Topic:"Rate Design" in M12349

............................................. 74 16 Figure 58: Historical and Forecast Annual NSR ........................................................................ 75 17 Figure 59: Forecast Components ..................................

AI summary The text lists figures related to energy demand forecasting, demand response programs, peak load analysis, and the impact of electric vehicles. Topics include system reliability, load management, and integration of renewable energy sources through advanced metering infrastructure.

94 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.3 Economic Information 2 3 Economic and other provincial statistics used in the load forecast are from the Conference Board 4 of Canada’s 20-year forecas...

AI summary The 2025 Load Forecast Report uses economic data from the Conference Board of Canada's 20-year forecast, including housing completions, to predict residential customer growth. The NSUARB directed a re-evaluation of housing completions due to population growth targets and housing initiatives like the Housing Accelerator.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Redacted 1 4.5 Price Data 2 3 Price data is an input to the SAE forecasts for the residential, small general and general services 4 classes, and the price series is calc...

AI summary The 2025 Load Forecast Report discusses price data as an input for SAE forecasts, calculating real revenue per kWh and using a 12-month moving average. Electricity prices are projected to increase by 3.8% in 2025 and 5% annually from 2026 to 2029, with subsequent increases at approximately inflation rates. Price elasticities of -0.15 are applied in the SAE models.

aily Price Elasticity -1.607 +/- 0.317 -0.017 +/- 0.173 Inter-Period Substitution Price Elasticity -0.105+/- 0.005 -0.029 +/- 0.001 18 19 The elasticity values have changed significantly from the prior report, and although the Daily Price...

AI summary The document discusses changes in price elasticity values from prior reports, noting that the Daily Price Elasticity for the TOU rate is significantly higher than previously used in load forecasts, while Inter-Period Substitution values remain similar. These elasticity values impact sales in the SAE model but are less influential than other factors like DSM and EVs.

tor REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2025 Load Forecast Report Appendix B Page 16 of 34 General Service The General Service rate class model is estimated on a total monthly sales basis where total monthly billed sales is a funct...

AI summary The General Service rate class model estimates monthly billed sales based on heating, cooling, and other use variables, incorporating price elasticity, GDP, employment, HDD, CDD, and various binary shift variables to improve model accuracy. An ARMA process is also added to the model.

ED) 2025 Load Forecast Report (NSEB M12349) NSPI Responses to CA Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Reference: Exhibit N-1, Section 10.0. 4 5 (a) Please provide a table summarizing NS Power’s forecasts for demand red...

AI summary NSPI is requested to provide a table summarizing forecasted and actual demand reductions from time-varying pricing (2021-2035) and explain the basis for actual reductions. The request references Exhibit N-1, Section 10.0, and is part of the 2025 Load Forecast Report (NSEB M12349).

1 CPP participation recorded in each TVP Season (as reported in the Company’s annual 2 Evaluation Reports) while accounting for line losses during peak periods. 3 4 (c) System peak demand reduction has already been achieved by AMI technolo...

AI summary NS Power reports AMI technology has reduced peak demand by 3.7 MW during the 2024/2025 winter season, with continued growth expected as programs scale. The text defines TOU peak periods and provides load data for top peak hours in the 2023–2024 winter season.

1 (e) Please see the table below. 2 Step Description Amount Reference Projected reduction in system 2025 Load A peak associated with TVP rates 33 MW Forecast Figure in 2035 60 B Winter 2023/24 Peak Reduction 1.3 MW CA IR-02 (a) 2,241 2023/...

AI summary The text outlines a Time-Varying Pricing (TVP) pilot aimed at reducing peak demand, referencing the Evergreen Integrated Resource Plan. It includes projected peak reductions, stakeholder collaboration, and plans to evaluate tariff effectiveness. Data sources include CA IR-02 (a) and EM&V.

pany intends to continue reviewing, assessing, and evaluating the 9 effectiveness of the Tariffs included in the pilot. This evaluation will help determine the 10 appropriate timing and approach for potentially migrating some, or all, of t...

AI summary NSPI plans to evaluate TVP tariffs from a pilot, considering migration to standard offer options. Stakeholder engagement and annual EM&V reports refine tariff performance assessments. The 2025 Load Forecast Report (NSEB M12349) and NSPI's responses to CA's information requests are referenced.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to CA Information Requests CONFIDENTIAL (Attachment Only) 1 Request IR-3: 2 3 Reference: Exhibit N-1, Section 10.4. 4 5 (a) Please provide the data supporting Figures 71 and 72. 6 7 (b...

