N-12024 Load Forecast Report + Appendices - Redacted
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ts from Batteries ........................................................................ 45 31 Figure 29: Residential End-Use Intensities................................................................................... 47 32 Figure 30:...
AI summary The document contains a list of figures related to energy use trends, electrification forecasts, demand-side management (DSM) savings, and residential/commercial electricity consumption patterns, including historical data and projections.
1 1.0 EXECUTIVE SUMMARY 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market 4 Rules, Nova Scotia Power Incorporated (NS Power, the Company) is required to provide 5 the Nova Scotia Utility and Review...
AI summary NS Power is required to submit a 10-year energy and demand forecast (Load Forecast) to the NSUARB, outlining considerations like weather, economic indicators, and energy efficiency program effectiveness. The forecast uses Statistically Adjusted End-Use (SAE) models for residential and commercial sectors, acknowledging inherent uncertainties from factors like technological changes and electrification impacts.
2022 unexplained variance and 2 report on its findings in the 2024 Load Forecast Report. 3 2F 3 4 In accordance with the Board’s direction, NS Power revised and enhanced the 2024 Load 5 Forecast in the following manner: 6 7 • The EV foreca...
AI summary NS Power revised the 2024 Load Forecast Report per the Board's direction, updating EV forecasts, integrating hybrid electrification scenarios, summarizing Smart Grid and Demand Response projects, discussing economic inputs, hydrogen production impacts, and analyzing residential forecast variances.
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.
100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Heat Pumps 2 3 Heat pump usage continues to grow in the province as more customers find heat pumps an 4 efficient way to heat and cool buildings as well a...
AI summary The 2024 Load Forecast Report discusses the growing adoption of heat pumps in Nova Scotia, noting a 44% saturation rate in 2024 and continued uptake supported by grants and financing. The forecast model has been updated to a hybrid scenario, where some heat pump users retain non-electric backup heating systems, aligning with the Nova Scotia Power Electrification Strategy.
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.
337 6 7 The overall heating intensity has increased compared to the 2023 forecast as a result of 8 adjustments made in response to the “unallocated” variance in the residential class results 9 (discussed in Section 9). The heat pump heatin...
AI summary The overall heating intensity has increased by approximately 39% compared to the 2023 forecast, primarily due to adjustments made in response to unallocated variance in residential class results, which were largely weather-dependent and occurred mainly in winter months. The increase may also be influenced by higher work-from-home activity and increased equipment intensity.
2024 Heating 2023 Heating Change Year Intensity Intensity (Percentage) (kWh/house) (kWh/house) 2024 2,066 1,511 +37 2025 2,271 1,639 +39 2026 2,454 1,770 +39 2027 2,636 1,899 +39 2028 2,814 2,026 +39 2029 2,973 2,140 +39 2030 3,128 2,250 +...
AI summary The text provides forecasts for heating intensity from 2024 to 2034, showing a steady increase in kWh per house. It also discusses NS Power's expectations regarding customer adoption of heat pumps and electric water heaters, with a joint demand response program involving E1 to manage water heater usage for system benefits.
all intensity over the forecast period. The efficiency improvements of heat pump 14 hot water heaters are still not incorporated into the forecast as uptake is still small (114 in 15 2021 and 163 in 2022 according the 2022 DSM Programs Eva...
AI summary The document highlights the low uptake of heat pump water heaters despite an available rebate, with only 114 units installed in 2021 and 163 in 2022. The efficiency improvements of these units are not yet incorporated into the load forecast due to their limited adoption.
Vehicle Avg Avg kW/vehicle Avg kWh/year Type km/year 20 19F on Peak LDV 17,427 3,485 0.9 MDV 22,779 8,205 1.6 HDV 62,888 113,890 7.3 3 4 The peak impact assumes that 70 percent of charging is managed (including direct control 5 through EV...
AI summary The text discusses the average energy consumption and peak demand contributions of different vehicle types, including LDV, MDV, and HDV, based on managed and unmanaged EV charging scenarios. It highlights the impact of managed charging, using technologies like DERMS, on reducing peak demand and electricity costs.
SGNS) Project. A final report was submitted to the 6 UARB in March 2024. 21 Through the project, 100 EV smart chargers were deployed under 20F 7 a ChargePoint pilot program where data collection and control of Electric Vehicle Supply 8 Equ...
AI summary The Smart Grid Nova Scotia (SGNS) Project deployed 100 EV smart chargers under a ChargePoint pilot and established an EV telemetry pilot with ev.energy, enabling the utility to influence charging times through curtailment events. A final report was submitted to the UARB in March 2024, and Appendix C discusses observed EV charging characteristics in Nova Scotia.
erage was 21 0.35 kW. 22 DATE: April 30, 2024 Page 41 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Solar Generation (PV) 2 3 Solar generation consists of two main types – distributed small-scale s...
AI summary The document discusses solar generation in Nova Scotia, distinguishing between distributed small-scale solar (under net metering) and larger-scale solar fed directly onto the grid. It notes that the 2023 Load Forecast predicted 2,268 new installations, but the actual number was 2,233, with a cumulative total of 8,221 installations (72 MW) by the end of 2023. The average installed capacity is approximately 8.3 kW.
still 9 relatively expensive, in the range of $15,000-$20,000 24 for a single battery and installation 3F 10 (for 5-7 kW batteries). This is more expensive than the cost of a gas-powered generator, 11 which varies from as little as $1,000...
