N-12024 Load Forecast Report + Appendices - Redacted
73 passages
..................................................... 14 6 4.0 Discussion of Major Inputs ................................................................................................ 16 7 4.1 Historical Class Sales and Energy Data .......
AI summary The document outlines a regulatory proceeding's structure, detailing sections on historical energy data, weather patterns, economic factors, end-use intensity trends, price data, demand-side management, renewable energy integration, and sector-specific analyses for residential and commercial sectors.
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 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.
..................................................................................................... 82 16 Figure 57: Peak Regression Coefficients ...................................................................................... 84 1...
AI summary The text lists technical figures related to energy demand forecasting, peak load analysis, and system sensitivity. It includes historical data, forecasts, and components contributing to peak loads, with references to demand response (DR) and load research data. Topics focus on forecasting methodologies, load management, and system reliability.
al and commercial rate classes. The SAE models explicitly 27 incorporate end-use energy intensity projections into the Load Forecast. End-use energy 28 forecasts derived from the residential and commercial SAE models are then combined with...
AI summary The 2024 Load Forecast Report details higher near-term growth due to customer additions and adjusted weather, mid-term EV growth impacts, and long-term reductions from DSM and solar. The net annual increase is projected at 0.2%. Forecasts combine SAE models, industrial econometric data, and customer-specific inputs to determine Net System Requirement (NSR).
rage annual increase of 0.2 14 percent. Annual historic and forecast NSR are shown below in Figure 1. 15 16 Figure 1: Historical and Predicted Annual Net System Requirement 17 18 DATE: April 30, 2024 Page 7 of 100 REDACTED (CONFIDENTIAL IN...
AI summary NS Power forecasts increased system peak demand due to customer growth and electrification, offset by DSM/DR initiatives. Near-term peaks rise from electric heating, while long-term peaks decrease with lower EV sales and hybrid heating adoption under Nova Scotia’s Clean Power Plan and NS Power’s Evergreen IRP. Annual system peak demand is projected to grow 1.4% annually.
customers to ensure system adequacy. 26 // 27 28 The Board expects NS Power to continue exploring all realistic scenarios 29 related to commercial hydrogen production and, where there is sufficient 30 data, incorporate them to the extent p...
AI summary The NSUARB directs NS Power to explore commercial hydrogen production scenarios in load forecasting and address the significant 2022 forecast variance (234 GWh) attributed to pandemic and heat pump impacts. The Board emphasizes revisiting unexplained variance and considering factors like cooling demand and home electrical upgrades in residential load projections.
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.
s population growth slows from the 13 expected peak in 2023. As noted in its Decision 9, the NSUARB directed that: 8F 14 o Given the continued population growth in Nova Scotia and ongoing housing 15 shortage, re-evaluate the use of housing...
AI summary The NSUARB directed NS Power to re-evaluate housing completions as a demand indicator, considering household demographics. NS Power maintains housing completions remain the best near-term proxy for residential customer growth despite ongoing housing shortages.
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.
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.
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.
ORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 23: Water Heater Forecast 2 Overall % Overall Intensity Year Saturation (kWh/household) 2024 73 1,633 2025 76 1,675 2026 78 1,711 2027 80 1,745 2028 82 1,777 2029 83 1,807 2030...
AI summary The 2024 Load Forecast Report includes a forecast for water heater saturation and intensity, as well as an overview of existing federal and provincial incentives for electric vehicles (EVs). It notes that there were approximately 4100 EVs in Nova Scotia as of the end of 2023 and references a report on ensuring zero-emission vehicle (ZEV) adoption.
2024 Load Forecast Report REDACTED 1 Figure 25: EV Mileage Assumptions and Load/Peak Modeling Results 2
AI summary The text references a 2024 Load Forecast Report and includes a figure (Figure 25) discussing EV mileage assumptions and load/peak modeling results, though the content is redacted and no details are provided.
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.
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.
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.
