Topic:"Energy Efficiency Resource Assessment Model" in M11689

REDACTED (CONFIDENTIAL INFORMATION REMOVED) Nova Scotia Utility and Review Board IN THE MATTER OF The Public Utilities Act, R.S.N.S. 1989, c.380, as amended 2024 Load Forecast Report April 30, 2024 REDACTED REDACTED (CONFIDENTIAL INFORMATI...

AI summary The document is a 2024 Load Forecast Report prepared under the Public Utilities Act, R.S.N.S. 1989, c.380. It includes sections on executive summary, introduction, forecasting approach, and discussion of major inputs. The report is part of a regulatory proceeding involving Nova Scotia's utility and review board.

1 Figure 42: Illustrative Contribution of Specific End Uses............................................................ 63 2 Figure 43: Commercial Class Sales ...................................................................................

AI summary The text lists figures illustrating energy sales, demand, and growth data across commercial, industrial, and residential sectors, including historical trends, forecasts, and demand response metrics. Visuals highlight sales vs. economic indicators, annual growth, and variance analysis.

............................ 98 30 Figure 71: System Peak Sensitivity .............................................................................................. 99 31 Figure 72: 2022 Evergreen IRP Scenarios Comparison ....................

AI summary The document is a redacted 2024 Load Forecast Report containing attachments and appendices detailing forecast models, stakeholder presentations, and partially confidential data. It outlines residential, commercial, and industrial demand projections, including sensitivity analyses and model inputs.

2024 Load Forecast Report REDACTED 1 Figure 11: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 Regarding household size, population is currently used in combination with customer count 6 to estimate average househo...

AI summary The 2024 Load Forecast Report discusses methodology for estimating household size in the SAE model using population and customer count data, impacting Heat Use, Cool Use, and Other Use variables. Figure 12 illustrates temporal changes in Household Size, influencing utilization rates of these variables.

omic Outlook, Dec 2023 11 RBC Provincial Forecast, Dec 2023 12 TD Provincial Economic Forecast Dec 2023 13 National Bank of Canada Monthly Economic Monitor, Dec 2023 DATE: April 30, 2024 Page 29 of 100 REDACTED (CONFIDENTIAL INFORMATION RE...

AI summary The document includes economic forecasts from multiple financial institutions and a redacted 2024 Load Forecast Report, with confidential information removed. The report is part of a regulatory proceeding and involves load forecasting, which is relevant to energy planning and resource allocation.

1 4.4 End-Use Intensity Trends 2 3 In addition to economic data, the SAE model also uses end-use data, in the form of 4 saturations and efficiencies, from NRCan and the US Energy Information Agency (EIA). 5 NRCan data for the residential s...

AI summary The SAE model uses end-use data from NRCan and EIA to develop end-use intensity trends, with adjustments made based on NS Power billing data. EVs and rooftop solar PV are modeled separately due to limited historical data. Forecasts for space heating and EV load shapes are based on third-party consultant E3's work.

As with the 2023 Load Forecast, the forecasts developed by third party consultant E3 for 26 space heating and EV load shapes are used. The space heating forecast uses the uptake 27 required to meet stated emission goals over the next 20 ye...

AI summary The 2024 Load Forecast Report uses third-party consultant E3's forecasts for space heating and EV load shapes, based on emission goals over the next 20 years, without assuming specific regulatory or incentive changes within the current 10-year forecast period.

aging hybrid (mini-split) systems and best-in-class performing heat 18 pumps.” 14 As stated in response to E1 IR-02 in the 2024 Annual Capital Expenditure 13F 19 proceeding, “The province’s 2030 Clean Power Plan calls for peak management,...

AI summary The text discusses the province's 2030 Clean Power Plan, which aims to reduce peak demand by 150 MW through peak management, demand response, and efficiency investments. It highlights the Hybrid Peak Mitigation electrification scenario, which includes heat pump saturation in residential areas to meet net-zero carbon reduction targets by 2050.

was adjusted to meet the E3 series by around 2040 as shown in Figure 19, 3 resulting in a slightly lower trajectory. 4 5 Figure 19: Commercial Space Heating Saturation Comparison 6 7 8 For the 2024 forecast, energy and peak values predicte...

AI summary The text discusses adjustments to energy and peak forecasts for commercial and residential heat pump installations, comparing models from NS Power and the E3 hybrid scenario, showing differences in energy and peak demand over time.

Overall Overall Overall Overall Total % Install % Install % Sat. Heating % Sat. Cooling Year Cumulative Non-Elec. Elec. for Intensity for Intensity New Installs Heat Heat Heating (kWh/house) Cooling (kWh/house) 2024 21,187 68 32 44 2,066 5...

AI summary The document provides a table showing cumulative new installations and heating and cooling intensity from 2024 to 2034. It notes that the overall heating intensity has increased compared to the 2023 forecast due to adjustments made in response to an 'unallocated' variance in the res.

dered as EVs) in the province as of the end of 2023. The EV forecast has been updated 14 for 2024 based on the report entitled “Ensuring ZEV Adoption in Nova Scotia” 17, with a 16F 15 provincial vehicle sales forecast that aligns with the...

AI summary The document discusses the forecast for zero-emission vehicle (ZEV) adoption in Nova Scotia, noting that current adoption rates are expected to lag behind federal targets due to regional differences. The forecast aligns with national goals but acknowledges the gap will narrow as targets increase over time.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 24: EV Forecast 2 3 4 The forecast includes both light-duty vehicles (LDV) as well as medium-duty vehicles 5 (MDV) such as delivery trucks and other me...

