Topic:"Energy Efficiency" in M12932

Background The TVP Pilot Program was developed by NS Power with the assistance of Brattle Group and stakeholder input in M09777. The Pilot, testing Time-of-Use (TOU) and Critical Peak Pricing (CPP) tariffs across the Domestic, Small Genera...

AI summary The TVP Pilot Program, developed by NS Power with stakeholder input, was approved by the Board in 2021. The program includes TOU and CPP tariffs, with extensions and updates, including a 2024 Consensus Agreement. Following a 2025 cybersecurity incident, NS Power requested an extension for the Year Four EM&V report, which was approved by the NSEB.

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.

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 document defines key terms and metrics used in the regulatory proceeding, including hourly net system requirements, customer rate classes, energy consumption measurements, and income definitions aligned with provincial tax brackets.

Residential TOU participants reduced load during peak periods (0.16 kW / 7.6%) with minimal snapback (0.014 kW during evenings only) while also reducing overall annual energy (300 kWh per year), resulting in an average annual electricity b...

AI summary Residential TOU participants reduced peak load by 0.16 kW (7.6%) with minimal snapback and annual energy use by 300 kWh, leading to a 5.5% reduction in electricity bills. Participants with smart devices and Eco Shift achieved greater load reduction. Electrification of heating systems also showed significant peak load reduction without increasing annual energy use.

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 TOU Participants reduced load during peak periods (0.80 kW / 7.5%) with a statistically significant evening snapback (0.90 kW). However, the overall snapback effect of 0.11 kW for TOU participants was not statistically significa...

AI summary Commercial TOU and CPP participants showed mixed responses to pricing signals. TOU participants reduced load during peak periods but had an overall increase in daily usage. CPP participants showed significant load reduction during peak events, especially in colder months. General Tariff participants were more responsive than Small General Tariff participants.

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 evaluated load and economic impacts, showing a 0.48 kW overall load reduction during peak periods, with significant reductions only during evening peak hours. Load increased significantly in March, except during the winter period.

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).

Group Energy (kWh) SOR 10,225 TVP 12,678 2.1.1 Eco Shift

AI summary The document includes a table showing energy usage by group, with SOR and TVP as categories, and a section titled '2.1.1 Eco Shift' which likely discusses energy efficiency or related initiatives.

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.

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.

[Table 29](#page-76-1) 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 changes in daily electricity usage levels by space heating type for residential CPP participants during the pilot period compared to the pre-pilot period. Electrified participants saw a 13% increase, while de-electrified and steady electric participants saw decreases. Steady non-electric participants showed no significant change.

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.

The analysis reveals that the type of space heating has a significant effect on the bill savings for the CPP participants. The steady electric participants achieved the highest bill savings compared to steady non-electric. While CPP partic...

AI summary The analysis shows that the type of space heating significantly impacts bill savings for CPP participants. Steady electric heating systems achieved the highest daily and annual bill savings, while electrified customers did not experience statistically significant annual savings. Non-Winter days saw the most savings across all heating types.

Parameters De- Electrified Electrified Steady Electric Steady Non-Electric Winter Avg. Bill Savings ($/day) a 0.36 ± 0.04 -0.47 ± 0.03 $0.56 \pm 0.02$ 0.14 ± 0.01 Avg. Bill – Residential CPP, Pre- Pilot ($/day) 6.3 8.0 10.3 3.4 Relative Av...

AI summary The table presents average bill savings and significance levels for different electrification scenarios (De-Electrified, Electrified, Steady Electric, and Steady Non-Electric) across winter, non-winter, and annual periods, highlighting statistical significance and relative savings percentages.

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 and relative usage reduction across various time periods, including annual average, winter overall, winter non-holiday weekdays, winter holiday weekends, and non-winter. The data includes statistical significance indicators and standard deviations.

5.2 Economic Impacts This section presents and discusses the impact of commercial TOU tariffs on the electricity bill as well as price elasticity. a Positive values represent 'Load Reduction' and Negative values represent 'Load Increase'

AI summary This section discusses the economic impacts of commercial time-of-use (TOU) tariffs on electricity bills and price elasticity, noting that positive values indicate load reduction while negative values indicate load increase.