AI summary Residential battery storage systems are currently expensive, ranging from $15,000 to $20,000 for a 5-7 kW battery and installation. Gas-powered generators are a more cost-effective alternative for backup power. Time Variable Pricing (TVP) rates may encourage battery use in the future as costs decrease with technological advancements.
s be able to discharge their full capacity 18 to the grid (in practice the available demand reduction would be lower than shown). 19 20 Figure 28: Potential Peak Impacts from Batteries Residential Share (%) Technology 50% 25% 10% 5% Batter...
AI summary The text discusses the potential peak impact of battery storage technologies on residential demand, showing significant reductions under optimal demand response (DR) control. It also mentions that direct load control (DLC) of heating and hot water loads is covered in Section 10 of the document.
and EV forecasts. 25 DATE: April 30, 2024 Page 46 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 29: Residential End-Use Intensities 2 3 4 The intensity trends are similar to those in prior f...
AI summary The 2024 Load Forecast Report discusses residential and commercial end-use intensities, noting trends such as increasing use of heat pumps, decreasing electric baseboard heating, and changes in cooling intensity. The report also highlights the impact of EV sales and PV generation on residential energy use.
2024 Load Forecast Report REDACTED 1 Figure 31: Historical and Projected General Commercial End-Use Intensity 2 (kWh/m2) 3 4 5 Supporting data for General commercial end-use intensities is included in Attachment 3. 6 7 For the 2024 Load Fo...
AI summary The 2024 Load Forecast Report discusses the inclusion of small-scale solar and EV load in the Miscellaneous category and highlights projected growth in the commercial and industrial sectors due to electrification programs aimed at reducing carbon emissions.
2022 2021 20.4 2.6% 9,531 482,771 4,601 4,661 59.5 1.3% 2023 2022 48.9 6.4% 9,641 488,654 4,711 4,822 110.6 2.3% 2024 2023 15.4 1.8% 10,064 495,055 4,982 4,986 3.8 0.1% 6 7 The adjusted heating intensities are expected to result in a small...
AI summary The text discusses forecast adjustments for residential energy consumption, including the impact of the COVID-19 variable and changes in load due to factors like RTR market migration, EV forecasts, and behind-the-meter solar. The adjusted heating intensities are expected to reduce unexplained variance in future years.
2024 Load Forecast Report REDACTED 1 Figure 60: Weather-Normalized Firm Peak (including DR) 2 3 4 5 Figure 61 below shows the breakdown of the peak forecast by the various components. 6 7 Figure 61: Peak Contribution Components (MW) 8 Mode...
AI summary The 2024 Load Forecast Report provides a detailed breakdown of peak load contributions, including modeled peak, residential heating, electric vehicle (EV) usage, demand response (DR), hybrid loads, commercial and industrial (C&I) demand, large customer contributions, demand-side management (DSM), and system peak. It compares scenarios with and without EV mitigation.
Page 90 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 End Use Peak Estimates 2 3 While the Load Forecast is a good statistical fit for the historical data, it presents challenges 4 when trying to a...
AI summary The 2024 Load Forecast Report discusses end use peak estimates, noting that while the forecast is statistically accurate, individual end use contributions to peak demand by class are challenging to assess. The report highlights that electric vehicle (EV) contributions to peak demand are expected to increase significantly, while contributions from electric heating sources are projected to decrease.
CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 71: System Peak Sensitivity 2 3 4 This analysis provides a potential range of outcomes for the 2024 Load Forecast. Energy 5 is most sensitive to economics over t...
AI summary The 2024 Load Forecast Report outlines the sensitivity of energy demand to economic and temperature factors, highlighting the variability of peak load. It compares the 2023 and 2024 forecasts with the Evergreen IRP cases, noting differences in load served through the RTR market and initial peak expectations. The report also mentions future policy changes related to decarbonization targets.
(698) (310) Change 9.0% 7.0% 6.0% -8.0% -1.4% -3.4% -6.9% 2.3% to load Res Sales = Existing Customer + New Customer + EV + Solar + RTR + Hybrid + DSM Existing customer load is calculated as Res Average Use (10,468 kWh/customer in 2024, 11,...
AI summary The document discusses the calculation of residential load, including existing customer load, new customer load, EV load, solar load, RTR load, hybrid load, and DSM load. It provides data on residential average use and its components, such as heating, cooling, and other uses, and includes a regression analysis for 2024 and 2034.
sidential model (including heat pump programs inside the SAE model). Adjustments done outside the regression include estimates for other commercial and industrial growth programs, PV, EV, RTR and DSM. Historically the XHeat, XCool and XOth...
AI summary The document discusses the Small General Load model used in the Load Forecast Report, including adjustments for EV, solar, RTR, and DSM. It provides load data for 2024 and 2034, showing changes in load and customer counts, and explains how the load from the regression model is calculated based on average use and customer numbers.
TIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 29 of 35 Combined Model for Commercial and Industrial DSM Coefficient NonResSalesm = b1×NonResEESavingsProfiledm + b2×GenWtXHeatm + b3×GenWtXCoolm + b3×GenWtXOtherm + b4×N...
AI summary This section presents a combined model for commercial and industrial demand-side management (DSM) coefficients, including variables such as non-residential energy efficiency savings, weighted end-use factors, and customer counts. The model uses historical data and binary variables to address billing issues in February 2018 and October 2022. The EESavings variable coefficient indicates the amount of DSM required to explain historical sales trends beyond end-use changes.
Forecast DSM (base case) -143 -26 -1483 -266 Solar PV -55 0 -681 0 EV (current forecast) 18 6 566 156 Other Possible Scenarios EV (current forecast, no peak 18 10 566 281 mitigation) 2 Hydrogen Production 0 0 824 130 Facilities (firm suppl...