AI summary The 2024 Load Forecast Report revises previous estimates, projecting over 150,000 electric vehicles (EVs) on Nova Scotia roads by 2034, primarily light-duty vehicles. The report highlights the impact of EVs on energy sales and peak demand, influenced by factors such as vehicle type, charging capacity, and driving patterns. E3’s EV Load Shaping Tool provides load shapes based on a bottom-up modeling approach.

rom the load shapes provided by E3. 21 DATE: April 30, 2024 Page 38 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 25: EV Mileage Assumptions and Load/Peak Modeling Results 2

AI summary The document discusses the 2024 Load Forecast Report, focusing on EV mileage assumptions and load/peak modeling results, as provided by E3.

arging that would occur during switches 17 from peak to off-peak rate periods. 18 19 Figure 26 provides the estimated energy and peak impacts that correspond to the number 20 of EVs in the forecast, along with a sensitivity showing potenti...

AI summary The text discusses the potential energy and peak demand impacts of electric vehicles (EVs) based on the number of EVs in the forecast, including a sensitivity analysis that assumes an average peak demand of 1.6 kW/vehicle. It references a survey from NRCan 2009 and highlights the importance of managed charging measures.

2024 Load Forecast Report REDACTED 1 Figure 26: EV Impact to Energy and Peak Forecasts (cumulative) 2 Peak @ Peak @ New Load Year 0.9kW/vehicle 1.6kW/vehicle EVs (GWh) (MW) (MW) 2024 3,387 12 3 6 2025 9,491 34 9 16 2026 13,466 51 12 22 202...

AI summary The 2024 Load Forecast Report discusses the impact of electric vehicles (EVs) on energy and peak load forecasts in Nova Scotia up to 2034. The report highlights the cumulative growth in EVs and their increasing influence on energy demand and peak load. It also mentions the Smart Grid Nova Scotia (SGNS) Project and its findings, which were submitted to the UARB in March 2024.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Intensities 2 3 Figure 29 provides an estimate of the resulting residential end-use intensities. This figure 4 represents the intensities that are inputs to t...

AI summary The document provides an overview of residential end-use intensities used in the 2024 Load Forecast Report, including categories such as heating, cooling, lighting, and other appliances. It notes that PV and EV are modeled separately within the 'Other' category to illustrate their impact.

Avg Sales Count Forecast Sales (GWh) Variance Report (kWh/cust) (GWh) (GWh)

AI summary The text presents a table with columns for average sales, count, forecast, sales in gigawatt-hours (GWh), and variance, indicating a comparison of energy sales data and forecasted values.

1 6.0 COMMERICAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General 4 rate classes. Like the residential model, the commercial SAE models express monthly sales 5 as a function of heating, coolin...

AI summary The Commercial SAE model forecasts energy use for Small General and General rate classes based on heating, cooling, and other loads, incorporating factors like GDP, employment, and weather. The model accounts for the impact of the pandemic and shows a rebound in 2022. For 2024, the impact of RTR has increased, and EV load growth has slowed compared to previous forecasts.

1 normalization are the same as those used in the 2023 forecast and are described in Figure 2 57 below: 3 4 Figure 57: Peak Regression Coefficients 5 Coefficient Value Description Weekdays 30.5 Peaks that occur on weekdays will be 30.5MW h...

AI summary The document discusses peak load forecasting, including factors such as weekday vs. weekend differences, wind speed, and temperature lag. It notes that the forecast system peak is expected to increase by 1.4% annually, with near-term increases due to heating load and updated temperature averages, and long-term decreases due to reduced EV impact and hybrid heating scenarios.

4 degrees Celsius. In the long term the peak forecast 19 has decreased from the 2023 forecast due to less impact from EVs as well as the impact of 20 the hybrid heating scenario. 21 DATE: April 30, 2024 Page 84 of 100 REDACTED (CONFIDENTIA...

AI summary The 2024 Load Forecast Report discusses historical and forecasted system peak demand, noting a long-term decrease in peak forecasts due to reduced impact from EVs and hybrid heating scenarios. The firm peak is expected to increase by 1.4 percent annually, with normalized data showing improved alignment between historical trends and forecasts.

Appendix A – Forecast Values 1.1 Table A1: Energy Requirement – 2024 NS Power Forecast Energy Forecast

AI summary Appendix A provides a table titled 'Energy Requirement – 2024 NS Power Forecast Energy Forecast,' which outlines forecasted energy requirements for the year 2024.

s defined as: CoolIndex = f(Cooling Saturation, Efficiency, Shell Integrity, Square Footage) XOther captures non-weather sensitive end uses: XOther = OtherUse × OtherIndex OtherUse is calculated as 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑦𝑦,𝑚𝑚 𝐻𝐻𝐻𝐻𝐻𝐻𝐻...

AI summary The text defines the CoolIndex and XOther variables, which are used to calculate energy consumption based on factors like cooling saturation, efficiency, and household characteristics. It also outlines the calculation of OtherUse, incorporating variables such as employment compensation, household size, and electricity prices, with reference to a baseline year of 2015.

Type for a particular year y, EffyType is the efficiency of an end-use of given Type for a particular year y, and EI15Type is a reference year (2015) calibration weight per end-use Type. The factors: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑆𝑆𝑆𝑆𝑆𝑆𝑦𝑦 � � 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 � 𝑇𝑇𝑇𝑇𝑇𝑇...

AI summary The text describes efficiency factors and formulas used in calculating energy use intensities for residential end-use types, including the impact of demand-side management (DSM) on load reduction. It highlights how DSM savings are embedded in billed sales data, with a regression coefficient indicating a negative correlation between DSM activity and load.

y the coefficients to calculate the overall impact. For example, the contribution of Efurn is calculated as [(Efurn2034-Efurn2024) x HeatUse x Coeff]/WtXHeat2024 Residential Input Variables – XCool Intensities Econ + Struct Regression Cent...

AI summary The text discusses residential input variables related to cooling (XCool) and other residential factors (XOther), including calculations based on intensity values, usage variables, and coefficients. It outlines the methodology for calculating contributions from different cooling technologies and their impact on load forecasts.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 10 of 35 Residential Input Variables – XOther Intensities Econ + Reg Struct Water Cook Ref/Frz Wash/ TV Light Misc Other Coeff Total Heat Dry Use Xother...