AI summary NSPI responds to CA's requests regarding the 2025 Load Forecast Report, providing data on figures 71 and 72, class load data for 2024, and noting no peak forecast using class-specific growth rates has been developed.

1 Request IR-2: 2 3 Please describe the scope of the Time Varying Pricing Pilots (TVP) and Distributed Energy 4 Resources Integration Roadmap (DERIR) as it relates to the business case for the 5 deployment of behind-the-meter battery energ...

AI summary The Time Varying Pricing (TVP) pilot aims to use dynamic pricing signals to support behind-the-meter battery storage by enabling load shifting and peak shaving. The DERIR roadmap seeks stakeholder input on integrating distributed energy resources, including battery storage, with a final report due by September 30, 2025 (M12177). Considerations include export restrictions under current tariffs and ongoing rate design discussions.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to SBA Information Requests NON-CONFIDENTIAL 1 Request IR-7: 2 3 Refer to Report, Section 4.5, Price Data, page 52 of 94 and respond to the following: 4 5 (a) These price elasticity es...

AI summary NSPI responds to SBA's request about the TVP pilot's customer participation (0.17% of small businesses) and explains that increased participants in the TVP pilot led to changes in price elasticity values from the 2024 report.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to SBA Information Requests NON-CONFIDENTIAL 1 Request IR-11: 2 3 Refer to Report, Section 10.0, Peak Demand, page 83 of 94 and respond to the following: 4 5 (a) At lines 1-2, NS Power...

AI summary NSPI explains 'Utility Managed' EV charging as active control by the utility or time-varying rates, classifying it as demand response. Small businesses can manage EV charging via time-varying pricing and enroll in Efficiency Nova Scotia's Smart Synergy program.

2025 Load Forecast Report (NSEB M12349) NSPI Responses to Solar Nova Scotia Information Requests NON-CONFIDENTIAL 1 Request IR-2: 2 3 Please describe how “Time Variable Pricing (TVP) rates currently being piloted might start 4 to encourage...

AI summary NSPI refers to an external document (ESC IR-2) in response to a request about how Time Variable Pricing (TVP) rates might encourage battery installations with solar panels. The response is part of the 2025 Load Forecast Report (NSEB M12349) and does not directly address the question.

t residential load, accounting for the load-reducing effects of DSM programs, decreases by about 1.6 percent over the forecast period. Without DSM programs, the increase would be about 3.9 percent. 10 The two largest contributors to the in...

AI summary Residential load is projected to decrease by 1.6% with DSM programs, versus a 3.9% increase without them. Key drivers include new customers (7.6% growth) and EV load (7.9% growth), partially offset by solar PV and DSM. The forecast uses a regression-based SAE model incorporating heating, cooling, work-from-home trends, and time-fixed effects.

t, page 38. 33 Response to Synapse IR-9(e). 34 2025 Load Forecast, Figure 29. 35 2025 Load Forecast, Figure 29. 36 2025 Load Forecast, Figure 3, Figure 29, Figure 65. 37 Response to Synapse IR-9h. Synapse Energy Economics, Inc. Evidence Re...

AI summary Synapse recommends NSPI monitor EV sales impacts, adjust forecasts, detail managed charging assumptions, and develop incentives for managed charging as EV adoption grows. Solar generation forecasts show increased installations and a projected 1,023 GWh load reduction, with updated coincidence factors based on 2024 data.

generation with monthly system peaks, NSPI confirmed that it had updated the coincidence factors based on 2024 data. These factors are based on both weather patterns and the timing of system peak. 39 Recommendations and considerations NSPI...

AI summary NSPI updated solar coincidence factors using 2024 data and recommends ongoing evaluation of solar projections. Solar-plus-battery systems may have limited near-term impact but warrant re-evaluation. New customer load growth is projected to increase residential demand by 7.6% by 2035. Rate design and incentives could influence solar adoption.

’s forecast will yield accurate projections. Recommendations and Considerations NSPI should monitor the accuracy of its projections of housing completions, and consider changes to this methodology. Price elasticity NSPI demonstrated that t...

