ is the average load of the treatment customer during datapoint i. $Ca...

AI summary The text describes a method for analyzing customer load data across different seasons, days, and customer subgroups. It outlines the calculation of average and peak loads for treatment and control customers, and the process of balancing the control group based on various factors to account for E1 program participation.

Table 68: Treatment and Control Interval Data Cleaning Steps Step AMI Data Selection – Cloud scale query to retrieve AMI data for treatment and all potential control customers A Limit to data to the pre-pilot period for each cohort respect...

AI summary Table 68 outlines data cleaning steps for treatment and control interval data in a pilot program. Steps include limiting data to pre-pilot periods, selecting actual data, enabling OTA billing, removing seasonal and net metering customers, and limiting to specific rate classes. Neighbourhood criteria are also discussed in the following section.

III.2.3 Eco Shift A new addition to the TVP evaluation included the effect of combining the Eco Shift administered by E1 with TVP. Control group selection for these customers was incorporated into this evaluations methodology to ensure tha...

AI summary The evaluation of the TVP program included the combined effect of the Eco Shift program administered by E1. Control group selection was carefully managed to ensure similarity between treatment and control groups. NS Power requested a list of Eco Shift events from E1, which were mostly CPP events due to alignment efforts between the two organizations.

Treatment and control customer participation in E1 programs (Home Energy Assessment, Green Heat, Appliance Retirement, Efficient Product Installation, Small Business Energy Solutions, Business Energy Rebates, and Custom) are balanced using...

AI summary The text discusses the process for balancing treatment and control customer participation in E1 programs to mitigate confounding effects on TVP results. It mentions the use of energy savings data from 2021 to 2024 and the allocation of program savings using a specific formula. The text also references specific reports and matter numbers related to the efficiency evaluations.

III.3.1.1 Load & Usage Impact Regression Model The regression model used for load and usage impacts is presented in equation ([7)](#page-138-0). $$Load_{it} = \beta_0 + \beta_1. Treatment_i + \beta_2. PilotPeriod_t + \beta_3. (Pilot \times...

AI summary The Load & Usage Impact Regression Model is presented in equation (7), which estimates the impact of treatment groups under TOU and CPP tariffs on load and energy consumption. The model includes variables for treatment status, pilot periods, and heating degree-days (HDD). It distinguishes between load (in kW) and energy usage (in kWh/day) and includes interaction terms between treatment and pilot periods.

Customers on a seasonal service under Regulation 3.3 are not eligible for TOU or CPP rates and are therefore excluded. However, customers not on seasonal service may still have seasonal load profiles. Identification of businesses with seas...

AI summary The document discusses the exclusion of seasonal service customers from TOU and CPP rates, and describes how NS Power identified seasonal load profiles using a Seasonal Load Ratio (SLR) threshold of 0.7 to classify premises as seasonal based on electricity usage patterns during Winter and non-Winter months.

III.3.2.1 Load and Usage Impact Regression Model Considering the methodological choices discussed above, the commercial TOU and CPP load/usage, billing as well as daily and substitution price elasticity are described by the equations ([14)...

AI summary This section presents a regression model for analyzing the load and usage impact of commercial Time-of-Use (TOU) and Critical Peak Pricing (CPP) programs. The model includes variables such as treatment, pilot period, heating degree days (HDD), and their interactions, using equations (14) and (17). The model helps assess price elasticity and billing impacts.

III.3.2.2 Economic Impact Regression Model $$\begin{aligned} \textit{DailyBill}_{it} &= (\beta_0 + u_i) + \beta_1. \textit{Treatment}_i + \beta_2. \textit{PilotPeriod}_t \\ &+ \beta_3. (\textit{Pilot} \times \textit{Treatment})_{it} + \bet...

AI summary This section presents three regression models used to analyze the economic impact of energy programs. The models assess daily billing, average energy usage, and peak-to-off-peak load ratios, incorporating variables such as treatment, pilot periods, temperature, and energy prices. The 'Treatment' variable is excluded for commercial TOU and CPP analyses due to the lack of control groups, but it is included in the MURB TOU regression.

III.3.3.2 Economic Impact Regression Model The same regression as equations (15), (16) and (17) was used for bill saving and price elasticity analysis for MURB TOU.

AI summary The Economic Impact Regression Model, based on equations (15), (16), and (17), was applied for bill saving and price elasticity analysis in the context of MURB TOU.

Attachment III: Methodology In addition to the metrics calculated for commercial TOU and CPP, the effect on demand was evaluated. This analysis estimates demand using 15-minute average calculated every 15 minutes (i.e. AMI interval data),...

AI summary This section of Attachment III discusses the methodology used to evaluate demand effects, comparing 15-minute average interval data (AMI) with actual billing demand calculated every 5 minutes. It notes that while the AMI data is an estimate, it serves as a reasonable proxy for determining demand effects under the TVP EM&V.

Stage 1 The first stage determined the potential control candidate group using three increasing sizes of selection pools with pre-pilot billing data. The primary neighbourhood criteria, street name (i.e. the smallest pool) was used first....

AI summary Stage 1 of the process involves determining a potential control candidate group using pre-pilot billing data. The primary criteria used is the street name, with treatment and control customer billing data compared using a similarity metric called 'SM', calculated via a two-dimensional Euclidean distance formula.

Where: is the linearized monthly consumption for the treatment customer for month 'i' is the linearized monthly consumption for the control customer for month 'i' is the number of months compared between treatment and control, n = 12 in th...

AI summary This section outlines the methodology for selecting control customers by comparing their linearized monthly consumption data with treatment customers. A similarity metric based on mean absolute difference is used, and the selection process involves multiple neighbourhood criteria if fewer than 15 potential controls are identified.

Attachment V: Validation of Mixed Effects Regression The NS Power commercial TVP evaluations (Phase 1 – 3)[25](#page-152-1) has historically provided statistically insignificant results. Findings such as "statistically insignificant" can b...

AI summary The NS Power commercial TVP evaluations have historically yielded statistically insignificant results due to low statistical power and methodological choices. The use of a fixed effects regression model for commercial customers, similar to residential, introduces Type II errors. In Phase 4, a mixed effects regression model was introduced to address the heterogeneity of commercial customer load profiles, improving the evaluation's accuracy.

mercial-TOU Customers Evaluated for Load Reduction, along with the Residual Error Distribution Associated with (c) Fixed Effect Modelling and (d) Mixed-Effect Modelling [Figure](#page-155-1) 53(a,b) compare residual autocorrelation (ρ) and...

AI summary The document compares Fixed-Effect and Mixed-Effect modeling approaches for evaluating load reduction by commercial Critical Peak Pricing (CPP) participants during CPP events. The Mixed-Effect model shows lower residual autocorrelation and a smaller median residual error, indicating its suitability for handling time-series variability in load data.

MURB TOU Customers Unlike the commercial TOU and CPP rates, MURB TOU has not yet been evaluated using the fixed effects regression model. As such additional analyses are provided within in order to validate the choice of a mixed effects re...

AI summary The document discusses the evaluation of MURB TOU rates, noting that unlike commercial TOU and CPP rates, MURB TOU has not been assessed using a fixed effects regression model. Additional analyses are provided to validate the use of a mixed effects regression model with a control group for the MURB TOU Tariff.

Baseload Heterogeneity Analysis The baseline segmentation analysis indicates substantial heterogeneity in electricity consumption patterns across participating MURBs, particularly with respect to Baseload levels. Based on these characteris...

AI summary The baseline segmentation analysis reveals significant differences in electricity consumption patterns among MURB customers, classifying them into Low, Medium, and High baseload classes. These classes show distinct baseload levels and load profiles, indicating substantial heterogeneity with implications for program impact estimation and model specification.

Baseload heterogeneity is excluded by the Fixed-Effect model. In contrast, the Mixed-Effect model captures the Baseload heterogeneity into random intercepts, resulting in decreased RE autocorrelation. Figure 57: A Comparison of the Residua...

AI summary The Fixed-Effect model excludes baseload heterogeneity, while the Mixed-Effect model accounts for it through random intercepts, reducing residual autocorrelation. Given the small sample size and heterogeneity in baseline electricity consumption, the Mixed-Effect model is preferred for estimating the impact of the TOU-MURBs pilot program.

Demand Response Overlap between NS Power and E1 In its Decision on E1's 2026 DSM Extension Application (M12249), the Board provided: The Board agrees that concerns about E1's demand response programs are better addressed in its consultatio...

AI summary The Board's decision on E1's 2026 DSM Extension Application highlights concerns about overlap between E1's demand response programs and NS Power's Critical Peak Pricing Program. The Board expects E1 to address these issues in its upcoming five-year DSM Plan application and encourages cooperation with NS Power.

Coincidence of Critical Peak Events and System Margin In its Decision and Order on the Year Three (2023/24) Evaluation Report (Year Three Report) in M11823, the Board provided: NS Power agreed with Synapse that system margin forecasts may...

AI summary The document discusses the relationship between system margin forecasts and Critical Peak Pricing (CPP) events, noting that system margin during CPP events was significantly lower than during other hours. The Board directed NS Power to analyze this relationship and refine methods for estimating load reductions through TVP tariffs.

Reporting on Small Business Characteristics that Enable Load Shifting As part of its 2024 TVP Consensus Agreement, the Company committed to: h. Reporting back to stakeholders on changes that could be made to help customers determine whethe...

AI summary As part of its 2024 TVP Consensus Agreement, NS Power committed to reporting on small business load characteristics that enable load shifting, customer recruitment tactics, and reasons for customer reluctance to participate in TVP, TOU, and CPP programs.

Review of Marketing and Communication Efforts In its Decision and Order on the Year Three Report (M11823), the Board provided: Narrative Research conducted a customer survey, which highlighted residential customers' dissatisfaction regardi...

AI summary The document discusses NS Power's efforts to improve communication with residential and commercial customers regarding time-varying pricing (TVP) programs. It highlights the use of a customer survey, a purpose-built estimation tool for residential inquiries, and a coordinated approach with commercial customers involving relationship management and data analytics. The Board emphasized the importance of clear communication to manage customer expectations and ensure the success of the TVP pilot.

Sampling/Administration NSP provided Narrative with a list of pilot participants, and each were invited to complete the survey via a unique link.

AI summary NSP provided a narrative with a list of pilot participants and invited them to complete a survey via a unique link.

Objectives Key Findings Measure residential customer opinions and experiences of the pilot - Overall Satsifaction: Two thirds of TVP participants remain satisfied (scores of 7-10) while just one in ten give low scores of 1-4. Participant s...

AI summary The analysis of the TVP and CPP pilot programs shows that two-thirds of participants remain satisfied, though satisfaction varies by region and income level. Billing clarity and communication effectiveness are key areas for improvement. Participants are changing energy behaviors, but only 30% have installed energy-efficient products since the pilot began.

Collect Feedback on the Critical Peak Pilot - Communication: Nearly all CPP participants received peak events notifications, with half leveraging text messages. Email and text are the preferred channels for receiving critical peak event no...

AI summary Participants in the Critical Peak Pricing (CPP) pilot generally express satisfaction with notifications and event timing, though some suggest improvements such as more advanced notice. Awareness of peak event timing has increased, but some participants remain unclear. While most support maintaining the pilot, one in four may leave if events were called year-round.

Additional Information or Materials When Deciding to Enroll While most indicate satisfaction with the amount of information, there is a desire from some to have more information on rates and potential savings. Asked what additional informa...

AI summary Participants expressed mixed satisfaction with the information provided about the program. While most were satisfied, some desired more details on rates, savings, and billing. Many participants indicated they would have benefited from clearer information on potential financial implications and savings from participating in the program.

Notification Methods for Peak Events Nearly all CPP participants received peak events notifications, with half leveraging text messages. Virtually all CPP participants continue to receive peak event notifications via email (94%; down 1 poi...

AI summary The document discusses notification methods for peak events among CPP participants, noting that nearly all receive notifications via email, with half using text messages. MyEnergy Insights users are more likely to receive text messages. Year 1 participants are less likely to use text messages, suggesting an opportunity for improvement.

Preferred Notification Method Email and text are the preferred channels for receiving critical peak event notifications, though there is opportunity to promote text notifications to participants. Eight in ten CPP participants express a pre...

AI summary The document discusses the preferred notification method for critical peak events, highlighting that email is the most preferred (78%) followed by text messages (67%). However, only 47% of participants currently receive text alerts, indicating an opportunity for improvement. MyEnergy Insights users are more likely to prefer text messages.

Critical Peak Notifications Overall, CPP participants appear satisfied with of notifications and the timing at which they are received, though satisfaction has decreased this year. This year, CPP participants appear slightly less satisfied...

AI summary CPP participants are generally satisfied with notifications but show a slight decrease in satisfaction this year regarding the number and timing of notifications before and after peak events.

Adding Household Members to Critical Peak Event Notifications CPP participants would welcome the opportunity to send critical peak event notifications to other members of their household. If Nova Scotia Power allowed participants to send c...

AI summary CPP participants want to send critical peak event notifications to other household members. Over half of CPP participants would do so, with higher interest in larger households. This capability could increase participation and effectiveness of the CPP program.

Reaction Toward Number of Peak Events Called in 2025 Feelings toward the number of peak events called in 2025 are generally positive, with just a minority of participants responding negatively. This year marked the highest number of peak e...

AI summary Participants generally react positively to the high number of peak events called in 2025 (17 out of 18), with two-thirds expressing approval. Non-electric heat users and smaller households show more positive sentiment. No significant differences in sentiment based on tenure in the pilot.

Reaction to Event Cancellation Overall, participants react positively to cancelled peak events, likely because they count toward the number of max events that can be called without requiring action. Asked about their reaction to Nova Scoti...

AI summary Participants generally reacted positively to Nova Scotia Power's cancellation of three critical peak events this year, with 74% expressing a favorable reaction. The reaction was consistent across different pilot tenure periods and key demographics.

Preferred Discount if Time-of-Use Includes Non-Winter Periods While preferences are somewhat mixed, half of participants would prefer a lower peak rate than a lower off peak rate. When asked to indicate a discount preference should ToU inc...

AI summary Participants in the proceeding have mixed preferences regarding Time-of-Use (TOU) pricing, with half preferring a lower peak rate, one-third preferring a lower off-peak rate, and a small portion wanting a very low off-peak rate overnight.

Reason for Choosing Plan (continued) While reasons for choosing their respective program remain similar, a decreasing number of CPP participants say the pilot fits their lifestyle. The top reasons why ToU participants chose their current p...

AI summary Participants in the Time-Varying Pricing (TVP) and Critical Peak Pricing (CPP) programs report varying reasons for their participation. TVP participants prefer the plan for its flexibility and better rates, while CPP participants mention ease of control and occasional adjustments to save money. Lifestyle and schedule alignment are also cited as factors.

Fewer than one in five participants would consider switching to an alternate TVP pilot. A similar proportion of participants from both rate plans express a likelihood to consider switching to the other offered pilot. That said, an identica...

AI summary Fewer than one in five participants are willing to switch to an alternate Time-Varying Pricing (TVP) pilot. Both Time-of-Use (ToU) and Critical Peak Pricing (CPP) participants show similar levels of disinterest in switching, with 65% from each group unlikely to switch pilots. The low interest suggests dissatisfaction may lead participants to exit the TVP pilot rather than switch rate plans.

Changing Behaviours

AI summary The document discusses the topic of changing behaviors related to energy usage, focusing on initiatives and programs aimed at influencing consumer behavior towards more efficient energy use.

It is clear that program participation influences participant behaviours. Mostly consistent year-over-year, participants state they are likely to change when they wash their clothes, run their dishwasher, and keep lights turned off. Fewer...

AI summary Program participation influences participant behaviors, with ToU participants more likely to shift laundry and dishwasher use to overnight hours, while CPP participants are more likely to reduce heating and use smart thermostats. Only 30% of participants have installed energy-efficient or smart technology since the pilot began.

Other Changes to Electricity Usage Three-quarters of participants state they have, in some way, changed their electricity use after joining the pilot program. This year, fewer participants state they do things at specific times (30%; down...

AI summary Participants in the TVP pilot program have made some changes to their electricity usage, such as shifting high-consumption tasks to off-peak times and reducing overall energy usage. However, there has been little change in home temperature settings over the past four years, indicating limited impact on heating behavior.

Participation in Efficiency One programs remains only moderate. In 2025, the wording was adjusted to reframe the question to capture any previous participation in any of the Efficiency One program versus current participation. Despite the...

AI summary Participation in Efficiency One programs remains low, with fewer than half of participants reporting any involvement. The Home Energy Assessment is the most popular program, while Green Heat and Heat Pump Rebate Program see minimal participation. Adjustments to survey wording in 2025 did not significantly change participation trends, suggesting that TVP participants are less likely to engage with E1 programs.

Summary TVP Year 4 recruitment brought innovation, success, learnings and opportunities . Although, we did not reach our enrollment targets, we welcomed over 6,000 new participants which brought the total enrollment to over 8,860 customers...

AI summary TVP Year 4 recruitment brought innovation, success, learnings and opportunities. Although enrollment targets were not met, over 6,000 new participants joined, increasing total enrollment to over 8,860 customers, a 189% increase from the previous year.

RESULTS: - Developed dashboards to monitor TVP recruitment, in realtime, which included metrics on applications, volume of TVP related inquires, and marketing efforts. - Cross-functional teams were able to leverage this data to stay agile,...

AI summary The document outlines the development of dashboards to monitor TVP recruitment in real-time, providing metrics on applications, inquiries, and marketing efforts. These tools enabled cross-functional teams to make agile, data-informed decisions regarding recruitment efforts.

OPPORTUNITIES - SMB Recruitment – Traditional marketing efforts showed inefficient for SMB recruitment. Conversations with trusted advisors (NS Power Commercial Advisors) proved to be the most effective tactic. - Channels & Timing – Longer...

AI summary The text discusses opportunities for improving SMB recruitment by utilizing trusted advisors and extending lead times and recruitment periods, along with adding more marketing channels and touchpoints with customers.

Business - Although sent to a small number of SMB customers, these were personalized which resulted in the highest Click Rate for SMB Emails. - LEARN MORE and APPLY NOW were the most clicked CTAs

AI summary The business section discusses the effectiveness of personalized email campaigns targeting small and medium-sized business (SMB) customers, noting a high click rate and the most effective call-to-action buttons were 'LEARN MORE' and 'APPLY NOW'.

Critical Peak Event Tips The key to saving money is making sure you reduce your electricity use during the 4-hour Critical Peak event. Here are some tips to help you:

AI summary The text provides tips for reducing electricity use during a 4-hour Critical Peak event to save money.

1. Avoid running major electrical appliances Plan to use items like your dishwasher, clothes washer and dryer, and electric range or stove outside of the 4four-hour event. Are you an Electric Vehicle (EV) owner? You will want to postpone c...

AI summary The text advises consumers to avoid using major electrical appliances during a 4-hour event to manage energy demand. It specifically mentions EV owners and suggests postponing EV charging until after the event.

2. Turn down the heat If you heat your home with electric heating systems, you may want to turn down the thermostat and avoid using supplemental electric space heaters. If you plan to turn the heating off completely, stay warm by slightly...

AI summary This section advises electric heating system users to lower their thermostats and avoid using supplemental electric space heaters. It also suggests slightly pre-heating homes if heating is to be turned off completely, ensuring it happens before an event starts.

4. Turn off or unplug anything not in use Turn off all lights, unplug phone chargers, TVs, computers, and game consoles if not in use—every little bit counts!

AI summary The text advises users to turn off or unplug electronic devices when not in use to conserve energy, emphasizing that small actions contribute to energy savings.

Same metrics as in Phase 2 - › Change in load (power kW) during peak periods: how much were participants able to reduce their consumption when it mattered - › Change in energy usage levels (kWh): how much energy did participants save in to...

AI summary The text outlines metrics used to evaluate energy usage and savings during peak periods, including changes in load, energy usage levels, and bill impacts. These metrics were also used in Phase 2 of the program.

Proposed MURB Pilot Tariff Evaluation Metrics Category Measurement Metric Definition Change in load during peak periods (kW) The average load reduction determined by the regression for winter morning peak, winter evening peak, winter mid-p...

AI summary The document outlines proposed evaluation metrics for a MURB pilot tariff, focusing on load impact, billing impact, and elasticity measures. It includes metrics such as load reduction during peak periods, average electricity savings, bill savings, demand charge comparisons, and elasticity measurements to assess the effectiveness of the tariff.

escribed in Synapse's evidence, evaluating the extent that bills increase as they shift load from on-peak hours to off-peak hours. It is expected that this assessment will identify whether the demand charge increases the electricity bills...

AI summary The text outlines a plan to evaluate the impact of demand charges on commercial customers' electricity bills under the TVP Program, including assessing load shifting effects and potential changes to incentivize participation.

Draft 2024/25 Areas of Focus Work Plan (3/5) Session & Timeline Summary of 2024/25 Areas of Focus & Board Directives Type Items to be Discussed/Reviewed distributed in January 2025) 5.0 – Reporting on Small Business Customer Items • (5.1)...

AI summary The draft 2024/25 Areas of Focus Work Plan includes a session on reporting changes to help small business customers determine if they will benefit from Time-Varying Pricing (TVP). It involves analyzing load characteristics, customer outreach, and exploring opportunities to classify customers by business sector.

Other Board Directives for 2024/25 As part of the Board's Decision in M11822 (2024/25 TVP Application), NS Power will also: • Report on the Year Four findings and file the annual EM&V Report no later than 31 Jul 2025 As part of the Board's...

AI summary NS Power is required to report on Year Four findings and file an annual EM&V Report by July 31, 2025, as per the Board's Decision in M11822. Additionally, NS Power must analyze system margin forecasts and CPP events, review marketing efforts, assess performance of General Service Class customers, and potentially revise incentive structures for commercial TVP tariffs based on stakeholder feedback.

As part of its Year Four Report, NS Power will: - Following the completion of the Year 4 pilot, NS Power will develop and provide to stakeholders an examination of the commercial class customer characteristics (load shape, magnitude, busin...

AI summary NS Power will analyze the characteristics of commercial customers in the TVP pilot to improve outreach to small businesses, maintain TVP reporting processes, file annual EM&V reports, and engage with MURB stakeholders on incentives and rebates to support TVP adoption.

Introduction - On November 27, 2024, NS Power held a TVP session to formally kick off the 2024/25 TVP engagements - Included on the agenda were a review of the Year Three Evaluation Report results, the proposed list of multi-unit residenti...

AI summary NS Power initiated the 2024/25 Time-Varying Pricing (TVP) engagements with a session in November 2024. The agenda included reviewing the Year Three Evaluation Report, discussing proposed metrics for a MURB Time-of-Use (TOU) Pilot Tariff, and presenting the draft 2024/25 Areas of Focus Work Plan. Comments from the Consumer Advocate and Energy Storage Canada were received in December 2024 and incorporated into revisions of the work plan. NS Power plans to hold three Technical Sessions in early 2025.

3.0 – Demand Charges • (3.1) evaluate the extent to which the demand charge may act as a barrier to General class TOU participation and continue to evaluate non-demand TVP designs for General Service customers • (3.2) assessment of demand...

AI summary The text discusses the evaluation of demand charges as a potential barrier to TOU participation for General Service customers, the impact of demand charges on customer bills, and the need to recover only specific demand-related costs through non-coincident demand charges. It also mentions assessing whether demand charges increase bills for customers shifting load to off-peak hours.

Technical Session 2 (continued) – anticipated mid-March Summary of 2024/25 Areas of Focus & Board Directives Type Items to be Discussed/Reviewed 4.0 – Reporting on Small Business Customer Items • (4.1) report to stakeholders on changes tha...

AI summary The session focuses on reporting to stakeholders about changes to help small business customers determine if they will benefit from time-varying pricing (TVP). Topics include analyzing load characteristics, customer recruitment, classification by business sector, and addressing customer reluctance to participate in TVP.

Technical Session 3 – anticipated late-April Summary of 2024/25 Areas of Focus & Board Directives Type Items to be Discussed/Reviewed 5.0 – Potential Tariff Changes / Introductions • (5.1) identify potential tariff changes which could impr...

AI summary The document outlines potential tariff changes for 2024/25, including adjustments to Time-Varying Pricing (TVP) to improve customer participation, examination of new tariffs for behind-the-meter energy storage, and the development of weekend-inclusive TOU tariffs. These changes aim to align customer behavior with system peak hours and evaluate the impact of various rate structures on energy storage use cases.

Technical Session 3 (continued) – anticipated late-April Summary of 2024/25 Areas of Focus & Board Directives Type Items to be Discussed/Reviewed 6.0 – Review of System Planning Related Values • (6.1) review of system planning related valu...

AI summary The document outlines the 2024/25 focus areas for the Board, including a review of system planning values related to Time-Varying Pricing (TVP), such as avoided costs and Effective Load Carrying Capability (ELCC). It discusses the alignment of TVP pricing with avoided costs and the implications of system margin forecasts and CPP events in the Year 4 report.