AI summary The document provides a forecast of demand-side management (DSM) and solar PV impacts on energy load, along with scenarios for electric vehicle (EV) adoption, hydrogen production, and battery storage. It also notes the evaluation of potential impacts from proposed hydrogen facilities on the Net System Requirement and System Peak.
61 246 9 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 10 of 18 Small Scale Solar • Small scale solar uptake continues to be strong: as of 2023 approximately 71.7 MW of solar generation has been inst...
AI summary The document discusses the strong uptake of small-scale solar installations in Nova Scotia, with 71.7 MW installed as of 2023, and forecasts increased solar penetration due to legislative changes and rebate programs. It also outlines forecast changes for residential and commercial load, noting the impact of warmer weather, electric heating, and the introduction of Renewable to Retail (RTR).
N-6NSPI (Synapse) RIR-1 to RIR-54 - Redacted
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2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-4: 2 3 Weather Data (Section 4.2, pp 17-22) 4 5 (a) Refer to the following statement on page 17: “18°C is assumed to be...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, focusing on HDD/CDD calculation methodologies, temperature data, and forecast assumptions. Requests include explanations of 18°C thresholds, internal/solar heat gains, and spreadsheet data for historical and forecasted HDD/CDD values across Nova Scotia weather stations.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-7: 2 3 Residential and Commercial Heating and Heat Pumps (Section 4.4, pp 30-35) 4 5 (a) Refer to the E3 scenario on pag...
AI summary The NSUARB M11689 document outlines NSPI's responses to Synapse Information Requests regarding the 2024 Load Forecast Report. Key questions focus on emissions scenarios, heating saturation forecasts, and customer heating technology distribution across forecast years.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (d) Please indicate the number of new residential customers from 2024 through 2050 and 2 the breakout of their space heating techno...
AI summary NSPI is responding to Synapse's information requests regarding the 2024 Load Forecast Report. The report includes questions about residential customer growth, heat pump saturation rates, and detailed breakdowns of heating technologies in commercial buildings.
1 (g) Refer to Figure 18 and 19. 2 3 (i) Please provide NSPI’s own commercial space heating saturation forecasts by 4 technology and fuel type, reflecting NSPI’s own commercial electric heating 5 share provided in Figure 19. For heat pumps...
AI summary The text requests detailed information on NSPI's commercial space heating saturation forecasts by technology and fuel type, including heat pump usage breakdowns. It also asks for clarification on the models used for peak load forecasting and the definition of the 'hybrid scenario'. Specific data on peak load impacts per customer type and technology are requested.
ng, heat pumps, and hybrid heat pumps. Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 4 of 12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document is a non-confidential portion of NSPI's responses to Synapse's information requests related to the 2024 Load Forecast Report under NSUARB matter M11689. It includes data on load forecasting and energy efficiency programs such as heat pumps and hybrid heat pumps.
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.
1 (c) 2 (i-xi) Please see the following table estimating the number of customers for each 3 category in the forecast: 4 Customers Customers Customers Customers Customers Customers Existing with with Heat with with Electric with Heat with H...
AI summary The text presents a table forecasting the number of customers in various categories, including residential, electric resistance, heat pumps, and others, from 2024 to 2032. The data shows trends in customer distribution across different heating and cooling sources over time.
New New New Customers New Customers New Customers Customers Residential with Heat Pump with Electric with Non Electric with Customers Heat Baseboard Heat Heating Supplementary Year Electric Heat 2024 7,021 4,213 1,755 1,053 2,457 2025 13,6...
AI summary The text provides a table showing the number of new residential customers with various heating types from 2024 to 2034. It also references the 2024 Load Forecast Report (LFR) and Attachment 1 Residential Intensities for calculations related to end-use saturation, particularly heat pump saturation based on annual sales data from installers.
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.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Residential Water Heaters (WH) (Section 4.4, p 35-36) 4 5 (a) Please provide NSPI’s projection of electric resist...
AI summary The document outlines a series of information requests from NSPI to Synapse regarding projections and standards related to electric water heaters, including heat pump water heaters, load control strategies, and program offerings to promote efficiency. These requests cover forecasting, load impacts, efficiency standards, and program evaluations for the period 2024 through 2034.
1 A third charge management type, charge management with Vehicle-Grid 2 Integration (VGI), still features drivers that shift their times of charging to minimize 3 charging costs, but also features an aggregator’s involvement to smooth peak...
AI summary The text discusses charge management strategies for electric vehicles, including Vehicle-Grid Integration (VGI) and the impact of managed charging on peak loads. It references a blended scenario with 70% of EV owners using an aggregator and 30% on flat rates. The findings from the Smart Grid Nova Scotia project are detailed in M11621 and are based on a small pilot group in Nova Scotia.
Customer Forecast Data: Year Customer Count Residential Total (December 31) 2034 546,929 Best estimates based on data available as of April 12, 2021. Still many uknowns and much uncertainty with values presented. REDACTED (CONFIDENTIAL INF...
AI summary The document provides customer forecast data and load forecast scenarios for residential customers in Nova Scotia, including DER peak impact and DR program impacts. The data is based on estimates as of April 12, 2021, with significant uncertainty noted.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-11 Attachment 1 Page 3 of 3 Residential Uptake 50% 25% 10% 5% Battery Peak Impact - No Control (MW) 0 0 0 0 Battery Peak Impact - Optimal DR Control (MW) (1,3...