Page 40 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 The maximum contribution to the 17:00 – 19:00 evening peak demand from participants 2 in the SGNS project was calculated in both the ChargePoin...
AI summary The document discusses the evening peak demand contributions from participants in the SGNS project under the ChargePoint and ev.energy programs. ChargePoint customers had an average contribution of 0.24 kW/vehicle, while ev.energy customers had 0.65 kW/vehicle. During pilot periods, peak contributions reached 2.33 kW and 1.92 kW per vehicle, respectively. Demand Response observed during system peak times averaged 0.35 kW per enrolled EV.
) 2024 Load Forecast Report REDACTED 1 Figure 27: PV Impact to Energy (cumulative) 2 Total New Year Load (GWh) Peak (MW) Installs 2024 3,019 -33 0 2025 6,361 -70 0 2026 10,062 -111 0 2027 14,163 -158 0 2028 18,708 -209 0 2029 23,746 -267 0...
AI summary The 2024 Load Forecast Report indicates a cumulative impact of photovoltaic (PV) installations on energy load and peak demand, with negative values reflecting reduced load. The report notes that distributed solar and battery storage combinations are not assumed to be significant due to the high cost of home batteries, ranging from $15,000 to $20,000.
e Appendix A – 8 Impact Analysis Report of the Project Final Report. 25 Key results included demonstration 24F 9 of load following demand response modes in evening peak periods of 1 – 1.5kW over four- 10 hour events, shifting load to perio...
AI summary The report discusses the impact of demand response and vehicle-to-grid (V2G) technologies in managing load and reducing peak demand. It highlights the demonstration of load following demand response during evening peaks and the potential of EV batteries to support demand charge management and lower emissions.
Page 44 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 The technology required for V2G is still in development and is not widely available, but a 2 few manufacturers have included the capability in...
AI summary The text discusses the current state of vehicle-to-grid (V2G) technology, noting that it is still in development and not widely available. It highlights the potential of coordinated distributed energy resources, such as batteries and EVs, to smooth energy demand and mitigate peak loads when managed through a utility DERMS.
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.
rt of the 2023-2024 GRA and the BA Rider approved 14 in April of 2024. 26 2025 is forecast to increase to account for projected increased fuel costs 25F 15 while 2026 is forecast to increase to account for recovery of outstanding fuel cost...
AI summary The document discusses the 2023-2024 GRA and BA Rider approved in April 2024, with forecasts for 2025 and 2026 showing increases to account for fuel costs and recovery of outstanding fuel costs over three years. The forecast also indicates an average annual increase of 2% from 2027 onwards, with 2029 showing a return to inflation rates. Price elasticity is discussed in relation to the SAE model and the TVP Pilot EM&V in matter M11267.
ion behaviour in response to year-over-year price changes. The estimated 2 elasticities for the two TVP rates are shown in Figure 34. 3 4 Figure 34: TVP Price Elasticity Estimates 5 6 7 Although the Daily Price Elasticity for the TOU rate...
AI summary The document discusses price elasticity estimates for TVP rates and their impact on load forecasts, noting that elasticity has a minimal effect on sales compared to other factors. It also mentions the role of Demand Side Management (DSM) in electricity use forecasting, referencing E1's proposed supply agreement and 2019 study for DSM projections.
8 DSM. 9 10 In order to highlight the impact of DSM in the province, the forecast amounts are subtracted 11 from the results of the forecast regression models. As with other jurisdictions and utilities 12 with significant DSM activity, NS...
AI summary The document discusses the impact of Demand Side Management (DSM) on load forecasting, highlighting the challenge of double counting DSM savings. It explains that historical DSM savings are incorporated into regression models and end-use data, making it difficult to accurately forecast sales without DSM. A method is proposed to account for this by using historical DSM savings as a load modifying variable.
to historic activities, the coefficient applied to historic DSM will also apply to the forecast 28 DSM. This does not imply that a portion of DSM activities is not taking place or that 29 M10473, EfficiencyOne 2023-2025 DSM Resource Plan F...