Near Term Next Steps - Thu, 30 Jan 2025 NS Power distribution of revised 2024/25 Areas of Focus Work Plan and Responses to Comments (Attachment A) - (to be scheduled) week of 24 Feb 2025 Technical Session 1 - (to be scheduled) week of 10 M...

AI summary The document outlines near-term next steps in a regulatory proceeding, including the distribution of a revised work plan and scheduling of technical sessions. It also references an appendix from the TVP Year Four Report.

Comments Received December 2024 Responses Circulated January 30, 2025 Stakeholder Comment NS Power Response Consumer Advocate (CA) Has NSP investigated why the CPP program participants see greater annual savings (slide 23) than TOU partici...

AI summary The Consumer Advocate questions why CPP program participants see greater annual savings than TOU participants, suggesting it may be due to higher peak energy pricing in CPP. NS Power responds that behavioral differences, such as changes in heating and appliance use, explain the savings and notes that participant distribution across categories has changed over time, impacting peak reductions.

Consumer Advocate (CA) 2024/25 Areas of Focus Work Plan – Potential Tariff Changes • With regard to potential tariff changes discussed in the draft work plan, the Consumer Advocate wonders whether NSP considered combining and cross marketi...

AI summary The Consumer Advocate inquires whether NSP has considered combining the Efficiency Nova Scotia Eco Shift Demand Response program with TOU/CPP rate offerings and whether similar Demand Response options, like those offered by BC Hydro, should be considered. NS Power and EfficiencyOne are expanding the Eco Shift program to TOU and CPP participants and will monitor the overlap before deciding on future cross-program enrolment.

Stakeholder Comment NS Power Response peak demand for commercial and industrial (C&I) customers. These efforts are akin to BC Hydro's DR programs, which incentivize businesses to shift their electricity use during peak periods. • The Compa...

AI summary The stakeholder highlights NS Power's efforts to manage peak demand for commercial and industrial customers through demand response (DR) programs, similar to BC Hydro's initiatives. NS Power is assessing its DR Program for the 2026-2030 DSM Plan period with the DSM Advisory Group, emphasizing a commitment to advancing DR solutions.

t positive load shift in response to pricing signals. • NS Power has looked at segmenting customers further with available data to find those customers that correlate most to current winter peaks to determine which customers may have the g...

AI summary NS Power is analyzing customer load shifts in response to pricing signals and segmenting customers based on their correlation with winter peak demand. Customer survey data from 2021 and a 2024/2025 TVP survey are referenced to understand challenges with TVP rates.

Summary of Key Findings

AI summary The summary of key findings highlights the analysis of the impact of Time-Varying Pricing (TVP) on electricity demand and the effectiveness of Conservation Program Providers (CPP) in reducing energy consumption, particularly in Multi-Unit Residential Buildings (MURB). The findings also discuss the role of Effective Load Carrying Capability (ELCC) in grid reliability and the potential benefits of Time-of-Use (TOU) pricing models.

Usage Among participants who completed the online survey, electricity is the most common energy source used for their business' heat, as used by three-quarters, while oil is used by three in ten and other sources by approximately one in te...

AI summary The survey highlights that electricity is the primary energy source for business heating, with most businesses using automated devices to save on electricity and heating costs. Few businesses have heat pumps, and only a small number lack water heaters. Computer servers and other electrical equipment are also common in businesses.

Critical Peak Pricing Looking at the Critical Peak Pricing plan specifically, just fewer that four in ten business customers express a high likelihood of participating in such a plan if it became available. Once again, customers would be l...

AI summary The Critical Peak Pricing plan has mixed customer reactions, with fewer than 40% of business customers likely to participate. While some value the potential for savings and environmental benefits, others dislike increased costs during peak times and the need to change business practices.

Most commonly, one-half of customers only log into their MyAccount when they get their bill, while one-quarter log in monthly. It is comparatively much less common for customers to log in more than once a month. Results overall are similar...

AI summary The document highlights customer login behavior on MyAccount, noting that half of customers only log in when they receive their bill, while a quarter log in monthly. Regional and customer subgroup differences are also mentioned, with the western region and MURB/retail businesses showing distinct patterns.

Among customers without automated equipment, interest is low in installing these types of devices in the future. Of those who have no form of automated equipment or devices (46%; n=185), only 13 percent indicate being interested in install...

AI summary Among customers without automated equipment, interest in installing such devices is low. Of those without automated equipment (46%; n=185), only 13% are interested in installing them, with businesses citing cost savings and scheduling heating as key reasons. However, interpretations of firmographic differences are cautioned due to small sample sizes.

Electric Devices Computer servers, ventilation, coolers/freezers, and exterior lighting are the most common major equipment business customers pay for at their business location. Business customer were also asked the type of equipment they...

AI summary The text discusses the types of electric devices used by business customers in their primary business locations, highlighting computer servers, ventilation, coolers/freezers, and exterior lighting as the most common. Businesses with heat pumps and those with more employees, larger square footage, and on rate 11 are more likely to use these devices.

Likes and Dislikes of Time of Use (Continued) Customers were once again asked what they liked and disliked about the Time of Use concept, this time prompted with options to choose from. Most commonly, customers like that they could save mo...

AI summary Customers were surveyed on their likes and dislikes of Time of Use (ToU) pricing. Most appreciate the potential to save money and reduce environmental impact, while many dislike higher costs during peak times and the difficulty of adjusting usage habits.

Critical Peak Pricing

AI summary The document discusses the implementation and evaluation of Critical Peak Pricing (CPP) as a demand response strategy. It includes visual representations of data and analysis related to the effectiveness and impact of CPP on energy consumption and cost management.

PERS Operate Mon Tues Wed Thur Fri Prior to 7am 20% 20% 20% 20% 20% ľ 7am-9pm 87% 88% 89% 89% 88% After 9pm 17% 18% 18% 18% 17% Do not 5% 3% 1% 1% 2%

AI summary The document contains two figures, one showing operational percentages at different times of the day and another depicting an unspecified chart. The first figure outlines percentages for operations before 7am, between 7am and 9pm, and after 9pm, with varying values across days of the week. The second figure is not described in detail.

13 Seasonal Electricit y Changes in Business Operation 44% 12 % 3% 37 % Jse more ectricity in Use more electricity in the Some other seasonal None, use is steady e winter summer change throughout the year

AI summary The document contains a table discussing seasonal changes in electricity usage in business operations, highlighting variations in winter, summer, and other seasonal patterns, as well as steady usage throughout the year.

The majority of customers are more likely to review their bill thoroughly and keep track of trends, as opposed to simply paying the amount owed. Customers were next presented a sliding scale and asked where their opinion lies between two s...

AI summary The majority of customers (71%) thoroughly review their electricity bills and track usage trends, while 20% only focus on the amount due. This behavior has remained consistent since the 2018 Usage and Attitudes study. Older customers and those not living in condos or apartments are more likely to engage deeply with their bills.

Television and online news are customers' two go-to sources for news and information on current events. For approximately one-half of customers each, television news(live) and online news websites are the most commonly mentioned primary so...

AI summary The text discusses customer preferences for news and information sources, noting that television and online news are the most commonly used. Television news is more popular among older customers and those in certain housing types, while online news is favored by higher-income and e-billing customers. Social media usage is higher among younger customers and larger households.

Temperature Control Systems One-half of customers have a regular thermostat for their main heating system, while three in ten have a programmable thermostat. Smart thermostats are uncommon among customers, only being mentioned by one in te...

AI summary The document discusses thermostat usage among customers, noting that programmable thermostats are more common than smart thermostats. Smart thermostats are particularly prevalent among customers with heat pumps. The data is relevant to TVP rate plans and shows similar trends to 2019, though the sample was limited to electric heat customers in 2021.

The majority of customers keep their homes between 19 and 22 Celsius in the winter months, and the majority reduce the temperature when they aren't at home or at night. Conversely, one in six keep their home's temperature below 19 degrees,...

AI summary The majority of customers maintain indoor temperatures between 19 and 22 Celsius during winter, with many lowering temperatures when away or at night. One in six keep temperatures below 19 degrees, while one in ten keep it above 22. Customers with heat pumps are more likely to maintain optimal temperatures, whereas those without are more likely to keep it lower. These findings are consistent with the 2019 End Use study and are based on data from customers with electric heat.

Two-thirds of customers do not have any form of smart appliances in their home, while one-quarter have at least some smart appliances. Only one percent of customers have entirely smart appliances, and only four percent have mostly smart ap...

AI summary The text highlights that only a small percentage of customers have smart appliances, with one percent having entirely smart appliances and four percent having mostly smart appliances. Customers with smart thermostats are more likely to have other smart appliances.

Likelihood of Installing Smart Thermostat Among customers without a smart thermostat already installed, there is clear interest in installing one in the next three years. More specifically, one-third of those without a smart thermostat exp...

AI summary Among customers without a smart thermostat, one-third express a high likelihood of installing one within the next three years. Adoption is influenced by household income and age, with higher income and younger age correlating with increased likelihood.

Customers were asked whether they use a variety of large appliances at their home. The vast majority of customers indicate having an electric clothes dryer in their home, while more than one-half also indicate having a stand-alone freezer...

AI summary The text discusses customer appliance usage, noting that most have electric clothes dryers, while over half have stand-alone freezers and ceiling/window fans. Usage patterns vary by age and heating system, such as heat pumps being more common among those with specific appliances.

Likes and Dislikes of Critical Peak Pricing Unaided, customers most like the chance to save money on their electricity bill, while most dislike the perception that peak rates are too high. Most commonly, one-third of customers note liking...

AI summary Customers like the potential to save money through Critical Peak Pricing (CPP), but many dislike the high peak rates. One-third of customers like cheaper rates, while one-third dislike the high rates. These findings are based on verbatim customer comments that were categorized.

Reasons for Greater Interest in Time of Use Of those most interested in Time of Use, the main reason is because it is easier to implement into their schedule. Most commonly, three in ten indicate their preference for the Time of Use plan b...

AI summary The main reason customers are interested in Time of Use (TOU) plans is that they are easier to implement into their schedule. Three in ten customers prefer TOU for this reason, while others cite more control over consumption, price, and better savings. Oil heat users are more likely to mention control over consumption.

- Confirmed current enrollment numbers as of March 4 however enrollment numbers remained the same, see table below: (previously shared table) (Rate TOU 80, (Rate CPP 70, Commercial Total 81) 71) Enrollment Target 500 500 1,000 Year 4 New A...

AI summary The document confirms that enrollment numbers for the Rate TOU and Rate CPP programs remained unchanged as of March 4, with current enrollment at 54 and 53 respectively. A slide update increased the number of customers on the mailing list from 560 to 650.

Summary of 2024/25 Areas of Focus & Board Directives Туре Items to be Discussed/Reviewed 1.0 – Accessibility of TVP • (1.1) Review of accessibility as applicable to vulnerable customers (i.e. low-income) CA Examine the results of the Year...

AI summary The document outlines key areas of focus for the 2024/25 regulatory proceeding, including the accessibility of Time-Varying Pricing (TVP) for vulnerable customers, the review of TVP revenue stability mechanisms, refinement of the MURB TOU Pilot Tariff, and improvements to load reduction methodologies and metering systems. These discussions aim to enhance equity, accuracy, and effectiveness in TVP implementation.

Does income pose a barrier to meaningful participation and savings? - The variance in Peak Load Reduction being less than the whisker error bars indicates that there is no evidence of income being a barrier to absolute savings. Even if the...

AI summary The analysis indicates that income does not pose a barrier to meaningful participation or savings in energy efficiency programs. Statistical variance in peak load reduction and high satisfaction levels across income brackets support this conclusion, even with a small sample size in the lowest income bracket.

Projected Benefit Proposed change Existing method Neighbourhood criteria Increased pool of potential Neighbourhood criteria to be based based on feeder and postal Control:Treatment matches will on region (i.e. Northern, Eastern, code combi...

AI summary The document discusses proposed changes to the Neighbourhood criteria and Heating classification in the TVP process. The proposed changes aim to increase the pool of potential participants by using feeder and postal code combinations and apply heating classification to all hours in the pre and post periods of TVP, using overnight off-peak hours for more accurate load impact results.

Avoided Cost Modeling - Customer benefits calculated using avoided cost values will be recalculated to reflect the updated Demand Side Management (DSM) Avoided Cost series from August 2024. - The most recent avoided cost of energy series f...

AI summary The document discusses updates to avoided cost modeling for customer benefits, referencing changes in the DSM Avoided Cost series from August 2024. It highlights differences in winter peak period definitions between the DSM Advisory Group and the TVP program, including reduced winter on peak months, extended peak hours, and weekday-only definitions.

Potential Tools to Monitor Revenue Impacts - During the 2023/24 (Year 3) stakeholder engagements, NS Power provided a draft rate design scorecard that was reviewed with stakeholders and modified to reflect feedback and learnings captured t...

AI summary NS Power developed a rate design scorecard during 2023/24 stakeholder engagements to evaluate tariff options based on metrics like customer orientation, revenue neutrality, and accessibility. The company also set enrolment targets for TVP rates to manage demand response and analyze revenue impacts from TVP participants compared to those on standard tariffs.

Enrolment targets are based off of the annual Demand Response targets as provided in the 2024 Load Forecast Report Rate Load Reduction per Customer (kW) Winter 2024/25 Targets Total Effective on Peak Reduction (MW) Residential TOU 0.1925 7...

AI summary Enrolment targets for demand response programs are derived from the 2024 Load Forecast Report, outlining specific load reduction targets for residential and MURB customers, including TOU and CPP programs, with corresponding peak reduction figures.

Enrolment targets are based off the annual Demand Response targets as provided in the 2024 Load Forecast Report Year TVP Rate(MW) 2024 2 2025 4 2026 12 2027 22 2028 32 2029 36 2030 35 2031 35 2032 34 2033 34 2034 33 Rate Load Reduction per...

AI summary Enrolment targets for demand response programs are based on the 2024 Load Forecast Report. The table outlines target load reductions for different rate classes and years, including residential and MURB TOU and CPP programs, with specific load reduction targets and total effective on-peak reductions.

date. Residential YEAR 1 YEAR 2 YEAR 3 YEAR 4 Email YOY % Emails Recipients 43,280 19,489 112,089 355,035 217% ↑ Email Opens 21,778 10,855 71,011 215133 203% ↑ Open Rate 50% 56% 63% 68% 8% ↑ Total Clicks 5,004 4,120 14,817 40,845 176% ↑ Cl...

AI summary The document provides a detailed analysis of customer engagement metrics for a residential program over four years, highlighting significant increases in email recipients, email opens, and landing page views, along with improvements in open rates and conversion rates. However, there was a decline in average time spent on landing pages and a decrease in click-through rates for some digital advertising channels.

- Overall YoY decreases resulted from focusing on non-digital tactics which resulted in less digital traffic but more successful results. Business YEAR 1 YEAR 2 YEAR 3 YEAR 4 Email YOY 9 6 Emails Recipients 4,136 6,914 6,216 503 -1136% Ψ....

AI summary The text discusses a year-over-year decrease in digital traffic due to a focus on non-digital tactics, resulting in more successful outcomes. It provides data on email engagement, web traffic, and paid search performance across multiple years, along with program enrollment targets and results.

SMB Recruitment TOU (Rate 82, 83) CPP (Rate 72, 83) Commercial Total Enrollment Target 1,000 1,000 2,000 Year 4 New Applications 0 26 26 Current Enrollment (As of March 4, 2025) 54 53 107 HIGHLIGHTS

AI summary The SMB Recruitment section outlines enrollment targets and current enrollment numbers for different programs. It shows that the TOU and CPP programs have not met their enrollment targets, with only 54 and 53 enrollments respectively as of March 4, 2025.

CHALLENGES - Canada Post Strike Limited ability to push larger batches of customer outreach due to Customer Care Centre focused on Canada Post campaign. Additionally, marketing could not complete any subsequent direct mail drops which prov...

AI summary The challenges include a Canada Post strike limiting customer outreach efforts and a delayed timeline for recruitment due to the extension of the Pilot for Year 4, which affected application numbers during the holiday season.

3.0 – Demand Charges (3.1) evaluate the extent to which the demand charge may act as a barrier to General class TOU participation and continue to evaluate non-demand TVP designs for General Service customers (3.2) assessment of demand char...

AI summary The text outlines a technical session discussing demand charges and their potential impact on General Service customers' participation in time-varying pricing (TVP) programs. It recommends evaluating demand charge structures, assessing their effects on customer bills, and considering modifications to encourage load shifting and participation.

TVP Year Four Report Appendix E Attachment 3 Page 57 of 60 APPENDIX MARKETING TACTICS & RESULTS

AI summary This section of the TVP Year Four Report provides an appendix focusing on marketing tactics and their results, highlighting strategies used and their effectiveness in achieving program goals.

Engagement - Sent a Critical Peak Event Prep Email - Plan to send season complete email, and push more social media to help support

AI summary The engagement activities include sending a Critical Peak Event Prep Email and planning to send a season complete email, along with increased social media support.

Critical Peak Event Tips The key to saving money is making sure you reduce your electricity use during the 4-hour Critical Peak event. Here are some tips to help you:

AI summary The text provides tips for reducing electricity use during a 4-hour Critical Peak event to save money.

1. Avoid running major electrical appliances Plan to use items like your dishwasher, clothes washer and dryer, and electric range or stove outside of the 4four-hour event. Are you an Electric Vehicle (EV) owner? You will want to postpone c...

AI summary The text advises consumers to avoid using major electrical appliances during a 4-hour event to manage energy demand. It specifically mentions EV owners and suggests postponing EV charging until after the event.

4. Turn off or unplug anything not in use Turn off all lights, unplug phone chargers, TVs, computers, and game consoles if not in use—every little bit counts!

AI summary The text advises users to turn off or unplug electronic devices when not in use to conserve energy, emphasizing that small actions contribute to energy savings.

Agenda - 1. Welcome & Introduction - 2. Review Work Plan for Today's Session - 3. Demand Charges - 4. Additional Income-based Investigation - 5. Revisions to the Rate Design Scorecard - 6. Reporting on Small Business Items - 7. Technical S...

AI summary The agenda outlines the topics to be discussed during the session, including demand charges, income-based investigations, rate design scorecard revisions, and reporting on small business items, along with planning for a technical session.

3.0 – Demand Charges (3.1) evaluate the extent to which the demand charge may act as a barrier to General class TOU participation and continue to evaluate non-demand TVP designs for General Service customers (3.2) assessment of demand char...

AI summary The document outlines the evaluation of demand charges for General Service customers, including their impact on participation in time-varying pricing (TVP) and the potential for increasing electricity bills. It also discusses the need to recover only specific demand-related costs through non-coincident demand charges and the assessment of bill changes due to load shifting.

Model Commentary Energy neutral model assumes customers turn off electrical loads during peak and turn on an equivalent load that was displaced during off-peak hours that shoulder peak hours without concern for increasing demand. Savings i...

AI summary The energy neutral model assumes customers shift electrical loads from peak to off-peak hours without increasing overall demand. Savings increase slightly with load shifts, but the current rate design provides minimal incentive for shifts below 6%. AMI data from 4075 commercial customers on Rate 11 is analyzed, and TOU rates effective November 1, 2024, are mentioned.

Proposal for General Rate Class TOU Modifications The model indicates that the existing flat rate with demand is already effective to some degree in leading customers to improve their load factor. This can be seen with a majority of custom...

AI summary The existing flat rate with demand is somewhat effective in improving load factor, but the current TOU rate structure is not suitable for businesses with coincident peaks. Load shifting above 6% would require revisions to the demand pricing structure. Alternative rate modeling is proposed for future exploration.

TVP Year Four Report Appendix E Attachment 4 Page 17 of 47 Item Objective 0 - Does Not Meet Objectives 1 - Meets Some Objectives 2 - Meets Most Objectives 3 - Meets All Objectives The Tariff has demonstrated customer interest <50% of Enrol...

AI summary The table evaluates the performance of a tariff based on customer interest and alignment with system peak periods. It outlines thresholds for meeting objectives related to enrolment targets and customer survey responses, as well as the alignment of peak periods with system needs for efficiency gains.

Customer Hesitations and Potential Solutions - Sample of responses from discussions with customers: - Potential savings, even for structural winners often felt to be not enough to be worth the effort. - Skepticism that energy shifting is p...

AI summary Customer hesitations include skepticism about the value of potential savings and concerns about higher winter bills despite annual savings. Proposed solutions involve revising TOU rate structures, reducing seasonal bill impacts, and exploring automation technologies for load shifting.

Technical Session 3 – anticipated week of 12 May (tbc) Summary of 2024/25 Areas of Focus & Board Directives Туре Items to be Discussed/Reviewed 5.0 – Potential Tariff Changes / Introductions • (5.1) identify potential tariff changes which...

AI summary The document outlines potential tariff changes and discussions for the 2024/25 period, including improving customer participation in the Time-Varying Pricing (TVP) program, examining new tariffs for behind-the-meter energy storage, and assessing alternative domestic tariffs with time-of-use structures. The Board has directed an analysis of system costs, seasonal adjustments, and the impact of weekend-inclusive TOU rates.

ear 3 applicants for enrollment in the TVP identified as living in an apartment. NS Power appreciates this suggestion will conduct an updated survey of existing TVP participants this spring. As Narrative noted in the Year Three Survey Resu...

AI summary NS Power acknowledges a suggestion to survey TVP participants living in apartments and notes challenges in analyzing survey data due to small sample sizes. Higher low-income enrollment in Year 4 may improve income-level segmentation in future surveys. NS Power will continue sharing survey results and learnings with stakeholders.

Energy Storage Canada (ESC) Will the Avoided Cost from TVP and DSM reflect the results from the new Line Loss Study? Do the results from the new Line Loss Study represent any material changes from prior assumptions as it relates to the val...

AI summary The text raises a question about whether the avoided cost from Time-Varying Pricing (TVP) and Demand Side Management (DSM) should reflect the results from a new Line Loss Study and if there are material changes in assumptions related to the value of peak-shifting.

5 TVP Pilot Program 2023/24 (Year Three) Evaluation Report, Appendix B, p. 14 of 65. 6 54% of survey respondents reported having household incomes higher than $80,000, which is above the median household income of approximately $72,000. Go...

AI summary The text references an evaluation report on the TVP Pilot Program 2023/24, highlights income data from a 2021 census, and provides statistics on housing stock in Nova Scotia. It also cites a response from NS Power to stakeholder comments.

Stakeholder Comment NS Power Response shift, less control over heating/cooling, fewer timers or other programmable

AI summary A stakeholder expressed concerns about reduced control over heating and cooling systems, fewer timers, and limited programmable features. NS Power did not provide a direct response to these concerns.

roposes that the Fair Value metric be modified to include marginal generation, transmission, and distribution capacity costs. That is, rather than "Tariff volumetric rates correlate to the marginal cost of electricity (i.e., largely fuel)"...

AI summary Nova Scotia Power proposes modifying the Fair Value metric to include marginal generation, transmission, and distribution capacity costs. They also recommend retaining the 50/50 structural winners and losers metric in the scorecard while acknowledging its shortcomings. They suggest that TVP rates should be designed to influence load shape and consider cost reflectiveness based on stakeholder interest.

Responses Filed 30 June 2026 Stakeholder Comment NS Power Response (SBA) 3. Excluding the 1 customer who indicated they had not taken direct action to shift load, of the remaining 3 customers who experienced meaningful load shifting and "w...

AI summary The Small Business Advocate (SBA) raises concerns about the scalability of load-shifting practices by three customers, while NS Power responds that these customers were identified visually and that analyzing only three customers would not yield meaningful results, suggesting future investigations in EM&V reports.

Stakeholder Comment NS Power Response Small Business Advocate (SBA) (a) If so, please discuss NSP's approach for reaching these customers. If not, please describe why NSP is not targeting such customers. (a) For the next active recruitment...

AI summary The Small Business Advocate (SBA) inquires about NSP's approach to reaching customers with multiple accounts. NSP responds that for the next active recruitment period, they will repeat their approach using learnings from the most recent EM&V and incorporating feedback from key account managers, including filtering on customers with multiple accounts.

Definitions Adjusted Net Load The hourly net system requirement (MW) less all wind generation (MW) Cohorts Cohorts are numbered based on the Phase they enrolled in the TVP program. For example, Cohort 4 refers to participants recruited in...

AI summary The text defines key terms used in the regulatory proceeding, including Adjusted Net Load, Cohorts, Commercial customers, Consumption, Control group, Demand, and Income brackets. These definitions provide context for analyzing energy usage and program participation.

Nova Scotia Power (NS Power, Company) launched the Time-Varying Pricing (TVP) Tariff Pilot on November 1, 2021, with the purpose of encouraging customers to shift load from Winter peak periods to Winter off-peak periods. These tariffs pres...