AI summary NSPI should monitor housing completion projections and adjust methodology. Price elasticity of -0.15 aligns with SAE models. NSPI revised COVID-19 work-from-home modeling, removing the variable from General Service models while retaining binary shift variables. Synapse supports NSPI's approach to phase out the separate COVID-19 variable.

ces. We strongly support NSPI’s new commitment to analyzing AMI data and encourage the utility to prioritize this effort as it works to refine its modeling of electric heating impacts. 3. We recommend that NSPI begin modeling heat pump wat...

AI summary The text emphasizes the importance of modeling heat pump water heaters as a separate end use technology in load forecasts, monitoring the impact of the carbon levy removal on EV sales, and updating solar installation projections. These actions are recommended to improve forecast accuracy and align with electrification goals.

ign or other programmatic options. 5. NSPI should continue to evaluate and update its solar installation projections and coincidence factors for solar so they align with the latest data. 6. NSPI should begin to incorporate the impacts of r...

AI summary The text outlines several recommendations for NSPI regarding the accuracy and comprehensiveness of its load forecasting and analysis, including updates to solar projections, incorporation of rate design impacts, and scenario analysis for uncertain technologies and programs.

of making a simplified adjustment based on E3’s hybrid scenario. 3. We recommend that NSPI model heat pump water heaters as a separate end-use technology in the next load forecast. 4. NSPI should carefully monitor EV adoption and update it...

AI summary The document outlines several recommendations for NSPI, including modeling heat pump water heaters as a separate end-use technology, monitoring EV adoption, updating load forecasts with empirical analysis, examining solar generation coincidence factors, investigating battery storage incentives, and validating the use of new home construction as a proxy for customer growth.

ially for peak management. 7. NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. 8. We ask that NSPI reassess its modeling approach...

AI summary The document outlines several requests for NSPI to refine its load forecasting and modeling approaches, including validating proxies for customer growth, reassessing the impact of the pandemic on residential load, and considering the effects of solar, DSM, and industrial electrification on load forecasts. It also emphasizes the need to explore real-time rates and time-of-use rates to manage peak load increases.

gs. 16. We ask NSPI to investigate what can be done with time-of-use rates and other measures to mitigate the peak load increases for all these components, especially for the C&I sectors. 17. NSPI should conduct an analysis of portfolio EL...

AI summary The text outlines several recommendations for NSPI regarding load management, including investigating time-of-use rates, analyzing ELCC values for demand response, evaluating impacts of electrification and EVs, and developing scenarios for uncertain future technologies such as heat pumps and demand-side management.

Page 6 of 17 2025 Load Forecast Report Reply Evidence Non-Confidential 1 NS Power Response: 2 3 NS Power agrees with this recommendation and will add a separate category in the intensity 4 calculations for heat pump water heaters. 5 6 2.1....

AI summary NS Power agrees to adjust load forecasts for heat pump water heaters, update solar projections annually, and clarify that managed charging strategies will be addressed through rate design rather than load forecasting. Responses align with recommendations on EV sales monitoring and solar coincidence factors.

S Power Response: 29 30 Solar installation projections and coincidence factors are updated annually. 31 8 M12349, Exhibit N-8, page 27. 9 M12349, Exhibit N-8, page 27. DATE FILED: November 6, 2025 Page 7 of 17 2025 Load Forecast Report Rep...

AI summary NS Power agrees to incorporate rate design and incentives for solar-plus-battery adoption into load forecasts but notes these will be handled separately. They commit to monitoring housing completion projections and adjusting forecasts using CBoC data. The document also references recommendations to explore industrial RTR participation impacts.

1 data provided by the LRS, the Consumer Advocate would request that NS Power 2 be directed to conduct its own independent analysis and provide an update to the 3 Board through a compliance filing, or alternatively, at the Board’s directio...

AI summary The Consumer Advocate requests NS Power to conduct an independent analysis and update the Board on load forecasting. The Small Business Advocate (SBA) criticizes the proposed 2% electricity price increase as arbitrary and urges NS Power to consider additional factors. NS Power responds that rate modeling is outside the load forecast's scope and that their assumptions are based on a robust internal forecast, with uncertainties increasing beyond 2029.

outcome was forecast for Medium Industrial customers. Overall, between 2025 and 2035, General demand sales will decrease 7.3% and Medium Industrial demand sales? will have an average decline of 1.4%. The CA did not find fault in the applic...