AI summary The text discusses the application of a coefficient to historic Demand Side Management (DSM) activities, which will also apply to the forecast DSM. It references two filings related to DSM resource plans and potential studies.
1 forecast DSM is overstated; rather, it is a way of accounting for DSM that is captured 2 elsewhere in the forecast. The methodology is not specific to DSM and could be applied to 3 other variables that need to be highlighted in the forec...
AI summary The text discusses the inclusion of historical DSM in forecasting models, noting that DSM is not specific to demand-side management and can be applied to other variables. The Residential model shows a slight improvement in fit with DSM included, indicating that 55.6% of DSM savings are not captured by other variables and must be included in the forecast.
nd Industrial customers by rate class or by month, so by creating a combined 23 model for these classes, the level of uncertainty around allocating historical DSM savings 24 across rate classes and months of the year is reduced. The DSM va...
AI summary The document discusses the use of a combined model for industrial customers by rate class and month to reduce uncertainty in allocating historical DSM savings. The DSM variable coefficient remains at -0.448, indicating a 45% adjustment to future load forecasts based on DSM amounts. The model has a high adjusted R-squared of 0.821 and a low MAPE of 2.75, showing a strong fit.
2024 Load Forecast Report REDACTED 1 Figure 35: Annual Forecast Residential DSM Savings (incremental) 2
AI summary The 2024 Load Forecast Report includes a figure illustrating annual forecast residential DSM savings, highlighting incremental savings from demand-side management programs.
Year Forecast Forecast Forecast DSM DSM DSM DSM Residential Commercial Industrial captured by captured by Adjustment Adjustment DSM DSM savings DSM savings Residential Comm/Ind for for savings (GWh) (GWh) end use end use Residential Comm/I...
AI summary The table presents forecasts for energy savings from demand-side management (DSM) programs across residential, commercial, and industrial sectors from 2024 to 2034, including captured savings and adjustments with coefficients for each year.
2033 71.3 43.4 7.7 31.7 28.2 39.6 22.9 2034 69.1 44.0 7.8 30.7 28.6 38.4 23.2 3 4 The methodology used to determine the DSM coefficient only works for levels of DSM 5 that have been relatively consistent throughout the historical data set...
AI summary The text discusses the methodology for determining the DSM coefficient and its limitations, noting that it works best for consistent historical levels of DSM and may need revision if future forecasts change significantly. It also mentions a third-party application to the NSUARB for approval as a Licensed Retail Supplier under the RTR tariffs starting in 2025.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 41: Residential Sales Components by Year 2 Year Regression New Hybrid Solar EV RTR DSM Total Total Res. DSM Model Cust. Adjust. Impact Impact Sales Adj...
AI summary The document presents a section of the 2024 Load Forecast Report, focusing on residential sales components by year, including data on regression model outputs, customer growth, hybrid adjustments, solar and EV impacts, retail tariff rates, and demand-side management (DSM) adjustments.
response to NSUARB IR-12 (e) from the 2020 Load Forecast, where the end-use intensity 8 is multiplied by the number of existing customers, the appropriate X coefficient from the 9 regression model, and either the HeatUse variable or the Co...
AI summary The text discusses the methodology used in the 2020 Load Forecast to calculate end-use intensity by multiplying factors such as the X coefficient and variables like HeatUse or CoolUse. It notes that these calculations are illustrative and do not account for DSM amounts impacting specific end uses. Total DSM is adjusted for losses and allocated to Municipal class customers.
TION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 44 Commercial Sales vs Economic Indicators 2 3 4 5 6.1 Small General Service 6 7 Historical and forecast Small General service loads are shown in Figure 45. Small General 8 service...
AI summary The 2024 Load Forecast Report discusses historical and forecast Small General Service loads, noting an average annual increase of 1.8 percent. Commercial electrification of heating is expected to be offset by demand-side management (DSM) and decreased intensity forecasts for ventilation, lighting, and miscellaneous end uses.