Chapter 5: Commercial TOU Tariff Impacts The results demonstrated a Daily Price Elasticity of 0.13 (± 0.20) during Winter season and -0.21 (± 0.08) annual overall. The daily price elasticity during Winter season may imply that for 1% incre...

AI summary The analysis in Chapter 5 shows that the Daily Price Elasticity during Winter is 0.13 (not statistically significant), while the annual elasticity is -0.21 (statistically significant), indicating reduced usage over time with price increases. The inter-period substitution elasticity of -0.065 suggests load shifting from peak to off-peak periods during Winter due to rate changes.

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.

Table 49: Change in Daily Electricity Bill for Commercial CPP Participants Parameters Winter Non-Winter Annual Average Bill Savings ($/day)a Avg. 11.7 ± 10.1 -1.5 ± 1.9 10.3 ± 7.0 Avg. Bill, Commercial CPP Participants, Pre-Pilot ($/day) 2...

AI summary Commercial CPP participants saw no significant change in average daily bills despite increased energy consumption during non-Winter months, due to decreased CPP volumetric energy costs. Annual electricity bills were reduced despite higher overall energy use.

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.

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 but not statistically significant, which a...

AI summary The analysis of Multi-Unit Residential Building Time-of-Use (TOU) rates shows that customers did not increase monthly demand during Winter, and while energy and bill savings were positive, they were not statistically significant. Modest load reductions were observed, particularly during evening peak periods, with no evidence of snapback.

Table 60: Top 88 ANL Hours 2020 - 2021 Load - Wind % of Halifax Temp. TOU Period 2 6-Feb-22 6:00 PM 2,027.3 99.8% -10.9 Off-Peak 3 11-Jan-22 5:00 PM 2,007.0 98.7% -16.1 PM 4 6-Feb-22 8:00 PM 1,993.4 98.1% -11.3 Off-Peak 5 27-Jan-22 8:00 AM...

AI summary Table 60 presents data on the top 88 ANL (Apparent Net Load) hours from 2020 to 2021, including load values, percentages of maximum load, Halifax temperatures, and TOU (Time-of-Use) periods. This data is used to analyze energy demand patterns and their correlation with temperature and pricing periods.

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.

III.2.2 Heating Classification Customers are first classified into three categories (Primary, Secondary, Non-Electric) based on their electricity consumption as visualized in [Figure](#page-132-1) 48. Figure 48: Example of Heating Correlat...

AI summary Customers are classified into primary, secondary, or non-electric heating categories based on electricity consumption patterns and temperature data. The classification uses correlation coefficients and a minimum consumption threshold during cold days to determine heating type, with the model simplified for evaluation purposes.

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.

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 For example, if a Co...

AI summary Table 72 presents a savings correction factor for the E1 Program based on the year of the home energy assessment and TVP Cohort. For example, a Cohort 2 customer who completed an energy assessment in 2022 would have their program savings adjusted by a factor of 0.67, resulting in 2,910 kWh annually in Phase 4.

or. For daily electricity usage impact analysis, Load it in equation ([7)](#page-138-0) is replaced by Energyit which is the energy consumption in kWh/day by customer ' i' at time ' t' . TVP customers in Eco Shift will be evaluated from De...

AI summary The document discusses the modification of the Load & Usage Impact Regression Model to evaluate the impact of the Eco Shift program on TVP customers. A mixed-effects model was chosen over a fixed effects model due to the small sample size and heterogeneity of participants, with the addition of a binary variable and a random intercept to improve model accuracy.

III.3.1.2 Economic Impact Regression Model The regression model used for billing impact is as follows in equation ([9)](#page-140-0). $$\begin{aligned} \textit{DailyBill}_{it} &= \beta_0 + \beta_1. \textit{Treatment}_i + \beta_2. \textit{P...

AI summary The document outlines a regression model used to analyze the economic impact of billing changes, specifically focusing on the relationship between treatment status, pilot periods, and heating degree days (HDD) on daily bills. It also mentions models for price elasticity.

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.

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.