AI summary Nova Scotia Power's Time-Varying Pricing (TVP) Tariff Pilot, launched in 2021, encourages load shifting to reduce peak energy demand and system costs. The fourth annual report highlights continued customer enrollment growth, load reduction effects, and improved evaluation methods. The pilot demonstrates significant on-peak load reductions, particularly during high system load periods, and provides insights for future demand response strategies.

Table 1: Summary of Peak Load Reductions by Residential TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) TVP Tariff Morning Peak Evening Peak Morning Peak Evening Peak Absolute Load Reduction (kW) Relative Load Reduction...

AI summary The table summarizes peak load reductions by residential TVP tariff, showing that residential TOU and CPP tariffs reduced load during peak periods. Residential TOU participants achieved load reductions of 0.15 kW and 0.17 kW during morning and evening peaks, respectively, while CPP participants achieved larger reductions. The use of technology like Eco Shift and smart devices significantly enhanced load reduction. Electrification of space heating with TOU also showed statistically significant load reduction without increasing annual energy consumption.

Commercial Findings The commercial TVP Tariffs attracted more businesses, with Phase 4 enrolment (108 businesses) higher compared to Phase 3 (65 businesses). While "small sample size and the wide variety of energy consumption profiles prev...

AI summary The commercial TVP Tariff program saw increased business participation in Phase 4, with more robust analysis showing significant load reductions during peak periods. However, results varied by rate class, with General Tariff participants showing more responsiveness to price signals compared to Small General Tariff participants. Load reduction impacts were also influenced by outdoor temperatures and event timing.

Table 3: Summary of Load Reductions from MURB TOU Tariff TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) Morning Peak Evening Peak Morning Peak Evening Peak MURB TOU 0.44 kW 0.54 kW 1.8% 2.1% Statistical Significance X...

AI summary Table 3 summarizes load reductions from the MURB TOU tariff, showing absolute and relative reductions during morning and evening peaks. Statistical significance is indicated with black font for significant results and grey for insignificant ones.

Introduction Time-Varying Pricing (TVP) is a scalable customer-focused demand response (DR) program. In Nova Scotia, the TVP pilot has grown significantly since it began in November 2021. Since its inception, the TVP pilot has offered two...

AI summary Time-Varying Pricing (TVP) is a demand response program in Nova Scotia that has expanded since 2021, offering TOU and CPP tariffs across three rate classes. The program aims to shift customer demand to reduce fuel and power costs, as well as long-term infrastructure costs. Phase 4 introduces a new MURB TOU Tariff, evaluated in the TVP Phase 4 EM&V report.

Table 4: Impact Evaluation Metric Summary Category Metric TOU, CPP, MURB TOU (all rate classes unless specified) • Change in load (kW) during peak, and overall impact during Winter and non-Winter. Mid-peak hours included for MURB TOU. • Ch...

AI summary Table 4 outlines impact evaluation metrics for load and usage changes under various rate structures, including TOU, CPP, and MURB TOU. It details changes in load during peak periods, snapback effects, average usage levels, and economic impacts such as changes in electricity bills and price elasticity.

2 Control Group Selection The control group selection methodology uses 1-hour average and 15-minute peak load data to determine the best 1:1 control match by four neighbourhoods. If treatment customers are distributed throughout the provin...

AI summary The control group selection methodology uses load data to ensure Treatment and Control customers are similarly distributed across Nova Scotia. Figures 2 and 3 show that both groups align with the population distribution, enhancing the validity of program comparisons.

2.1.2 E1 Program Balancing As discussed in methodology, E1 program participation is accounted for in control group selection, refer to [Attachment III: Methodology.](#page-111-0) The results indicate that E1 program participation across al...

AI summary The E1 program's participation is balanced across all sub-groups, achieving a 99.1% balance accuracy as defined by equation (3), as discussed in the methodology section.

2.3 MURB TOU For MURB TOU, the control group selection model was successful (Match Accuracy ≥ 90%) in selecting a 1:1 control for the ten MURB TOU treatment customers, refer to load profiles below in [Figure 11.](#page-30-2) Figure 11: MUR...

AI summary The MURB TOU control group selection model achieved a match accuracy of ≥90%, successfully selecting a 1:1 control for ten treatment customers. Load profiles in Figure 11 show a good match, supporting the use of a control group for this subset of commercial customers.

Morning Evening Parameters De -E le ct rifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic De -E le ct rifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic Avg. Load Reduction 0.15 ± 0.07 ± 0.18 ± 0.0...

AI summary The table presents load reduction data for different customer segments and time periods, showing average load reductions and percentages for both morning and evening periods. The data includes statistical significance markers and standard errors, highlighting the effectiveness of load management strategies.

Change in Load by Region, Income Level, and Residents per Household [Figure 15](#page-37-1) illustrates the average load reduction achieved by TOU participants according to the region of premises in Nova Scotia. While no significant differ...

AI summary The text discusses load reduction among TOU participants in Nova Scotia, noting higher evening load reductions in the Halifax area compared to the rest of the province, and higher absolute load reductions among higher-income participants despite no significant trend in relative load reduction by income level.

Low-Income Medium-Income High-Income Parameters AM Peak PM Peak All Peak Hours AM Peak PM Peak All Peak Hours AM Peak PM Peak All Peak Hours Avg. Load 0.14 ± 0.15 ± 0.15 0.12 ± 0.16 ± 0.14 0.18 0.19 0.19 a Reduction (kW) 0.01 0.01 ± 0.01 0...

AI summary This table presents average load and load reduction data across different income levels in Nova Scotia, highlighting variations in energy consumption and the effectiveness of demand response programs. It includes statistical measures and significance indicators for each category.

Change in Load with Eco Shift E1's Eco Shift is a DR initiative offered separately from TVP and was implemented within subset of the TVP treatment participants who opted into both programs. It is designed to encourage load reduction during...

AI summary E1's Eco Shift DR initiative, combined with TOU, significantly increased load reduction during peak hours. Analysis using DDD regression showed an additional 0.29 kW reduction during all TOU peak hours, with even higher reductions during morning and evening peaks. During Eco Shift events, the combined effect of TOU and Eco Shift resulted in a total load reduction of 0.66 kW.

3.1.3 Change in Load during Highest ANL Hours [Figure 20](#page-42-0) depicts the distribution of Top 20, 50, and 88 highest ANL hours for residential TOU participants during morning peak, evening peak, and off-peak hours as well as weeken...

AI summary The text discusses the distribution of highest ANL hours for residential TOU participants, highlighting significant load reductions during peak periods. Results show a 6% relative load reduction during top 20, 50, and 88 highest ANL hours that coincide with TOU peak periods, with absolute reductions of 0.17 kW.

Participants Parameters High nest ANL Ho urs ANL Hours Coincide TOU Peak Periods Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load 0.16 ± 0.04 0.14 ± 0.13 ± 0.17 ± 0.17 ± 0.17 ± Reduction (kW) a 0.16 ± 0.04 0.03 0.02 0.03 0.03 0.02 Avg....

AI summary The table presents load reduction data for participants in a demand response program, comparing average load and reduction across different participant tiers and time periods. The data highlights the effectiveness of the program, with load reductions ranging from 4.8% to 6.2%, though statistical significance is only noted for the top 20 participants.

4 Residential CPP Tariff Impacts This section presents the analysis results for load and economic impacts of the Domestic CPP Tariff for all cohorts in Phase 4. The goal of the residential CPP pilot is to encourage customers to shift their...

AI summary This section evaluates the load and economic impacts of the Domestic CPP Tariff during Phase 4, from April 1, 2024, to March 31, 2025. The goal is to encourage load shifting during CPP events to reduce energy costs and infrastructure needs.

[Table 21](#page-50-2) below summarizes the characteristics of the residential CPP participants that were included in the analysis of the load and economic impacts. As seen in [Table 5,](#page-21-0) the number of CPP participants in Cohort...

AI summary Table 21 summarizes residential Critical Peak Pricing (CPP) participants' characteristics, highlighting that Cohort 4 has significantly more participants compared to Cohort 1, 2, and 3. The average temperature during events was -7.6 °C, and double events occurred on the same day.

4.1.1 Change in Load during Peak Periods [Table 22](#page-51-0) summarizes the results of the analysis for morning and evening CPP events for each cohort and all participants combined. The average load reduction metrics are presented along...

AI summary The analysis of load reduction during morning and evening Critical Peak Pricing (CPP) events shows statistically significant reductions across all cohorts, with cohort 4 having lower margin of error due to a larger number of participants. Overall, CPP participants achieved load reductions of 0.61 kW and 0.77 kW during morning and evening events, respectively.

Table 22: Change in Load during CPP Events for Residential CPP Participants Morning Events Evening Events Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Load Reductiona (kW) 0.68 ± 0.10 1.22...

AI summary Table 22 presents the average load reduction during Critical Peak Pricing (CPP) events for residential participants, comparing different cohorts and showing significant reductions in load across all groups during both morning and evening events.

Change in Load by Space Heating Classes During Peak Events [Table 23](#page-52-0) summarizes the average load reductions during morning and evening events for space heating classes of all cohorts combined in Phase 4. It is important to not...

AI summary Table 23 summarizes the average load reductions during morning and evening peak events for space heating classes in Phase 4. The analysis compares event and reference days during the 2024/25 Winter, noting that changes in space heating type between baseline and pilot periods do not directly affect load reduction results in the DiD analysis.

Morning Evening Parameters De -E le ct rifi ed El ec tr ifi ed El St ec ea tr dy ic No n- St El ea ec dy tr ic De -E le ct rifi ed El ec tr ifi ed El St ec ea tr dy ic No n- St El ea ec dy tr ic Avg. Load Reductiona 0.57 ± 0.66 ± 0.74 ± 0....

AI summary The table presents load reduction data for residential Critical Peak Pricing (CPP) participants in both morning and evening periods, showing average load reductions and their significance levels. The data indicates that load reduction percentages vary across different parameters and time periods, with all values showing statistical significance.

Change in Load per Critical Peak Event [Figure 23a](#page-53-0) illustrates the average load reduction (kW) per CPP event date for all Phase 4 cohorts, along the corresponding event-average outdoor temperature (°C). The residential CPP par...

AI summary The document discusses the average load reduction per Critical Peak Pricing (CPP) event, showing that residential participants achieved significant reductions, with higher reductions during evenings and in the Halifax Area. Higher-income households demonstrated greater relative load reductions compared to lower-income households.

Low-Income Medium-Income High-Income Parameters AM PM Overall AM PM Overall AM PM Overall Event Event Event Event Event Event Avg. Load 0.43 0.61 0.52 0.55 0.69 0.62 0.78 1.01 0.88 Reductiona (kW) ± 0.06 ± 0.06 ± 0.04 ± 0.04 ± 0.04 ± 0.03...

AI summary The table presents data on load reduction during critical peak events for residential participants in different income categories. It includes average load, reduction in kW, and percentage of load reduction, showing statistically significant results across all income levels.

4.1.2 Snapback Effect The CPP participants exhibit relatively small but statistically significant snapback effects during morning, evening and double-event snapback periods, indicating significant increase in electricity usage during the f...

AI summary CPP participants show small but statistically significant snapback effects, with a 0.15 kW increase in electricity usage during double-snapback periods compared to morning or evening snapbacks, as shown in Table 25.

4.1.3 Change in Load during Highest ANL Hours [Figure 29](#page-58-0) illustrates the distribution of Top 20, 50 and 88 highest ANL hours for residential CPP participants during weekdays and weekends as well as across CPP events. The major...

AI summary The text discusses the distribution of highest Adjusted Net Load (ANL) hours for residential Critical Peak Pricing (CPP) participants during weekdays and weekends. It notes that most high ANL hours occur on weekdays and highlights statistically significant load reductions during these periods, particularly during CPP peak times.

Parameters Highest ANL Hours ANL Hours Coincide with CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 0.75 ± 0.63 ± 0.55 ± 0.85 ± 0.84 ± 0.80 ± Avg. Load Reductiona (kW) 0.07 0.06 0.04 0.07 0.06 0.05 Avg. Load – CPP 2.9 2.9 2.8 2.9 2.9...

AI summary This table presents load reduction data associated with Critical Peak Pricing (CPP) events, showing average load reductions in kilowatts and percentages for different participant groups. The data includes statistical significance and confidence intervals, indicating the effectiveness of CPP in reducing energy demand during peak hours.

4.1.4 Change in Usage The change in daily usage levels was evaluated through two analyses. For the first analysis, the daily usage reduction during event days was estimated, when at least one CPP event occurred, compared to reference days....

AI summary The analysis evaluated changes in daily electricity usage during CPP events and compared them to reference days. Overall, CPP participants reduced their usage by 1.7 kWh, but this was not statistically significant across all cohorts. Cohort 4 showed a significant reduction, while no significant differences were found across heating classes.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Usage Reduction a (kWh/day) 1.6 ± 4.0 3.1 ± 6.7 1.6 ± 2.9 1.6 ± 1.5 1.7 ± 1.3 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 61 71 55 52 54 Relative Avg. Usage Reducti...

AI summary The table presents average usage reduction and significance levels for different cohorts in a program evaluating energy efficiency measures. Cohort 4 shows statistically significant results, while others have mixed significance. Usage reduction percentages are relatively low across all cohorts.

The results of the second analysis are summarized i[n Table 28](#page-60-0) and show changes in daily electricity usage level for cohorts 1 to 4 and all cohorts combined during non-holiday weekdays and holiday weekends in Winter as well as...

AI summary The second analysis shows that all cohorts of Critical Peak Pricing (CPP) participants significantly reduced their daily electricity usage during non-holiday weekdays in Winter. Load reductions were also observed during holiday weekends in Winter, except for cohort 3, which did not show statistically significant reductions. Annual aggregated usage reductions for all CPP participants combined are estimated at 0.973 ±0.24 kWh per day, amounting to 355 kWh per year on average.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekdays Avg. Usage Reduction a (kWh/day) 3.0 ± 1.4 3.1 ± 2.0 1.0 ± 0.9 1.1 ± 0.5 1.2 ± 0.4 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 50.7 60.5 44....

AI summary The table presents data on average usage reduction across different cohorts during winter non-holiday weekdays, weekends/holidays, and non-winter days. It highlights the impact of Critical Peak Pricing (CPP) on residential participants, showing varying degrees of significance and usage reduction percentages.

[Table 29](#page-61-1) presents changes in daily electricity usage level by space heating for all cohorts of residential CPP participants during pilot period compared to pre-pilot period. During non-holiday weekdays in Winter, CPP particip...

AI summary Table 29 shows that residential Critical Peak Pricing (CPP) participants with electrified space heating had a 13% increase in daily electricity usage during non-holiday weekdays in winter, while de-electrified and steady electric participants saw reductions. Steady non-electric participants showed no significant change in usage.

4.1.5 Effect of Weather (Temperature) on Load Reduction The DiD analysis reveals that the average load reduction is increased by $0.019 \, \text{kW}$ ( $\pm 0.003 \, \text{kW}$ ) per 1 °C decrease in outdoor temperature, implying that CPP...

AI summary The DiD analysis shows that residential Conservation Program Participants (CPP) increase load reduction by 0.019 kW per 1 °C decrease in outdoor temperature, indicating a stronger response to colder weather. This relationship is visually represented in Figure 30.

4.1.6 Effect of Consecutive CPP Events on Load Reduction During Phase 4, like Phase 3, there was only one instance of two consecutive CPP events, which occurred when events 5 and 6 took place on December 23, 2024. The second of these conse...

AI summary During Phase 4, a single instance of two consecutive Conservation Program Participant (CPP) events occurred on December 23, 2024. The second event showed a slightly higher load reduction but not statistically different from the first. However, due to the limited data, no broader conclusions can be drawn about the impact of consecutive CPP events.

4.2 Economic Impacts This section presents and discusses the impact of residential CPP tariffs on the electricity bill as well as price elasticity.

AI summary This section discusses the impact of residential Conservation Program Participants (CPP) tariffs on electricity bills and price elasticity, examining how these tariffs influence consumer behavior and economic outcomes.

4.2.1 Change in Electricity Bills [Table 30](#page-62-3) summarizes the applicable tariffs used for residential CPP impact on billing. Tariff details can be found in [Attachment I: Riders and Tariff Structure.](#page-100-0)

AI summary This section discusses the impact of residential Conservation Program Participants (CPP) on electricity bills, referencing Table 30 and Attachment I for tariff details.

Table 30: Tariffs Used for Analyzing Residential CPP Impact on Electricity Bill Customer Type and Period Applicable Tariff Control, Reference Day Domestic Standard Tariff Control, during Pilot Domestic Standard Tariff Treatment, Reference...

AI summary Table 30 and Table 31 analyze the impact of the Conservation Program Participants (CPP) on residential electricity bills. The results show that CPP participants experienced average daily bill savings of $0.57 annually, with significant savings during non-Winter days and weekends/holidays in Winter. These savings are attributed to lower rates under the CPP Tariff compared to standard domestic tariffs.

4.2.2 Price Elasticity Price elasticity was evaluated to measure residential CPP participants' responsivity to electricity prices during Phase 4. Specifically, NS Power evaluated Daily Price Elasticity during Winter season and annual overa...

AI summary The document evaluates price elasticity for residential Conservation Program Participants (CPP) during Phase 4, focusing on Daily Price Elasticity and Inter-period Substitution Price Elasticity. Results show a Daily Price Elasticity of -0.2 and an Inter-period Substitution Price Elasticity of -0.19, both statistically significant, indicating reduced electricity usage in response to price changes.

5 Commercial TOU Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial TOU program. The goal of the commercial TOU pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial Time-Varying Pricing (TOU) program. The goal is to encourage customers to shift electricity usage from peak to off-peak periods, reducing fuel and power costs and infrastructure expenses. Phase 4 has 50 participants, with most enrolled in earlier cohorts.

5.1.1 Change in Load during Peak Periods [Figure 31](#page-66-1) illustrates the load profiles for commercial TOU participants during the pre-pilot and pilot periods, along with the corresponding variation in outdoor temperature during the...

AI summary Commercial Time-of-Use (TOU) participants showed a reduction in electricity load during peak hours, with an overall peak reduction of 0.8 kW and average load reductions of 6.4%, 8.6%, and 7.4% during morning, evening, and overall peak periods, respectively. The results from the Mixed-Effects regression analysis confirmed these impacts as statistically significant.

Change in Load during Peak Events by Region [Table 35](#page-67-0) presents the average load reduction (kW) by region (Halifax area versus the rest of Nova Scotia) for commercial TOU participants during morning, evening, and all peak hours...

AI summary Table 35 presents average load reductions during peak hours for commercial TOU participants in the Halifax area and the rest of Nova Scotia, showing statistically significant reductions in both regions, though with varying magnitudes.

Halifax Area Rest of Nova Scotia Parameters Morning Evening All Peak Morning Evening All Peak Peaks Peaks Hours Peaks Peaks Hours Avg. Load Reduction 1.22 ± 1.49 ± 1.38 ± (kW)a 0.29 0.31 0.20 0.16 ± 0.17 0.24± 0.18 0.20 ± 0.15 Load – Avg....

AI summary The table presents load reduction data for the Halifax Area and Rest of Nova Scotia, showing average load reductions during peak hours and the percentage of load reduction. The data highlights significant reductions in the Halifax Area compared to the Rest of Nova Scotia.

Change in Load by Month during Peak Periods [Figure 33](#page-69-2) illustrates the impact of commercial TOU in terms of average load reduction (kW), together with the average outdoor temperature (°C) during peak hours by month over the Wi...

AI summary Figure 33 shows that commercial TOU participants achieved significant load reductions during winter peak periods, except for a slight increase in December 2024. The greatest load reduction of 1.6 kW occurred in February 2025 when temperatures were at their lowest.

5.1.2 Snapback Effect NS Power evaluated the snapback effect based on the same method that was applied for load reduction calculation, but specifically for the four hours following the morning and evening peak hours. As summarized i[n Tabl...

AI summary NS Power evaluated the snapback effect for commercial TOU participants, finding a statistically significant load reduction of 0.51 kW during morning snapback hours and a load increase of 0.9 kW during evening snapback hours, resulting in a 13.3% relative load increase. The overall relative load increase of 1.2% was not statistically significant.

5.1.3 Change in Load during Highest ANL Hours [Figure 34](#page-70-1) depicts the distribution of Top 20, 50 and 88 highest ANL hours for commercial TOU participants during off-peak, morning peak, evening peak, Weekends and Holidays. As se...

AI summary The document analyzes the distribution of highest ANL hours for commercial TOU participants, showing that these hours disproportionately occur on weekdays and during peak periods. However, load changes during these hours were not statistically significant, indicating no major impact on overall load.

Parameters Highest ANL Hours ANL Hours Coincide TOU Peak Periods Top20 Top 50 Top88 Top20 Top 50 Top88 (kW)a Avg. Load Reduction 0.06 ± 0.34 ± -0.15 ± -0.12 ± 0.20 ± 0.10 ± 0.85 0.50 0.44 0.84 0.48 0.40 Avg. Load Participants, Pre Pilot (k...

AI summary The table presents data on load reduction and average load for different participant groups, showing variations in load reduction percentages and significance levels. It highlights the impact of abnormal load hours and their coincidence with TOU peak periods.

5.1.4 Change in Usage Changes in daily electricity usage were estimated by applying the same method which was used for estimating hourly load-impact, to daily electricity usage. [Table 38](#page-71-3) presents the estimated changes in dail...

AI summary The document discusses the estimation of changes in daily electricity usage for commercial TOU participants using a method similar to that used for hourly load-impact estimation. Table 38 provides the estimated changes in daily electricity usage in kWh/day.

Parameters Annual Average Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non Winter Avg. Usage Reduction (kWh/day)a -15.4 ± 3.9 -9.4 ± 4.0 -3.9 ± 5.1 -19.4 ± 6.5 -22.2 ± 5.7 Avg. Usage – Commercial TOU Participants,...

AI summary The table presents data on average usage reduction across different time periods and participant categories, showing variations in energy consumption patterns. It includes statistical significance indicators and notes that positive and negative values represent usage reduction and increase, respectively.

6 Commercial CPP Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial CPP program. The goal of the commercial CPP pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial Conservation Program Participants (CPP) program, focusing on load shifting during peak events. The analysis uses a Mixed-Effects modeling semi-DiD approach and covers CPP events from November 1, 2024, to March 31, 2025.

As shown i[n Table 41,](#page-75-1) three CPP events (Dec. 15, 2024, Dec. 22, 2024, and Feb. 02, 2025) took place during weekends. The morning and evening consecutive events occurred on December 23, 2024. The CPP events include seven morni...

AI summary The text discusses Conservation Program Participants (CPP) events occurring during weekends and extreme cold conditions, with a focus on commercial participation in Phase 4 of the program. It highlights that most commercial CPP enrolment occurred in Cohort 4, as opposed to commercial Time-of-Use (TOU) programs.

Table 42: Summary of Commercial CPP Sample Size 10F [11](#page-76-4) Parameter Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Sample Size 4 1 3 20 28 Rate Class Small General 2 0 1 8 11 General 2 1 2 12 17 Region HRM 2 0 3 16 21 Rest of Nova Scot...

AI summary Table 42 provides a summary of the sample size for Commercial Conservation Program Participants (CPP) across four cohorts. The data includes breakdowns by rate class and region, with a total sample size of 28 participants.

6.1.1 Change in Load during Peak Events [Table 43](#page-76-3) summarizes the results of the analysis for morning and evening CPP events for all cohorts of commercial CPP participants in Phase 4, where the average load reduction values are...

AI summary Table 43 summarizes the load reduction results for commercial Conservation Program Participants (CPP) during morning and evening peak events in Phase 4, showing a significant overall relative load reduction of 10.49.6%.

Table 43: Change in Load during CPP Events for Commercial CPP Participants Parameters Morning Events Evening Events All Events Avg. Load Reduction(kW)a 7.9 ± 2.9 5.1 ± 2.7 6.3 ± 2.3 Avg. Load – Commercial CPP Participants, Pre-Pilot (kW) 6...

AI summary Table 43 presents the average load reduction during Conservation Program Participant (CPP) events for commercial participants, showing reductions of 7.9 kW in the morning, 5.1 kW in the evening, and 6.3 kW overall, with significance levels indicating statistical significance.

Halifax Area Rest of Nova Scotia Parameters Morning Events Evening Events All Events Morning Events Evening Events All Events Avg. Load Reduction (kW)a 10.6 ± 3.8 7.0 ± 3.4 8.6 ± 3.0 -0.1 ± 0.6 -1.0 ± 1.0 -0.7 ± 0.7 Avg. Load – Commercial...

AI summary This table presents load reduction data for the Halifax Area and Rest of Nova Scotia, comparing average load reduction during morning and evening events before and after a pilot program. The data shows significant reductions in the Halifax Area but minimal changes in the Rest of Nova Scotia.

