Topic:"Rate Design" in M10569

1 Figure 29: PV Impact to Energy (cumulative) .............................................................................. 47 2 Figure 30: Potential Peak Impacts from Batteries ...............................................................

AI summary This document lists figures from a regulatory proceeding, covering topics such as PV energy impact, battery potential, end-use intensities, electricity price forecasts, and demand-side management (DSM) savings. The figures include historical and projected data on residential, commercial, and industrial energy use, electrification trends, and sales components.

21, pages 6 (M10109). DATE: April 29, 2022 Page 29 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Both retail sales and disposable income show distinct impacts from COVID, while 2 household compensat...

AI summary The document discusses the impact of economic factors like retail sales, disposable income, and work-from-home activity on residential load forecasts. It highlights the use of housing completions as a key indicator for residential customer forecasts, referencing a decision by the NSUARB to re-evaluate this approach due to population growth and housing shortages.

Total New Year Load (GWh) Peak (MW) Installs 2022 2,610 -24 0 2023 3,947 -36 0 2024 5,351 -49 0 2025 6,825 -63 0 2026 8,505 -78 0 2027 10,420 -95 0 2028 12,604 -115 0 2029 15,093 -138 0 2030 17,931 -164 0 2031 20,724 -185 0 2032 23,069 -20...

AI summary The document discusses the projected load growth from 2022 to 2032, noting that distributed solar and battery storage combinations are not significantly assumed in the 2022 Load Forecast. It highlights the high cost of home batteries compared to gas generators, with the latter being more cost-effective for backup power. New pricing mechanisms like CPP and TOU may encourage battery use, but current costs remain high.

18 60 15 16 16 9 18 2032 18 60 15 16 16 9 18 6 7 DATE: April 29, 2022 Page 53 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 4.5 Price Data 2 3 Price data is an input to the SAE forecasts for the res...

AI summary The document discusses the methodology for calculating price data used in load forecasts, including the use of a 12-month moving average of real revenue per kWh. It references the 2020-2022 Fuel Stability Plan Compliance Filing and the General Rate Application (GRA) for price increases over the forecast period.

1 4.6 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the 4 use of electricity in Nova Scotia, and the forecast takes the projected energy and demand 5 savings into account. NS Powe...

AI summary The document discusses Demand Side Management (DSM) in Nova Scotia, highlighting the use of DSM targets approved by the Board in matter M09096. It explains the challenges of double-counting DSM savings in forecasting models and the approach to address this issue by incorporating historical DSM savings.

1 Request IR-17: 2 3 Respecting the Board’s direction to “evaluate improvements to the weather normalization 4 estimate and examine the impact of incremental cold on loads in the temperature ranges 5 where peak loads occur,” (Report, p. 12...

AI summary The Board directed NS Power to evaluate improvements to the weather normalization estimate and examine the impact of incremental cold on loads during peak temperature ranges. NS Power has not made an interim adjustment to its demand change metric, and the request asks for an explanation and a list of planned tasks to complete the Board's directed updates to the load forecast report.

Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL 1 Request IR-10: 2 3 Reference: NS Power 2022 Load Forecast, page 43, lines 12-13 4 5 “The peak impact assume...

AI summary NSPI responds to EfficiencyOne's questions about EV charging management, acknowledging demand response programs and CPP rate structures as potential tools. NSPI emphasizes the need for detailed program specifics and studies on rate impacts, while addressing concerns about double-counting capacity savings from overlapping initiatives.

1 11. Tariffs: 2 a. E3 assumes a mix of flat rate (residential and small commercial) and Time-of- 3 Day Rate Pilot Program pricing (TOU residential and small commercial TOU 4 pricing) 5 b. E3 assumes 100% access to public chargers, 77% acc...

AI summary The text outlines E3's assumptions about tariff structures, EV charging access rates, and Dunsky's modeling approach for forecasting EV adoption in Nova Scotia. Key elements include flat and time-of-day rate structures, EV charging cost assumptions, and a three-step modeling approach involving market segmentation, calibration, and input assumptions.

1 • Statistics Canada: population, area of population centers, and housing 2 composition. 3 • Nova Scotia Power Inc.: electricity rates 4 • NRCan Comprehensive Energy Use Database: number of vehicles and 5 annual vehicle sales, 6 and fuel...