IV.1.2 E1 Program Balancing (Phase 1) No balancing for E1 program effects was completed. As a result, the effects associated with E1 programs were not controlled for and could influence TVP EM&V results. Subsequent evaluations (i.e. Phase...

AI summary No balancing for E1 program effects was completed in Phase 1, which may have influenced TVP EM&V results. Subsequent phases have addressed this by balancing control group selection for E1 participation.

IV.1.3 Neighbourhood Criteria & Load Similarity Metric Data (Phase 1, 2, 3) In the previous TVP evaluations (i.e. Phases 1-3) control selection included 2 stages.

AI summary The document discusses the two-stage control selection process used in previous Thermal Value Program (TVP) evaluations, specifically Phases 1 through 3, focusing on neighbourhood criteria and load similarity metric data.

IV.2 Heating Classification by Phase The heating classification methodology has changed in each Phase of the evaluation, the method used during each Phase is described below.

AI summary The document discusses the changes in the heating classification methodology across different Phases of the evaluation, with each Phase using a different method. The specific methods used in each Phase are outlined in the text.

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 improved but failed to account for changes in heating type between the pre-pilot and pilot periods.

IV.2.3 Phase 3 To account for customers changing heating types between pre-pilot and pilot period, Phase 3 included a pre-pilot and pilot period evaluation of heating classification using the same methodology as in Phase 2, with the distin...

AI summary Phase 3 of the proceeding evaluates changes in customer heating types between the pre-pilot and pilot periods by defining four new heating classifications based on the difference in classifications, using the same methodology as in Phase 2.

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.

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.

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.

ff to see if changes can address some of the concerns, (i.e., create successful recruitment).[34](#page-168-0) In its Decision and Order on the Year Three Report (M11823), the Board further provided: NS Power agreed with Synapse's recommen...

AI summary The document discusses efforts by NS Power to improve customer education and collect feedback as part of the 2024/25 Areas of Focus. It also highlights the analysis of commercial class customer characteristics that influence load shifting in Time-Varying Pricing (TVP) tariffs, primarily driven by the business' scale of energy consumption.

Methodology Mode Online survey Audience NSP TVP Pilot Participants (Years 1-4) 2,103 completes Time of Use: 1,332 Critical Peak: 771 Data Collection Dates April 10 - 16, 2025 Response Rate 27.0% (7,801 invitations sent) Average Completion...

AI summary The methodology section describes an online survey conducted among NSP TVP Pilot Participants over four years, with 2,103 completes and a 27% response rate. The survey was conducted between April 10-16, 2025, and took an average of 23 minutes to complete.

Recent Joiners Main Reason For Choosing Plan Saving money dominates as the main reason for participating in a rate plan, distantly followed by non-monetary motivations. New participants were asked why they chose to join ToU or CPP. As expe...

AI summary The main reason for joining Time-of-Use (ToU) or Critical Peak Pricing (CPP) rate plans is to save money, with less than 10% of participants citing non-monetary motivations. Participants highlight the need to reduce electricity costs, particularly for seniors and retirees on fixed incomes.

Energy Efficient Products Participants commonly did not have energy-efficient technology installed in their homes prior to joining the program. Reports of having smart devices installed before starting the pilot program are relatively unch...

AI summary Most participants did not have energy-efficient technology installed before joining the program. Only two in ten had a smart thermostat, while fewer had smart outlets or hot water heater timers. Two-thirds had no such devices prior to joining the pilot program.

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.

Tactics and Schedule\ - Email – October 22 – December 13 - Introduced Retargeting and Segmentation/Personalization - Paid Channels – October 21 – November 30, Extended until December 2 - Direct Mail Postcard - Digital Ads Social, Google/Bi...

AI summary The document outlines a marketing and engagement strategy for promoting Time-Varying Pricing (TVP) programs, including email campaigns, paid and owned media channels, internal communications, and earned media efforts. Tactics span from October to December, with a focus on segmentation, personalization, and customer onboarding.

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.

- › Average energy savings compared to the pre-participation year are 476 ± 364 kWh per year Parameters Steady Electric Electrified De-Electrified Steady Non electric Winter, Non-Holiday Weekdays Average Daily Electricity Savings (kWh/day)...