6.1.2 Snapback Effect The snapback effect refers to commercial CPP customers that use more electricity than the baseline during the hours immediately following CPP event hours, compared to the pre-pilot period. This effect was evaluated ba...

AI summary The snapback effect refers to increased electricity usage by commercial Conservation Program Participants (CPP) immediately after CPP event hours. Analysis using a Mixed Effect approach found statistically insignificant overall snapback, but significant increases of 6.9 kW during event days with both morning and evening CPP events.

Table 45: Snapback Effect for Commercial CPP Participants Parameters Load Reduction a Avg. (kW) Significance (p_Value≤0.05) Morning Snapback 0.6 ± 0.9 X Evening Snapback -0.1 ± 1.3 X b Morning & Evening Snapback -6.9 ± 3.4 ✓ Overall Snapba...

AI summary Table 45 presents the snapback effect for commercial Conservation Program Participants (CPP), showing load reduction and increase values for morning and evening periods. The data includes statistical significance indicators and standard deviations, with the overall snapback effect showing a slight load increase.

6.1.3 Change in Load during Highest ANL Hours [Figure 39](#page-82-0) depicts the distribution of Top 20, 50 and 88 highest ANL hours for commercial CPP participants during weekdays and weekends as well as across CPP events. As seen in [Fi...

AI summary The text discusses the distribution of highest ANL (Abnormal Load) hours for commercial Conservation Program Participants (CPP) during weekdays and weekends, and across CPP events. It also highlights load reductions achieved during these periods, particularly during CPP peak times.

Parameters Highest ANL Hours ANL Hours Coincide CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load Reduction (kW)a 6.8 ± 3.3 6.6 ± 2.8 5.0 ± 2.1 7.4 ± 3.4 8.2 ± 3.1 7.4 ± 2.6 Avg. Load Participants, Pre Pilot (kW) 64.4 61.4 61....

AI summary The table presents load reduction data for different participant groups in a program, including average load reduction, average load before the pilot, and relative load reduction percentages. The data is analyzed for statistical significance.

6.1.4 Change in Usage Changes in electricity usage were estimated by applying the same Mixed-Effect modeling approach that was used for estimating hourly load impact, where the change in daily usage levels was evaluated through two analyse...

AI summary The analysis evaluated changes in electricity usage by commercial Conservation Program Participants (CPP) during event days and different seasons. It found significant reductions in daily electricity usage during Winter and non-holiday weekdays, with a notable increase in non-Winter seasons.

Parameters Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non Winter Annual Average Avg. Usage Reduction (kWh/day)a 54.0 ± 10.9 51.2 ± 11.6 62.3 ± 16.6 -21.4 ± 3.7 -12.2 ± 6.7 Avg. Usage – Commercial CPP Participant...

AI summary Table 47 presents the average usage reduction and relative usage reduction percentages for Commercial Conservation Program Participants (CPP) during different periods, including winter overall, winter non-holiday weekdays, winter holiday weekends, and non-winter. The data includes statistical significance indicators and standard deviations.

6.1.5 Effect of Weather (Temperature) on Load Reduction The DiD analysis reveals that the average load reduction is increased by 0.06 kW (± 0.35 kW) per 1 °C decrease in outdoor temperature, which is not statistically significant. It impli...

AI summary The DiD analysis shows that commercial Conservation Program Participants (CPP) do not exhibit statistically significant changes in load reduction with colder temperatures, despite an upward trend in correlation between load reduction and outdoor temperature during CPP event days.

6.1.6 Effect of Consecutive CPP Events on Load Reduction During Phase 4, like Phase 3, there was only one instance of two consecutive CPP events, which occurred when events 5 and 6 took place on December 23, 2024. The second of these conse...

AI summary During Phase 4, a single instance of two consecutive Conservation Program Participant (CPP) events on December 23, 2024, showed that the second event resulted in a statistically significant higher load reduction compared to the first. However, due to limited data, no broad conclusions can be drawn about the impact of consecutive CPP events.

6.2 Economic Impacts This section presents and discusses the impact of commercial CPP tariffs on the electricity bill as well as price elasticity.

AI summary This section discusses the economic impacts of commercial Conservation Program Participants (CPP) tariffs on electricity bills and price elasticity.

6.2.1 Change in Electricity Bills The bill impact analysis for the commercial CPP uses the same method that was used for estimating the commercial CPP load impact analysis. In the bill impact analysis, applicable tariffs were applied in ac...

AI summary The bill impact analysis for the commercial Conservation Program Participants (CPP) uses the same method as the load impact analysis. Applicable tariffs were applied based on customer type and pilot period, with details provided in an attachment.

Table 48: Tariffs Used for Analyzing Commercial CPP Impact on Electricity Bill Customer Type Period Applicable Tariff Treatment – General Pre-Pilot General Tariff – Rate Code 11 Treatment – General Pilot General CPP Tariff – Rate Code 73 T...

AI summary Table 48 outlines the tariffs applied to commercial Conservation Program Participants (CPP) before and during the pilot phase, with different rate codes for general and small general customers. Table 49 indicates that commercial CPP participants experienced an average daily bill saving increase of $1211, but this saving increase of 5.84.7% is not statistically significant.

Table 49: Change in Daily Electricity Bill for Commercial CPP Participants Parameters Winter Non-Winter Annual Average Bill Savings ($/day)a Avg. -11.7 4 ± -1.50.1 ± 10.3-9.83 ± 10.113.1 1.91.6 7.011.1 Avg. Bill, Commercial CPP Participant...

AI summary The table shows that while commercial Conservation Program Participants (CPP) increased energy consumption during non-Winter months, their average daily electricity bills did not change significantly due to a decrease in CPP volumetric energy costs.

6.2.2 Price Elasticity NS Power evaluated price elasticity to measure commercial CPP participants' responsivity to electricity prices during Phase 4 of the pilot. Specifically, NS Power evaluated Daily Price Elasticity during Winter season...

AI summary NS Power evaluated price elasticity for commercial Conservation Program Participants (CPP) during Phase 4 of a pilot. Daily Price Elasticity was -0.3839 in winter and -0.48 annually, showing decreased usage with higher prices. Inter-period substitution elasticity was -0.14, but not statistically significant.

7 MURB TOU Tariff Impacts As this Phase represents the first year of the MURB TOU pilot program, participation was limited to 10 enrolled MURBs, with the pilot period beginning on November 1, 2024. The goal of the MURB TOU pilot is to info...

AI summary The MURB TOU pilot program, starting in November 2024, involves 10 enrolled MURBs and aims to assess the impact of removing the demand charge on load shifting potential compared to the commercial TOU pilot. This section discusses the estimated load and economic impacts of the commercial TOU pilot.

7.1 Load Impacts This section presents and discusses the electrical load impact from the commercial MURB TOU pilot.

AI summary This section discusses the electrical load impact from the commercial MURB TOU pilot, providing insights into how this initiative affects overall load management.

7.1.1 Change in Load during Peak Periods [Figure 41a](#page-88-0) illustrates the load profiles for MURB TOU participants and the control group during the pre-pilot and pilot periods with the corresponding variation in outdoor temperature....

AI summary The text discusses load reduction patterns among MURB TOU participants during peak and off-peak hours, showing statistically significant reductions in electrical load, particularly during morning and evening peak periods. The DiD analysis reveals an overall load reduction of 1.9%, with higher reductions observed during midpeak and off-peak hours.

7.1.2 Snapback Effect The snapback effect refers to premises that usinge more electricity than the baseline during the hours immediately following peak periods, compared to the pre-pilot period. This effect is evaluated based on the same D...

AI summary The snapback effect refers to increased electricity usage following peak periods, evaluated using a DiD approach. MURB TOU participants showed average load reductions during morning and evening snapback periods, with only the morning reduction being statistically significant.

7.1.3 Change in Load during Highest ANL Hours [Figure 43](#page-91-0) depicts the distribution of Top 20, 50 and 88 highest ANL hours for MURB TOU participants during morning peak, evening peak, mid-peak, and off-peak hours. As seen in [Fi...

AI summary The text discusses the distribution of highest ANL (Abnormal Load) hours for MURB TOU (Multi-Unit Residential Building Time-of-Use) participants during peak and off-peak hours. It highlights that load reductions occur during the top 20, 50, and 88 highest ANL hours, with statistically significant reductions during the top 50 and 88 hours. Load reductions are also noted during TOU peak periods.

Parameters Highest ANL Hours ANL Hours Coincide TOU Peak Periods Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load (kW)a Reduction 3.3 ± 4.0 4.2 ± 2.9 3.2 ± 2.1 2.9 ± 4.2 3.7 ± 3.0 2.8 ± 2.5 Avg. Load – MURB TOU Participants, Pre-Pilot (...

AI summary The table presents load reduction data for participants in a Time-of-Use (TOU) program, comparing average load reductions before and during the pilot period. It highlights statistical significance and variability in load reduction percentages across different participant groups, with some reductions being statistically significant.

7.1.4 Change in Usage Changes in daily electricity usage were estimated by applying the same DiD approach that was used for estimating hourly load impact[. Table 53](#page-92-3) presents the estimated changes in daily electricity usage (kW...

AI summary The document discusses changes in daily electricity usage estimated using a DiD approach for MURB TOU participants, with results presented in Table 53.

7.1.5 Change in Demand Demand for this evaluation is defined as the maximum 15-minute electricity consumption in terms of power (kW) calculated at AMI interval data frequency (i.e. every 15 minutes)[. Figure 44](#page-93-0) illustrates a c...

AI summary The evaluation examines changes in electricity demand among MURB TOU participants during the Winter months. While some months showed potential demand reductions or increases, these changes were not statistically significant, indicating no overall impact on demand patterns.

Winter Winter Months Parameters Overall January February March November December Avg. Monthly Demand Reduction (kW)a -0.120.48 ± 1.624.45 3.2812. 9 ± 6.1322. 7 -1.666.60 ± 6.0124.0 0.873.5 1 ± 1.385.5 1 -1.656.60 ± 2.7611.2 -0.843.35 ± 2.6...

AI summary The table presents data on average monthly demand reduction and changes in demand for MURB TOU participants during winter and winter months. It includes values for different months and indicates statistical significance for all entries. Positive values indicate demand reduction, while negative values indicate demand increase.

[Figure 45](#page-95-0) illustrates the estimated relative demand reduction (%) overall and across specific TOU periods, including pre-morning peak, morning peak, morning snapback, pre-evening peak, evening peak, and evening snapback. Duri...

AI summary The text discusses estimated demand reductions during specific TOU periods for MURB TOU customers during the Winter 2024/25 evaluation period. While some periods show statistically significant reductions, others are not. The overall demand reduction of 1.4% is significant, with notable reductions during morning snapback and pre-evening peak periods.

Table 55: Changes in Demand by MURB TOU Participants during Winter Parameters Pre Morning b Peak Morning c Peak Morning Snapback d Pre Evening e Peak Evening Peakf Evening Snapbackg Overall Daily Avg. Demand Reduction (kW)a -0.2 80 ± 0.280...

AI summary Table 55 shows changes in demand by MURB TOU participants during winter, including average demand reduction, average demand, relative demand change percentages, and significance levels. The data highlights variations in demand reduction across different time periods and the statistical significance of these changes.

7.2.1 Change in Electricity Bills For the assessment of the economic impacts of the MURB TOU program, the same DiD approach used to estimate load and demand impacts was applied to evaluate changes in electricity bills for MURB TOU particip...

AI summary The economic impacts of the MURB TOU program were assessed using a DiD approach, applying applicable tariffs from 202511F to evaluate changes in electricity bills for participants during the pilot period.

7.2.2 Price Elasticity NS Power evaluated participants' responsiveness to energy price under MURB TOU pricing program, focusing on estimating daily price elasticity and inter-period Substitution Price elasticity. While Daily Price Elastici...

AI summary NS Power evaluated the price elasticity of MURB TOU participants, finding a daily price elasticity of -1.19 and an inter-period substitution elasticity of -0.032. Both values are not statistically significant, indicating limited responsiveness to price changes in electricity usage.

Findings Summary The TVP pilot tariffs have demonstrated success in reducing load during peak periods while providing participating customers with bill savings. Both residential tariff options evaluated continue to demonstrate a strong cap...

AI summary The TVP pilot tariffs have successfully reduced peak load and provided bill savings for residential customers. Both residential and commercial rates show positive load shift, though only in general rate participants. The evaluation supports the continued progress of TVP tariffs in shifting electricity usage to off-peak periods.

Residential TOU & CPP - Participants continue to demonstrate statistically significant reduced load during peak periods with minimal snapback. - For the first time, TVP combined with technology (e.g. smart thermostats) and DR (i.e. Eco Shi...

AI summary Residential Time-Varying Pricing (TVP) combined with technology and Demand Response (DR) has led to significant load reduction during peak periods. Customers who electrified their space heating also showed reduced peak load without significant increases in annual energy consumption.

Commercial TOU & CPP - Participants for the first time have demonstrated statistically significant reduced load during peak periods. This new finding is largely attributable to improvement in methodology and increased sample sizes. - The l...

AI summary The text discusses the effectiveness of Commercial Time-of-Use (TOU) and Conservation Program Participants (CPP) tariffs in reducing load during peak periods, particularly during cold weather. Results show significant load reductions during specific events, though variability exists across rate classes and months.

Multi-Unit Residential Building TOU - Despite the absence of a demand charge, MURB TOU customers did not increase their monthly demand during Winter. - Winter energy and bill savings were positive negative but not statistically significant...

AI summary The analysis of Multi-Unit Residential Building Time-of-Use (MURB TOU) customers showed no increase in monthly demand during Winter despite the absence of a demand charge. Winter energy and bill savings were not statistically significant, but overall, participants showed modest load reductions, particularly during evening peak periods, with no evidence of snapback.

Riders Although riders can change throughout a pilot Phase, to control for economic impacts associated with tariff change, a consistent tariff is used for all economic analyses. Riders are applied to all volumetric energy charges on a per...

AI summary Riders are applied consistently across all volumetric energy charges on a per kilowatt-hour basis to control for economic impacts from tariff changes. Table 58 provides a summary of rider rates for different rate classes, including TVP, DSM, and SCR.

Table 60: Top 88 ANL Hours 2020 - 2021 Load - Wind % of Halifax Temp. TOU Period Rank Date Start Time (Adjusted Net Load) Maximum (°C) AM Peak / PM Peak / (MW) Load Off-Peak 1 19-Feb-21 8:00 AM 1,722.6 100.0% -8.7 AM 2 21-Jan-21 5:00 PM 1,...

AI summary Table 60 presents the top 88 ANL (Abnormal Load) hours between 2020 and 2021, including details such as date, start time, adjusted net load in MW, percentage of maximum load, Halifax temperature, and TOU (Time-of-Use) period. The data shows peak load times and their corresponding temperatures and periods.

Energy Savings Programs E1 program participation14F [15](#page-112-1) and evaluation reports15F [16](#page-112-2) are used for control group selection balancing. Refer t[o Attachment III: Methodology](#page-111-0) (section III.2.4 E1 [Prog...

AI summary The document discusses the E1 program's use in control group selection balancing for the TVP evaluation, including the Eco Shift program's role in demand response through behind-the-meter technologies. It also references evaluation reports and matter numbers related to the DSM program.

III.2 Control Group Selection Control group selection in Phase 4 is comprised of four modified elements that will improve the evaluation relative to previous iterations. Changes are summarized i[n Table 66.](#page-113-1)

AI summary Control group selection in Phase 4 includes four modified elements aimed at improving evaluation compared to previous iterations, as summarized in Table 66.

Table 66: Summary of Changes to Control Group Methodology 17F [18](#page-113-3) Previous Method New Method Projected Benefit Neighbourhood criteria based on feeder and postal code combination Neighbourhood criteria to be based on region (i...

AI summary This table outlines changes to the control group methodology, including the use of region-based neighborhood criteria, incorporating heating classification and Eco Shift participation into the selection process, and integrating E1 program participation. These changes aim to improve load similarity metrics and streamline the control group selection process.

evaluated sub-group combinations (Table 67) have acceptable group level load similarity and balancing for E1 programming. Sub-Group Possible Sub-Groupings (784 Possible Combinations) TVP Tariff TOU / CPP / MURB TOU Rate Class Domestic / Sm...

AI summary The document evaluates sub-group combinations for E1 programming, focusing on load similarity and balancing. Table 67 outlines various sub-groupings, including TVP tariff, rate class, cohort, neighbourhood, heating classification, and Eco Shift participation, to assess their suitability for control group selection.

Where: i are datapoints corresponding the 24 hours in both seasons (Winter/non-Winter) and day types (weekend/weekday) for a total of 96 different combinations. $Tavg_i$ is the average load of the treatment customer during datapoint i. $Ca...

AI summary The text describes a method for analyzing customer load data across various timeframes and customer categories to identify potential control groups for E1 program participation. It outlines the calculation of average and peak load metrics and the balancing process across different subgroups.

III.2.2 Heating Classification Customers are first classified into three categories (Primary, Secondary, Non-Electric) based on their electricity consumption as visualized i[n Figure 48.](#page-117-1) Figure 48: Example of Heating Correlat...

AI summary Customers are classified into primary, secondary, and non-electric categories based on their electricity consumption and correlation with temperature data. The model uses AMI data and weather station temperatures during overnight hours to determine heating types, with specific thresholds for correlation coefficients and consumption levels.

Table 70: Heating Classification Definition Heating Classification Pre-Pilot Pilot Period Steady Electric Electric Electric Steady Non-Electric Non-Electric Non-Electric Electrified Non-Electric Electric De-electrified Electric Non-Electri...

AI summary Table 70 defines heating classifications for customers during the Pre-Pilot and Pilot Period, distinguishing between steady electric, steady non-electric, electrified, and de-electrified classifications. The classification ensures consistent treatment of customers in control group selection.

III.2.3 Eco Shift A new addition to the TVP evaluation included the effect of combining the Eco Shift administered by E1 with TVP. Control group selection for these customers was incorporated into this evaluations methodology to ensure tha...

AI summary The evaluation of the TVP (Transmission Voltage Planning) included the combined effect of Eco Shift and TVP. Control group selection was used to ensure similarity between treatment and control groups. NS Power requested a list of Eco Shift events from E1, which were mostly Critical Peak Pricing (CPP) events.

a Participants Eco Shift Event Date Event Hour Window Is CPP Event? Is Weekend? All 04-Dec-2024 5 PM – 9 PM ✓ x CPP 15-Dec-2024 5 PM – 9 PM ✓ ✓ All 20-Dec-2024 7 AM – 11 AM ✓ x CPP 22-Dec-2024 5 PM – 9 PM ✓ ✓ All 23-Dec-2024 5 PM – 9 PM ✓...

AI summary The table outlines Eco Shift events scheduled for various dates, specifying event hour windows, participant types (All or CPP), and whether the event occurs on a weekend. The section 'III.2.4 E1 Program Balancing' introduces a discussion on program balancing related to the E1 program.

Treatment and control customer participation in E1 programs (Home Energy Assessment, Green Heat, Appliance Retirement, Efficient Product Installation, Small Business Energy Solutions, Business Energy Rebates, and Custom) are balanced using...

AI summary The text discusses the process of balancing treatment and control customer participation in E1 programs to mitigate confounding effects on TVP results. It outlines methods for calculating energy savings using average per participant savings adjusted for line loss factors and allocates these savings based on program year and TVP cohort.

E1 Program Year (y) TVP Cohort (c) 2021 2022 2023 2024 Cohort 1 0.67 1 1 0.75 Cohort 2 0 0.67 1 0.75 Cohort 3 0 0 0.67 0.75 Cohort 4 0 0 0 0.42 Table 72: Phase 4 E1 Program Year and TVP Cohort Savings Correction Factor

AI summary Table 72 presents the Phase 4 E1 Program Year and TVP Cohort Savings Correction Factor for the years 2021 to 2024, showing varying correction factors for different cohorts.

III.2.5 Match Accuracy Once a final control group has been established, the quality of the control group selection match is defined by the 'Match Accuracy', refer to equation (4). $$Match\ Accuracy = \left(1 - \frac{\overline{ T_t - C_t }}...

AI summary The 'Match Accuracy' is a metric used to evaluate the quality of the control group selection in a study. It is calculated using equation (4), which compares the average absolute difference between treatment and control power data over 96 time intervals, relative to the average treatment power.

III.2.6 CPP Reference Days Although the regression model controls for time synchronous (i.e. zero hour lag-lead) temperature effects in the form of heating degree days (HDD), it is critical to find reference days with similar temperature p...

AI summary The document outlines the methodology for selecting reference days in Critical Peak Pricing (CPP) evaluations. It emphasizes the need for similar temperature profiles to event days, using criteria such as weekday type, proximity in time, and temperature comparisons via Euclidean distance to ensure accurate regression results.

III.3 Regression Models The regression models are divided into three sections, Residential TOU & CPP, Commercial TOU & CPP and MURB TOU. Notably, these regression models utilize hourly AMI data on a per-customer basis. This shift from prev...

AI summary The regression models are categorized into Residential TOU & CPP, Commercial TOU & CPP, and MURB TOU. They use hourly AMI data on a per-customer basis, leading to smaller margins of error compared to earlier TVP evaluation reports due to larger sample sizes in Phase 4.

III.3.1.1 Load & Usage Impact Regression Model The regression model used for load and usage impacts is presented in equation [(7).](#page-123-0) $$Load_{it} = \beta_0 + \beta_1.Treatment_i + \beta_2.PilotPeriod_t + \beta_3.(Pilot \times Tr...

AI summary The Load & Usage Impact Regression Model is presented in equation (7), which estimates the impact of treatment groups under TOU and CPP tariffs on load and energy consumption, incorporating variables such as HDD and pilot periods.

III.3.1.2 Economic Impact Regression Model The regression model used for billing impact is as follows in equation (9). $$DailyBill_{it} = \beta_0 + \beta_1.Treatment_i + \beta_2.PilotPeriod_t + \beta_3.(Pilot \times Treatment)_{it} + \beta...

AI summary The text presents a regression model used to analyze the economic impact of a billing program, incorporating variables such as treatment status, pilot period, HDD, and energy price. The model evaluates daily bill changes and price elasticity, with specific coefficients for each variable and their interactions.

III.3.2 Commercial TOU & CPP This section presents the methodology used to determine the load, usage, and economic impact of the commercial TOU and CPP tariffs. The estimates for load and economic impacts are rounded and reported according...

AI summary This section discusses the methodology used to evaluate the load and economic impacts of commercial Time-of-Use (TOU) and Critical Peak Pricing (CPP) tariffs. It outlines the use of a Linear Mixed Effect model and semi-DiD method due to the absence of a control group and small sample sizes, highlighting the challenges in analyzing commercial load impacts compared to residential.

III.3.2.1 Load and Usage Impact Regression Model Considering the methodological choices discussed above, the commercial TOU and CPP load/usage, billing as well as daily and substitution price elasticity are described by the equations (14),...

AI summary The document describes a regression model used to analyze the load and usage impact of commercial time-of-use (TOU) and critical peak pricing (CPP) programs. The model incorporates variables such as treatment, pilot period, heating degree days (HDD), and their interactions to estimate load and energy consumption changes.

III.3.2.2 Economic Impact Regression Model $$\begin{aligned} \textit{DailyBill}_{it} &= (\beta_0 + u_i) + \beta_1. Treatment_i + \beta_2. \textit{PilotPeriod}_t \\ &+ \beta_3. (\textit{Pilot} \times \textit{Treatment})_{it} + \beta_4. \tex...

AI summary The text presents three regression models used to analyze the economic impact of energy programs. The models include variables such as treatment, pilot period, heating degree days, and energy prices. The equations are used to assess the effects of demand-side management and time-varying pricing on daily billing, energy usage, and load patterns. The treatment variable is excluded for commercial time-of-use and critical peak pricing analyses due to the lack of control groups.

III.3.3.2 Economic Impact Regression Model The same regression as equations (15), (16) and (17) was used for bill saving and price elasticity analysis for MURB TOU. In addition to the metrics calculated for commercial TOU and CPP, the effe...

AI summary The economic impact regression model was applied to MURB TOU for bill saving and price elasticity analysis. The model uses 15-minute average AMI interval data to estimate demand, serving as a proxy for billed demand in TVP EM&V.

Stage 1 The first stage determined the potential control candidate group using three increasing sizes of selection pools with pre-pilot billing data. The primary neighbourhood criteria, street name (i.e. the smallest pool) was used first....

AI summary Stage 1 of the process used pre-pilot billing data to identify potential control candidate groups based on neighborhood criteria and a similarity metric (SM) calculated using a two-dimensional Euclidean distance formula.

Where: is the linearized monthly consumption for the treatment customer for month 'i' is the linearized monthly consumption for the control customer for month 'i' is the number of months compared between treatment and control, n = 12 in th...