AI summary The document includes a request and response related to the 2024 Load Forecast Report. The request pertains to residential end-use intensities and appliance efficiency data, and the response indicates that the data is sourced from Itron and is available in the provided attachments.
original EIA data or documentation. Date Filed: June 19, 2024 NSPI (Synapse) IR-12 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDE...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report. The response includes data sources for commercial and industrial growth calculations, including customer outreach, heat pump installations, and electrification efforts. The focus is on demand growth, particularly from heat pumps and customer expansion.
1 Request IR-24: 2 3 General Service (Section 6.2). 4 5 (a) Please explain and quantify the specific reasons for the differences from the previous 6 forecast. 7 8 (b) Please quantity separately and explain the derivation of the effects for...
AI summary The request seeks explanations for differences in load forecasts between 2023 and 2024, focusing on factors such as EV loads, space heating, DSM programs, and solar generation. The response notes a decrease in general service class load, a larger drop attributed to increased solar generation, and a reduced impact from EVs compared to previous forecasts.
Heat Heat Heat Cool Cool Cool Cool share efficiency efficiency efficiency efficiency efficiency efficiency 2022 2024 2023 2022 2024 2023 2022 2014 79% 1.472 1.472 1.472 3.352 3.352 3.353 2015 80% 1.517 1.517 1.518 3.386 3.386 3.387 2016 80...
AI summary The text presents a table with data on heat and cool efficiency percentages and values from 2014 to 2032. It shows trends over time, including efficiency levels and corresponding numerical values for both heat and cool categories.
2022 Other Shares 2024 Other Efficiencies Vent EWHeat Cooking Refrig I.Light Office Misc Vent EWHeat Cooking Refrig I.Light Office Misc 2014 100% 68% 28% 48% 100% 100% 100% 0.504 1.035 0.686 2.828 56.272 1.000 1.000 2015 100% 68% 28% 48% 1...
AI summary The document presents a table comparing energy efficiency data across multiple years, focusing on percentages and efficiency values for various categories such as ventilation, electric water heating, cooking, refrigeration, and others. The data spans from 2014 to 2024, showing trends in efficiency improvements over time.
2023 Other Efficiencies 2022 Other Efficiencies Vent EWHeat Cooking Refrig I.Light Office Misc Vent EWHeat Cooking Refrig I.Light Office Misc 2014 0.498 1.035 0.686 2.631 58.168 1.000 1.000 0.498 1.035 0.686 2.631 58.168 1.000 1.000 2015 0...
AI summary The document presents tables comparing 'Other Efficiencies' for the years 2014 to 2024, detailing energy consumption across various categories such as Vent, EWHeat, Cooking, Refrig, I.Light, Office, and Misc. The data shows a consistent increase in energy usage over the years, particularly in the Office category.
on from demand response measures? 28 Date Filed: June 19, 2024 NSPI (Synapse) IR-32 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 includes information requests related to demand response programs, energy efficiency evaluations, and load forecasting. NSPI is asked to provide reports on DSM programs, the Eco Shift Pilot, and details on figures related to load forecasts. Responses reference prior filings, including a 2021 load forecast report.
8 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-32 Attachment 1 Page 1 of 12 INTRODUCTION EfficiencyOne (EOne), an independent, non-profit organization, is responsible for helping Nova Scotians impro...
AI summary EfficiencyOne (EOne) is an independent non-profit organization that provides energy efficiency and demand response services in Nova Scotia through the Efficiency Nova Scotia (ENS) franchise. EOne's 2023 demand response (DR) program includes residential and BNI components evaluated by Econoler. The DR program uses pathways such as Domestic Hot Water Direct Load Control and DR Aggregator.
Customer count and housing New housing usage NSPI and other Programs Before future DSM Year SAE model Historical Increment Cumlative New Cumulative Forecasted New New Structural Forecast RTR Hybrid Electric Solar PV Total Total SAE + Regre...
AI summary The text presents data on customer counts, housing usage, and energy consumption from 2014 to 2016, including information on new housing, structural changes, and energy programs such as Solar PV and Electric Vehicles. The data includes forecasted and historical usage figures.
Year Month ResEndUse.ResCool NResEndUse.SmlGenCool NResEndUse.GenCool mVars.CoolLoad mVars.Days mVars.Cool_AvgMW mPkDayWthr.PkCDDIdx mVars.Cool_Var 2020 8 58,848.7 5,353.0 42,085.2 106,286.9 31.0 142.9 1.0 148.6 2020 9 27,124.0 2,479.2 19,...
AI summary The data presents monthly cooling load and related metrics for residential and non-residential end-use in Nova Scotia from 2020 to 2021. The table includes cooling load, number of days, average cooling demand, and peak cooling degree day index, providing insights into energy usage patterns during cooling seasons.
2024 Load Forecast Report Synapse IR-45 Attachment 1 Page 1 of 1 Sensitivity: 2025 Peak Sensitivity: 2025 No DSM Total Sales Sensitivity: 2034 No DSM Total Sales Assumptions ContributionToVariance RankCorrelation Assumptions ContributionTo...
AI summary The 2024 Load Forecast Report by Synapse IR-45 discusses sensitivity analyses for peak load and total sales under different assumptions, including temperature, HDD, CDD, and economics. The report highlights the contribution to variance and rank correlation for various factors affecting load forecasting.
.279911425 † Economics 0.395313574 0.598796045 Wind at Peak 0.027100012 0.139415143 Sensitivity: 2025 Peak Sensitivity: 2025 No DSM Total Sales Sensitivity: 2034 No DSM Total Sales 1% 3% † Monthly HDD † Monthly CDD † Economics 9% 40% 42% 4...