AI summary The text provides average energy savings data from a pilot program, showing that electrified participants saved significantly more energy than steady electric and de-electrified participants, with the highest savings observed during winter non-holiday weekdays.

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.

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.

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 As part of the Board's decisions in M11822 and M11823, NS Power is required to report on the Year Four findings, analyze system margin forecasts and CPP events, review marketing and communication efforts, and assess the performance of General Service Class customers, potentially leading to tariff changes.

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 how the distribution of low- and high o supply cushion hours are expected to evolve. • Why NS Power still views the winter evening peak to be the most likely and impactful time for a system peak moving...

AI summary The document discusses how the distribution of low- and high-supply cushion hours is expected to evolve, with a focus on NS Power's perspective regarding the winter evening peak being the most likely and impactful time for a system peak moving forward.

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.

More than one-half of customers use electric heat as their primary heat source in their business, while two in ten use oil. Electric heat is greater among customers with fewer employees, and customers in buildings under 2,500 square feet....

AI summary More than half of customers use electric heat as their primary heat source in their business, while two in ten use oil. Electric heat is more common among customers with fewer employees, smaller buildings, e-billing customers, newer buildings, businesses in the Western region, and certain business types and rate classes.

Home Heating Energy Sources In terms of total heat sources customers have in their business, electricity is used by three-quarters, while oil is used by three in ten. Other sources of heat are much less common among business customers. Oil...

AI summary The document discusses the distribution of home heating energy sources among business customers in Nova Scotia. Electricity is the most common source, used by three-quarters of customers, while oil is used by three in ten. Oil heat is more common in non-Metro areas and older buildings, while natural gas is more common in Metro customers. Electric heat is most prevalent in the Western region.

Four in ten customers have automated heating in their business, while a third have automated cooling and just over one in ten have automated lighting. Businesses were asked whether they had various forms of automated or scheduled electroni...

AI summary A survey indicates that 40% of businesses have automated heating, 33% have automated cooling, and 10% have automated lighting. The primary reasons for installation include saving on electricity (60%) and heating costs (57%), with environmental concerns and scheduling also being significant factors.

Electric water heaters are most common among business customers. The majority of business customers have an electric water heater , while very few (2%) have a heat pump water heater . Meanwhile, two in ten have some other type of water hea...

AI summary The majority of business customers in Nova Scotia use electric water heaters, with only 2% using heat pump water heaters. Non-electric/heat pump water heaters are more common among customers who do not use electricity as their primary heating source, those without heat pumps, and in specific regions and building types.

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.

Reasons for Greater Interest in Critical Peak Pricing Of those most interested in Critical Peak Pricing, the main reasons are because of potential cost savings and providing more control over their usage. Most commonly, one-quarter indicat...

AI summary The text discusses the reasons why customers are interested in Critical Peak Pricing, highlighting potential cost savings, lower rates, and greater control over energy usage as the main factors. A quarter of respondents prefer this plan for these benefits, while a smaller proportion find it more suitable for their business needs.

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.

More than one-half of customers use electric heat as their primary heat source in their home, while three in ten use oil. This same question was posed in the previous 2019 End Use survey and 2018 U&A survey with results being relatively co...

AI summary More than half of customers use electric heat as their primary heating source, with three in ten using oil. This has been consistent across recent surveys, with mobile home residents most likely to use electric heat. Other demographics show similar primary heat sources.

Reasons for Installing Smart Thermostat The small proportion who have already installed a smart thermostat primarily did so to save on electricity, and to make their heating more efficient. Other key mentions made by a small proportion of...

AI summary The text discusses the reasons why a small proportion of customers have installed smart thermostats, primarily to save on electricity and improve heating efficiency. Other reasons include receiving the thermostat as part of their home, environmental concerns, and having the latest technology. Caution is advised when interpreting demographic subgroup data due to small sample sizes.

Reason for Interest in Installing Smart Thermostat Among customers expressing a high likelihood of installing a smart thermostat, interest is primarily related to electricity savings. Among customers interested in installing a smart thermo...

AI summary Customers interested in installing smart thermostats are primarily motivated by the potential for electricity savings, particularly those with electric heat. This interest is higher among older customers and those living in mobile homes, who may benefit more from reduced electricity costs.

