Topic:"Electric Resource Assessment Model" in M10569

2022 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 In the commercial models, non-manufacturing gross domestic product (GDP) and non- 6 manufacturing employment continue t...

AI summary The 2022 Load Forecast Report discusses the use of economic drivers in forecasting load demand, including GDP and employment data for residential, commercial, and industrial sectors. The industrial models use longer regression timescales to improve the relevance of economic variables and model fit.

NSPI (E1) IR-4 Page 2 of 2 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to EfficiencyOne Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 Reference: NS Power 2022 Load Forecast, p...

AI summary NSPI responds to IR-5 by asserting heat pumps are the primary electrification technology due to efficiency, with the model focused on emission reduction targets rather than economic factors. Other heating technologies are excluded due to lower efficiency.

is less than 100%, what other types of back-up heating are 27 expected to be in place for heat pumps in 2050, and what percent of the market are 28 they expected to serve? 29 Date Filed: July 8, 2022 NSPI (E1) IR-9 Page 1 of 4 10 - Year En...

AI summary NSPI responds to EfficiencyOne's questions about heat pump backup systems, modeling efficiency tiers (Base, Mid, Best in Class) with corresponding COPs. The analysis includes climate change impacts on heating/cooling degree days and low-GWP refrigerant transitions, though specific technologies are not detailed.

29 coincident peak time of a weekday evening in January at hour ending 1800, so the 30 difference between the E3 models would be 0.6 kW/vehicle. Not all of the charging will Date Filed: July 8, 2022 NSPI (CA) IR-4 Page 1 of 2 REDACTED (CON...

AI summary NSPI discusses challenges in managing EV charging demand during peak winter hours, noting 30% of vehicles remain unmanaged. Temperature impacts EV efficiency and battery performance, though traffic data analysis during peak periods has not been conducted.

temperature and load, and stated that temperature “is the predominant driver, so the impact 5 of other factors may not be statistically significant when combined with temperature.” 20 6 Q: Is wind speed potentially a significant driver of...

AI summary The expert confirms temperature is the primary driver of load but notes wind speed also significantly impacts load, particularly on cold days. Regression analysis shows adding wind speed slightly improves the model's R-squared value, though the effect is minimal. The expert advocates including wind speed in NS Power's weather normalization equation.

M10569 – SBA IRs – June 16, 2022 Page 4 of 5 1 Request IR-18: Understanding these are proprietary models, please provide a detailed 2 description of the methodology used in E3’s RESHAPE and EV Load Shaping Tool models 3 to determine load s...

AI summary The document includes requests for detailed methodologies of E3's EV load shaping models and questions about NS Power's EV forecast, including validation against past technology adoption and adjustments made to the forecast.

Date Filed: June 16, 2022 CA (NSPI) Page 6 of 11 1 b. Provide documentation of the methodology for calculating weather-normalized sales, 2 requirements and peak load. If the method is to multiply the difference in actual 3 temperature vs t...

AI summary The document requests detailed documentation and methodology from NS Power regarding weather-normalized sales, peak load calculations, and load shape data. Specific inquiries include the use of a 25 MW/°C peak adjustment, lighting load adjustments, and data sources for Figure 60 and Exhibit N-34.

EV forecast, investigation of new technologies to reduce energy and peak demand, and the management of peak through the Smart Grid Nova Scotia (SGNS) project results and direct control water heaters. The CA filed evidence prepared by John...

AI summary The document discusses John Wilson's recommendations for NS Power to improve climate change scenario analysis, enhance weather station integration, refine peak load forecasting models, and address modeling errors in residential energy models. Wilson also suggests adjustments to heat pump assumptions and EV usage during peak periods, alongside completing the line loss determination model with quarterly reporting.