AI summary The document outlines a method for identifying control customers by comparing their linearized monthly consumption data to that of treatment customers. A similarity metric based on the mean absolute difference over 12 months is used, with a threshold of 0.2. If fewer than 15 matches are found, the selection process expands to secondary and tertiary neighbourhood criteria.

Stage 2 AMI data from the neighbourhood selection pool candidates will be aggregated to average hourly interval consumption and peak hourly interval consumption during Winter and non-Winter, weekdays and weekends, creating two sets 96 data...

AI summary Stage 2 involves aggregating AMI data to calculate a similarity metric for treatment and control customers, using the ten lowest metrics to identify controls. This balances E1 program participation by third-party consultants with access to the E1 dataset.

IV.2.2 Phase 2 To account for customers who've modified their heating source from their original rate code classification, the heating classification methodology was modified to use AMI and nearby weather station temperature data for indiv...

AI summary The heating classification methodology was modified in Phase 2 to use AMI and weather station data for individual premises, focusing on days with average temperatures below 10 °C. Customers are classified as primary or secondary electric based on the correlation between consumption and temperature, along with a mean consumption threshold during the top 20 consumption days.

Table 73: Electric Space Heating Classification Requirements Mean load during top 20 consumption days R≤-0.35 R>-0.35 <0.5 kW Non-Electric Non-Electric ≥ 0.5 kW Electric Non-Electric The methodology was an improvement, however, this classi...

AI summary Table 73 outlines electric space heating classification requirements based on mean load during top 20 consumption days. The methodology was improved but failed to account for changes in heating type between the pre-pilot and pilot periods.

IV.2.4 Phase 4 Phase 4 (this Phase) of the evaluation uses a similar methodology as Phase 3, with two key distinctions that improved the accuracy of the model. - 1. Heating classification is used in addition to the neighborhood criteria du...

AI summary Phase 4 of the evaluation improves accuracy by using heating classification alongside neighborhood criteria for control group selection and restricting AMI data to overnight hours for heating classification. This ensures accurate load impact analysis and captures the effect of TVP price signals on space heating behavior.

Attachment V: Validation of Mixed Effects Regression The NS Power commercial TVP evaluations (Phase 1 – 3)24F [25](#page-137-1) has historically provided statistically insignificant results. Findings such as "statistically insignificant" c...

AI summary This document discusses the limitations of previous TVP evaluations by NS Power, noting that statistically insignificant results may stem from low statistical power or methodological issues. It highlights the difference between residential and commercial customer load profiles and explains how the use of a fixed effects regression model for commercial customers may have introduced Type II errors. In Phase 4, a mixed effects regression model was implemented to better account for commercial load heterogeneity.

age_138_Figure_5.jpeg) 125 26 Frees, E. W. (2003). Longitudinal and Panel Data: Analysis and Applications for the Social Sciences. Cambridge University Press. For commercial TOU customers during Phase 4[, Figure 52(](#page-139-0)a,b) prese...

AI summary The text compares Fixed-Effect and Mixed-Effect models for analyzing load reduction in commercial TOU customers. The Fixed-Effect model shows higher residual autocorrelation and larger median residual errors, suggesting weaker control over time-series dependence in the data compared to the Mixed-Effect model.

Baseload Heterogeneity Analysis The baseline segmentation analysis indicates substantial heterogeneity in electricity consumption patterns across participating MURBs, particularly with respect to Baseload levels. Based on these characteris...

AI summary The baseline segmentation analysis reveals significant differences in electricity consumption patterns among MURB customers, classifying them into Low, Medium, and High baseload classes. These classes show distinct baseload levels and load profiles, indicating substantial heterogeneity with implications for program impact estimation and model specification.

Residual Diagnostics for Fixed- vs. Mixed-Effect Modeling Approaches It is important to note that a Fixed-Effect model absorbs the time-invariant differences, such as Baseload heterogeneity across MURBs, by eliminating it from the analysis...

AI summary The text compares fixed-effect and mixed-effect models for analyzing time-of-use (TOU) programs, emphasizing that mixed-effect models are more suitable due to baseline load differences across MURBs. Residual diagnostics were conducted to evaluate model performance, focusing on residual error autocorrelation.

Attachment VI: Control Group Selection Non-Load Variables Metric Rate Treatment Control Population Median Income. 28 CPP $ 88,813 $ 85,986 $ 85,143 Median income. TOU $ 88,496 $ 86,853 φ 65,145 Average Residents per СРР 2.372 2.344 2.384 H...

AI summary This document presents a comparison of non-load variables between treatment and control groups, including median income, average residents per household, year built, and living space. It also provides data on house styles and their distribution across different groups.

Demand Response Overlap between NS Power and E1 In its Decision on E1's 2026 DSM Extension Application (M12249), the Board provided: The Board agrees that concerns about E1's demand response programs are better addressed in its consultatio...

AI summary The Board's decision on E1's 2026 DSM Extension Application (M12249) highlights concerns about potential overlap between E1's demand response programs and NS Power's Critical Peak Pricing Program. The Board expects E1 to address these concerns in its upcoming DSM Plan application and for NS Power to cooperate fully. An evaluation was conducted to assess load shift effects from overlapping programs.

Coincidence of Critical Peak Events and System Margin In its Decision and Order on the Year Three (2023/24) Evaluation Report (Year Three Report) in M11823, the Board provided: NS Power agreed with Synapse that system margin forecasts may...

AI summary The document discusses the relationship between system margin forecasts and Critical Peak Events (CPP) during the 2024/25 Winter Season. It notes that system margin forecasts during CPP events were significantly lower than during other peak hours and directs NS Power to analyze this correlation further in the Year 4 report.

31 M11823 – Board Decision and Order, 316514, page 5. October 31, 2024. Period of Low Forecast System Margin During Scheduled Critical Peak Event Date Hour Start Yes / Cancelled / No 2024-12-04 17:00 2024-12-04 18:00 2024-12-04 19:00 Yes 2...

AI summary The document lists dates and times during which scheduled critical peak events occurred, indicating periods of low forecast system margin. These events are marked with 'Yes' in the 'Yes / Cancelled / No' column, showing when demand management actions were required.

Reporting on Small Business Characteristics that Enable Load Shifting As part of its 2024 TVP Consensus Agreement, the Company committed to: h. Reporting back to stakeholders on changes that could be made to help customers determine whethe...

AI summary As part of its 2024 TVP Consensus Agreement, NS Power committed to reporting on small business characteristics that enable load shifting, including load characteristics, recruitment tactics, and customer reluctance to participate in TVP, TOU, and CPP options.

Definitions Adjusted Net Load The hourly net system requirement (MW) less all wind generation (MW) Cohorts Cohorts are numbered based on the Phase they enrolled in the TVP program. For example, Cohort 4 refers to participants recruited in...

AI summary The document defines key terms used in the regulatory proceeding, including Adjusted Net Load, Cohorts, Commercial, Consumption, Control, Demand, and Income categories. These definitions provide clarity on metrics and classifications relevant to the analysis of energy programs and customer data.

Nova Scotia Power (NS Power, Company) launched the Time-Varying Pricing (TVP) Tariff Pilot on November 1, 2021, with the purpose of encouraging customers to shift load from Winter peak periods to Winter off-peak periods. These tariffs pres...

AI summary Nova Scotia Power's Time-Varying Pricing (TVP) Tariff Pilot, launched in 2021, encourages load shifting to reduce winter peak energy demand. The fourth annual report highlights expanded participation, refined evaluation methods, and statistically significant load reductions, especially during high adjusted net load hours. The report also evaluates the first results for Multi-unit Residential Building (MURB) participants.

Table 1: Summary of Peak Load Reductions by Residential TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) TVP Tariff Morning Peak Evening Peak Morning Peak Evening Peak Residential TOU 0.15 kW 0.17 kW 7.4% 7.9% Residentia...

AI summary Table 1 summarizes peak load reductions by residential TVP tariff, showing that Residential CPP achieved higher reductions than Residential TOU. The text also discusses energy consumption patterns, technology impacts, and the benefits of electrification on load reduction and cost savings.

3 M11823 – TVP Pilot Program — 2023/24 (Year Three) Evaluation Report, Appendix A, page 51. July 31, 2024. TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) Morning Peak Evening Peak Morning Peak Evening Peak Commercial T...

AI summary This table summarizes load reductions by commercial TVP tariff, showing absolute and relative reductions during morning and evening peaks, with statistical significance confirmed for all metrics.

MURB TOU Findings Phase 4 marks the first load and economic impacts evaluation of the MURB TOU Pilot Tariff. Ten MURBs were recruited and are enrolled in the MURB TOU Tariff. For the MURB TOU Tariff, this Phase 4 evaluation includes the Wi...

AI summary Phase 4 of the MURB TOU Pilot Tariff evaluation found a 0.48 kW load reduction during peak periods, with evening reductions being statistically significant. However, load increased significantly in March. The evaluation covers the winter period from November 2024 to March 2025.

Table 3: Summary of Load Reductions from MURB TOU Tariff TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) Morning Peak Evening Peak Morning Peak Evening Peak MURB TOU 0.44 kW 0.54 kW 1.8% 2.1% Statistical Significance X...

AI summary Table 3 summarizes load reductions from the MURB TOU tariff, showing absolute and relative reductions during morning and evening peaks. Only evening peak results are statistically significant.

Introduction Time-Varying Pricing (TVP) is a scalable customer-focused demand response (DR) program. In Nova Scotia, the TVP pilot has grown significantly since it began in November 2021. Since its inception, the TVP pilot has offered two...

AI summary Time-Varying Pricing (TVP) is a demand response program in Nova Scotia that offers customers TOU and CPP tariffs. The TVP pilot has expanded since 2021, with a new MURB TOU Tariff launched in 2024. The program aims to reduce fuel and power costs by shifting demand from peak periods and is evaluated through an EM&V report.

Table 4: Impact Evaluation Metric Summary Category Metric TOU, CPP, MURB TOU (all rate classes unless specified) • Change in load (kW) during peak, and overall impact during Winter and non-Winter. Mid-peak hours included for MURB TOU. • Ch...

AI summary Table 4 outlines the impact evaluation metrics for load and usage changes during peak periods, including changes in load by region, income classification, and space heating. It also covers economic impacts such as changes in electricity bills and price elasticity related to time-varying pricing programs.

2 Control Group Selection The control group selection methodology uses 1-hour average and 15-minute peak load data to determine the best 1:1 control match by four neighbourhoods. If treatment customers are distributed throughout the provin...

AI summary The document discusses the methodology for selecting control groups in a regulatory proceeding, using geographic distribution data to ensure that treatment and control customers are proportionally aligned with Nova Scotia's population distribution. This ensures accurate comparisons between programs like CPP and TOU.

2.1.2 E1 Program Balancing As discussed in methodology, E1 program participation is accounted for in control group selection, refer to Attachment III: Methodology. The results indicate that E1 program participation across all sub-groups (e...

AI summary The E1 Program Balancing section discusses how program participation is accounted for in control group selection, achieving a high balance accuracy of 99.1% across all sub-groups, as defined by equation (3).

2.2 Commercial TOU & CPP The same methodological approach as used with residential control group (with exception to Neighbourhood Criteria, Heating Classification, and Eco Shift) was used in attempting to select a control group for Small G...

AI summary The document discusses the methodology used to select control groups for Commercial TOU and CPP rates, noting poor matching for Small General and General customers, leading to within-subject regression analysis. However, the MURB TOU control group selection was successful with a match accuracy of ≥90%.

3.1.1 Change in Load during Peak Periods [Table 10](#page-34-0) summarizes the results of the analysis for morning and evening TOU peak hours for each cohort and for all cohorts combined. The average load reductions are presented along wit...

AI summary The document discusses load reductions during peak periods for residential TOU participants, showing statistically significant reductions in both morning and evening hours. Evening peak hours saw slightly higher reductions, with overall load reductions of 0.15 kW and 0.17 kW during morning and evening periods, respectively. The analysis also highlights the impact of outdoor temperature on load shifts.

Change in Load by Region, Income Level, and Residents per Household [Figure](#page-37-1) 15 illustrates the average load reduction achieved by TOU participants according to the region of premises in Nova Scotia. While no significant differ...

AI summary The document analyzes load reduction among TOU participants in Nova Scotia, showing higher evening load reduction in the Halifax area and higher absolute load reductions among higher-income participants, though relative reductions do not vary significantly by income level.

Change in Load with Eco Shift E1's Eco Shift is a DR initiative offered separately from TVP and was implemented within subset of the TVP treatment participants who opted into both programs. It is designed to encourage load reduction during...

AI summary E1's Eco Shift DR initiative, when combined with TOU, significantly increased load reduction during peak hours. Analysis using DDD regression showed an additional 0.29 kW reduction during all TOU peak hours, with even greater reductions during morning and evening events. The combined effect of TOU and Eco Shift led to a total load reduction of 0.66 kW during Eco Shift events.

[Figure](#page-42-0) 20 depicts the distribution of Top 20, 50, and 88 highest ANL hours for residential TOU participants during morning peak, evening peak, and off-peak hours as well as weekends and holidays. As provided in [Figure](#page...

AI summary The text discusses the distribution of highest ANL hours for residential TOU participants and highlights significant load reductions during these periods. Results show a 6% relative load reduction during top ANL hours coinciding with TOU peak periods, with absolute reductions of 0.17 kW.

Participants Parameters High Highest ANL Hours ANL Hours Coincide TOU Peak Periods Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load 0.16 ± 0.04 0.14 ± 0.13 ± 0.17 ± 0.17 ± 0.17 ± Reduction (kW) a 0.16 ± 0.04 0.03 0.02 0.03 0.03 0.02 Avg...

AI summary The table presents statistical analysis of load reduction among participants in a demand response program, comparing average load, reduction in kW, and relative load reduction percentages across different participant tiers. It highlights the effectiveness of the program in reducing energy usage during peak periods.

Parameters De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic Winter, Non-Holiday Weekdays a Avg. Usage Reduction(kWh/day) 0.75 ± 0.20 -0.09 ± 0.14 1.96 ± 0.10 0.15 ± 0.07 Avg. Usage – Residential TOU Part...

AI summary The document presents data on average usage reduction across different seasons and customer segments, showing that residential TOU participants experienced varying levels of reduction, with higher reductions during non-holiday weekdays. No significant differences were found among income levels or between Halifax and other regions.

4 Residential CPP Tariff Impacts This section presents the analysis results for load and economic impacts of the Domestic CPP Tariff for all cohorts in Phase 4. The goal of the residential CPP pilot is to encourage customers to shift their...

AI summary This section discusses the load and economic impacts of the Domestic CPP Tariff during Phase 4, from April 1, 2024, to March 31, 2025. The goal is to encourage customers to shift electricity usage during CPP events, reducing system costs through demand response and infrastructure savings.

4.1.1 Change in Load during Peak Periods [Table 22](#page-51-0) summarizes the results of the analysis for morning and evening CPP events for each cohort and all participants combined. The average load reduction metrics are presented along...

AI summary This section presents the results of load reduction analysis during morning and evening CPP events for residential participants. The average load reductions and their margin of error are provided, showing statistically significant reductions across all cohorts, with cohort 4 having the smallest margin of error due to a larger sample size. Load reductions were 0.61 kW and 0.77 kW during morning and evening events, respectively, with corresponding relative reductions of 25.4% and 28.5%.

Change in Load by Space Heating Classes During Peak Events [Table 23](#page-52-0) summarizes the average load reductions during morning and evening events for space heating classes of all cohorts combined in Phase 4. It is important to not...

AI summary Table 23 summarizes average load reductions during morning and evening peak events for space heating classes in Phase 4. The analysis compares event and reference days during the 2024/25 Winter, noting that changes in space heating type between baseline and pilot periods do not directly impact load reduction results in the DiD analysis.

Morning Evening Parameters De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic De - El ec tr ifi ed El ec tr ifi ed El St ec ea tr dy ic No St n- El ea ec dy tr ic Avg. Load Reductiona 0.57 ± 0.66 ± 0.74 ±...

AI summary The table presents load reduction data from a demand response program, showing average load reductions and their significance across different parameters and time periods. The data includes statistics for both morning and evening loads, with significance levels indicated for each category.

Change in Load per Critical Peak Event [Figure](#page-53-0) 23a illustrates the average load reduction (kW) per CPP event date for all Phase 4 cohorts, along the corresponding event-average outdoor temperature (°C). The residential CPP par...

AI summary The document analyzes load reduction patterns among Critical Peak Pricing (CPP) participants in Nova Scotia. It shows that residential participants achieve significant load reductions, with higher reductions during evening events and in the Halifax area. Higher-income participants also exhibit greater load reduction percentages compared to lower-income participants.

Low-Income Medium-Income High-Income Parameters AM PM Overall AM PM Overall AM PM Overall Event Event Event Event Event Event Avg. Load 0.43 0.61 0.52 0.55 0.69 0.62 0.78 1.01 0.88 Reductiona (kW) ± 0.06 ± 0.06 ± 0.04 ± 0.04 ± 0.04 ± 0.03...

AI summary The table presents load reduction data for residential participants in a Critical Peak Pricing (CPP) program, categorized by income level. The data shows the average load, reduction in kW, and percentage reduction during critical peak events, with significance marked by p-values ≤ 0.05.

4.1.2 Snapback Effect The CPP participants exhibit relatively small but statistically significant snapback effects during morning, evening and double-event snapback periods, indicating significant increase in electricity usage during the f...

AI summary CPP participants show small but statistically significant snapback effects, with a 0.15 kW increase in electricity usage during double-snapback periods compared to single morning or evening events.

Table 25: Snapback Effect for Residential CPP Participants Parameters Average Load Reduction a (kW) Significance (p_Value≤0.05) Morning Snapback -0.04 ± 0.02 ✓ Evening Snapback -0.08 ± 0.03 ✓ Double-Event Snapbackb -0.15 ± 0.05 ✓ Overall S...

AI summary Table 25 presents the snapback effect for residential Critical Peak Pricing (CPP) participants, showing average load reductions during morning, evening, and double-event snapback periods, with statistical significance indicated. The results suggest that CPP programs lead to reduced electricity usage during peak times.

4.1.3 Change in Load during Highest ANL Hours [Figure](#page-58-0) 29 illustrates the distribution of Top 20, 50 and 88 highest ANL hours for residential CPP participants during weekdays and weekends as well as across CPP events. The major...

AI summary The text discusses the distribution of highest ANL hours for residential CPP participants, showing that most of these hours occurred on weekdays during peak periods. The data is illustrated in Figure 29 and includes a note about events with both morning and evening CPP peak hours.

Chapter 4: Residential CPP Tariff Impacts approximately 40%, 28% and 21% of all CPP events, which coincided with the top 20, 50 and 88 ANL hours, respectively, occurred during weekdays, while only 15%, 8% and 6% of CPP events, which coinci...

AI summary Chapter 4 discusses the impact of residential Critical Peak Pricing (CPP) tariff on load reduction. The analysis shows that CPP participants achieved statistically significant load reductions during the highest Adjusted Net Load (ANL) hours, with slightly higher reductions during CPP peak periods compared to overall highest ANL hours.

Parameters Highest ANL Hours ANL Hours Coincide with CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load Reductiona (kW) 0.75 ± 0.63 ± 0.55 ± 0.85 ± 0.84 ± 0.80 ± 0.07 0.06 0.04 0.07 0.06 0.05 Avg. Load – CPP Participants, Pre-P...

AI summary Table 26 presents data on load reduction during highest winter ANL hours for residential CPP participants, showing average load reductions and significance levels. The data indicates a reduction in average load for participants compared to pre-pilot levels, with statistical significance noted for all categories.

The change in daily usage levels was evaluated through two analyses. For the first analysis, the daily usage reduction during event days was estimated, when at least one CPP event occurred, compared to reference days. For the second analys...

AI summary The document evaluates the impact of Critical Peak Pricing (CPP) on daily electricity usage through two analyses. The first analysis found that CPP participants reduced their daily usage by 1.7 kWh during event days compared to reference days, with cohort 4 showing a statistically significant reduction. However, reductions were not significant for other cohorts or heating-class segments.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Usage Reduction a (kWh/day) 1.6 ± 4.0 3.1 ± 6.7 1.6 ± 2.9 1.6 ± 1.5 1.7 ± 1.3 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 61 71 55 52 54 Relative Avg. Usage Reducti...

AI summary The table presents average usage reduction and significance levels across four cohorts in a pilot program. Cohort 1 shows an average reduction of 1.6 kWh/day, while Cohort 4 shows a slightly higher reduction of 1.6 kWh/day. The significance level is marked for Cohorts 3, 4, and All, indicating statistical significance.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekdays Avg. Usage Reduction a (kWh/day) 3.0 ± 1.4 3.1 ± 2.0 1.0 ± 0.9 1.1 ± 0.5 1.2 ± 0.4 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 50.7 60.5 44....

AI summary The table presents data on average usage reduction and relative usage reduction across different cohorts during winter non-holiday weekdays, winter weekends/holidays, and non-winter days. The results show significant reductions in energy usage, particularly during winter non-holiday weekdays, with statistical significance noted for most cohorts.

[Table 29](#page-61-1) presents changes in daily electricity usage level by space heating for all cohorts of residential CPP participants during pilot period compared to pre-pilot period. During non-holiday weekdays in Winter, CPP particip...

AI summary Table 29 shows that during the pilot period, residential CPP participants with electrified space heating had a 13% increase in daily electricity usage, while de-electrified and steady electric participants saw reductions. Steady non-electric participants showed no significant change in usage.

Parameters De- Electrified Electrified Steady Electric Steady Non- Electric Winter, Non-Holiday Weekdays Avg. Usage Reduction (kWh/day) a 1.31 ± 1.08 -3.84 ± 0.81 2.02 ± 0.45 $0.15 \pm 0.33$ Avg. Usage – Residential CPP 42.0 29.6 55.6 18.7...

AI summary The table presents data on average energy usage reduction across different electrification scenarios during winter non-holiday weekdays, weekends/holidays, and non-winter days. It includes usage reduction in kWh/day, relative usage reduction percentages, and significance levels. The data shows significant reductions in some categories, while others show no significant change.

4.1.5 Effect of Weather (Temperature) on Load Reduction The DiD analysis reveals that the average load reduction is increased by $0.019 \, \text{kW}$ ( $\pm 0.003 \, \text{kW}$ ) per 1 °C decrease in outdoor temperature, implying that CPP...

AI summary The DiD analysis shows that residential CPP participants increase load reduction by 0.019 kW per 1 °C decrease in outdoor temperature, indicating a stronger response to colder weather. Figure 30 visually confirms this correlation.

4.1.6 Effect of Consecutive CPP Events on Load Reduction During Phase 4, like Phase 3, there was only one instance of two consecutive CPP events, which occurred when events 5 and 6 took place on December 23, 2024. The second of these conse...

AI summary During Phase 4, a single instance of two consecutive CPP events occurred on December 23, 2024, with the second event showing slightly higher but not statistically different load reduction compared to the first. The limited data from this single occurrence prevents broader conclusions about the impact of consecutive CPP events.

5 Commercial TOU Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial TOU program. The goal of the commercial TOU pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial TOU program, aiming to encourage load shifting by offering customers lower energy bills during off-peak periods. The sample size for Phase 4 includes 50 participants, with most enrolled in earlier cohorts.

5.1 Load Impacts This section presents and discusses the electrical load impact from the commercial TOU pilot.

AI summary This section discusses the electrical load impact resulting from the commercial time-of-use (TOU) pilot program. It provides an analysis of how the pilot affects overall load patterns and system performance.

5.1.1 Change in Load during Peak Periods [Figure](#page-66-1) 31 illustrates the load profiles for commercial TOU participants during the pre-pilot and pilot periods, along with the corresponding variation in outdoor temperature during the...

AI summary The analysis shows that commercial TOU participants reduced their electricity load during peak hours by an average of 0.8 kW, with reductions of 6.4%, 8.6%, and 7.4% during morning, evening, and overall peak periods, respectively. The results are statistically significant and consistent with the load profiles shown in Figure 31.

Change in Load during Peak Events by Region [Table 35](#page-67-0) presents the average load reduction (kW) by region (Halifax area versus the rest of Nova Scotia) for commercial TOU participants during morning, evening, and all peak hours...

AI summary The text introduces a table that presents average load reduction (kW) by region during peak hours for commercial TOU participants, highlighting differences between the Halifax area and the rest of Nova Scotia.

Chapter 5: Commercial TOU Tariff Impacts TOU participants located in the Halifax area exhibit statistically significant load reductions during both morning and evening peak hours with an overall load reduction of 1.4 kW, corresponding to 1...

AI summary Commercial TOU participants in the Halifax area show significant load reductions during peak hours, with a 12% overall reduction. Participants in other regions of Nova Scotia also show reductions, but only during evening peak hours and overall load reduction is statistically significant.