AI summary The document provides details on the 2024 Load Forecast Report, focusing on residential and commercial input variables used in modeling energy consumption. It outlines formulas for calculating XOther and XHeatm, which are used to forecast load based on factors like heating and cooling requirements, GDP, employment, and price trends.

(CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 13 of 35 Small General Model Statistics Model Statistics Iterations 12 Adjusted Observations 120 Deg. of Freedom for Error 109 R-Squared 0.950 Adjusted R-Squared...

AI summary The Small General Demand customer forecast model is similar to the residential model, incorporating heat pump programs within the SAE model. Adjustments outside the regression include estimates for other commercial and industrial growth programs, PV, EV, RTR, and DSM.

d as Small Gen Average Use (12,763 kWh/customer in 2024, 13,895 kWh/customer in 2034) x number of customers (26,675 in 2024, increasing to 29,466 in 2034). Small General Average Use –Regression XHeat XCool XOther Binaries ARMA Avg Sales (k...

AI summary The document provides a detailed breakdown of the Small General Average Use for electricity consumption, including regression analysis and input variables for heating and cooling. It outlines the projected increase in usage from 2024 to 2034 and the factors influencing these changes.

general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total sales rather than average use as is the case in the small general and residential classes. Like the small general model, a flat s...

AI summary The text discusses the forecast for general demand load in the commercial sector, highlighting the use of a flat scaling factor in the regression model and adjustments for factors like EV load, PV, RTR, Hybrid, and DSM. It also provides a comparison between 2024 and 2034 load forecasts and includes a formula for calculating general demand sales.

energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coefficients found after running the sales model. ResOther can be written as: ResOtherm =ResSalesm- b1×ResXHeatm-b2×ResXCoolm...

AI summary The document presents a regression model for residential energy sales, separating weather-dependent and non-weather-dependent variables. It also describes normalization of load requirements and includes adjustments for specific months due to external factors like billing delays.

r a high residual and for October 2022 to account for the impact of billing delays related to hurricane Fiona in the energy models. Variable Coefficient StdErr T-Stat P-Value mVarsNew.Heat_Var 1.541 0.080 19.375 0.00% mVarsNew.Cool_Var 1.3...

AI summary The text discusses statistical analysis of energy models, including coefficients, standard errors, t-statistics, and p-values for various variables related to heating, cooling, and monthly energy usage. A specific adjustment was made for October 2022 due to billing delays caused by Hurricane Fiona.

TED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 34 of 35 Peak Model Statistics Model Statistics Iterations 1 Adjusted Observations 120 Deg. of Freedom for Error 103 R-Squared 0.984 Adjusted R-Squared 0.982...

AI summary This section presents statistical details of a peak load forecasting model, including metrics such as R-squared, AIC, BIC, and error measures. The model has a high R-squared value of 0.984, indicating a strong fit, but some statistics like the F-statistic and Durbin-H statistic are not available.

REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendix C Page 4 of 9 Figure C4: Energy Forecast Accuracy NSR less mills Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forecast Forec...

AI summary The table presents forecasted and actual energy values over multiple years, comparing forecasts issued in different years against actual outcomes, adjusted for mills. It highlights the accuracy of load forecasts from 2013 to 2022.

Appendix D – Forecast Sensitivity Analysis Figure D6: Sensitivity of Energy Forecast (2025) Figure D7: Sensitivity of Energy Forecast (2034) Page 7 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast Report Appendi...

AI summary The document presents a forecast sensitivity analysis, highlighting that demand-side management (DSM) and electric vehicles (EVs) are the main drivers of energy forecast sensitivity, while DSM, EVs, hybrid heating peak mitigation, and weather/economics similarly influence peak forecast sensitivity.

FORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 5 of 18 Changes from 2023 Input Data Source Heating intensity Residential heating intensity updated to reflect 2022 and 2023 actual results. EVs Vehicle sales updated to reflect...

AI summary The 2024 Load Forecast Report Appendix E highlights updates to heating intensity, EVs, and electrification of heating based on the 'Hybrid' scenario. Variance analysis shows significant unexplained load differences in residential heating during winter months, leading to an adjustment in heat pump heating intensity for more accurate forecasting.

Forecast Comparison - Commercial The significant changes to the forecast for 2024 are a larger drop in load between 2025 and 2026 due to the introduction of RTR, with lower growth from EVs. 12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...

AI summary The document discusses forecast comparisons for 2024, highlighting changes in load forecasts for commercial, industrial, energy, and peak demand. Key factors include the impact of RTR, changes in EV growth, heating intensity, and new customer forecasts.

1 (b) Distribution software is not used to prepare the load forecast. Updates to the distribution 2 planning process are being discussed in the ongoing Cost of Service Study. AMI data is 3 currently being used in CYME distribution analysis...

AI summary The text discusses load forecasting practices, noting that AMI data is used in CYME software for capacity planning and generator impact studies, while pilot projects like Smart Grid Nova Scotia are not typically required for load forecasting. Electrification of heating and EVs is modeled, but more detailed data is needed for granular forecasts. Solar generation is already included in the forecast.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (vii) Please provide NSPI’s estimates of the average of the maximum winter peak 2 load impacts per building (kW/building) for 2024...

AI summary NSPI is responding to Synapse's information requests regarding load forecasts for commercial electric heating systems from 2024 to 2034, including peak load impacts and heat pump efficiencies. NSPI also discusses adjustments made to heating component intensities based on weather-dependent variances.

provided in 2024 LFR 4 Attachment 1 Residential Intensities. The heat pump saturation is estimated based 5 on annual sales numbers from heat pump installers in the province. 6 7 (ii) This refers to residential customers. 8 9 (iii) The 44 p...