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 preferences regarding Time of Use (TOU) pricing. Two-thirds liked the potential to save money, while over half disliked higher costs during peak times. Many also appreciated environmental benefits and increased control over their bills, though some disliked the need to change behavior to save money.

Required Savings On average, customers are looking to save more than $300 per year to make the programs worthwhile to them, though a majority would take part in order to save less than $300. Regardless of a customer's interest in the progr...

AI summary The text discusses customer expectations for savings from energy programs, noting that on average, customers expect to save around $381 annually, with variations based on interest in specific programs and demographics. Customers with electric heat or heat pumps, and larger households, require higher savings to participate.

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.

Tactics and Schedule - Email October 22 – December 13 - Introduced Retargeting and Segmentation/Personalization - Paid Channels October 21 November 30, Extended until December 2 - Direct Mail Postcard - Digital Ads Social, Google/Bing Disp...

AI summary The document outlines a marketing and engagement strategy for a program, including email campaigns, paid and owned media channels, internal communications, earned media efforts, and customer onboarding and retention initiatives. The plan spans from October to December and includes tactics like retargeting, direct mail, digital ads, and web content updates.

- 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.

5. Gain insights into your energy use Find out which appliances you should avoid using and dive into other home energy insights with MyEnergy Insights 3 days after the event. Keep track of how you are doing and how you can further save ene...

AI summary This section introduces MyEnergy Insights, a tool that provides users with energy use insights and recommendations for reducing energy consumption. It also mentions the Critical Peak Pricing Rate Pilot (CPP) and encourages sign-up for event notifications. The text includes references to an appendix and images, suggesting it is part of a larger report.

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.

• What we are proposing: - Leverage NS Power Commercial Advisors as they are the trusted partners, who know the correct decision makers. - Allowing NS Power Commercial Advisors to begin conversations now and build out application informati...

AI summary The proposal outlines strategies to engage larger commercial customers through NS Power Commercial Advisors, leveraging trusted partnerships, targeted outreach, and digital marketing to promote TVP Rates and increase participation.

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.

rically, which is a 34% decrease from the number of pilot participants who identified that they heat electrically last year, is the decrease reflective of there being an increase in the number of and this the pool of (CPP) applicants rathe...

AI summary The text discusses a 34% decrease in the number of participants who heat electrically in a pilot program, noting that this decrease may be due to an increase in the number of applicants rather than a reduction in the overall pool. The decline in electrically heated customers from 44% in Year 3 to 31% in Year 4 is relative to those who applied in the respective enrolment periods.

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.

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.

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.

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.

Residential CPP participants reduced load during peak periods (0.69 kW / 27%) with a statistically significant overall snapback (0.060 kW). Participants reduced overall annual energy (355 kWh per year), resulting in an average annual elect...

AI summary Residential Critical Peak Pricing (CPP) participants reduced load during peak periods by 27%, with significant load reduction when combined with Eco Shift and smart devices. Participants with electric heating achieved the highest load reduction, while those with non-electric heating had the lowest. Electrification of heating systems showed potential benefits despite marginal increases in annual energy use.

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.

Group Energy (kWh) SOR 10,225 TVP 12,678 2.1.1 Eco Shift

AI summary The document includes a table showing energy usage by group, with SOR and TVP as categories, and a section titled '2.1.1 Eco Shift' which likely discusses energy efficiency or related initiatives.

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.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekdays Avg. Usage Reduction 1.71 ± 2.70 ± 1.64 ± 1.10 ± 1.39 ± a (kWh/day) 0.21 0.31 0.14 0.08 0.06 Avg. Usage – Residential TOU Participants, Pre-Pilot (kWh/day) 39....

AI summary The table presents average usage reduction and relative usage reduction percentages across different cohorts during winter non-holiday weekdays, winter weekends/holidays, and non-winter periods. All cohorts show statistically significant results (p_Value≤0.05), indicating the effectiveness of the program in reducing residential TOU participants' energy usage.

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

AI summary This section discusses the economic impacts of residential time-of-use (TOU) tariffs, focusing on their influence on electricity bills and price elasticity.

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.

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.