AI summary The analysis leverages data from Statistics Canada, Nova Scotia Power Inc., and NRCan to model EV adoption. It calculates vehicle costs using Dunsky's database and forecasts adoption based on local dealership availability and market constraints.

d Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Small Business Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Please refer to Page 43, Lines 12-22. This section states that it is assumed 70% of...

AI summary The NSPI response to IR-9 discusses the management of EV charging, combining time-based rates and aggregator-based charge management. It clarifies that total kWh load remains unchanged between managed and unmanaged scenarios, but peak demand varies. The forecast assumes no impact on overall consumption, with annual load being the same across all cases.

Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Reference Report p. 59: “The non-weather variance in 2021 is mainly related to an inc...

AI summary NSPI responds to a consumer advocate's inquiry about factors influencing demand forecasts, noting population growth since 2016, no pandemic-driven migration analysis, and lack of concrete housing policies. The response addresses residential and commercial model assumptions, efficiency in new construction, and forecasted customer growth.

d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Reference Report p. 86 Figure 60 “Weather-Normalized Firm Peak.” For items a-d bel...

AI summary The document outlines a request (IR-14) made to NSPI regarding the weather-normalized sales, requirements, and peak load data from the 2022 Load Forecast Report. The request includes detailed inquiries about methodology, calculations, and supporting workpapers for the weather normalization process.

and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 DailyEnergy = Constant + b1×HDD13 + b2×HDD0 + b3×Lag1HDD13 + 2 b4×Lag2HDD13 + b5×JanHDD13 + b6×FebH...

AI summary The text describes a model used to calculate daily energy demand based on temperature variables, including HDD and CDD factors. It references attachments containing model inputs, outputs, and coefficients, and explains the allocation of weather impact across residential, commercial, and municipal sectors. The normalization factor for weather adjustments was updated from 20 MW/degree to 25 MW/degree in 2016, with a revised figure provided for 2019.

2022 LFR CA IR-14 Attachment 2 Page 6 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 CDD18 0 232.686 -97.305 358.84 Summary Jun 2021 heating load 7323 MWh Normal heating load 6995 MWh 2021 Varinace to Normal 328 MWh

AI summary The document provides data on heating load for June 2021, showing a total of 7323 MWh, compared to a normal heating load of 6995 MWh, resulting in a variance of 328 MWh. It also includes variable coefficients and other metrics related to heating degree days and load forecasting.

1 Request IR-17: 2 3 Respecting the Board’s direction to “evaluate improvements to the weather normalization 4 estimate and examine the impact of incremental cold on loads in the temperature ranges 5 where peak loads occur,” (Report, p. 12...

AI summary The request asks NS Power to explain why it has not made an interim adjustment to its demand change metric following the Board's direction, referencing Wilson testimony from M10109. It also requests a list of tasks for updating the load forecast report. The response indicates that NS Power agreed with the Board's direction to re-evaluate weather normalization and peak load forecasting methods.

son, please state your name, occupation, and business address. 3 A: I am John D. Wilson. I am the research director of Resource Insight, Inc., 10 Court Street, 4 Arlington, Massachusetts. 5 Q: Summarize your professional education and expe...

AI summary John D. Wilson, research director at Resource Insight, Inc., testifies about his 30+ years of experience in utility regulation, including work on cost-effectiveness of energy projects, conservation program design, ratemaking, and performance-based ratemaking for electric utilities. He has testified in multiple jurisdictions, including Nova Scotia.

SUMMARY OF PROFESSIONAL EXPERIENCE 2019– Research Director, Resource Insight, Inc. Provides research, technical assist- Present ance, and expert testimony on electric- and gas-utility planning, economics, and regulation. Reviews electric-u...

AI summary Summary of professional experience in energy regulation, including roles in utility planning, rate design, conservation programs, renewable energy evaluation, and regulatory policy. Highlights work with organizations focused on energy efficiency, market data, and air pollution reduction.

end-use. The electrification of vehicles (identified as EV) is also a major growth factor that can be mitigated with time- of-charge controls. We also wonder if more can be done with demand response. The interruptible load representing pri...

AI summary The text discusses concerns about peak load growth driven by electric vehicle adoption and industrial demand, urging NSPI to explore time-of-use rates and expanded demand response measures. It highlights the need for updated forecasts incorporating post-2026 demand response programs from the IRP Action Plan and acknowledges adjustments in load forecasting methodology.