AI summary The text discusses heat pump adoption rates in Nova Scotia, referencing the 2024 Load Forecast Report and noting that actual adoption has exceeded estimates from the Energy Efficiency and Conservation Act (E3). The report includes data on residential heat pump saturation based on installer sales.

1 (f) 2 (i-ix) The E3 numbers in Figure 18 are provided in Attachment 1 (HP Stock tab). The 3 commercial numbers in the forecast are not produced on the same basis as the 4 residential numbers, the intensities are based on kWh per area rat...

AI summary The document discusses the methodology used in forecasting commercial energy usage, highlighting differences between residential and commercial forecasting approaches. It references the E3 model, heat pump saturation impacts, and the hybrid scenario, while noting limitations in forecasting peak values at the individual class or end-use level.

1 (i) 2 (i) Values in Figure 21 values can be found in 2024 LFR Attachment 1 as follows: 3 • Total Cumulative new installs are the sum of column B on the HP tab 4 • Percent install non-electric heat is column D on the HP tab 5 • Percent in...

AI summary The text provides detailed references to data sources and tabs within the 2024 LFR Attachment 1 and 2 for heating and cooling intensities, saturation percentages, and efficiency metrics, particularly for heat pumps and electric resistance heating. It explains how specific data points are derived and highlights the absence of equivalent data for commercial classes.

and intensities can be found in 2024 LFR Attachment 2 and 3, but are 26 applicable to heating and cooling end uses as a whole and not specific to heat 27 pumps. 28 Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 10 of 12 REDACTED (CONFI...

AI summary The document discusses the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It references attachments 2 and 3 of the LFR, which provide intensity and efficiency values for heating and cooling, though not specific to heat pumps or individual end uses. Efficiency data is sourced from the EIA and uses Btu out/Btu in, not COP.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (v) For LDV, MDV, and HDV, please provide peak load reductions in kW per 2 vehicle for each vehicle category that NSPI expect and a...

AI summary The document outlines a series of information requests from the NSUARB to NSPI regarding the impacts of managed charging programs and time of use rates on peak load reductions for different vehicle categories, as well as the status of time of use tariffs in pilot stages. NSPI is asked to provide supporting evidence for assumptions about managed and unmanaged EV charging.

and rationales for these assumptions. Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 2 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFID...

AI summary The document discusses questions raised about assumptions in the 2024 Load Forecast Report, specifically regarding vehicle types and peak load impacts from electric vehicles. NSPI provides context about aligning with federal targets and references the Dunsky report as a source for forecast assumptions.

Nova Scotia are likely 21 to lag those of larger markets. The Dunsky report 1 cited in the forecast provides the 0F 22 following on page 18: 23 24 From this starting point, we produced a low adoption in NS scenario (Figure 25 5) that repre...

AI summary The document discusses the likelihood of Nova Scotia lagging behind larger markets in zero-emission vehicle (ZEV) adoption, based on the Dunsky report. It outlines a low adoption scenario for Nova Scotia, which could prevent the province from meeting its 30% ZEV sales target by 2030 if certain factors, such as federal ZEV regulations and provincial action, are not addressed.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 In the near term, growth in EV adoption in recent years has exceeded that of the low 2 scenario, but overall sales are expected to...

AI summary The 2024 Load Forecast Report discusses EV adoption growth exceeding the low scenario in the near term, but aligning with it over 10 years. It references differences in driving behavior between summer and winter months and the use of the E3 EV Load Shape Tool for forecasting EV charging loads.

CTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-9 Attachment 2 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Info...

AI summary The NSPI responded to IR-10 requests regarding PV generation data, summer peak impacts, and Load (GWh) definitions. They referenced the Net Metering Report and provided attachments for supporting data and calculations.

r to Attachment 1, Solar Peak Impact tab. 25 26 (c) Please refer to Attachment 1, 2024 tab. 27 1 M11553, Exhibit N-1, NS Power, 2023 Net Metering Report, January 31, 2024. Date Filed: June 19, 2024 NSPI (Synapse) IR-10 Page 1 of 2 REDACTED...

AI summary The document discusses the 2024 Load Forecast Report, specifically the Solar Peak Impact tab and the 2024 tab, which include data on solar production and its impact on load forecasting. It also references the 2023 Net Metering Report and mentions the Load column representing total solar production over a year, though it does not account for the timing mismatch between energy injection and consumption offset.

Total Cumulative Total Cumulative Comm Cumulative Res Cumulative Res Comm Cumulative Cumulative Capacity Res Install Install Year Installs (MW) GWh Installs (MW) Comm GWh GWh (MW) Est Est Total Installs 2024 22 24 9 10 33 31 2,570 450 3,01...

AI summary The text presents cumulative data on installations and energy generation from 2024 to 2034, including metrics such as total installs (MW), cumulative GWh, and estimated capacity. The data highlights increasing trends in both installations and energy production over time.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Introduction (Section 2.0, citing to Board Decision concerning 2023 Load Forecast). Please 4 explain in detail h...

AI summary NSPI responds to Synapse Information Requests regarding the 2024 Load Forecast Report, addressing the Board's direction to include the IRP, SGNS, AMI, and TVP outcomes; reviewing carbon emission reduction assumptions; and assessing historical load data compared to survey results. Citations are provided for each section in the report.

1 Request IR-16: 2 3 Introduction (Section 2.0, citing to Board Decision concerning 2023 Load Forecast). Please 4 describe in detail any analyses conducted and results obtained in conducting the following, 5 which the Board encouraged NSPI...

AI summary The document requests NSPI to evaluate the elasticity in the SAE model using data from the TVP Pilot EM&V in matter M11267 and assess the robustness of the model. It also asks for an evaluation of input variables in the residential model, including housing completions, household size, economic inputs, EV adoption rates, and infrastructure projects.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 Price Data (Section 4.5, pp 52-53) 4 5 (a) Please provide the data and calculations used to produce the electric...