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 usage reduction and significance levels across different electrification scenarios during winter weekdays, weekends/holidays, and non-winter days. It highlights significant reductions in residential energy usage for the 'Electrified' category, with some categories showing statistically significant results.

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.

The analysis reveals that the type of space heating has a significant effect on the bill savings for the CPP participants. The steady electric participants achieved the highest bill savings compared to steady non-electric. While CPP partic...

AI summary The analysis shows that the type of space heating significantly affects bill savings for Conservation Program Participants (CPP). Steady electric heating systems achieved the highest bill savings, particularly on non-Winter days, while electrified customers did not achieve statistically significant annual savings.

Parameters De- Electrified Electrified Steady Electric Steady Non-Electric Winter Avg. Bill Savings ($/day) a 0.36 ± 0.04 -0.47 ± 0.03 $0.56 \pm 0.02$ 0.14 ± 0.01 Avg. Bill – Residential CPP, Pre- Pilot ($/day) 6.3 8.0 10.3 3.4 Relative Av...

AI summary The table presents average bill savings and significance levels for different electrification scenarios across winter, non-winter, and annual periods. It indicates that De-Electrified and Electrified scenarios show mixed results, with some periods showing significant savings and others not. The data highlights the impact of electrification on residential bill savings for Conservation Program Participants (CPP).

5.2.2 Price Elasticity Daily Price Elasticity reflects the change in overall daily electricity usage caused by changes in the average daily price ($) per kWh. It is expressed as the percent (%) change in the average electricity usage assoc...

AI summary The text discusses price elasticity in electricity usage, noting a Daily Price Elasticity of 0.13 during Winter and -0.21 annually. The inter-period substitution elasticity is -0.065, indicating load shifting from peak to off-peak periods during Winter. These results suggest that price changes influence consumption patterns, though some findings are not statistically significant.

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.

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.

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.

6.2.2 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.

7.1.5 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.

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.

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.

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.

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.

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.

r. For daily electricity usage impact analysis, Load it in equation [(7)](#page-123-0) is replaced by Energyit which is the energy consumption in kWh/day by customer ' i' at time ' t' . TVP customers in Eco Shift will be evaluated from Dec...

AI summary The document discusses modifications to the Load & Usage Impact Regression Model for TVP customers in the Eco Shift program, including the addition of a binary variable for enrolment and a random intercept to account for small participant numbers and heterogeneity. A mixed-effects model was selected for improved accuracy.

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.

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.

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.

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.

o see if changes can address some of the concerns, (i.e., create successful recruitment).33F [34](#page-153-0) In its Decision and Order on the Year Three Report (M11823), the Board further provided: NS Power agreed with Synapse's recommen...

AI summary The document discusses efforts by NS Power to improve customer education and analyze commercial customer characteristics that influence load shifting under the Time-Varying Pricing (TVP) tariff. It highlights that load shifting is primarily driven by the business' scale of energy consumption and provides an estimated breakdown of participating commercial customers by TVP tariff and business vertical.

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.

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.

2.1.2 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).

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 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.

Parameters Cohort 1 Cohort 2 Cohort 3 Cohort 4 All Winter, Non-Holiday Weekdays Avg. Usage Reduction 1.71 ± 2.70 ± 1.64 ± 1.10 ± 1.39 ± a (kWh/day) 0.21 0.31 0.14 0.08 0.06 Avg. Usage – Residential TOU Participants, Pre-Pilot 39.4 50.4 43....

AI summary The table presents usage reduction data across four cohorts during different seasons, showing average kWh/day reductions and relative usage reductions in percentages. Statistical significance is marked with a check, indicating consistent results across all cohorts.

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.

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.

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.

The analysis reveals that the type of space heating has a significant effect on the bill savings for the CPP participants. The steady electric participants achieved the highest bill savings compared to steady non-electric. While CPP partic...

AI summary The analysis shows that residential CPP participants with steady electric heating systems achieve the highest bill savings, particularly on non-Winter days. Electrified participants have lower savings and do not achieve statistically significant annual savings. The study does not account for additional fuel costs for non-electric heating sources.