AI summary The text contains a table and chart discussing sensitivity analyses related to energy demand, including peak wind, HDD (Heating Degree Days), CDD (Cooling Degree Days), and economics for different years, such as 2025 and 2034. The content is partially redacted due to confidentiality.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-46: 2 3 Nova Scotia Power Electrification Support Overview by E3 4 5 (a) Please provide any supplemental materials produ...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report. The responses included references to heat pump and EV load shapes, as well as modeling based on federal sales targets and net zero emission goals.
1 Request IR-50: 2 3 Peak Load Forecast (Section 10.0, p. 81): 4 5 (a) Has NSPI studied any sensitivities where the peak load occurs in the summer rather 6 than the winter? If so, please provide such sensitivities and the subsequent foreca...
AI summary NSPI has not studied summer peak load scenarios. The winter peak is significantly higher than the summer peak due to factors like electric heating and EV charging. The temperature differential between winter and summer also contributes to higher winter energy demand. Other regions are also seeing a shift to winter peaking due to similar trends.
1 Request IR-54: 2 3 End-Use Intensity Trends (Section 4.4, pp. 30-50): 4 5 (a) For each end-use discussed in Section 4.4 End-Use Intensity trends, is it NSPI’s view 6 that forecast uptake used by NSPI has the highest probability of occurr...
AI summary NSPI does not conduct probabilistic analysis of end-use uptake but considers current trends as the most likely outcome. EV sales are forecast based on federal targets, and heat pump uptake is modeled to meet net zero by 2050. Most other forecasts are based on simple trends or expected patterns.
N-7-(i)Attachment 1 - CV of John Wilson
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JOHN D. WILSON Vice President Grid Strategies, LLC SUMMARY OF PROFESSIONAL EXPERIENCE 2023– Vice President, Grid Strategies, LLC. Provides research, technical assistance, Present and expert testimony on electric- and gas-utility planning,...
AI summary John D. Wilson's professional experience includes roles in utility regulation, energy efficiency, and renewable resource evaluation. He has provided expert testimony on rate design, cost recovery mechanisms, and resource planning for electric utilities, as well as directed regulatory policy and litigation activities related to clean energy and air quality.
cs (with honors) and history, Rice University, 1990. MPP, John F. Kennedy School of Government, Harvard University, 1992. Concentration areas: Environment, negotiation, economic and analytic methods. PUBLICATIONS “Urban Areas,” with Judith...
AI summary The text outlines an individual's academic and professional background, emphasizing expertise in environmental policy, energy efficiency, and power market analysis, with publications on topics including climate change impacts, energy efficiency programs, and market behavior in power generation.
t Economy Summer Study on Energy Efficiency in Buildings, August 2010. “Monopsony Behavior in the Power Generation Market,” with Mike O’Boyle and Ron Lehr, Electricity Journal, August-September 2020. REPORTS “Policy Options: Responding to...
AI summary The text lists academic studies and policy reports on energy efficiency, climate change, and environmental management, including works by the Houston Advanced Research Center, US EPA, and Texas Water Commission, spanning topics from monopsony behavior in power markets to coastal management practices.
John D. Wilson Grid Strategies, LLC Page 2 “Smoke in the Water: Air Pollution Hidden in the Water Vapor from Cooling Towers – Agencies Fail to Enforce Against Polluters,” Galveston Houston Association for Smog Prevention, February 2004....
AI summary The text lists publications by the Galveston Houston Association for Smog Prevention and Southern Alliance for Clean Energy (SACE), addressing air pollution, toxic emissions, mercury contamination, and renewable energy initiatives in the U.S. Southeast. These reports highlight environmental compliance challenges and energy efficiency programs.
st,” Southern Alliance for Clean Energy, November 2014. “Cleaner Energy for Southern Company: Finding a Low Cost Path to Clean Power Plan Compliance,” Southern Alliance for Clean Energy, July 2015.
AI summary Two reports by the Southern Alliance for Clean Energy (SACE) from 2014 and 2015 discuss compliance with a 'Low Cost Path to Clean Power Plan' for Southern Company. The documents analyze strategies for achieving cleaner energy goals through cost-effective measures.
John D. Wilson Grid Strategies, LLC Page 3 “Analysis of Solar Capacity Equivalent Values for Duke Energy Carolinas and Duke Energy Progress Systems,” prepared for and filed by Southern Alliance for Clean Energy, Natural Resources Defense...
AI summary The text lists publications and reports by Southern Alliance for Clean Energy (SACE) and collaborators on solar capacity, energy efficiency, decarbonization, and integrated resource planning in the Southeastern U.S. and Nova Scotia. Key references include a 2021 review of Nova Scotia Power’s Integrated Resource Plan for the Nova Scotia Consumer Advocate.
i Power Company 2024 Integrated Resource Plan,” with Michael Goggin, Grid Strategies LLC, prepared for Southern Renewable Energy Association, for submission in MPSC Docket No. 2019-UA-231, June 2024. SELECTED PRESENTATIONS “Clean Energy So...
AI summary The text lists presentations by Michael Goggin on energy efficiency, renewable energy, and integrated resource planning, involving organizations like Southern Alliance for Clean Energy (SACE) and Grid Strategies LLC. Key topics include energy efficiency, renewable energy, and IRP, with a cross-reference to MPSC Docket No. 2019-UA-231.
tives, February 2011. “Rates vs. Energy Efficiency,” 2013 ACEEE National Conference on Energy Efficiency as a Resource, September 2013. “TVA IRP Update,” TenneSEIA Annual Meeting, November 19, 2014.