AI summary NSPI responds to Synapse's information request regarding the 2024 Load Forecast Report, specifically addressing price elasticity data, technical definitions, and whether more recent studies have been reviewed. NSPI references Itron's experience and notes that most studies on price elasticity are from the 1980s.

9 Canada, the US, and in both summer and winter-peaking jurisdictions. There have been 30 studies on the price elasticity of electricity consumption, most dating from the 1980s and Date Filed: June 19, 2024 NSPI (Synapse) IR-17 Page 1 of 2...

AI summary The document references historical studies on the price elasticity of electricity consumption, noting a range of results from -0.05 to -0.30, with a mean estimate of -0.16 in 1997 and similar findings in a 2006 study.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (b) Please refer to the table below. The values for 2024-2025 are from E1’s supply agreement, 2 while the values from 2026-2034 are...

AI summary The text refers to the 2024 Load Forecast Report and NSPI's responses to Synapse Information Requests. It provides load forecast data from 2024 to 2034, citing E1’s supply agreement and the E1 Potential Study. It also references a Nova Scotia Energy Efficiency and Demand Response Potential Study from 2019, filed as Exhibit N-1 in M08929.

so, please explain in detail. 27 28 (g) Please provide the inputs and calculations used to create Figure 39. 29 30 (h) Please provide the inputs and calculations used to create Figure 40. Date Filed: June 19, 2024 NSPI (Synapse) IR-20 Page...

AI summary The document contains a series of information requests related to the 2024 Load Forecast Report, including requests for inputs and calculations used in specific figures and discussions on the impact of proposed building efficiency regulations. NSPI provides partial responses, including references to attachments and explanations related to the impact of the COVID variable on load forecasts.

26 27 (e) No. 28 29 (f) No. 30 Date Filed: June 19, 2024 NSPI (Synapse) IR-20 Page 2 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL...

AI summary NSPI provides responses to Synapse's information requests regarding the 2024 Load Forecast Report, including data locations and assumptions related to building efficiency and floor space estimates.

a consequence of increased forecast solar penetration, owing to 24 continued strong uptake of small-scale solar installations and changes to legislation which 25 will likely result in more interest from commercial customers. Increased sola...

AI summary The text discusses increased forecast solar penetration due to strong uptake of small-scale solar installations and legislative changes, which may lead to greater interest from commercial customers. This is elaborated in Section 4.4 of the report.

2.770 80.257 1.000 1.000 0.582 1.166 0.694 2.770 80.083 1.000 1.000 2024 0.590 1.186 0.695 2.797 83.040 1.000 1.000 0.591 1.177 0.695 2.797 82.884 1.000 1.000 2025 0.599 1.199 0.695 2.821 85.612 1.000 1.000 0.599 1.187 0.695 2.822 85.498 1...

AI summary The text presents a series of numerical data points, likely related to energy load forecasts for the years 2024 to 2034. It references the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests, indicating a regulatory proceeding involving energy forecasting and utility responses.

e included in the Commercial class. Date Filed: June 19, 2024 NSPI (Synapse) IR-30 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDE...

AI summary The document contains responses from NSPI to information requests regarding the 2024 Load Forecast Report, specifically addressing the source data and calculations for figures 53, 54, and 55. The responses direct the requester to specific tabs and cells in the attached document.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iii) How many water heaters and smart thermostats does NSPI assume for its peak 2 reduction estimates for the TVP rates? 3 4 (iv)...

AI summary The document outlines a series of information requests from NSPI to Synapse regarding load forecasting assumptions, including peak load reductions, program participation, demand response impacts, and electrification assumptions in the 2024 Load Forecast Report.

acy 18 assessments. Date Filed: June 19, 2024 NSPI (Synapse) IR-32 Page 4 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 In its 2...

AI summary The document discusses load forecasting and demand response (DR) programs, referencing ELCC values applied by BC Hydro and NB Power, as well as the ELCC Class Rating for Demand Response in PJM's 2025/2026 Base Residual Auction. It highlights the importance of developing load capacity curves and identifying energy potential from DR programs.

ual Auction the ELCC Class Rating for Demand 10 Response is 76 percent 4 and its preliminary ELCC rating for Demand Response for 3F 11 the 2026/2027 delivery period through to the 2034/2035 delivery period begins at 12 70 percent and decre...

AI summary The document discusses the ELCC (Energy Loss Correction Coefficient) class rating for Demand Response, noting a decrease from 76% to 51% over the 2026/2027 to 2034/2035 delivery period. It also estimates that around 50,000 participants would be required to achieve 19MW savings, based on average customer savings of 0.385kW. Water heaters and smart thermostats are not considered for peak reductions under TVP rates.

Yes, but the timing has been shifted to account for the timing of the associated pilot 22 projects. 23 24 (ix) The values are the same but are shifted by 2 years. 25 5 EfficiencyOne’s (E1) Nova Scotia Energy Efficiency and Demand Response...

AI summary The document references a load forecast report and responses to information requests, noting that timing has been adjusted to align with pilot projects and that the values are shifted by two years. It also mentions a study on energy efficiency and demand response potential filed under a specific matter.

margin, the Evaluator used the following scenario: › Average load of the 10 eligible non-event days prior to the event without adjusting for the load observed a few hours before the event. This scenario is coherent with the finding that DH...

AI summary The Evaluator used a baseline scenario with a 6.1% MPE for mornings and 2.3% for evenings, significantly improving upon the previous baseline's 18.5% and 14.2% MPE. The new method resulted in an average DR capacity of 385 W per enrolled controller with a margin of error below 10%.

t being the result of lower available DR capacity in 2023 compared to 2022. Indeed, using the same methodology as in 2022 would have resulted in a unitary available DR capacity 14% lower than in 2022. 3.2.3 Interactive Effects In a home, i...

AI summary The document discusses the calculation of available DR capacity for the DHW Direct Load Control pathway, noting a 14% decrease in 2023 compared to 2022. It also addresses interactive effects and the effective useful life (EUL) of DR capacity, determining an EUL of 1 year for total capacity and 7 years for new capacity based on reenrollment rates.