Parameters De- Electrified Electrified Steady Electric Steady Non-Electric Winter Avg. Bill Savings ($/day) a $0.36 \pm 0.04$ -0.47 ± 0.03 $0.56 \pm 0.02$ 0.14 ± 0.01 Avg. Bill – Residential CPP, Pre- Pilot ($/day) 6.3 8.0 10.3 3.4 Relativ...

AI summary The text presents a table comparing average bill savings and relative bill savings percentages for different electrification scenarios during winter, non-winter, and annual periods. The data shows significant variations in savings, with some scenarios showing bill increases. The significance of these findings is indicated with p-values.

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 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.

Table 40: Change in Daily Electricity Bill for Commercial TOU Participants Parameters Winter Non-Winter Annual Overall Bill Savings ($/day)a Avg. -11.9 ± 3.4 6.2 ± 3.1 -1.1 ± 3.1 Avg. Bill, Commercial TOU Participants, Pre-Pilot 36.7 37.4...

AI summary Table 40 shows the change in daily electricity bills for commercial TOU participants, with winter savings of -11.9 \/day and non-winter savings of 6.2 \/day. Annual overall savings are -1.1 \/day. Savings percentages are -32% in winter, 16.7% in non-winter, and -3.1% annually. Significance is indicated for winter and non-winter, but not for annual overall.

Chapter 5: Commercial TOU Tariff Impacts The results demonstrated a Daily Price Elasticity of 0.12 (± 0.17) during Winter season and -0.21 (± 0.08) annual overall. The daily price elasticity during Winter season may imply that for 1% incre...

AI summary The chapter discusses the impact of commercial Time-of-Use (TOU) tariffs, showing a daily price elasticity of 0.12 during winter and -0.21 annually, indicating a statistically significant reduction in electricity usage with price increases. The inter-period substitution elasticity of -0.065 suggests load shifting from peak to off-peak periods during winter.

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.

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.

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.

Table 53: Change in Daily Electricity Usage (kWh/day) for MURB TOU Participants Winter Winter Months Parameters Overall January February March November December Avg. Usage Reduction 16.5 ± 30 22 ± 45 33 ± 97 -34 ± 35 66 ± 68 11 ± 34 (kWh/d...

AI summary Table 53 presents the change in daily electricity usage for MURB TOU participants during winter months, showing reductions and increases across different months with statistical significance noted for all parameters.

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.

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.

18 Please refer to Appendix E, Attachment 3, page 23. 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) TV...

AI summary The document references a table that outlines possible sub-groupings for evaluating load similarity and balancing in E1 programming, including factors like TVP tariff, rate class, cohort, neighbourhood, heating classification, and Eco Shift participation.

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.

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](#page-117-1) 48. Figure 48: Example of Heating Correlat...

AI summary Customers are classified into primary, secondary, and non-electric categories based on their electricity consumption and heating patterns. The classification uses AMI data and temperature data from nearby weather stations, focusing on overnight hours when temperatures are below 10°C. A correlation coefficient 'R' is used, where lower values indicate a higher likelihood of electric heating.

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.

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 For example, if a Co...

AI summary Table 72 provides a savings correction factor for the E1 Program Year and TVP Cohort in Phase 4. For example, a Cohort 2 customer with a home energy assessment completed in 2022 would have their program savings adjusted by a factor of 0.67, resulting in 2,910 kWh annually in energy savings.

age daily bill. The regression models for daily price elasticity and inter-period substitution price elasticity are presented in equations ([10)](#page-125-1) and ([11)](#page-127-0), respectively. $$ln(Daily\ Avg\ Usage\ kWh)_{it} = \\ =...

AI summary The text presents regression models for daily price elasticity and inter-period substitution price elasticity, including equations and variables related to energy usage, temperature, and pricing structures. These models aim to analyze customer behavior and price responsivity in relation to energy consumption.

IV.1.2 E1 Program Balancing (Phase 1) No balancing for E1 program effects was completed. As a result, the effects associated with E1 programs were not controlled for and could influence TVP EM&V results. Subsequent evaluations (i.e. Phase...

AI summary No balancing for E1 program effects was completed in Phase 1, potentially influencing TVP EM&V results. Subsequent phases have addressed this by balancing control group selection for E1 participation.