AI summary The text lists citations from energy efficiency conferences and integrated resource plan updates, including references to the American Council for an Energy-Efficient Economy (ACEEE) and the Tennessee Valley Authority (TVA). These sources discuss energy efficiency initiatives and IRP developments.
John D. Wilson Grid Strategies, LLC Page 5 “Views on TVA EE Modeling Approach,” presentation with Natalie Mims to Tennessee Valley Authority’s Evaluating Energy Efficiency in Utility Resource Planning Meeting, February 10, 2015. “The Cle...
AI summary John D. Wilson of Grid Strategies, LLC has presented on energy efficiency modeling, renewable energy reliability, carbon markets, solar capacity value, power plant procurement, resource adequacy, and energy transitions at various conferences and forums between 2015 and 2024.
John D. Wilson Grid Strategies, LLC Page 6 Center. Cost recovery mechanism for energy efficiency, including shareholder incentive and lost revenue adjustment mechanism. 2009 North Carolina NCUC Docket No. E-7, Sub 831, direct testimony o...
AI summary John D. Wilson of Grid Strategies, LLC testified in multiple regulatory proceedings across North Carolina, Florida, South Carolina, and Georgia from 2009-2010, focusing on energy efficiency cost recovery mechanisms, shareholder incentives, lost revenue adjustments, and adequacy of integrated resource plans in considering energy efficiency.
outhern Alliance for Clean Energy. Adequacy of consideration of energy efficiency in Georgia Power’s 2010 integrated resource plan, including cost effectiveness, rate and bill impacts, and lost revenues. Georgia PSC Docket No. 31082, direc...
AI summary Southern Alliance for Clean Energy (SACE) participated in multiple regulatory dockets from 2010-2011, advocating for adequate consideration of energy efficiency in utility plans. Key focus areas included cost-effectiveness, stakeholder engagement, resource mix analysis, and evaluation of demand-side management programs for Georgia Power, South Carolina Electric & Gas, and Carolinas utilities.
John D. Wilson Grid Strategies, LLC Page 7 including resource mix, sensitivity analysis, alternative supply and demand side options, cost escalation, uncertainty of nuclear and economic impact modeling. 2013 Georgia PSC Docket No. 36498,...
AI summary John D. Wilson of Grid Strategies, LLC provided expert testimony in multiple U.S. regulatory proceedings from 2013–2016, focusing on energy efficiency adequacy, renewable energy integration, and system reliability. Testimonies addressed Georgia Power’s integrated resource plans, South Carolina’s capacity needs, and Florida’s reserve margin requirements, often representing the Southern Alliance for Clean Energy.
John D. Wilson Grid Strategies, LLC Page 11 Massachusetts DPU Docket No. 22-22, direct, surrebuttal and supplemental testimony on Eversource Energy’s 2022 Base Distribution Rate Case on behalf of the Cape Light Compact. Allocation of dis...
AI summary John D. Wilson of Grid Strategies, LLC provided testimony in multiple regulatory proceedings, including Nova Scotia UARB matters and Massachusetts DPU dockets, addressing rate cases, fuel adjustment mechanisms, distribution revenue allocation, and impacts of power delivery projects like Maritime Link on electrification and energy efficiency.
N-8Evidence of Synapse (BCC)
13 passages
esidential forecast, including the load-reducing effects of DSM programs, increases by 2.3 percent over the forecast period, from 2024 to 2034. Without DSM programs, the increase would be 9.1 percent. The two largest contributors to the in...
AI summary The residential load forecast from 2024–2034 shows a 2.3% increase with DSM programs, versus 9.1% without them. Key drivers include new customers (7.0%) and EV load (6.0%), offset by solar PV and DSM. The forecast uses a regression-based SAE model incorporating factors like heating, cooling, and time-fixed effects, with XHeat influenced by heating degree days, income, and equipment efficiency.
ding Nova Scotia Power’s 2024 Load Forecast 13 is unclear how exactly NSPI estimated this value, and we cannot observe any connection between the savings value and any value presented in Figure 20. The forecast growth in heat pumps explain...
AI summary The document critiques NSPI's 2024 load forecast for unclear savings estimation and lack of connection to Figure 20. Heat pumps are identified as a major driver of residential electricity growth (9% increase by 2034), contributing 5.6% of load increases. However, fossil fuel displacement from heat pumps is noted as unreported savings, raising concerns about model accuracy.
there are additional unreported savings there. It is important to note in particular two potential issues with the accuracy of NSPI’s model in estimating energy and peak load impacts from heat pumps: • Heating intensities have increased by...
AI summary The text highlights two issues with NSPI’s model for estimating heat pump impacts: a 39% increase in heating intensities due to unallocated variance adjustments and discrepancies in peak load calculations compared to E3’s data. These inaccuracies may affect energy and load forecasts.
4 Load Forecast, Appendix B, page 8. 26 2024 Load Forecast, Appendix B, page 9. 27 2024 Load Forecast, Figure 41, Figure 41, 42. 28 2024 Load Forecast, page 34. 29 2024 Load Forecast, page 34. Synapse Energy Economics, Inc. Evidence Regard...
AI summary The text highlights discrepancies between NSPI's and E3's 2030 heat pump energy usage estimates (242 GWh vs. 74 GWh) due to differing assumptions about heating scenarios and saturation rates. It recommends validating assumptions about heat pump displacement of fossil-based heating and using AMI data for more accurate modeling.
ptions about customer usage of secondary heating equipment. Finally, NSPI should model hybrid electric heating within its model instead of making a simplified adjustment based on E3’s hybrid scenario. Water heaters For water heaters, the f...