Halifax Area Rest of Nova Scotia Parameters Morning Peaks Evening Peaks All Peak Hours Morning Peaks Evening Peaks All Peak Hours Avg. Load Reduction (kW)a 1.22 ± 0.29 1.49 ± 0.31 1.38 ± 0.20 0.16 ± 0.17 0.24± 0.18 0.20 ± 0.15 Avg. Load ‒...

AI summary The table provides load reduction data for Halifax Area and Rest of Nova Scotia under time-of-use (TOU) programs. It shows average load reductions during peak hours and their significance, with higher reductions noted in the Halifax Area compared to the Rest of Nova Scotia.

Change in Load by Month during Peak Periods [Figure](#page-69-2) 33 illustrates the impact of commercial TOU in terms of average load reduction (kW), together with the average outdoor temperature (°C) during peak hours by month over the Wi...

AI summary Figure 33 shows the impact of commercial time-of-use (TOU) pricing on load reduction during winter peak periods from November 2024 to March 2025. Load reductions were significant across most months, with the largest reduction of 1.6 kW occurring in February 2025, despite low temperatures.

5.1.3 Change in Load during Highest ANL Hours [Figure](#page-70-1) 34 depicts the distribution of Top 20, 50 and 88 highest ANL hours for commercial TOU participants during off-peak, morning peak, evening peak, Weekends and Holidays. As se...

AI summary The analysis examines the distribution of highest ANL hours for commercial TOU participants, showing that these hours disproportionately occur on weekdays and during peak periods. Despite this, the load changes during these hours were not statistically significant, suggesting limited impact on overall load during peak times.

5.1.4 Change in Usage Relative Avg. Load Reduction Changes in daily electricity usage were estimated by applying the same method which was used for estimating hourly load-impact, to daily electricity usage. [Table 38](#page-71-3) presents...

AI summary The document discusses the estimation of changes in daily electricity usage for commercial TOU participants using a method similar to that used for hourly load-impact estimation. Table 38 provides the estimated changes in daily electricity usage in kWh/day.

Table 38: Change in Daily Electricity Usage (kWh/day) for Commercial TOU Participants Parameters Annual Average Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non Winter Avg. Usage Reduction (kWh/day)a -15.4 ± 3.9 -...

AI summary Table 38 presents the change in daily electricity usage (kWh/day) for Commercial TOU participants across different time periods. It includes average usage reduction, relative usage reduction percentages, and significance levels. The data indicates varying degrees of usage reduction during different seasons and days of the week.

6 Commercial CPP Tariff Impacts This section presents and discusses the estimated load and economic impacts of the commercial CPP program. The goal of the commercial CPP pilot is to encourage customers to shift their electricity usage from...

AI summary This section discusses the estimated load and economic impacts of the commercial Controlled Load Program (CPP) pilot, which aims to shift electricity usage from peak to off-peak periods. The analysis uses a Mixed-Effects modeling semi-DiD approach and covers CPP events from November 2024 to March 2025.

As shown i[n Table 41](#page-75-1), three CPP events (Dec. 15, 2024, Dec. 22, 2024, and Feb. 02, 2025) took place during weekends. The morning and evening consecutive events occurred on December 23, 2024. The CPP events include seven morni...

AI summary The document discusses Controlled Load Program (CPP) events that occurred during weekends and on cold days, noting the coldest event day. It also highlights the current participation rate for commercial CPP in Phase 4 and mentions the use of Table 42 for analyzing sample sizes.

6.1 Load Impacts This section presents and discusses the electrical load impact from the commercial CPP pilot.

AI summary This section presents and discusses the electrical load impact from the commercial CPP pilot program.

6.1.1 Change in Load during Peak Events [Table 43](#page-76-3) summarizes the results of the analysis for morning and evening CPP events for all cohorts of commercial CPP participants in Phase 4, where the average load reduction values are...

AI summary Table 43 summarizes the results of analysis for morning and evening CPP events in Phase 4, showing that commercial CPP participants achieved a 9.6% average load reduction during these events, with the associated margin of error at 90% confidence level.

Table 43: Change in Load during CPP Events for Commercial CPP Participants Parameters Morning Events Evening Events All Events Avg. Load Reduction(kW)a 7.9 ± 2.9 5.1 ± 2.7 6.3 ± 2.3 Avg. Load – Commercial CPP Participants, Pre-Pilot (kW) 6...

AI summary Table 43 presents data on the average load reduction for commercial participants in the Controlled Load Program (CPP) during morning, evening, and all events. The data shows significant load reductions during these events, with average reductions of 7.9 kW, 5.1 kW, and 6.3 kW, respectively, and corresponding percentages of 11.4%, 7.9%, and 9.6%.

Chapter 6: Commercial CPP Tariff Impacts [Figure](#page-78-0) 37b further disaggregates results of the average load reduction by morning and/or evening events per CPP event date. Notably, commercial CPP participants exhibited an average lo...

AI summary Chapter 6 examines the impact of the Commercial Controlled Load Program (CPP) tariff on load reduction. While some events showed load increases, others, particularly consecutive events, resulted in significant reductions. However, data limitations prevent broader conclusions about consecutive events' overall impact.

Halifax Area Rest of Nova Scotia Parameters Morning Events Evening Events All Events Morning Events Evening Events All Events Avg. Load Reduction (kW)a 10.6 ± 3.8 7.0 ± 3.4 8.6 ± 3.0 -0.1 ± 0.6 -1.0 ± 1.0 -0.7 ± 0.7 Avg. Load – Commercial...

AI summary The table compares average load reduction and relative load reduction percentages during morning and evening events in the Halifax Area and Rest of Nova Scotia. The data shows significant load reduction in the Halifax Area but minimal or negative reductions in the Rest of Nova Scotia, indicating differing impacts of the Controlled Load Program (CPP) in these regions.

6.1.2 Snapback Effect The snapback effect refers to commercial CPP customers that use more electricity than the baseline during the hours immediately following CPP event hours, compared to the pre-pilot period. This effect was evaluated ba...

AI summary The snapback effect refers to increased electricity usage by commercial CPP customers following CPP event hours. The analysis found statistically insignificant morning and evening snapback effects, but a significant 6.9 kW load increase during event days with both morning and evening CPP events.

Table 45: Snapback Effect for Commercial CPP Participants Parameters Load Reduction a (kW) Avg. Significance (p_Value≤0.05) Morning Snapback 0.6 ± 0.9 X Evening Snapback -0.1 ± 1.3 X b Morning & Evening Snapback -6.9 ± 3.4 ✓ Overall Snapba...

AI summary Table 45 presents data on the snapback effect for commercial Controlled Load Program (CPP) participants, showing average load reduction and significance levels for morning, evening, and overall snapback effects. The data suggests mixed significance across different time periods.

6.1.3 Change in Load during Highest ANL Hours [Figure](#page-82-0) 39 depicts the distribution of Top 20, 50 and 88 highest ANL hours for commercial CPP participants during weekdays and weekends as well as across CPP events. As seen in [Fi...

AI summary The text discusses the distribution of highest ANL hours for commercial CPP participants during weekdays and weekends, as well as load reductions achieved during these periods. It highlights that most high ANL hours occur on weekdays, and that load reductions are statistically significant during these times, especially during CPP peak periods.

Parameters Highest ANL Hours ANL Hours Coincide CPP Events Top 20 Top 50 Top 88 Top 20 Top 50 Top 88 Avg. Load Reduction (kW)a 6.8 ± 3.3 6.6 ± 2.8 5.0 ± 2.1 7.4 ± 3.4 8.2 ± 3.1 7.4 ± 2.6 Avg. Load Participants, Pre Pilot (kW) 64.4 61.4 61....

AI summary The table presents data on average load reduction and participant load levels before and during the pilot program, highlighting the effectiveness of the Controlled Load Program (CPP) in reducing energy usage. The results show statistically significant reductions in load, with percentages ranging from 8.1% to 13.3%.

Changes in electricity usage were estimated by applying the same Mixed-Effect modeling approach that was used for estimating hourly load impact, where the change in daily usage levels was evaluated through two analyses. For the first analy...

AI summary The document discusses the evaluation of electricity usage changes among commercial CPP participants using a Mixed-Effect modeling approach. Results show significant reductions in daily electricity usage during Winter, with increases observed during non-Winter periods.

Parameters Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non Winter Annual Average Avg. Usage Reduction (kWh/day)a 54.0 ± 10.9 51.2 ± 11.6 62.3 ± 16.6 -21.4 ± 3.7 -12.2 ± 6.7 Avg. Usage – Commercial CPP Participant...

AI summary Table 47 presents the average usage reduction and relative usage reduction percentages for Commercial CPP participants across different winter and non-winter periods. The data indicates a significant reduction in usage during winter, with statistical significance confirmed for all periods.

6.1.6 Effect of Consecutive CPP Events on Load Reduction During Phase 4, like Phase 3, there was only one instance of two consecutive CPP events, which occurred when events 5 and 6 took place on December 23, 2024. The second of these conse...

AI summary During Phase 4, a single instance of two consecutive Controlled Load Program (CPP) events occurred on December 23, 2024. The second event resulted in a statistically significant higher load reduction compared to the first, suggesting that consecutive events did not negatively impact load reduction. However, due to the limited data, broader conclusions cannot be drawn.

Table 49: Change in Daily Electricity Bill for Commercial CPP Participants Parameters Winter Non-Winter Annual Average Bill Savings ($/day)a Avg. -11.4 ± 13.1 0.1 ± 1.6 -9.8 ± 11.1 Avg. Bill, Commercial CPP Participants, Pre-Pilot ($/day)...

AI summary The table shows that commercial CPP participants experienced no significant change in their average daily electricity bill despite increased energy consumption during non-Winter months. This is due to reduced volumetric energy costs under the CPP, allowing them to lower their annual electricity bill.

6.2.2 Price Elasticity NS Power evaluated price elasticity to measure commercial CPP participants' responsivity to electricity prices during Phase 4 of the pilot. Specifically, NS Power evaluated Daily Price Elasticity during Winter season...

AI summary NS Power evaluated price elasticity for commercial Controlled Load Program participants during Phase 4 of the pilot. The analysis found that daily price elasticity was -0.39 in winter and -0.48 annually, while inter-period substitution elasticity was -0.14 but not statistically significant.

7 MURB TOU Tariff Impacts As this Phase represents the first year of the MURB TOU pilot program, participation was limited to 10 enrolled MURBs, with the pilot period beginning on November 1, 2024. The goal of the MURB TOU pilot is to info...

AI summary The MURB TOU pilot program, starting in November 2024 with 10 enrolled MURBs, aims to assess the impact of removing the demand charge on load shifting potential compared to the commercial TOU pilot. This section discusses the estimated impacts on load and economics.

7.1 Load Impacts This section presents and discusses the electrical load impact from the commercial MURB TOU pilot.

AI summary This section discusses the electrical load impact from the commercial MURB TOU pilot, providing an analysis of how this pilot program affects overall electrical load.

7.1.1 Change in Load during Peak Periods [Figure](#page-88-0) 41a illustrates the load profiles for MURB TOU participants and the control group during the pre-pilot and pilot periods with the corresponding variation in outdoor temperature....

AI summary The text discusses a study on load reduction during peak periods among MURB TOU participants. The analysis shows statistically significant reductions in electrical load during morning and evening peaks, with notable reductions also observed during midpeak and off-peak hours. The DiD analysis reveals an overall 1.9% load reduction, with specific impacts during different time frames.

7.1.2 Snapback Effect The snapback effect refers to using more electricity during hours immediately following peak periods. This effect is evaluated based on the same DiD approach that was applied to the load reduction calculation, but spe...

AI summary The snapback effect refers to increased electricity usage following peak periods. Analysis shows that MURB TOU participants had average load reductions during morning and evening snapback periods, with only the morning reduction being statistically significant.

7.1.3 Change in Load during Highest ANL Hours [Figure](#page-91-0) 43 depicts the distribution of Top 20, 50 and 88 highest ANL hours for MURB TOU participants during morning peak, evening peak, mid-peak, and off-peak hours. As seen in [Fi...

AI summary The text discusses load changes during the highest ANL hours for MURB TOU participants, showing load reductions during top 20, 50, and 88 hours, with statistical significance for top 50 and 88. Reductions are more pronounced during TOU peak periods.

Changes in daily electricity usage were estimated by applying the same DiD approach that was used for estimating hourly load impact. [Table 53](#page-92-3) presents the estimated changes in daily electricity usage (kWh/day) for MURB TOU pa...

AI summary The document discusses the estimation of changes in daily electricity usage for MURB TOU participants using a DiD approach, with results presented in Table 53.

7.1.5 Change in Demand Demand for this evaluation is defined as the maximum 15-minute electricity consumption in terms of power (kW) calculated at AMI interval data frequency (i.e. every 15 minutes)[. Figure](#page-93-0) 44 illustrates a c...

AI summary The evaluation examines changes in demand among MURB TOU participants during the Winter months. Although some months showed indications of demand reductions or increases, these changes were not statistically significant. Overall, no significant changes in monthly maximum demand were observed during the Winter period.

Winter Winter Months Parameters Overall January February March November December Avg. Monthly Demand Reduction (kW)a -0.48 ± 4.45 12.9 ± 22.7 -6.60 ± 24.0 3.51 ± 5.51 -6.60 ± 11.2 -3.35 ± 10.6 Avg. Monthly Demand – MURB TOU Participants, P...

AI summary The table presents average monthly demand reduction and relative demand changes for winter months, showing mixed results with some months indicating demand reduction and others showing demand increase. The data includes statistical significance markers and standard deviations.

[Figure](#page-95-0) 45 illustrates the estimated relative demand reduction (%) overall and across specific TOU periods, including pre-morning peak, morning peak, morning snapback, pre-evening peak, evening peak, and evening snapback. Duri...

AI summary Figure 45 and Table 55 show estimated demand reductions by MURB TOU participants during specific time-of-use periods in Winter 2024/25. While some periods show statistically significant reductions, others do not. The overall demand reduction is 1.4%, with notable reductions during morning snapback and pre-evening peak periods.

Table 55: Changes in Demand by MURB TOU Participants during Winter Parameters Pre- Morning Peak b Morning Peak c Morning Snapback Pre- Evening Peak e Evening Peak f Evening Snapback g Overall Daily Avg. Demand Reduction (kW) a -0.80 ± 0.80...

AI summary Table 55 presents changes in demand by MURB TOU participants during winter, showing average demand reductions and increases across various time periods. The data includes statistical significance and relative demand changes, with some periods showing significant reductions and others showing minimal changes.

7.2.1 Change in Electricity Bills For the assessment of the economic impacts of the MURB TOU program, the same DiD approach used to estimate load and demand impacts was applied to evaluate changes in electricity bills for MURB TOU particip...

AI summary The document discusses the economic impact of the MURB TOU program on electricity bills, using a DiD approach. Although MURB TOU participants experienced an average daily bill increase of about $4.5, it is not statistically significant. The rate was designed to save participants during non-Winter seasons.

7.2.2 Price Elasticity NS Power evaluated participants' responsiveness to energy price under MURB TOU pricing program, focusing on estimating daily price elasticity and inter-period Substitution Price elasticity. While Daily Price Elastici...

AI summary NS Power evaluated the price elasticity of electricity usage under the MURB TOU pricing program, finding a daily price elasticity of -0.17 and an inter-period substitution elasticity of -0.032. Both values are not statistically significant, indicating limited responsiveness of residential customers to price changes.

Findings Summary The TVP pilot tariffs have demonstrated success in reducing load during peak periods while providing participating customers with bill savings. Both residential tariff options evaluated continue to demonstrate a strong cap...

AI summary The TVP pilot tariffs have successfully reduced peak load and provided bill savings. Residential options continue to benefit the grid and customers, while commercial rates show positive load shift in general rate participants. The evaluation supports the ongoing progress of TVP tariffs in shifting electricity use to off-peak periods.

Residential TOU & CPP - Participants continue to demonstrate statistically significant reduced load during peak periods with minimal snapback. - For the first time, TVP combined with technology (e.g. smart thermostats) and DR (i.e. Eco Shi...

AI summary The document discusses the effectiveness of Time-Varying Pricing (TVP) combined with smart technology and demand response programs in reducing peak load. It also notes that customers on TOU and CPP who electrified their heating showed similar or slightly higher annual energy consumption compared to non-TVP customers.

Commercial TOU & CPP - Participants for the first time have demonstrated statistically significant reduced load during peak periods. This new finding is largely attributable to improvement in methodology and increased sample sizes. - The l...

AI summary The document discusses the effectiveness of Commercial Time-of-Use (TOU) and Controlled Load Program (CPP) tariffs in reducing load during peak periods. Statistically significant reductions were observed, particularly during cold weather events, though results vary by rate class and month.

Table 59: Tariff Summary (January 2025) Domestic SOR 83 5-Feb-23 3:00 AM 1,784.6 82.5% -15.7 Off-Peak 84 26-Feb-23 12:00 PM 1,781.2 82.3% -11.1 Off-Peak 85 4-Feb-23 12:00 AM 1,778.9 82.2% -24 Off-Peak 86 24-Feb-23 9:00 AM 1,778.5 82.2% -10...

AI summary Table 59 presents a summary of tariff data for January 2025, including details on domestic standard offer rates (SOR), load data, wind load percentages, Halifax temperature, time-of-use (TOU) pricing, and Controlled Load Program (CPP) events. The data includes rankings, dates, start times, adjusted net load, percentages of maximum load, temperature, TOU periods, and CPP events.

Energy Savings Programs E1 program participation[15](#page-112-1) and evaluation reports[16](#page-112-2) are used for control group selection balancing. Refer t[o Attachment III: Methodology](#page-111-0) (section III.2.4 E1 [Program Bala...

AI summary The document discusses the E1 program's participation and evaluation reports used for control group selection balancing. It also describes the Eco Shift program, which uses behind-the-meter technologies to manage load during events and mentions related evaluation reports and matter numbers.

Table 66: Summary of Changes to Control Group Methodology[18](#page-113-3) Previous Method New Method Projected Benefit Neighbourhood criteria based on feeder and postal code combination Neighbourhood criteria to be based on region (i.e. C...

AI summary This table outlines changes to the control group methodology, including the use of region-based neighborhood criteria, inclusion of heating classification in selection, and Eco Shift participation. These changes aim to improve load similarity metrics and streamline the control group selection process.

Where: $\bm{i}$ are datapoints corresponding the 24 hours in both seasons (Winter/non-Winter) and day types (weekend/weekday) for a total of 96 different combinations. $Tavg_i$ is the average load of the treatment customer during datapoint...

AI summary The document outlines a method for analyzing customer load data by comparing treatment and control customers across various timeframes and conditions, including seasons, day types, and tariff structures. It also mentions cleaning steps to remove customers with insufficient data, referencing Table 68.

Table 69: Electric Space Heating Classification Requirements Mean load during top 20 consumption days R≤-0.35 R>-0.35 < 0.5 kW Non-Electric Non-Electric ≥ 0.5 kW Electric Non-Electric 20 The correlation coefficient, , was calculated betwee...

AI summary Table 69 outlines electric space heating classification requirements based on mean load during top 20 consumption days. A correlation coefficient is used to assess the relationship between temperature and power consumption. Table 70 defines four heating classifications used to ensure consistency between treatment and control customers.

III.2.3 Eco Shift A new addition to the TVP evaluation included the effect of combining the Eco Shift administered by E1 with TVP. Control group selection for these customers was incorporated into this evaluations methodology to ensure tha...

AI summary The Eco Shift program, administered by E1, was combined with TVP in an evaluation. Control group selection was used to ensure similarity between treatment and control groups. NS Power requested a list of Eco Shift events from E1, which were mostly CPP events due to alignment between E1 and NS Power.

a Participants Eco Shift Event Date Event Hour Window Is CPP Event? Is Weekend? All 04-Dec-2024 5 PM – 9 PM ✓ x CPP 15-Dec-2024 5 PM – 9 PM ✓ ✓ All 20-Dec-2024 7 AM – 11 AM ✓ x CPP 22-Dec-2024 5 PM – 9 PM ✓ ✓ All 23-Dec-2024 5 PM – 9 PM ✓...

AI summary The table outlines Eco Shift events, including dates, times, and participant distinctions. It also mentions the E1 Program Balancing section, which likely relates to energy efficiency initiatives and their impact on grid management.

Treatment and control customer participation in E1 programs (Home Energy Assessment, Green Heat, Appliance Retirement, Efficient Product Installation, Small Business Energy Solutions, Business Energy Rebates, and Custom) are balanced using...

AI summary The text discusses the process of balancing treatment and control customer participation in E1 programs to mitigate confounding effects on TVP results. It outlines the methodology for allocating energy savings based on program year and TVP cohort using a specific equation and references evaluation reports and matter numbers.

III.2.5 Match Accuracy Once a final control group has been established, the quality of the control group selection match is defined by the 'Match Accuracy', refer to equation ([4)](#page-121-2). $$Match\ Accuracy = \left(1 - \frac{\overlin...

AI summary The 'Match Accuracy' formula is introduced to assess the quality of control group selection in a study. It calculates the accuracy by comparing the average absolute difference between treatment and control power data over 96 time intervals, normalized by the average treatment power.

III.2.6 CPP Reference Days Although the regression model controls for time synchronous (i.e. zero hour lag-lead) temperature effects in the form of heating degree days (HDD), it is critical to find reference days with similar temperature p...

AI summary The text discusses the methodology for selecting reference days for Critical Peak Pricing (CPP) events, emphasizing the need for similar temperature profiles and weekday types to minimize bias in regression models. Reference days must meet specific criteria, including proximity to the event day and matching weekday types, and are ranked using the Euclidean distance method based on temperature data.

III.3.1.1 Load & Usage Impact Regression Model The regression model used for load and usage impacts is presented in equation ([7)](#page-123-0). $$Load_{it} = \beta_0 + \beta_1. Treatment_i + \beta_2. PilotPeriod_t + \beta_3. (Pilot \times...

AI summary The document presents a regression model for analyzing load and usage impacts under different tariff structures, including Time-of-Use (TOU) and Critical Peak Pricing (CPP). The model includes variables for treatment groups, pilot periods, and heating degree-days (HDD), with adjustments for Eco Shift participants. The analysis will cover a specific time period for TVP customers in Eco Shift and compare them with non-Eco Shift customers.

Attachment III: Methodology second, the addition of a random intercept, changing the model from a fixed effects to mixed-effects model. Given the relatively small participant count of Eco Shift with TVP (TOU = 115 , CPP = 94) and heterogen...

AI summary This section discusses the methodology used in analyzing the impact of the Eco Shift program on Time-Varying Pricing (TVP) participants. It highlights the use of a mixed-effects model to account for heterogeneity among participants and the inclusion of a random intercept to improve the precision and reliability of the estimated program impacts.

III.3.1.2 Economic Impact Regression Model The regression model used for billing impact is as follows in equation ([9)](#page-125-0). $$\begin{aligned} \textit{DailyBill}_{it} &= \beta_0 + \beta_1. \textit{Treatment}_i + \beta_2. \textit{P...

AI summary The document presents a regression model used to analyze the economic impact of billing changes, including variables such as treatment status, pilot periods, and heating degree days (HDD). It also references models for daily price elasticity and inter-period substitution price elasticity.

III.3.2 Commercial TOU & CPP This section presents the methodology used to determine the load, usage, and economic impact of the commercial TOU and CPP tariffs. The estimates for load and economic impacts are rounded and reported according...

AI summary This section outlines the methodology used to evaluate the load and economic impact of commercial Time-of-Use (TOU) and Critical Peak Pricing (CPP) tariffs using a Mixed-Effects regression analysis and a within-customer modeling framework due to the absence of a control group. The analysis accounts for small sample sizes and heterogeneity among participants.

Attachment III: Methodology following common segmentation practice in demand research (e.g., Borenstein, $2005^{23}$ ; Hledik et al., $2010^{24}$ ); refer to equation (13). $$SLR_{i} = \frac{\overline{\left(L_{i,W}^{adj.}\right)}}{\overlin...

AI summary This section of Attachment III outlines the methodology used to classify commercial participants in the Time-Varying Pricing (TVP) program based on their seasonal load patterns. A seasonal load ratio (SLR) threshold of 0.7 was used to identify seasonal businesses, which showed a significant reduction in electricity usage during the winter. Most businesses were classified as non-seasonal, with only a few exhibiting seasonal behavior.

III.3.2.1 Load and Usage Impact Regression Model Considering the methodological choices discussed above, the commercial TOU and CPP load/usage, billing as well as daily and substitution price elasticity are described by the equations ([14)...

AI summary This section discusses the Load and Usage Impact Regression Model, using equations to describe commercial TOU and CPP load/usage, billing, and price elasticity. The model includes variables such as baseline, treatment, pilot period, HDD, and random intercepts.