• We also raise a point about the appropriateness of the commercial electrification programs. We ask NSPI to provide further information about their relative benefits and costs (p.14). • It is not clear in the report how much of the commer...

AI summary The text outlines requests for clarification and further analysis from Synapse Energy Economics, Inc. regarding NSPI's 2022 load forecast, focusing on commercial electrification programs, demand savings, EV impacts, time-of-use rates, DR measures, thermal storage, and emerging technologies like induction cooking. Questions emphasize cost-benefit evaluation, sector-specific DSM effects, and load management strategies.

as not modelled) M07393, E-1, 2015 DSM Evaluation Reports, Green Heat, Table 13, page 29. 2016 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M07964, E-1, 2016 DSM Evaluation Reports, • Row 238; (Column AA 1000)/(Column Existing Residential Pro...

AI summary EfficiencyOne (E1) responds to Nova Scotia Power Incorporated (NS Power) information requests regarding its 10-Year Energy and Demand Forecast (2022 Load Forecast Report) under regulatory proceeding M10569. The text references prior DSM Evaluation Reports and related documents from 2015–2017, including calculations and tables from various regulatory filings.

2 Page 5 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 2.0 RESPONSE 2 3 As outlined below, the evidence from Synapse, the CA, and E1, as well as the SBA’s submission 4 are primarily focused on the following issues: 5 6 1. Fur...

AI summary The 2022 Load Forecast Report rebuttal highlights three key areas: electrification's impact on energy/peak demand, new technologies (smart grid, time-variable rates), and weather normalization improvements. NS Power acknowledges the need for further investigation and will incorporate data from initiatives like the Integrated Resource Plan and Smart Grid Nova Scotia into future forecasts.

32 rates and other measures to mitigate the peak load increases for 33 all these components, especially for the commercial and industrial 34 sectors”. DATE FILED: September 26, 2022 Page 12 of 25 2022 Load Forecast Report Rebuttal NON-CONF...

AI summary The text emphasizes the need for rates and other measures to address peak load increases, with a focus on commercial and industrial sectors. It is part of a 2022 Load Forecast Report Rebuttal submitted on September 26, 2022.

ILED: September 26, 2022 Page 12 of 25 2022 Load Forecast Report Rebuttal NON-CONFIDENTIAL 1 NS Power Response: 2 3 NS Power agrees with this recommendation. Current pilot programs include 4 time variable pricing, water heater control and...

AI summary NS Power agrees to incorporate demand response measures from the IRP Action Plan into future forecasts and highlights pilot programs like time-variable pricing and a new tariff for multi-unit buildings. It acknowledges limited adoption of electric thermal storage for peak load moderation.

(Section 4.4, pp 41-45) 26 a. How do the daily load shapes for various types of electric vehicles correspond to NS’s 27 system load patterns? How does the peak demand pattern for electric vehicles correspond 28 to NS’s summer and winter pe...

AI summary The text outlines questions regarding electric vehicle (EV) load patterns, the EV Load Shaping Tool, time-of-use tariffs, SGNS project results, and calculations in figures. It focuses on aligning EV demand with NS’s system load, tool specifics, tariff details, and data validation.

rows 87-101. 28 29 a. Please provide the most recent scaled class load shapes, including all assumptions, 30 source data, and methods with formulas in the workpapers intact and working. 31 32 b. Please provide NS Power’s best estimate of m...

AI summary The text includes requests for NS Power to provide detailed load shape data, explain loss factor methodologies, and clarify forecasting practices. It also references a discrepancy between weather-normalized actual and forecasted system peaks, highlighting NS Power's use of a 25 MW/°C demand change metric.

ion Requests to Nova Scotia Power Inc. (NS Power) In the Matter of Nova Scotia Power Incorporated’s 2022 Load Forecast Report – M10569 NON-CONFIDENTIAL 1 (a) As part of its 2023-2025 DSM Plan, E1 has included EV managed charging in its 2 d...

AI summary The document contains requests to NS Power regarding its 2022 Load Forecast Report, focusing on demand response programs (including EV managed charging and Critical Peak Pricing), capacity savings double-counting, and electrification measures. E1 questions NS Power's integration of updated demand response programs and decarbonization comparisons in the forecast.