) 2024 Load Forecast Report REDACTED 1 Figure 45: Historical and Forecast Annual Small General Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2024 6 to 2034. Total change between 2024 and...
AI summary The 2024 Load Forecast Report indicates a 19% increase in small general sales from 2024 to 2034. General class load is expected to decrease by 0.1% annually over the 10-year period, influenced by lower EV load, hybrid heating scenarios, and DSM programs. A drop in sales in 2026 is attributed to shifting to the RTR market and increased solar generation.
2024 Load Forecast Report REDACTED 1 Figure 46: Historical and Forecast Annual General Demand Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2024 6 to 2034. Total change between 2024 and 2...
AI summary The 2024 Load Forecast Report discusses the Large General class showing slower growth due to revised estimates of large project completion. The forecast uses customer surveys and historical sales data, with adjustments made based on the 2024 Board Decision to account for overestimations in prior years.
r over year changes, and the resulting 2024 forecast which has been adjusted 2 to account for the over estimation in prior years. 3 4 Figure 47: Large General Annual Growth (GWh) 5 Year 2022 2023 2024 2025 2026 2027 2028 2022 Forecast 12 2...
AI summary The text discusses changes in large general annual growth forecasts for electricity consumption, noting adjustments in the 2024 forecast due to overestimations in prior years. Growth is expected to be driven by institutional facilities, particularly hospital expansions, but overall demand is projected to decrease by 2034 due to demand-side management (DSM) efforts.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 55: Forecast Components 2 GWh Res Comm Ind Other Losses NSR 2024 Forecast 5,180 3,118 2,267 159 767 11,490 Model 466 352 41 -82 69 986 New Customers 366 32...
AI summary The 2024 Load Forecast Report includes a table showing forecast components for electricity demand across various sectors, including residential, commercial, industrial, and others, as well as adjustments for factors like solar, EV adoption, and demand-side management (DSM). The report also provides a 2034 forecast and highlights the impact of DSM initiatives.
1 10.0 PEAK DEMAND 2 3 The total system peak is defined as the highest single hourly average demand experienced 4 in a year. It includes both firm and interruptible loads. Due to the weather-sensitive load 5 component in Nova Scotia, the t...
AI summary The document defines total system peak demand as the highest hourly average demand in a year, typically occurring between December and February. NS Power uses an end-use approach to forecast peak demand, incorporating factors like heating, cooling, and EV impact. Demand response programs and hybrid heating scenarios are also considered in the 2024 Load Forecast.
nd BNI Curtailment. The achievable potential of these programs 25 was used in the Load Forecast. NS Power's IRP Action Plan has targeted 75 MW of 26 capacity for DR deployment by 2025. The estimates in the Load Forecast have been moved 27...
AI summary The Load Forecast Report discusses the achievable potential of demand response (DR) programs, including BNI Curtailment, and their alignment with NS Power's IRP Action Plan targeting 75 MW of capacity by 2025. The forecast timeline has been adjusted to 2028 to reflect current program development and expected ramp-up, with an effective load carrying capacity (ELCC) of 48% used for DR forecasts.
mation is gathered through the 2 implementation of DR programs. 3 4 Annual DR totals by program are provided in Figure 56. 5 6 Figure 56: Demand Response 7
AI summary The document discusses the collection of information through the implementation of Demand Response (DR) programs, with annual DR totals by program provided in Figure 56.
Year Direct TVP Business, Total Total Load Rate(MW) Non-Profit (MW) with Control & Industrial ELCC (MW) Curtailment (MW) (MW) 2024 0 2 0 2 1 2025 4 4 1 9 4 2026 12 12 2 26 12 2027 24 22 4 50 24 2028 36 32 6 74 36 2029 39 36 7 82 39 2030 39...
AI summary A pilot project conducted in 2021 and 2022 with E1 explored direct load control via water heater controls, installing 201 controllers in residential homes to test the benefits of utility-managed load shift events.
ter controllers 11 were procured and installed in residential homes, and testing was completed to demonstrate 12 the benefit of utility-managed load shift events. Results from the pilot, as presented in E1’s 13 2023 DSM Programs Evaluation...