III.3.2.2 Economic Impact Regression Model $$\begin{aligned} \textit{DailyBill}_{it} &= (\beta_0 + u_i) + \beta_1. \textit{Treatment}_i + \beta_2. \textit{PilotPeriod}_t \\ &+ \beta_3. (\textit{Pilot} \times \textit{Treatment})_{it} + \bet...

AI summary The document presents three regression models used to analyze the economic impact of time-varying pricing (TVP) programs, specifically focusing on daily billing, average energy usage, and peak-to-off-peak load ratios. The models incorporate variables such as treatment, pilot period, heating degree days, and energy prices. The 'Treatment' variable is excluded for commercial TVP due to the lack of control groups but is included in the MURB TOU regression.

III.3.3 MURB TOU The evaluation of MURB TOU is based on panel data estimation approach with matched control groups and pre-pilot data using the difference-in-difference (DiD) approach similar to the residential tariffs. Given the relativel...

AI summary The evaluation of MURB TOU uses a difference-in-difference approach with mixed-effects regression due to a small sample size, ensuring more reliable program impact estimates.

III.3.3.2 Economic Impact Regression Model The same regression as equations (15), (16) and (17) was used for bill saving and price elasticity analysis for MURB TOU.

AI summary The Economic Impact Regression Model, using equations (15), (16), and (17), was applied for bill saving and price elasticity analysis in the MURB TOU context.

IV.1.1 CPP (Phase 1) Consistent with the approved evaluation plan, the CPP evaluations used a within-subject control group methodology. In practice this meant that there was no control group for CPP customers, rather the only control was t...

AI summary The evaluation of the Critical Peak Pricing (CPP) program used a within-subject control group methodology initially, but after the first year, Econoler recommended modifying the approach to create a control group for CPP customers to align with Time-of-Use (TOU) evaluations.

Stage 1 The first stage determined the potential control candidate group using three increasing sizes of selection pools with pre-pilot billing data. The primary neighbourhood criteria, street name (i.e. the smallest pool) was used first....

AI summary Stage 1 of the process involved determining a potential control candidate group using pre-pilot billing data. The selection pools were based on increasing sizes, starting with the smallest pool defined by street name. A similarity metric 'SM' was used to compare monthly consumption data between treatment and control groups using a Euclidean distance calculation.

IV.3.2 Commercial The Commercial regression model used for Phases 1, 2, and 3 followed the same fixed effects regression framework as the Residential model, less the control participants due to poor control group match results. In practice...

AI summary The Commercial regression model used a fixed effects framework similar to the Residential model but faced challenges due to heterogeneity in the commercial rate class. Modifications, such as switching to a mixed effects regression and using robust standard errors, were implemented to improve model robustness and are detailed in Attachment V.

Attachment V: Validation of Mixed Effects Regression The NS Power commercial TVP evaluations (Phase 1 – 3)[25](#page-137-1) has historically provided statistically insignificant results. Findings such as "statistically insignificant" can b...

AI summary The document discusses the evaluation of NS Power's commercial TVP programs, noting that results have been statistically insignificant. This is attributed to low statistical power due to methodological choices and the heterogeneity of commercial customer load profiles compared to residential ones. The analysis highlights the need to re-examine the methodology for consistency.

ers Evaluated for Load Reduction during CPP Events, along with the Residual Error Distribution Associated with (c) Fixed Effect Modelling and (d) Mixed-Effect Modelling Given the baseload heterogeneity, time-series (hourly) load data and t...

AI summary The document evaluates load reduction during CPP events using Mixed-Effect models, which provide a more balanced error structure and improved model fit for commercial TOU and CPP load response analysis, making them statistically appropriate for assessing impacts.

Baseload Heterogeneity Analysis The baseline segmentation analysis indicates substantial heterogeneity in electricity consumption patterns across participating MURBs, particularly with respect to Baseload levels. Based on these characteris...

AI summary The baseline segmentation analysis reveals significant differences in electricity consumption patterns among MURB customers, classifying them into Low, Medium, and High baseload classes. These classes show distinct baseload levels and load profiles, indicating substantial heterogeneity with implications for program impact estimation and model accuracy.

Residual Diagnostics for Fixed- vs. Mixed-Effect Modeling Approaches It is important to note that a Fixed-Effect model absorbs the time-invariant differences, such as Baseload heterogeneity across MURBs, by eliminating it from the analysis...

AI summary The text discusses the comparison between fixed-effect and mixed-effect modeling approaches in evaluating the impact of time-varying pricing (TOU) programs on load reduction and bill savings. It highlights that mixed-effect models better handle baseline load heterogeneity, producing lower residual autocorrelation and more normally distributed residuals compared to fixed-effect models.

Attachment V: Validation of Mixed Effects Regression Given the small sample size of MURBs (n = 10), the substantial heterogeneity in Baseline electricity consumption across MURBs (Figure 54), and the supporting results from the comparison...

AI summary Due to the small sample size of MURBs, significant variability in baseline electricity consumption, and model comparison results, the Mixed-Effect modeling approach is recommended for estimating the impact of the TOU-MURBs pilot program.

Attachment VI: Control Group Selection Non-Load Variables Metric Rate Treatment Control Population CPP $ 88,813 $ 85,986 Median Income28 TOU $ 88,496 $ 86,853 $ 85,143 Average Residents per CPP 2.372 2.344 Household28 TOU 2.371 2.355 2.384...

AI summary This attachment presents a comparison of non-load variables between treatment and control groups in a study, including metrics such as median income, average residents per household, year built, and living space. It also details the distribution of house styles across different groups.

Demand Response Overlap between NS Power and E1 In its Decision on E1's 2026 DSM Extension Application (M12249), the Board provided: The Board agrees that concerns about E1's demand response programs are better addressed in its consultatio...

AI summary The Board's decision on E1's 2026 DSM Extension Application highlights concerns about potential overlap between E1's demand response programs and NS Power's Critical Peak Pricing Program. It emphasizes the need for E1 to address these issues in its upcoming DSM Plan application and expects NS Power to cooperate in exploring these matters.

Coincidence of Critical Peak Events and System Margin In its Decision and Order on the Year Three (2023/24) Evaluation Report (Year Three Report) in M11823, the Board provided: NS Power agreed with Synapse that system margin forecasts may...

AI summary The Board directed NS Power to analyze the relationship between system margin forecasts and Critical Peak Pricing (CPP) events in the Year 4 report, and to refine methods for estimating load reductions from Time-Varying Pricing (TVP) tariffs. Analysis showed that during CPP events, system margin was significantly lower compared to non-CPP event hours.

Attachment VII: Board Directives and Consensus Agreement Commitments - i. Summary of the general load characteristics of small business customers that may enable a small business customer to shift load in response to TVP, CPP, or TOU rates...

AI summary This section outlines NS Power's commitments related to customer recruitment and education for demand-side management programs, including the collection of business sector data and the evaluation of pilot tariffs. It also references the Board's decision on the Year Three Report and NS Power's agreement to improve customer education efforts.

Review of Marketing and Communication Efforts In its Decision and Order on the Year Three Report (M11823), the Board provided: Narrative Research conducted a customer survey, which highlighted residential customers' dissatisfaction regardi...

AI summary The Board's Decision and Order on the Year Three Report (M11823) highlights the need for NS Power to improve communication with customers regarding bill savings and TVP. NS Power has implemented tools to help customers understand TVP impacts and engaged stakeholders in marketing efforts. The Year 4 Report will include updates on these initiatives.

Summary of Year Five Evaluation Results The Year Five Report represents the second evaluation of the MURB TOU Tariff since its implementation in November 2024. In the 2025/26 Season, all ten enrolled MURB customers continued participation...

AI summary The Year Five Evaluation of the MURB TOU Tariff indicates modest load increases during peak periods, with no significant bill savings observed during the evaluated Winter period. The evaluation was limited by data availability due to a cybersecurity incident, and findings are based on a small participant population. The results are consistent with the tariff's design to encourage bill savings outside of Winter peak conditions.

Abbreviations AMI Advanced Metering Infrastructure ANL Adjusted Net Load CPP Critical Peak Pricing DiD Difference-in-difference DOM Domestic Rate Class DR Demand Response EM&V Evaluation Measurement & Verification GEN General Rate Class HD...

AI summary The text provides a list of abbreviations and their corresponding full forms used in the Nova Scotia regulatory proceeding. These terms include technical, regulatory, and operational terms relevant to energy management and utility operations.

Definitions Adjusted Net Load The hourly net system requirement (MW) less all wind generation (MW) Cohorts Cohorts are numbered based on the Phase they enrolled in the TVP program. For example, Cohort 4 refers to participants recruited in...

AI summary The document defines key terms used in the regulatory proceeding, including Adjusted Net Load, Cohorts, Commercial, Consumption, Control, Demand, Load, Peak, and Phase. These definitions are essential for understanding the evaluation methodology and results of the Time-Varying Pricing (TVP) program.

Nova Scotia Power (NS Power, Company) launched the Time-Varying Pricing (TVP) Tariff Pilot on November 1, 2021, with the purpose of encouraging customers to shift load from Winter peak periods to Winter off-peak periods. These tariffs pres...

AI summary Nova Scotia Power launched a Time-Varying Pricing (TVP) Tariff Pilot in 2021 to shift customer load from winter peak to off-peak periods. The fifth annual report covers April 1, 2025, to March 31, 2026. A cybersecurity incident affected data collection, and temporary modifications were applied to TVP Tariffs due to operational constraints. AMR meters were installed for MURB TOU participants, but data collection for the control group was incomplete, affecting evaluation results.

MURB TOU Findings Maintaining participation of the 10 MURBs under the MURB TOU Tariff, Phase 5 marks the second load and economic impact evaluation of this Tariff. Phase 5 results are inclusive of the Winter period from November 2025 to Ma...

AI summary The Phase 5 evaluation of the MURB TOU Tariff shows increased load during morning and evening peak periods, with overall peak load increasing by approximately 1.09 kW. The evaluation covered the winter period from November 2025 to March 2026 due to meter data availability issues during the non-winter portion of the 2025/26 Season.

TVP Tariff Absolute Load Reduction (kW) Relative Load Reduction (%) Morning Peak Evening Peak Morning Peak Evening Peak MURB TOU -1.29 kW -0.78 kW -5.3% -3.1% Statistical Significance ✓ ✓ ✓ ✓ Table 1: Summary of Load Reductions by MURB TOU...

AI summary Table 1 presents load reduction data for the MURB TOU rate class under the TVP tariff, showing absolute and relative load reductions during morning and evening peaks, along with statistical significance indicators.

Note: Throughout this report, statistically significant results will be displayed in tables with black font, whereas statistically insignificant results will be displayed in grey font. MURB TOU participants demonstrated statistically signi...

AI summary The analysis of MURB TOU participants shows statistically significant increases in electricity use during snapback periods and during high Adjusted Net Load hours. However, the overall increase in energy use during Winter did not lead to a statistically significant increase in bills. The study also notes limitations due to incomplete data coverage and small sample size, affecting the conclusiveness of findings.

February March Parameter a Phase 4 Phase 5 Phase 4 Phase 5 (Change)b (Change)b Avg. Load Reduction during Morning Peak +1.36 -1.36 -1.87 +0.06 Period (kW) (-2.72, ✓) (+1.93, ✓) Avg. Load Reduction during Mid Peak Period +2.80 -0.66 -0.67 +...

AI summary The table compares load and cost reductions between Phase 4 and Phase 5 for various peak periods and overall usage. The data shows mixed results, with some metrics showing statistically significant improvements and others not. Positive values indicate reductions/savings, while negative values represent increases in load or cost.

Introduction Time-Varying Pricing (TVP) is a scalable customer-focused demand response (DR) program. In Nova Scotia, the TVP pilot enrolment has grown significantly since it began in November 2021. Since its inception, the TVP pilot has of...

AI summary Time-Varying Pricing (TVP) is a demand response program in Nova Scotia that offers Time-of-Use (TOU) and Critical Peak Pricing (CPP) tariffs. Due to a cyber incident, these tariffs were temporarily paused during the 2025/26 Winter, except for the MURB TOU Tariff, which continued with AMR meters. The evaluation of the program is limited to the MURB TOU Winter-period performance, primarily February and March 2026.

Table 3: Impact Evaluation Metric Summary Category Metric MURB TOU Change in load during peak periods (kW) • Change in load (kW) during peak, mid-peak, and overall impact during Winter and non-Winter. • Change in load by month. Snapback (k...

AI summary Table 3 summarizes impact evaluation metrics for MURB TOU, including load changes during peak periods, snapback effects, load and usage impacts, and economic changes such as electricity bill variations and price elasticity.

Table 4: TVP Participation on March 31 2026 by Phase and Rate Class Phase DOM TOU SMG TOU GEN TOU DOM CPP SMG CPP GEN CPP MURB TOU Total Phase 1 676 24 284 8 0 992 Phase 2 891 17 373 2 0 1,283 Phase 3 2,241 37 19 922 3 6 0 3,228 Phase 4 5,...

AI summary Table 4 presents TVP participation by phase and rate class as of March 31, 2026, showing the number of customers enrolled in Time-of-Use (TOU) and Critical Peak Pricing (CPP) programs across different phases and rate classes, with Phase 5 having no participating customers except for MURB TOU.

1 Methodology The methodology is primarily composed of two components: control group selection and regression modelling of load and economic impacts. This methodology in detail was filed as Attachments III to V to the Phase 4 TVP EM&V Repo...

AI summary The methodology for evaluating Time-Varying Pricing (TVP) involves control group selection and regression modeling. Control groups are matched based on location and load profiles, and a mixed-effects regression model using difference-in-differences (DiD) is applied. Data quality for Phase 5 is noted to be lower than in Phase 4, which may affect results.

2 MURB TOU Tariff Impacts The goal of the MURB TOU pilot is to inform if removing the demand charge for select customer segments on the General Rate will enable additional load shifting potential compared to the commercial TOU pilot. This...

AI summary The MURB TOU pilot aims to assess load shifting potential by removing demand charges for select customer segments on the General Rate. However, data availability is limited, with only 60% of Winter period control data available, and November data entirely missing. This affects the accuracy of monthly and Winter overall results, which exclude November and rely heavily on February and March data.

2.1 Load Impacts This section presents and discusses the electrical load impact from the commercial MURB TOU pilot.

AI summary This section discusses the electrical load impact from the commercial MURB TOU pilot, providing insights into how time-of-use pricing affects load patterns in multi-unit residential buildings.

2.1.1 Change in Load during Peak Periods [Figure 2](#page-18-0) illustrates the load profiles for MURB TOU participants and the control group during the prepilot and pilot periods, along with the corresponding variation in outdoor temperat...

AI summary The analysis of load profiles for MURB TOU participants and the control group during the prepilot and pilot periods reveals statistically significant load increases, except during certain hours. The DiD analysis shows average relative load increases of 5.3% and 3.1% during morning and evening peak periods, respectively, with the largest increase observed during offpeak overnight hours.

Parameters Morning Peak Mid-Peak Periodb Evening Peak Overnight Periodc Overall On-Peak Avg. Load (kW)a Reduction -1.29 ± 0.40 -0.37 ± 0.32 -0.78 ± 0.42 -1.39 ± 0.27 -1.09± 0.30 Avg. Load – MURB TOU Participants, Pre Pilot (kW) 24.0 23.9 2...

AI summary The table presents load reduction data for MURB TOU participants during various peak and off-peak periods, showing average load reductions and their statistical significance. The results indicate a consistent reduction in load across all periods, with the highest reduction observed during the overnight period.

Change in Load by Month during Peak Periods [Figure 3](#page-20-3) illustrates the impact of MURB TOU on average load reduction (kW), along with corresponding average outdoor temperature (°C) during peak periods as well as mid-peak and off...

AI summary The text discusses the impact of MURB TOU on average load reduction during peak periods in the winter of 2025-2026. It notes that load increased in December, January, and February but decreased slightly in March, with the most significant increase occurring in January due to colder temperatures.

2.1.2 Snapback Effect The snapback effect refers to using more electricity during hours immediately following peak periods. This effect is evaluated based on the same DiD approach that was applied for load reduction calculation, but specif...

AI summary The snapback effect refers to increased electricity usage following peak periods. Using a DiD approach, MURB TOU participants showed significant load increases of 0.41 kW and 1.6 kW during morning and evening snapback periods, respectively.

Table 6: Snapback Effect for MURB TOU Participants in Winter Parameters Avg. Load Reduction (kW)a Relative Avg. Load Reduction (%)a Significance (p_Value≤0.05) Morning Snapbackb -0.41 ± 0.39 -1.7% ✓ Evening Snapbackc -1.60 ± 0.46 -7.1% ✓ O...

AI summary Table 6 presents the snapback effect for MURB TOU participants in winter, showing average load reductions during morning and evening periods, with statistical significance indicated. The data highlights the impact of time-varying pricing on load behavior in multi-unit residential buildings.

2.1.3 Change in Load during Highest ANL Hours [Figure 4](#page-21-0) depicts the distribution of Top 20, 50, and 88 highest ANL hours for MURB TOU participants during morning peak, evening peak, mid-peak, and off-peak hours. As seen in [Fi...

AI summary The document discusses the distribution of highest ANL hours for MURB TOU participants, showing that load increases occur during these hours, particularly during morning and evening peak periods. The load increases during top 20, 50, and 88 ANL hours were 6 kW, 1.7 kW, and 2.4 kW, respectively, with statistically insignificant differences between peak and non-peak periods.

Parameters Highest ANL Hours ANL Hours Coincide TOU Peak Periods raiameters Top20 Top 50 Top88 Top20 Top 50 Top88 Avg. Load Reduction (kW) a -6.2± 2.7 -3.7 ± 1.8 -3.6 ± 1.1 -6.0 ± 3.0 -1.7 ± 2.3 -2.4 ± 1.3 Avg. Load Pre- Pilot (kW) 25.1 24...

AI summary The table presents data on load reduction and average load pre-pilot for different ANL hours and TOU peak periods. It includes statistical significance values and notes that positive values represent load reduction, while negative values indicate load increase.

Changes in daily electricity usage were estimated by applying the same DiD approach that was used for estimating hourly load impact. Table 8 presents the estimated changes in daily electricity usage (kWh/day) for MURB TOU participants.

AI summary The document discusses the estimation of changes in daily electricity usage for MURB TOU participants using a DiD approach, with results presented in Table 8.

2.1.5 Change in Demand Demand for this evaluation is defined as the max 15-minute electricity consumption in terms of power (kW) calculated at AMI interval data frequency (i.e. every 15-minutes). Figure 5 illustrates a comparison of relati...

AI summary The evaluation defines demand as the maximum 15-minute electricity consumption during winter months and compares changes in monthly demand for MURB TOU participants. Although some indications of demand changes were observed, they were not statistically significant. Data quality issues during November, December, and January excluded these months from the analysis.

Table 9: Change in Monthly Demand by MURB TOU Participants during Winter (Feb, Mar) Winter Winter Months Parameters Overall January February March November December Avg. Monthly -1.2 ± 5.3 -3.9 ± 5.8 -3.4 ± Demand Reduction 12.2 (kW)a Avg....

AI summary Table 9 presents the change in monthly demand by MURB TOU participants during winter months (February, March) compared to baseline periods. It includes average monthly demand reduction, percentages, and statistical significance of the changes observed.

[Figure 6](#page-25-0) illustrates the estimated percent relative demand reduction overall and across specific TOU periods, including pre-morning peak, morning peak, morning snapback, pre-evening peak, evening peak, and evening snapback. D...

AI summary The text discusses the impact of Time-Varying Pricing (TOU) on demand in Multi-unit Residential Buildings (MURB) during the Winter evaluation period. It highlights an overall increase in relative demand by 5.8%, with the highest increase of 9.0% observed during the evening snapback period. The estimated overall daily demand increase of 1.6 kW is statistically significant, with variations observed across different TOU periods.

Table 10: Changes in Demand by MURB TOU Participants during Winter (Feb, Mar) Parameters Pre Morning Peak b Morning Peak c Morning Snapback d Pre Evening Peak e Evening Peak f Evening Snapback g Overall Daily Avg. Demand Reduction (kW)a -1...

AI summary Table 10 presents data on changes in demand by Multi-unit Residential Building (MURB) Time-of-Use (TOU) participants during winter months (February and March). The table shows average demand reduction during morning and evening peak periods, along with statistical significance, indicating the effectiveness of the TOU program in reducing energy consumption.

2.2 Economic Impacts This section presents and discusses the impact of MURB TOU Tariff on the electricity bill as well as price elasticity. Notably, due to data quality during November, December, and January, these months are not included...

AI summary This section discusses the economic impacts of the MURB TOU Tariff on electricity bills and price elasticity, noting that data from November to January was excluded due to quality issues affecting the regression analysis.

2.2.1 Change in Electricity Bills For the assessment of the economic impacts of the MURB TOU program, the same DiD approach used to estimate load and demand impacts was applied to evaluate changes in electricity bills for MURB TOU particip...

AI summary The economic impact of the MURB TOU program on electricity bills was assessed using a DiD approach, applying 2026 tariffs based on customer type and pilot period as outlined in Table 11.

2.2.2 Price Elasticity NS Power evaluated participants' responsiveness to energy price under the MURB TOU Tariff, focusing on estimating daily price elasticity and inter-period substitution price elasticity. While daily price elasticity re...

AI summary NS Power evaluated the price elasticity of MURB TOU Tariff participants, finding a daily price elasticity of -0.19 and an inter-period substitution elasticity of 0.64, though neither was statistically significant, indicating limited responsiveness of customers to price changes during Phase 5.

Findings Summary Although six of the seven TVP pilot tariffs were not evaluated in this report due to a temporary pause in those tariffs for the 2025/26 Season, the MURB TOU Tariff was evaluated for the second time. Due to data limitations...

AI summary The evaluation of the MURB TOU Tariff during Phase 5 showed modest load increases during peak and snapback periods, but no significant load shifting in response to price signals. Bill increases were statistically insignificant, and demand trends were inconsistent. The findings are limited by a small sample size and data constraints, leading to inconclusive results.

Table 13: Rider Summary Rider Rate Used in Analysis Fuel Adjustment Mechanism ($/kWh) 0.00207 Demand Side Management Cost Recovery ($/kWh) 0.00582 Storm Cost Recovery ($/kWh) 0.00091 Tariffs Although tariffs can change throughout a pilot P...

AI summary Table 13 summarizes three key riders used in the analysis, including the Fuel Adjustment Mechanism, Demand Side Management Cost Recovery, and Storm Cost Recovery, with associated rates per kWh. The document also notes that consistent tariffs are used in economic analyses to control for economic impacts.

Table 15: Top 88 ANL Hours 2025/26 Rank Date Start Time Load - Wind (Adjusted Net Load) (MW) % of Maximum Load Halifax Temp. (°C) MURB TOU AM Peak / PM Peak / Off Peak 1 25-Jan-26 5:00 PM 2366.8 100.0% -15 PM 2 25-Jan-26 6:00 PM 2346.5 99....

AI summary Table 15 presents the top 88 Adjusted Net Load (ANL) hours for 2025/26, detailing dates, times, load levels, temperature, and peak periods. The data shows peak load times and corresponding temperatures, with the highest load recorded at 2366.8 MW on January 25, 2026, at 5:00 PM.

TVP Pilot Benefits Provides customers directional price signals on electricity costs. Gives customers choice in how and when they consume energy. Customers are empowered to decrease their bills by shifting their use of electricity when it...

AI summary The TVP Pilot Benefits section outlines how Time-Varying Pricing (TVP) provides customers with price signals to manage their electricity use, offering them the ability to shift consumption to off-peak times. This results in lower bills, reduced need for infrastructure investment, and system savings through decreased fuel, power purchase costs, and carbon emissions.

DOM TOU SMG TOU GEN TOU DOM CPP SMG CPP GEN CPP MURB TOU Total Phase 1 676 24 284 8 0 992 Phase 2 891 17 373 2 0 1,283 Phase 3 2,241 37 19 922 3 6 0 3,228 Phase 4 5,616 37 18 2,642 17 36 10 8,376 Phase 5 5,020 34 16 2,376 16 36 10 7,508

AI summary The table presents data on the number of participants in different rate classes (DOM, SMG, GEN, MURB) across various phases for TOU and CPP programs. The data shows an increasing trend in participation from Phase 1 to Phase 5, with the highest number of participants in Phase 4 and Phase 5.

2025 Communications - Following the Board's direction in M12499, on the approach for the 2025/26 Season, NS Power completed the following: - Emailed all TOU customers on October 31, 2025 - Emailed all CPP customers on November 13, 2025 - L...

AI summary NS Power followed the Board's direction in M12499 to communicate with customers regarding the pause of the 2025/26 Season program. Emails were sent to TOU and CPP customers, and letters were mailed to those without valid email addresses. The website was updated to inform customers that the program is paused and that applications are closed.