AI summary The text discusses NSPI's modeling of hybrid electric heating and the forecasted increase in electric water heater adoption, which is expected to significantly impact energy and peak load growth. It also notes NSPI's collaboration with E1 on demand response projects and the use of pilot data for future forecasts.
itor the coincidence of solar generation with month system peaks and make updates to coincidence factors as warranted. 44 2024 Load Forecast, page 90. 45 2024 Load Forecast, Appendix D, page 9. Synapse Energy Economics, Inc. Evidence Regar...
AI summary The document discusses the impact of solar-plus-battery systems on load forecasting, noting limited effects but acknowledging potential peak management benefits. It also addresses the contribution of new residential customers to load growth, projecting a modest increase by 2034.
ohort represents approximately three-quarters of the commercial load. We reviewed the statistical models in NSPI’s Appendix B and found them satisfactory. NSPI’s Report has also provided specifics of Synapse Energy Economics, Inc. Evidence...
AI summary The 2024 load forecast projects a 0.8% decrease in total load between 2024 and 2034, primarily due to hybrid heating, commercial sales shifting to RTR, reduced EV sales, and increased distributed solar. The forecast also highlights greater proportional increases in Small General Service average loads.
he peak is first modeled statistically using historical data and economic and demographic projections to produce a Modeled Peak, and then NSPI applies various adjustments to arrive at the System Peak. Table 8. 2024 Peak contribution compon...
AI summary The document discusses the process of modeling peak electricity demand, starting with historical data and economic projections to produce a Modeled Peak, followed by adjustments to arrive at the System Peak. Table 8 provides peak contribution components for 2024 and 2034, including the impact of demand-side management and electric vehicle (EV) mitigation.
15 -145 2,670 147 1 Source: Figure 61 from 2024 Load Forecast Table 9. 2023 Peak contribution components Res Modeled Heat C&I Large Firm Inter. Peak Peak EV DR Elect. Cust. DSM Peak Cust. System (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW)...
AI summary The text discusses the projected increase in peak load demand in Nova Scotia, with electric vehicles (EVs) being the largest contributor. Residential heating electrification is the second-largest contributor. Time-of-use programs are suggested to mitigate these impacts, and an increase in capacity requirements of about 434 MW by 2034 is noted, representing a significant investment cost.
ed in Synapse’ August 2023 comments regarding the Evergreen IRP Update (M11307), “[i]t is analytically inconsistent to minimize the development and modeled representation of future demand response alternatives that are reasonably, if not l...
AI summary The text discusses recommendations for Nova Scotia Power Inc. (NSPI) to improve its analysis of demand response programs and electrification impacts. It highlights the need for a more comprehensive evaluation of technologies like thermal storage, heat pump water heaters, and induction cooking, particularly in the commercial sector.
d-use scenarios, NSPI should also evaluate the possibility of a higher than forecast peak in light of the systematic under-forecasting of peak that is noted above. Recommendations and Considerations For the major resources and end uses tha...
AI summary The document recommends that NSPI evaluate higher-than-forecast peak demand scenarios, develop multiple scenarios for uncertain resources like heat pumps and DSM, and conduct sensitivity analyses using new technologies. It also asks NSPI to explore increasing DSM levels and improve modeling of heat pump impacts on energy and peak load.
water heater demand response pilot in formulating its residential peak forecast. We further recommend consideration of heat pump-based hot water heating in the next forecast (page 18). 4. NSPI should carefully monitor EV adoption and updat...
AI summary The document outlines recommendations for NSPI regarding load forecasting, including monitoring EV adoption, incorporating heat pump-based hot water heating, validating assumptions about solar generation, and refining proxies for customer growth. It also raises questions about the impact of shifting EV loads to the commercial sector, DSM program expansion, on-site solar potential, and the RTR program.
ervice customers? Are they implementing DSM measures to reduce load? Adding solar generation? Entering into RTR contracts? Might all this reduce their loads to some degree? (page 25) Synapse Energy Economics, Inc. Evidence Regarding Nova S...
AI summary The text presents a series of questions and requests directed at Nova Scotia Power Inc. (NSPI) regarding load forecasting, demand-side management (DSM) program savings, industrial electrification, real-time rates, and the impacts of renewable energy contracts (RTR) and technologies like heat pumps and thermal storage.
N-9Rebuttal Evidence - NSPI
9 passages
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.
d the 28 impacts of the RTR and hybrid heating, NSPI should address the range of 29 possibilities (sensitivities) in these domains to produce a more robust forecast. 15 30 14 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2...
AI summary NS Power emphasizes the importance of the 2024 Load Forecast Report for planning and operations, noting year-over-year load variances. It urges NSPI to address sensitivities in RTR and hybrid heating impacts for robust forecasting, citing Synapse's report (M11689).
Page 15 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential 1 lack of forecast data (EIA does not provide estimates for either due to the small number) mean that 2 adjustments in the expected efficiency of water heaters and...
AI summary NS Power responds to recommendations in the 2024 Load Forecast Report, referencing Synapse's analysis. It addresses electrification impacts, scenario development for uncertain resources (heat pumps, EVs, DSM, demand response), and potential peak under-forecasting, citing Synapse's evidence (M11689).
1 NS Power Response: 2 3 NS Power’s The Path to 2030, filed on December 22, 2024 under the 2024 ACE Plan (M11458), 4 provided the following regarding hybrid peak scenario: 5 6 As a component of NS Power’s development of its electrification...