It should be noted that the program being only in its second year, only limited data are available on how long participants will remain in the program, thus this EUL value should be used with caution. 3.2.5 Evaluated New and Total Availabl...

AI summary The document discusses the evaluation of new and total available demand response (DR) capacity for residential programs in 2023, noting limited data availability and the use of an effective useful life (EUL) value with caution. It also outlines the calculation method for available DR capacity and references line loss factors submitted to the NSUARB.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-35: 2 3 Sensitivity Analysis (Section 11, 2020 IRP Comparison, pp 97-98) 4 5 (a) Please provide information about how th...

AI summary NSPI responds to Synapse's request regarding the impact of the evergreen IRP analysis on future loads. While the analysis does not directly affect load, it may identify long-term outcomes requiring policy or program support, such as addressing increasing peak demand through scenarios like hybrid space heating.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-37: 2 3 Appendix B: Residential Model 4 5 (a) Please provide in electronic spreadsheet format the data and the statistic...

AI summary NSPI has received a request (IR-37) from Synapse for detailed data and model parameters related to the residential model in the 2024 Load Forecast Report. The request includes data on end-use intensity, electric space heat and hot water usage, and factors influencing variables such as XHeat, XCool, and XOther. NSPI is asked to disclose whether it collaborated with E1 on common assumptions.

Heating Indices (kWh / HH) Cooling Indices (kWh / HH) Other Indices (kWh / HH) Year ResIndices.EFurn ResIndices.HPHeat ResIndices.SecHt ResIndices.FurnFan AContrib2Sales.HeatUse ResIndices.CAC ResIndices.HPCool ResIndices.GHPCool ResIndice...

AI summary The text presents data tables showing energy usage indices across various categories for the years 2014 to 2017, including heating, cooling, and other energy-related activities, measured in kWh per household.

MBin.Apr1 82.277 24.871 3.308 0.13% MBin.May1 87.249 24.604 3.546 0.06% MBin.Feb1 60.064 22.045 2.725 0.75% MBin.Covid 70.957 16.079 4.413 0.00% MBin.Oct22 145.757 23.017 6.333 0.00% MBin.Mar2 61.873 21.517 2.876 0.49% MA(1) 0.49 0.096 5.1...

AI summary The document includes load forecast data from the 2024 Load Forecast Report by Synapse, with specific values for different months and a redacted section indicating confidential information has been removed.

5,391.0 2030 11,078.0 495,055.0 1,227.8 12,818.9 2,945.7 26,529.8 534,403.7 16000 4860 0.996768 333.0 5,817.2 (74.8) (132) 93.3 (245.6) (359.6) 5,457.5 2031 11,157.0 495,055.0 1,089.3 13,908.1 2,719.3 29,249.1 538,212.2 16000 4860 0.994635...

AI summary The text presents a table with numerical data spanning from 2030 to 2034, including values such as load forecasts, costs, and other financial metrics. The table is part of the 2024 Load Forecast Report by Synapse, with a note indicating that some information has been redacted due to confidentiality.

1 (b) The calculation of the XHeat, XCool, and XOther values was completed in Metrix ND, the 2 forecasting software used by NS Power. The methodology used to calculate these variables 3 is outlined in Appendix B, Forecast Model Details in...

AI summary The text discusses the methodology used by NS Power in calculating XHeat, XCool, and XOther values using Metrix ND forecasting software, referencing various attachments and reports. It also mentions the inclusion of new variables in the 2024 model, such as those related to hurricane Lee and post-Covid usage shifts, which improved the model's fit.

orecast Report Synapse IR-38 Attachment 1 Page 6 of 9 Year XHeatNew XCoolNew XOtherNew Mar New Sept New Dec New Oct 22 New May 20 New Jun 20 New Yr22Plus NewARMA new Total (kWh)

AI summary The text presents a table with forecast data for various energy metrics, including XHeatNew, XCoolNew, XOtherNew, and others, with columns for different months and years. The table includes total kWh values, indicating energy usage or production forecasts.

2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-39: 2 3 Appendix B: General Service Model 4 5 (a) Please provide in electronic spreadsheet format the data and the stati...

AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report, providing references to attachments containing detailed data, model parameters, and calculations related to the General Service Model and end-use intensity data.

Heating Indices (kWh / HH) Cooling Indices (kWh / HH) Other Indices (kWh / HH) Year GenIndices.Heating AContrib2Sales.GenHeatUse GenIndices.Cooling AContrib2Sales.GenCoolUse GenIndices.Vent GenIndices.EWHea GenIndices.Cooking GenIndices.Re...

AI summary The text presents a table of energy usage indices for heating, cooling, and other purposes across various years from 2014 to 2020, including values for generation, contributions to sales, and specific usage categories such as ventilation, cooking, and lighting.

ed based on current and expected trends. Date Filed: June 19, 2024 NSPI (Synapse) IR-54 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CO...

AI summary NSPI's Load Forecast Report (M11689) outlines modeled trends for 2024, considering regulatory targets for EVs and net zero emissions as the most likely outcomes. These targets are used in the forecast based on current information, though their probability has not been assessed.

LYSIS...................................................................................... 30 5. QUESTIONS AND RECOMMENDATIONS ................................................................. 32 APPENDIX A. QUESTIONS AND RECOMMENDATIONS...

AI summary Nova Scotia Power, Inc. (NSPI) submitted a 2024 load forecast report showing reduced EV growth projections and increased Renewables to Retail (RTR) market forecasts, leading to more conservative energy and peak load growth estimates compared to prior years. Synapse Energy Economics notes improvements in expository quality and a moderation in growth rates post-2024, driven by EV adoption, electric heating, and customer growth.

ilding size. The major change drivers for XHeat are electric (resistance) heat, heat pumps, and, to a lesser extent, secondary heat. The net change over the forecast period is a 12.5 percent increase. The XCool variable is the product of t...