AI summary The document outlines forecasted demand declines for General and Medium Industrial customers between 2025-2035, with recommendations from the CA for improved class-specific peak load forecasting and verification of RtR sales data. The SBA challenges NS Power's arbitrary 2% annual electricity price increase projection, urging the use of measured factors for long-term sales forecasts.

for demand response. • Evaluate additional demand response programs, with a greater level of peak loads. 11. Assess the probability of “other possible scenarios” in Figure D8 and consider additional analyses aimed at mitigating projected p...

AI summary NS Power's rebuttal agrees with most Synapse recommendations but highlights constraints in implementing some, particularly regarding AMI data integration and managed charging strategies. They argue that certain analyses, like rate design, are better suited elsewhere. The discussion includes demand response, DSM, and load forecasting scenarios.

mendation #8, NS Power explained that the RtR forecast is based on the expectations developed by the Licensed Retail Supplier and is the best available information to be included in the Load Forecast. In response to Synapse’s recommendatio...

AI summary NS Power responds to recommendations from Synapse, SBA, ESC, and SNS, defending its load forecasting methods, rate assumptions, and stance on DER deployment. It asserts that full municipal utility loads must be included, rate projections use internal forecasts, and disagrees with incorporating DER value assessment in load forecasts.

M12349 NOVA SCOTIA ENERGY BOARD IN THE MATTER OF: The PUBLIC UTILITIES ACT -and- IN THE MATTER OF: NOVA SCOTIA POWER INCORPORATED’s 2025 Load Forecast Report NOTICE OF INTERVENTION OF: CONSUMER ADVOCATE TAKE NOTICE that the Consumer Advoca...

AI summary The Consumer Advocate intervenes in Nova Scotia Power's 2025 Load Forecast Report proceeding under the Public Utilities Act, representing residential ratepayers. The intervention focuses on addressing issues raised by the Energy Board and protecting consumer interests.

8 July, 2025 NOVA SCOTIA ENERGY BOARD C/O CRYSTAL HENWOOD ([email protected]) Dear Ms. Henwood, RE: Information Requests – NS Power 2025 Load Forecast Report (M12349) Please see below Information Requests to NS Power in the 202...

AI summary The letter from Leone Benson-King to Crystal Henwood outlines three information requests related to NS Power's 2025 Load Forecast Report proceeding (M12349). It asks about NS Power's role post-NSIESO formation, the scope of TVP and DERIR for behind-the-meter battery storage, and access to battery installation cost data for Efficiency One programs.

July 17, 2025 NOVA SCOTIA ENERGY BOARD C/O CRYSTAL HENWOOD ([email protected]) Dear Ms. Henwood, RE: SOLAR NOVA SCOTIA (SNS) – Information Requests - M12349 Please find below Information Requests to NS Power from Solar Nova Scotia (SNS)...

AI summary Solar Nova Scotia (SNS) requests NS Power to provide summer peak demand data, assess the impact of Time Variable Pricing (TVP) on battery installation, clarify assumptions about residential and non-residential behind-the-meter solar, and address curtailment and RES contributions in the 2025 Load Forecast proceeding (M12349).

variances in the residential NSR? Please address the reasonableness of any other 24 approaches to reducing this variance and please explain why any alternative approaches 25 weren’t ultimately adopted. 26 Request IR-4: 27 Billed versus Acc...

AI summary The text raises questions about residential NSR variances, the reasonableness of approaches to reduce them, and the methodology for forecasting energy sales using differing data periods. It also inquires about the implications of using shorter data periods for Medium Industrial forecasts and the accuracy of cloud cover data from NASA's SWin for Halifax.

elative to the estimates in prior years’ models? What are the 16 implications of any such differences? 17 d. Please explain how it is possible that the historical data in the General rate class model 18 captures COVID impact to sales, wher...

AI summary The document outlines regulatory requests to NS Power (NSPI) regarding rate model assumptions, EV impact on load forecasts, data sources, rebate effects, and managed charging. Questions focus on historical data discrepancies, EV classification, methodology, and data incorporation from external reports.

he text, please explain 22 how this value is being used, i.e., what residential peak estimate is included in each 23 column and what that value is intended to represent.