AI summary NS Power discusses the hybrid peak scenario in its The Path to 2030 report, collaborating with E3 to forecast load reductions through mini-split heat pumps and existing backup heating sources. The scenario aims for a 100 MW peak load reduction by 2030 under the Evergreen IRP, with a commitment to future studies on cost impacts.
Page 19 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential 1 Consistent with the Company’s comments in The Path to 2030 regarding further exploration of the 2 hybrid peak scenario, NS Power anticipates participating in a co...
AI summary NS Power disagrees with recommendations to remove peak reduction benefits from time-varying pricing and to use a lower kW/vehicle estimate for EV charging. They argue that TVP has shown positive results and that the Grid Strategies estimate is based on limited data.
Page 20 of 25 2024 Load Forecast Report Rebuttal Evidence Non-Confidential
AI summary This document is a rebuttal to the 2024 Load Forecast Report, submitted as part of a regulatory proceeding. It includes non-confidential evidence and analysis related to load forecasting for the year 2024.
1 necessarily representative of a typical peak. The 2024 Load Forecast report cites SGNS results 2 that indicate managed peak values of 0.35 kW/vehicle, with unmanaged averages across all winter 3 months of 0.2-0.6 kW/vehicle and single hi...
AI summary The text discusses load forecasting for electric vehicles based on SGNS project data, noting wide variation in peak load values and the need for revised assumptions in NS Power's load forecasts. It also references a recent NSUARB decision regarding an ATO application and highlights factors like population and economic growth affecting cost increases.
owth and Climate Change in 2016, 28 and therefore agreed to a net-zero energy-ready (NZER) code for new construction 29 by 2030 it must be expected that we will see changes to the building codes soon to 30 move towards NZER codes. It would...
AI summary The document discusses the potential impact of new net-zero energy-ready (NZER) building codes in Nova Scotia by 2030 on NS Power's long-term load forecasts. It suggests that NSPI should provide updates on the process in annual load forecast reports due to the possible significant effects of NZER codes.
of moving to a Tier 5 6 requirement by 2030. According to Efficiency Canada, 44 “Nova Scotia originally announced plans 43F 7 to adopt Tier 1 of the NBC and the NEBC, and to reach NBC Tier 3 in 2026, and NEBC Tier 3 in 8 2028. However, the...
AI summary The text discusses delays in Nova Scotia's plan to adopt higher energy efficiency building codes, citing labor and supply chain challenges. It notes that even with these delays, increased energy standards are expected through improved building efficiency, energy-efficient technologies, and solar generation, as captured by the SAE model.
94285BCC-Synapse (NSPI) IR-1 to IR-54
7 passages
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.
ters and heat pump water 30 heaters for 2024 through 2034. 31 b. Please provide the electric water heating intensity per household (kWh/household) for 32 2023 through 20234, for all electric water heating systems and separately for electri...
AI summary The text requests data on electric water heating intensity per household (2023-20234) and NSPI's estimates of winter peak load impacts (2024-2034) for electric water heating systems, including resistance heaters and heat pumps.
Date Filed: May 29, 2024 Synapse (NSPI) Page 7 of 24 1 systems and separately for electric resistance water heaters and heat pump water 2 heaters. 3 d. What are the existing or proposed standards for improving hot water efficiency? 4 e. Pl...
AI summary The document outlines requests for information regarding NSPI's programs for heat pump water heaters, load control strategies, impact evaluations, and EV scenario assumptions. It seeks data on program offerings, load management impacts, jurisdictional comparisons, and EV forecasting methodologies.
Date Filed: May 29, 2024 Synapse (NSPI) Page 13 of 24 1 f. Has the estimated impact of ongoing changes associated with COVID-19 changed since 2 the previous load forecast? If so, please explain in detail. 3 g. Please provide the inputs and...
AI summary The document includes regulatory requests for detailed explanations on load forecast assumptions, home size growth, EV charging behavior impacts, and commercial sector load effects from COVID-19. Questions focus on methodology, data inputs, and regulatory implications for energy efficiency and rate design.
Date Filed: May 29, 2024 Synapse (NSPI) Page 14 of 24 1 b. Please identify and quantify in detail the specific components in the forecast model that 2 are causing the increase starting about 2025 as shown in Figure 45. 3 4 5 6 7 Request IR...
AI summary The document contains regulatory requests (IR-24 to IR-26) seeking detailed explanations for forecast discrepancies in energy demand, including EV load impacts, DSM program effects, and solar generation growth. Requests focus on quantifying changes in model variables (XHeat, XCool, XOther) and differences between current and previous forecasts for General Service, Large General Service, and Small Industrial categories.
Date Filed: May 29, 2024 Synapse (NSPI) Page 19 of 24 1 i. Please note any changes in the model specification relative to the 2023 forecast 2 residential model, and please further quantify the impact of any such changes in 3 specification...
AI summary The document contains requests for detailed information and data related to residential and general service models, including statistical parameters, spreadsheet formats, and calculations for various variables and programs such as PV, EV, and DSM. The requests are part of a regulatory proceeding.
hroughout the report is a 50/50 or 90/10 31 forecast. Please describe the rationale for selecting the type of forecast. If it is neither 32 50/50 or 90/10, please describe how the forecast compares to 50/50 or 90/10 forecast 33 methodologi...
AI summary The text requests an explanation of the rationale for selecting a 50/50 or 90/10 forecast methodology and asks for a comparison of forecast uptakes to various optimal scenarios, including socially optimal, least-cost, and most carbon-abating uptakes. It also inquires about changes in optimizing variables over time and compliance with regulatory requirements.