AI summary The document details load forecast variables XHeat, XCool, and XOther, driven by factors like heating technologies, cooling saturation, and appliance efficiency. XHeat increases 12.5%, XCool surges 57.8% due to heat pump cooling, while XOther declines 2.1% from reduced lighting and TV use. Residential energy use is 45% heating, 4% cooling, 53% other, with existing customers seeing 5.6% higher heating loads over the forecast period.

mes that 70 percent of the EVs will be on managed charging programs or time varying rates and the rest of the EVs will be unmanaged. 37 However, NPSI does not explain how it developed this assumption. For 2033, this year’s forecast predict...

AI summary The document discusses EV load projections for 2033, noting a significant decrease from previous forecasts. It highlights that 70% of EVs are assumed to be on managed charging programs or time-varying rates, but NSPI does not explain how this assumption was developed. The new forecast is based on a scenario from a Dunsky report, which includes low and high EV adoption scenarios.

report provides EV forecasts for a low scenario, where sales in Nova Scotia lag the federal EV mandates, and a high scenario where EV sales in the country are distributed evenly across the provinces. NSPI’s 2024 EV forecast assumes that cu...

AI summary The document discusses NSPI's 2024 EV forecast, which assumes a low scenario for 2035 but notes that recent EV adoption has exceeded this. It highlights the need for rate designs and programmatic interventions to manage peak load from increased EV penetration and references the SGNS project's findings on EV impacts.

levels. We ask NSPI to explore the potential for greater industrial savings. We ask NSPI to explore the impacts of real time rates. We ask NSPI to explore the impacts of increases in industrial RTR. 2.5. Commercial and Industrial Electrifi...

AI summary The text discusses requests for NSPI to explore industrial savings, real-time rates, and impacts of industrial RTR. It outlines forecasts for commercial and industrial electrification, municipal sector load reductions, and the modest impact of DSM savings on energy forecasts, noting changes in the coefficient used to adjust future DSM savings.

• NSPI’s approach to apply an ELCC value specific to demand response does not consider any interactive effects with other resources in terms of its peak load impacts. A portfolio wide ELCC of various resources can be greater than a simple...

AI summary The text critiques NSPI's approach to evaluating the Effective Load-Carrying Capability (ELCC) of demand response, arguing that it should consider interactive effects with other resources like solar PV, wind, and battery storage. It references a 2020 E3 report and a 2023 Board decision (M11307) to emphasize the need for a portfolio-level ELCC analysis. The text also highlights the need to update peak load forecasts due to electrification and incorporate demand response resources into modeling.

lausible, although there are many uncertainties, and some aspects need refinement. There should be more discussion of the underlying factors causing peak growth and what can be done to mitigate it. 4. SENSITIVITY ANALYSIS The forecast Repo...

AI summary The text discusses the importance of sensitivity analysis in load forecasting, highlighting the impact of various factors such as hydrogen production facilities, battery adoption, and weather/economic drivers on peak load. It emphasizes the need to consider multiple scenarios, particularly for uncertain resources like heat pumps, EVs, and DSM, to ensure accurate and robust forecasting.

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 ELCC a...

AI summary The text outlines recommendations for NSPI to investigate time-of-use rates, analyze portfolio ELCC, evaluate technologies like thermal storage and heat pumps, quantify electrification impacts, develop scenarios for uncertain resource adoption, and conduct sensitivity analyses to mitigate projected peak load increases.

Page 6 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential 1 Synapse is also an active participant in the DSMAG and may raise the question of optimal DSM 2 investment with E1 as part of that forum. 3 4 2.1.2 Recommendation 2...

AI summary Synapse recommends NSPI investigate heat pump impacts on energy and peak load, validate assumptions about fossil fuel displacement, and model hybrid systems. NS Power agrees to evaluate heating components using AMI data and integrate hybrid scenarios into models. References to prior recommendations are noted.

1 should also continue to investigate the use of EV batteries, especially for peak 2 management. 11 3 4 NS Power Response: 5 6 Battery storage deployment and bi-directional EV charging were covered under the recently 7 concluded Smart Grid...

AI summary NS Power responds to recommendations on EV battery use for peak management and new home construction as a proxy for customer growth. They agree to further study EV batteries post-NSUARB decision on M11621 and argue historical data correlation validates new home construction as a proxy. They also note Synapse's advice to consider household demographics in load forecasts.

28 Synapse also advised that NS Power should consider household size and household resident age 29 in the Load Forecast. 13 Regarding the inclusion of household size, as outlined on pages 24-25 of 11 Synapse Evidence, 2024 Load Forecast Re...

AI summary Synapse advised NS Power to consider household size and resident age in the Load Forecast, citing pages 21-32 of their 2024 Load Forecast Report (M11689). This recommendation aims to improve accuracy in forecasting energy demand based on demographic factors.

1 impacts due to heating and transportation electrification. As part of its 2024/25 Time-Varying 2 Pricing (TVP) Tariff Application, filed July 31, 2024 under M11822, NS Power has proposed to 3 establish an ongoing pricing innovation proce...

AI summary NS Power proposes an ongoing pricing innovation process for Time-Varying Pricing (TVP) tariffs under M11822, including stakeholder collaboration and analysis of demand response programs. Recommendation 17 urges NSPI to analyze portfolio ELCC values for demand response, with NS Power referencing its 10-Year System Outlook (M11764) and collaboration with E1.

rgely the same but is now enhanced by AMI load factors. The Board considers the addition of AMI as an improvement to the model and directs NS Power to continue to add AMI data into the forecast model. NS Power applied E3’s scenario for hyb...

AI summary NS Power updated load forecasts using AMI data and hybrid electrification scenarios, aligning with its 2023 Electrification Strategy. The Board endorsed incorporating SGNS and TVP Pilot findings to enhance forecast accuracy. EV adoption estimates were revised to reflect Nova Scotia's lagging sales, with a low scenario adjusted for federal 2035 targets.