AI summary The text inquires about the use of residential peak estimates in different columns, seeking clarification on their representation and intended purpose.

Date Filed: July 28, 2025 CA (NSPI) Page 2 of 4 1 Request IR-2: 2 3 Reference: Exhibit N-1, Section 10.0. 4 5 (a) Please provide a table summarizing NS Power’s forecasts for demand reduction and 6 actual demand reduction due to time-varyin...

AI summary Request IR-2 asks NS Power to provide a table comparing forecasted and actual demand reductions from time-varying pricing (2021–2035) and explain the basis for actual reductions. The request emphasizes data transparency on demand-side management outcomes and forecasting accuracy.

vide a brief explanation of the basis for the actual demand reduction amount 10 in the response to part (a). 11 12 (c) Please provide NS Power’s current estimate for when it will fulfill the commitment that 13 its investment in AMI technol...

AI summary The text requests NS Power to explain demand reduction metrics, estimate AMI technology's impact on peak demand, analyze peak event periods, project TVP rate savings, and outline TVP deployment plans. Questions focus on system reliability, demand-side management, and tariff design.

M12349 – SBA IRs – July 28, 2025 Page 2 of 4 1 a) Please include the assumed effective dates and associated impacts, if available. 2 3 Request IR-5: 4 Refer to Report, Section 4.4.4, Solar Generation (PV), page 44 of 94 and respond to the...

AI summary The document outlines three information requests (IR-5, IR-6, IR-7) related to net-metering customer statistics, behind-the-meter solar installation projections, and price elasticity data for small businesses. Requests focus on customer distribution, incentive considerations, and program participation metrics, with references to NS Power's reports and forecasting methods.

The Consumer Advocate submits that an illustrative effort at a class-specific peak load forecast would be instructive, and that the Board should direct NS Power to submit such an effort, either as a compliance filing or with next year’s lo...

AI summary The Consumer Advocate urges NS Power to submit a class-specific peak load forecast to improve forecast accuracy and inform demand-side programs, rate design, and hybrid electrification scenarios. While acknowledging challenges, they suggest using alternative growth rates if class-specific data is unavailable, citing potential benefits for the Board's decision-making.

the forecast. Or the class-specific energy growth rate, for that matter. The reasonableness of such a forecast could be compared to the official peak demand forecast to understand what is missing (or in apparent excess). For these reasons,...

AI summary The Consumer Advocate requests a class-specific peak load forecast from NS Power, arguing it would improve forecast accuracy. NS Power anticipates increased system demand due to changes in RTR sales, including a new third-party Licensed Retail Supplier (LRS) expected to serve 420 GWh of load across residential, commercial, and industrial customers starting in 2026.

outcome was forecast for Medium Industrial customers. Overall, between 2025 and 2035, General demand sales will decrease 7.3% and Medium Industrial demand sales? will have an average decline of 1.4%. The CA did not find fault in the applic...

AI summary The document discusses forecasts for electricity demand, noting a projected decline in General and Medium Industrial demand between 2025-2035. The CA recommended class-specific peak load forecasts and questioned the reliability of RtR sales projections. The SBA criticized NS Power’s arbitrary 2% annual electricity price increase assumption, advocating for data-driven long-term sales projections.

for demand response. • Evaluate additional demand response programs, with a greater level of peak loads. 11. Assess the probability of “other possible scenarios” in Figure D8 and consider additional analyses aimed at mitigating projected p...

AI summary The text outlines recommendations to evaluate demand response programs, assess peak load scenarios, and develop high/low-case projections for uncertain resources like heat pumps and EVs. NS Power partially agrees with intervenors' recommendations but cites time/resource constraints, noting that managed charging strategies fall under rate design rather than load forecasting. The RtR forecast relies on Licensed Retail Supplier expectations.

mendation #8, NS Power explained that the RtR forecast is based on the expectations developed by the Licensed Retail Supplier and is the best available information to be included in the Load Forecast. In response to Synapse’s recommendatio...

AI summary NS Power responds to recommendations regarding load forecasting, integrated resource planning, and rate assumptions. It defends including municipal utilities' full load, using internal forecasts for rate projections, and disagrees with recommendations on DER deployment and EV forecasts, citing data alignment and annual evaluation practices.