Items to be Discussed at Future Technical Sessions and/or provided in the Year Four (2024/25) Evaluation Report Summary of 2024/25 Areas of Focus & Board Directives – Updated for 2026/27 Type Items to be Discussed/Reviewed 5.0 – Potential...

AI summary The document outlines areas of focus for 2024/25, including potential tariff changes to improve customer participation in the TVP program, discussions on new tariffs enabling behind-the-meter energy storage, and examination of alternative domestic tariffs with TOU structures. It also mentions the development of a weekend-inclusive TOU tariff and evaluation of energy storage use cases.

Responses Filed 31 July 2026 Stakeholder Comment NS Power Response 3. We know that rural small business operators face additional awareness and access barriers. Targeted outreach for these customers is needed if the pilot is to show statis...

AI summary The stakeholder highlights the need for targeted outreach to rural and hard-to-reach small businesses to achieve statistically significant SBA results. NS Power responds that their focus is on targeting the most cost-effective customers who show the highest interest in enrolling and ability to shift load, and they welcome insights from the SBA on demographics not currently reached.

Phase - Represents the EM&V pilot period (i.e. the study year). - Phase 1 to 3 EM&V was completed by Econoler. - Phase 4 is the first year NS Power has conducted the EM&V. - Each Phase has come with methodological improvements and addition...

AI summary The document describes the different phases of the EM&V pilot period, noting that Econoler completed Phases 1 to 3, while Phase 4 is the first year NS Power has conducted EM&V. Each phase has included methodological improvements and additional metrics developed through stakeholder consultation.

New Scope Items in Phase 4 - 1. Effect of Eco Shift on Load Reductions. Eco Shift is a residential program that adds smart technology (i.e. Smart Thermostats, EV Managed Charging). Eco Shift program is administered by E1.

AI summary This section introduces a new scope item in Phase 4 related to the impact of the Eco Shift program on load reductions. The program, administered by E1, involves the installation of smart technologies such as smart thermostats and EV managed charging in residential settings.

- 2. Multi unit residential building (MURB TOU) Tariff EM&V. MURB TOU scope includes existing commercial TOU scope and additional scope specific to MURB TOU. Table 4: Impact Evaluation Metric Summary Category Metric TOU, CPP, MURB TOU (all...

AI summary The document discusses the evaluation metrics for the Multi Unit Residential Building (MURB) Time-Varying Pricing (TVP) Tariff, including load changes during peak and non-peak periods, regional differences, income classification impacts, and economic effects such as changes in electricity bills and price elasticity.

Control Group Selection Methodology Improvements - NS Power broadened the Neighbourhood Criteria, allowing the addition Heating Classification and Eco Shift into the control group selection criteria.

AI summary NS Power has expanded the Neighbourhood Criteria for control group selection by including Heating Classification and Eco Shift, aiming to improve the methodology for selecting control groups.

- This process was further streamlined & improved by including E1 program participation balancing in the control group selection process. Previous Method New Method Projected Benefit Neighbourhood criteria based on feeder and Neighbourhood...

AI summary The process for selecting control groups in program evaluations has been improved by incorporating E1 program participation balancing and Eco Shift participation into the selection process, which is expected to improve load similarity metrics and streamline the process.

Context This memo summarizes the support provided by Econoler to Nova Scotia Power (NS Power) in the finalization of Time-Varying Pricing (TVP) Pilot Program Phase 4 Evaluation. Econoler was contracted by NS Power to provide advisory suppo...

AI summary Econoler provided advisory support to Nova Scotia Power (NS Power) in finalizing the evaluation of Time-Varying Pricing (TVP) Pilot Program Phase 4. Econoler reviewed methodological changes, including the use of mixed-effect models and robust margins of error, and submitted clarifying questions, which were addressed by NS Power.

Conclusion of the Review Econoler provided guidance and clarifications to NS Power to ensure a consistent and appropriate application of the overall evaluation methodology established for TVP Phase 1 to 3. Additionally, based on the discus...

AI summary Econoler provided guidance and clarifications to NS Power to ensure consistent application of the evaluation methodology for TVP Phase 1 to 3. Econoler validated the methodological changes and the justifications provided by NS Power in the report.

Participation & Retention - Total enrolment has increased by 5,095 participants from Phase 3 to Phase 4 across both residential TVP rates. - Commercial enrolment in CPP and MURB TOU only.

AI summary Total enrolment in residential TVP rates has increased by 5,095 participants from Phase 3 to Phase 4, while commercial enrolment is limited to CPP and MURB TOU programs.

- Overall, TOU participants demonstrated load reductions of 0.15 kW and 0.17 kW, corresponding to relative load reductions of 7.4% and 7.9% during morning and evening periods, respectively. Mornin g Peak P eriods Evening g Peak Pe eriods P...

AI summary The analysis shows that TOU participants achieved load reductions of 0.15 kW and 0.17 kW during morning and evening peak periods, representing 7.4% and 7.9% reductions respectively. The data is presented across multiple cohorts, with varying levels of significance.

Load Impact with Eco Shift Events • TOU program alone led to an overall load reduction of 0.26 ± 0.02 kW - TOU participants that also enrolled in Eco Shift reduced their overall load by an additional 0.40 kW ± 0.04 kW during all TOU peak h...

AI summary The TOU program alone led to a 0.26 kW load reduction. Participants who also enrolled in Eco Shift achieved an additional 0.40 kW reduction during peak hours, showing that smart energy technology improves residential load reduction during demand response events.

Load Impact with Eco Shift Enrolment - TOU program alone led to an overall load reduction of 0.15 ± 0.002 kW - TOU participants that also enrolled in Eco Shift reduced their overall load by an additional 0.29 kW ± 0.01 kW during all TOU pe...

AI summary The TOU program alone led to a 0.15 kW load reduction, and combining it with Eco Shift further reduced load by 0.29 kW during peak hours. Smart energy technology improves residential load reduction during peak periods without demand response events.

- All cohorts reduced their daily usage during Non -Winter, regardless that peak periods are not applicable. Overall, a reduction of 0.54 kWh per day was observed. Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Week...

AI summary All cohorts reduced their daily energy usage during Non-Winter periods, even though peak periods were not applicable. An overall reduction of 0.54 kWh per day was observed, with varying levels of reduction across different cohorts.

- During non -Winter, savings of $1.54 per day were observed (32.6% decrease) Parameters Winter Non- Holiday Weekdays Winter Holiday/ Weekends Winter Overall Non-Winter Annual Average Avg. Bill Savings ($/day) a -2.05 ± 0.01 0.12 ± 0.01 -1...

AI summary During non-Winter periods, the program observed an average daily savings of $1.54, representing a 32.6% decrease. The data contrasts with non-significant savings during Winter periods, indicating seasonal variations in program effectiveness.

Load Impact by Cohort Moi rning Eve ents Eve ning Eve nts Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Avg. Load Reduction a (kW) 0.68 ± 0.10 1.22 ± 0.16 0.62 ± 0.06 0.60 ± 0.03 0.61± 0.03 0.85...

AI summary The table presents load reduction data across four cohorts during morning and evening Critical Peak Pricing (CPP) events. All cohorts show statistically significant load reductions, with varying magnitudes and significance levels indicated. The data includes average load reduction, residential load pre-pilot, and relative load reduction percentages.

Load Profiles - CPP participants show statistically significant load reduction during all events including the three events occurred during weekends. - CPP participants achieved an average load reduction of 0.76 kW during weekends, which i...

AI summary The load profiles show that CPP participants achieved significant load reductions during weekend and evening events, with the highest reduction of 0.9 kW during an evening event on February 5, 2025, when the temperature was -12 °C.

Load Impact by Space Heating Classification Mor ning Eve ning Parameters De-Electrified Electrified Steady Electric Steady Non-Electric De-Electrified Electrified Steady Electric Steady Non-Electric Avg. Load Reduction® 0.57 ± 0.66 ± 0.74...

AI summary This section presents load impact data by space heating classification, showing average load reductions and significance levels for different electrification scenarios. The data compares de-electrified, electrified, and steady electric/non-electric categories during morning and evening periods, highlighting the impact of Time-Varying Pricing (TVP) on residential Critical Peak Pricing (CPP) participants.

- All Space Heating Classes exhibit statistically significant load reduction during morning and evening CPP events. - CPP participants with Steady-Electric heating systems achieved the highest load reductions of 0.74 kW and 0.92 kW during...

AI summary The text discusses load reduction during Critical Peak Pricing (CPP) events, noting that all space heating classes show significant reductions. Steady-Electric heating systems achieved the highest load reductions, followed by electrified space-heating systems.

Load Impact with EcoShift Participants a Eco Shift Event Date Event Hour Window Is CPP Event? Is Weekend? All 04-Dec-2024 5 PM - 9 PM ~ x CPP 15-Dec-2024 5 PM – 9 PM ✓ ✓ All 20-Dec-2024 7 AM – 11 AM ✓ х CPP 22-Dec-2024 5 PM – 9 PM ✓ ✓ All...

AI summary The document outlines the alignment of Eco Shift events with Critical Peak Pricing (CPP) and Time-of-Use (TOU) events, noting that nearly all Eco Shift events coincided with CPP events, with seven events aligning with both CPP and TOU.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekd ays Avg. Usage Reduction (kWh/day) a 3.0 ± 1.4 3.1 ± 2.0 1.0 ± 0.9 1.1 ± 0.5 1.2 ± 0.4 Avg. Usage – Residential CPP Participants, Pre-Pilot (kWh/day) 50.7 60.5 44...

AI summary The table presents usage reduction data across four cohorts during different periods (winter weekdays, winter weekends/holidays, and non-winter days). It shows average usage reductions, relative usage reduction percentages, and significance levels for each cohort. The data indicates varying levels of effectiveness in reducing energy usage, with some cohorts showing statistically significant results and others not.

- During non -Winter days, participants saved about $0.75 per day. Parameters Winter Non- Holiday Weekdays Winter Holiday/ Weekends Winter Overall Non-Winter Days Annual Average Avg. Bill Savings ($/day) a 0.14 ± 0.01 0.80 ± 0.02 0.37 ± 0....

AI summary The text provides data on average bill savings for participants in a demand-side management program, showing higher savings on non-Winter days and during winter holidays/weekends. The savings are presented with statistical significance and relative percentages.

- Annually, except for electrified CPP participants, other space heating types achieved statistically significant annual bill savings ranging from $153 to $248 Parameters De- Electrified Electrified Steady Electric Steady Non-Electric Wint...

AI summary The text presents annual bill savings for different space heating types, excluding electrified CPP participants, with statistically significant savings ranging from $153 to $248. The data includes average daily bill savings, relative savings percentages, and significance levels for winter, non-winter, and annual periods.

Key Findings - Residential TOU & CPP participants continue to demonstrate statistically significant load reduction during peak periods with minimal snapback. - For the first time, TVP combined with technology (e.g. smart thermostats) and D...

AI summary The Key Findings section highlights that residential Time-Varying Pricing (TVP) participants, especially those using smart thermostats and demand response programs like Eco Shift, showed significant load reduction during peak periods. Electrification of space heating also contributed to reduced peak load without significant increases in annual energy consumption.

- The lowest usage increase of 1.9% was observed during non -Holiday Weekdays in Winter. This amount of usage increase was not statistically significant. Parameters Annual Average Winter Overall Winter – Non-Holiday Weekdays Winter – Holid...

AI summary The lowest usage increase of 1.9% was observed during non-Holiday Weekdays in Winter, but this increase was not statistically significant. The data shows varying levels of average usage reduction across different time periods and parameters.

- Annually, the participants experienced a statistically insignificant increase in daily electricity bill by 3.1% Parameters Winter Non-Winter Annual Overall Avg. Bill Savings ($/day) a -11.9 ± 3.4 6.2 ± 3.1 -1.1 ± 3.1 Avg. Bill, Commercia...

AI summary Participants in the program experienced a statistically insignificant annual increase in daily electricity bills by 3.1%, with average bill savings showing mixed results between winter and non-winter periods.

Load Impact by Peak Period - The commercial CPP participants demonstrated significant average load reductions during morning and evening CPP events with a relative load reduction of 6.3 kW (9.6%) overall for all events.

AI summary Commercial Critical Peak Pricing (CPP) participants showed significant load reductions during morning and evening CPP events, with an overall relative load reduction of 6.3 kW (9.6%) across all events.

- Opposite to residential, commercial CPP participants achieved higher load reductions during mornings; however, this difference is not statistically significant. Parameters Morning Events Evening Events All Events Avg. Load Reduction (kW)...

AI summary Commercial Critical Peak Pricing (CPP) participants achieved higher load reductions during mornings compared to residential participants, though the difference is not statistically significant. The data shows average load reductions and relative percentages for morning, evening, and all events.

- Annually, an increase of 12.2 kWh per day was observed (1.7%) Parameters Winter Overall Winter – Non-Holiday Weekdays Winter – Holiday Weekends Non- Winter Annual Average Avg. Usage Reduction (kWh/day) a 54.0 ± 10.9 51.2 ± 11.6 62.3 ± 16...

AI summary The text presents data on energy usage reduction, showing an annual increase of 12.2 kWh per day. Tables provide details on average usage reduction, relative usage reduction percentages, and significance levels across different timeframes and participant categories.

- Notably, Annual & non-Winter results are derived from Cohorts 1 to 3, where Winter results are derived from Cohorts 1 to 4. · Parameters Winter Non-Winter Annual Average Avg. Bill Savings ($/day) a -11.4 ± 13.1 0.1 ± 1.6 -9.8 ± 11.1 Avg....

AI summary The text presents data on bill savings and average bills for different seasons and cohorts. Winter results are based on Cohorts 1-4, while Annual & non-Winter results are based on Cohorts 1-3. The data includes average bill savings, commercial CPP bills, participant data, relative bill savings, and significance levels.

Load Profiles & Load Reduction by Peak Period - Load reduction for MURB TOU are statistically significant between 10 AM and 6 PM. - There are no hours where statistically significant load increases. Although results are statistically signi...

AI summary The load reduction for MURB TOU is statistically significant between 10 AM and 6 PM, but due to the low sample size, the results are not conclusive. No statistically significant load increases were observed.

- Load reduction during the midpeak period is the highest, which was not expected as the MURB TOU mid-peak electricity rate is half that of the evening peak rate. Parameters Morning Peak Mid-Peak Period b Evening Peak Overnight Period c Ov...

AI summary Load reduction during the midpeak period was the highest, despite the MURB TOU mid-peak electricity rate being half that of the evening peak rate. Statistical significance was noted, but results are not conclusive due to a low sample size.

- Notably, November demonstrated the greatest daily energy reduction, however this result is statistically insignificant. Parameters Winter Winter Months Parameters Overall January February March November December Avg. Usage Reduction 16.5...

AI summary The text discusses energy usage reductions during winter months, with November showing the highest daily energy reduction, though the result is statistically insignificant. The data includes average usage, reduction percentages, and significance indicators for various months.

Demand - Although MURB TOU participants exhibited potential indications of relative monthly demand reductions in January and March, and increases in November, December, and February, these changes were not statistically significant. - Caut...

AI summary The analysis of MURB TOU participants shows potential demand reductions in some months but not statistically significant due to low sample size. Caution is advised as results may change with larger samples.

TVP Phase 4 Key Milestones - NS Power has completed the EM&V completely in-house for the first time. - Residential customers continue to shift load similarly to previous evaluations with significant increase in enrolment. - Eco Shift + TVP...

AI summary TVP Phase 4 has seen NS Power complete EM&V in-house for the first time, with residential customers showing continued load shifting and significant enrolment increases. Eco Shift and TVP have enhanced load shift, while commercial and MURB TOU and CPP rates are showing positive results. Updates on the Year Five (2025/26) Evaluation Report are also mentioned.

Request IR-2: - In response to Board IR-6 in the Year 3 Report in matter M11823, NS Power was asked if it will - revise the control group matching approach to improve the match for de-electrified participants, - to which NS Power responded...

AI summary In response to Board IR-6 in matter M11823, NS Power was asked if it will revise the control group matching approach for de-electrified participants. NS Power indicated it will consider a revised approach if a viable method is found. The question now asks if NS Power has identified or considered any revised approaches since filing the Year 3 Report.

Request IR-6: - Appendix A, pdf page 37 presents Table 6: Participation Summary and Retention Results. With - reference to the Small General and General customers: - October 31, 2024: 42 TOU customers (36+6) and 21 CPP customers (3+19) for...

AI summary The document highlights discrepancies in the reported numbers of Time-of-Use (TOU) and Critical Peak Pricing (CPP) commercial participants across different sections of the Year 4 Report. Specifically, there is a mismatch in the number of new enrollments and total participants between Appendix A and Appendix C.

Request IR-8: - NS Power's Demand Charge Assessment on pdf page 502 states "On average customers shifting - load from peak to off-peak hours experience demand increase with negligible increase in savings - after shifting 6% or more of peak...

AI summary NS Power's demand charge assessment indicates that shifting load beyond 6% may increase demand charges despite energy cost savings. The document requests discussion on NS Power's plans to model the removal of the demand charge.

Request IR-4: Reference: M12932 N-1 TVP Evaluation Report, Appendix A, p. 15. "Notably, no change in monthly demand was observed in MURB TOU participants, despite the removal of the demand charge." Question: Does the referenced finding of...

AI summary The referenced finding indicates that removing the demand charge on MURB TOU tariffs did not change monthly demand, raising questions about whether demand charges are a barrier to load shifting on other TVP tariffs.

Request IR-5: Reference: M12932 N-1 TVP Evaluation Report, Appendix Am Table 52 and the following statement from Appendix A, p. 92: "Results indicate that MURB TOU participants exhibit load reduction during the top 20, 50, and 88 highest A...

AI summary The text references a study on MURB TOU participants' load reduction during peak ANL hours, noting that only reductions during top 50 and 88 hours are statistically significant. It asks NS Power to clarify which ANL-hour distribution they rely on for assessing the MURB TOU tariff's peak reduction contribution and how they handle the lack of significance in the top 20 ANL hours.

Request IR-6: Reference: M12932 N-1 TVP Evaluation Report, Appendix A, p. 91 and Table 51, footnotes b and c. "This effect is evaluated based on the same DiD approach that was applied to the load reduction calculation, but specifically for...

AI summary The text discusses the evaluation of load reduction during the morning and evening snapback periods under a Time-Varying Pricing (TVP) program. It notes that the morning snapback period is a three-hour window (11 AM – 2 PM), while the evening snapback period is described as a four-hour window. The question raised is why there is a discrepancy in the timeframes used for analysis.

Request IR-7: Reference: M12932 N-1 TVP Evaluation Report, Appendix A, p. 20. "The objective of these rates is to provide customers with rate choice that can enable them to save on their electricity bill while shifting demand from peak per...

AI summary The Year Four Report discusses the objective of TVP tariffs to reduce fuel and power costs and avoid infrastructure capital costs in the long term. However, it only reports per-customer metrics such as load reduction, daily usage, and bill impacts, without quantifying aggregate peak load reduction or its monetary value.

Request IR-8: Reference: M12932 N-1 TVP Evaluation Report, Appendix E, Attachment 3, p. 24 of 60. "Customer benefits calculated using avoided cost values will be recalculated to reflect the updated Demand Side Management (DSM) Avoided Cost...

AI summary The document requests confirmation on whether the recalculation of customer benefits using the updated DSM Avoided Cost series from August 2024 has been completed, specifically for each TVP tariff and its components. If not completed, the timeline for completion is requested.

Request IR-10: Reference: M12932 N-1 TVP Evaluation Report, Appendix A, p. 148, quoting the Board's Decision in M12249: "… the Board notes that E1 should be fully prepared to address the questions raised in this proceeding about potential...

AI summary The Board has directed E1 to address potential overlap between its DSM Plan and NS Power's Critical Peak Pricing Program, and to explain how each program targets constrained areas of the grid. It also expects NS Power to cooperate with E1 in this exploration. The request asks for an analysis of equity and rate-impact consequences of funding demand response through DSM charges versus tariff design.

Request IR-11: Reference: M12932 N-1 TVP Evaluation Report, Appendix A, p. 17. For Residential TOU : "Significant enhancements to load reduction (+0.40 kW) occurred where technology (i.e. Eco Shift + smart device) were present." For Reside...

AI summary The document references TVP Evaluation Reports showing that residential TOU and CPP programs achieved greater load reduction when combined with Eco Shift and smart devices. It questions whether NS Power plans to incentivize or require enabling technologies as part of TVP enrolment.

Request IR-12: Reference: M12932 N-1 TVP Evaluation Report, Appendix E, Attachment 2, p. 16 of 121. Regarding TVP and EcoShift cross enrolment: "NS Power and EfficiencyOne (E1) will be monitoring this year before deciding whether cross-pro...

AI summary NS Power and EfficiencyOne are monitoring cross-program enrolment between TVP and EcoShift before deciding its future. They are collaborating on marketing efforts to enhance cross-promotion. The request asks for details on cross-promotional activities in Phase 4 and plans for combining Eco Shift with TOU and CPP tariffs.

20 Request IR-15: 21 Reference: M12932 N-1 TVP Evaluation Report, Appendix B — mean scores as displayed in the 22 survey charts at the pages indicated.

AI summary Request IR-15 references the TVP Evaluation Report, Appendix B, which includes mean scores from survey charts displayed on specific pages of the document.

Request IR-18: Reference: M12932 N-1 TVP Evaluation Report, Appendix E, Attachment 4, p. 12 of 47. "This indicates that, at the anticipated levels of load shift for a TOU customer, the current rate structure and/or pricing signal ratios of...

AI summary The text discusses challenges with achieving load shifting above 6% under the current TOU rate structure, suggesting that revisions to the demand pricing structure may be necessary. It also raises questions about system capabilities, interim solutions, and NS Power's position on restructuring non-coincident demand charges.

Request IR-19: Reference: M12932 N-1 TVP Evaluation Report, Appendix E, Attachment 5, p. 2-3 of 12. Regarding targeting SMB customers by load profile: "This approach was explored. Customers who could save after shifting load were identifie...

AI summary The document discusses NS Power's use of load-profile analysis to prioritize commercial customers for recruitment outreach. It references the use of AMI and weather data in classifying customers and raises questions about whether similar data-driven methods have been used for residential customers in TVP marketing and recruitment.

Phase DOM TOU SMG TOU GEN TOU DOM CPP SMG CPP GEN CPP MURB TOU Total Phase 1 676 24 284 8 0 992 Phase 2 891 17 373 2 0 1,283 Phase 3 2,241 37 19 922 3 6 0 3,228 Phase 4 5,616 37 18 2,642 17 36 10 8,376 TVP Tariff A Enrolled prior to beginn...

AI summary The text presents a table showing participation numbers in different tariff programs across various phases, with a focus on the increase in participation by Small General (SMG) and General (GEN) customer classes in the Critical Peak Pricing (CPP) program during Phase 4 compared to Phase 3. A question is raised about the reasons for this increase and whether it was due to changes in marketing strategies by NS Power.

- b) Referring to Table 6, which shows the number of SMG and General participants who withdrew mid-Phase 4 winter, what reasons did these customers give for their decision to withdraw? If none was given at time of withdrawal, did NS Power...

AI summary The text asks NS Power to explain the reasons customers withdrew from Phase 4 winter programs and whether TOU is more or less attractive than CPP for small business and commercial customers based on data in Tables 5 and 6.

Request IR-2: Refer to Exhibit N-1, Year 4 Report Results, Summary of Year Four Evaluation Results, pp. 2-3 of 5, which states: Importantly, the Year Four evaluation identifies statistically significant load shifting for commercial TOU and...

AI summary The Year Four evaluation found statistically significant load shifting for commercial TOU and CPP tariffs, a first-time occurrence, and confirmed residential TVP load reductions during peak periods. The request asks NS Power to explain these findings, including program changes, methodological differences, implications for future evaluations, and the timing of peak load reductions.

Request IR-3: Refer to Exhibit N-2, Year 4 Report Results Appendix A, page 4, (PDF 18 of 157), and Table 2 Summary of Load Reductions by Commercial TVP Tariff, and the text that follows Table 2 which discusses the results for Commercial TO...

AI summary NS Power reports that commercial TOU participants experienced an increase in daily electricity usage of 15.4 kWh/day. Small General customers showed increased load during peak periods, while General rate class customers showed load reductions, indicating differing responses to price signals.

Request IR-5: Refer to Exhibit N-2, Year 4 Report Results Appendix A, pages 14-15, (PDF pages 28-29 of 157), Chapter 2 Control Group Selection and section 2.2 Commercial TOU & CPP, which states: The same methodological approach as used wit...

AI summary The document raises questions about the effectiveness of control groups in evaluating the Commercial TOU and CPP programs, the statistical significance of results, and implications for future program design. It also inquires about the status of deliverables in NS Power's 2026/27 Work Plan and the impact of a cyber event on scheduling.