NS Power agreed to monitor several of the model’s inputs, including EV adoption, solar generation, battery storage deployment and bi-directional EV charging, and the associated load impacts for each. NS Power agreed to review pricing innov...

AI summary NS Power agreed to monitor EV adoption, solar generation, battery storage, and bi-directional EV charging impacts. It will review pricing innovations, update DSM Program savings, and analyze demand response capacity value. NS Power disagreed with removing TVP benefits from peak forecasts and adding certain weather metrics to the SAE model, but agreed to study temperature and cloud cover trends.

Document: 313458 Date Filed: 05/28/24 UARB Page 3 1 Request IR-8: 2 Figure 16: Economic Forecast Comparison employs data from three of Canada’s Big 5 banks and 3 National Bank. Board Staff find this table provides a useful comparison again...

AI summary The document contains four requests (IR-8 to IR-11) questioning NS Power's economic forecast data sources, adoption rates for emission goals, heat pump saturation assumptions, and verification of installation figures. It seeks clarification on omitted bank data, employment projection adjustments, uptake rate timelines, and evidence for 100% heat pump saturation by 2050.

Document: 313458 Date Filed: 05/28/24 UARB Page 5 1 Request IR-17: 2 Page 52 of the Report discusses the price impact to sales through imposed price elasticities and 3 explains that the SAE models use a price elasticity of -0.15. In matter...

AI summary The UARB requests clarification from NS Power on three issues: (1) why different price elasticities were not applied to rate classes despite evidence from Appendix C; (2) discrepancies in population growth forecasts between the 2023 Load Forecast Report and the Conference Board of Canada; and (3) the basis for the structural index in the Report. These questions challenge NS Power's modeling approaches and data reconciliation.

y Trends (Section 4.4, pp 30-50) 24 a. Please provide in electronic spreadsheet format the end-use data in the form of 25 saturations and efficiencies from NRCan and U.S. EIA used for the residential and 26 commercial models. 27 b. Please...

AI summary The text contains regulatory requests for data on residential and commercial energy modeling, including end-use efficiency data from NRCan and U.S. EIA, adjustments to align with NS Power billing data, and questions about the E3 scenario's net-zero emissions targets and load forecasting methods.

ts for its 2024 load forecast? If not, please 7 provide the space heating saturation forecasts that NSPI used for its load forecasts in 8 the same way as presented in Figure 17 and 18.

AI summary The text requests clarification on whether the entity provided its 2024 load forecast and specifically asks for space heating saturation forecasts used by NSPI, referencing Figure 17 and 18 for presentation format.

Date Filed: May 29, 2024 Synapse (NSPI) Page 5 of 24 1 5. Number of customers using heat pumps as primary heating with supplemental 2 resistance heating, 3 6. Number of customers using hybrid heat pumps with a breakdown of customers by 4 t...

AI summary The document requests detailed data on customer heating technology usage (heat pumps, oil, gas, propane) and clarifications on NSPI's commercial heating forecasts and modeling approaches, including hybrid scenario definitions and peak load impact calculations for 2030/2035.

ntial model more robust, considering the following: 24 o Given the continued population growth in Nova Scotia and ongoing housing 25 shortage, re-evaluate the use of housing completions for the near-term. 26 o Consider incorporating househ...

AI summary The text requests re-evaluation of housing completions, demographic factors, and economic data in energy models, along with updates to EV adoption rates and infrastructure communication. It also seeks clarification on price elasticity assumptions and data sources for electricity pricing models.

readsheet format the data and the statistical model 20 parameters and the full data and results used to produce the residential model coefficients 21 and statistics tables. 22 b. Please provide in electronic format the detailed end-use int...

AI summary The document requests detailed data and methodologies related to residential energy models, including end-use intensity data, statistical models, spreadsheet formats, and collaboration between NSPI and E1 on common assumptions for end-use components.

13 a. Please provide the model outputs in electronic form. 14 b. Please provide the full set of Oracle input data in electronic format so that it can be 15 replicated. 16 c. Please provide the historical data and forecasts used to develop...

AI summary The text outlines several information requests related to modeling, data replication, regulatory directives, and peak demand analysis. These requests pertain to providing detailed outputs, historical data, forecasts, and summaries of regulatory targets and generation mix.

Date Filed: May 29, 2024 Synapse (NSPI) Page 22 of 24 1 Electric Vehicles (Section 4.4, p. 40): 2 a. Please provide the percent of all vehicle sales, and electric vehicles as a percent of total 3 vehicle stock for each year through 2050. 4...

AI summary The document contains several requests for information related to electric vehicle adoption, peak load forecasting, municipal load differences, end-use peak shares, and forecast sensitivities. These requests are part of an ongoing regulatory proceeding involving Nova Scotia Power Incorporated.

on previous occasions. In the Board decision letter in matter M11108, the Board provided NS Power with direction on enhancements for continuous improvement in the development of the load forecast and 1 Based on Table A1 in each annual repo...

AI summary The Board directed NS Power to improve load forecasting and stakeholder engagement, with recommendations including incorporating hydrogen production scenarios, IRP/SGNS/AMI data, and evaluating model assumptions. A stakeholder consultation was held with entities like EOne, SBA, and CA.

efault-source/irp/electrification-strategy-report-february-2-2024- engagement-session-material.pdf?sfvrsn=4d233583_1 -6- Rebuttal Evidence - NS Power NS Power addressed the concerns raised by the intervenors in its Rebuttal evidence. As a...

AI summary NS Power rebutted intervenors' concerns by refusing to include pilot program data in forecasts until programs are comprehensive, committing to use AMI data for accuracy, and citing Bill 228's removal of DSM joint-filing requirements. It agreed to monitor factors like temperature trends, heat pump impacts, and EV adoption while shifting DSM responsibility to EOne.