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 updated to use AMI and weather data for premises with temperatures below 10 °C. Customers are classified as primary or secondary electric based on consumption correlation to temperature and 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 improved but failed to account for changes in heating type between the pre-pilot and pilot periods.

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.

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.

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.

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.

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.

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.

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.

MyEnergy Insights - The Customer Energy Management system (M10164, CI C0021839) also referred to as MyEnergy Insights – included the implementation of TVP tariffs. - MyEnergy Insights allowed customers to gain insights and manage their ene...

AI summary MyEnergy Insights is a customer energy management system that provides users with insights into their energy usage, enabling informed decisions about energy use and rates. The system, which includes TVP tariffs, is expected to be restored by the end of Q3 2026.

- 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.

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.

Energy Usage by Cohort - All cohorts exhibit statistically significant reduction in daily energy usage during non holiday weekdays in Winter, overall reducing 1.39 kWh per day. - All cohorts reduced their daily usage during holiday weekend...

AI summary All cohorts showed a statistically significant reduction in daily energy usage during non-holiday weekdays in winter, with an average reduction of 1.39 kWh per day. Additionally, all cohorts reduced their daily usage during holiday weekends, even though peak periods were not applicable, with an overall reduction of 0.60 kWh per day.

- 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, with an overall reduction of 0.54 kWh per day observed, despite peak periods not being applicable during this time.

Energy Usage by Space Heating Classification - The greatest energy reduction was achieved by steady electric heating classification as high as 1.96 kWh per day (3.5%) during non-holiday weekdays. - Electrified participants increased energy...

AI summary The text discusses energy usage differences across space heating classifications, noting significant reductions in energy consumption during non-Winter periods and increased usage by electrified participants during Winter weekends and holidays.

Bill Impact - Overall annual average bill saving of $0.30 per day (~$110 annually) was achieved, corresponding to 5.5% of average annual bill. - During Winter, participants increase their bill by $1.34 per day (16.5% increase)

AI summary The bill impact analysis shows an overall annual average savings of $0.30 per day (~$110 annually), representing 5.5% of the average annual bill. However, during winter, participants experienced a $1.34 per day increase in their bills, a 16.5% rise.

- 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.

Bill Impact by Space Heating Classification - Space heating has significant influence on the Winter bill impact for the TOU participants. With those relying on electric heating experiencing the highest winter bill increase. - Annually howe...

AI summary Space heating significantly affects winter bills for TOU participants, particularly those using electric heating, which see the highest increases. However, annually, TOU participants with steady electric and electrified space heating experience average bill savings of $120 and $153, respectively.

- 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.

Energy Usage - All cohorts significantly reduced their daily usage during non-holiday weekdays in Winter with an overall daily usage reduction of 1.2 kWh/day. - All cohorts exhibit daily usage reductions during weekends and holidays in Win...

AI summary All cohorts significantly reduced their daily energy usage during non-holiday weekdays in Winter, with an overall reduction of 1.2 kWh/day. They also reduced usage during weekends and holidays, though the reduction in cohort 3 was not statistically significant, with an overall reduction of 1 kWh/day.

Important: These effects represent the effect of TVP on customers relative to their control, of whom are in the same heating classification. Parameters De- Electrified Electrified Steady Electric Steady Non- Electric Winter, Non-Holiday We...

AI summary The text presents data on the impact of Time-Varying Pricing (TVP) on customer energy usage across different categories, including de-electrified, electrified, and steady electric/non-electric customers. It compares average usage reductions and significance levels during winter weekdays, weekends/holidays, and non-winter days, indicating varying effectiveness of TVP strategies.

Bill Impact - Overall annual average bill savings of $0.57 per day (~$209 annually) was achieved, corresponding to 8.6% of average annual bill. - During Winter, participants experienced bill savings of $0.37 per day, corresponding to 4.4%...

AI summary The document outlines the bill impact of a program, showing an overall annual average bill savings of $0.57 per day (~$209 annually), representing 8.6% of the average annual bill. During winter, participants experienced $0.37 per day in savings, which is 4.4% of the average winter bill.

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.

- 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.

- 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.

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-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-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.

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.