AI summary The document discusses a pilot project involving demand response (DR) controllers installed in residential homes, showing an average DR capacity of 385 W during winter peak periods. It also mentions a two-phase pilot with commercial and industrial customers, with a total available DR capacity of 2.365 MW reported in the 2023 DSM Programs Evaluation Report.
023 season, as presented in E1’s 2023 DSM Programs 4 Evaluation Report 37, indicated total available DR capacity at the generator of 2.365 MW 36F
AI summary The 2023 DSM Programs Evaluation Report 37, presented by E1, indicates that the total available demand response (DR) capacity at the generator level is 2.365 MW for the 2023 season.
5 for the 9 participating C&I customers. Results also indicated that available capacity tends 6 to vary from event to event and be lower than enrolled capacity, and that available capacity 7 tends to vary depending on time of day, with cap...
AI summary The document discusses the performance of demand response (DR) programs, including the recruitment of new customers and the evaluation of DR capacity results for the winter 2023/2024 season. It also mentions a pilot project with E1 involving residential smart thermostats and EVs. The impact of these initiatives on load forecasts is expected to be within the sensitivity analysis provided.
ed in the firm peak but is excluded from the system peak. 26 27 For the 2024 Load Forecast, as discussed in Section 4.2, the assumed peak temperature 28 inputs use a lagging 12-hour average as well as windspeed. The coefficients used for p...
AI summary The text discusses the 2024 Load Forecast Report, referencing peak temperature inputs and coefficients used for peak load calculations, and cites a final report on Business, Non-profit, and Institutional Demand Response.
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.
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.
-down model), and the resulting 2 forecast informed by the LRS-AMI historical series (bottom-up). 3 4 Figure 69: Monthly historical Residential LRS load at peak and forecasts 5 6 7 8 Both the current residential peak demand forecast (green...
AI summary The document discusses two forecasting methods for residential peak demand: a top-down approach and a bottom-up model based on LRS-AMI historical data. The top-down method uses annual load factors updated for the system peak month, while the bottom-up model provides more detailed forecasts. The top-down approach is currently preferred due to its simplicity and similar results in early forecast years.
1 11.0 SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, 4 including economics, weather, adoption of distributed generation, electricity rates and 5 DSM. Although each of these...
AI summary The text discusses a sensitivity analysis of sales and peak forecasts, highlighting the uncertainty influenced by factors like economics, weather, and DSM. A Monte Carlo simulation approach was used to estimate a probable distribution of future load, resulting in a P10/P90 range of 468-646 GWh over 10 years, primarily influenced by weather and economic variations.
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.
Interruptible Demand Firm Net System Temp at 12hr Lag Contribution to Response Contribution Growth Peak Peak Temp Year Peak (reduction in to Peak Notes Firm Peak only, (%) MW) (MW) (deg C) (deg C) (MW) (MW) - January 2 weekday 2014 83 2,03...
AI summary The table presents data on interruptible demand, firm peak contribution, response, and net system peak growth from 2014 to 2017, including temperature data at 12-hour lag and notes on specific dates and times.
� � ×� � 𝐻𝐻𝐻𝐻𝐻𝐻15 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻15 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅15 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃15 Where HDDy,m is the Heating Degree Day for a given month m of the year y, HHSize is the average household size, ResEcon is Employment Compensation divided by House Hold populat...
AI summary The text defines formulas for calculating heating and cooling demand based on factors like Heating Degree Days, household size, employment compensation, and electricity prices. It also introduces variables like HeatIndex and CoolIndex, which depend on efficiency, shell integrity, and square footage. XOther represents non-weather-sensitive electricity use.
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.
d regression coefficient, b4, assesses the portion of embedded DSM activity that is already included in the billed sales information. The negative sign of b4 indicates that DSM activity reduces load. The COVID variable is a binary that sta...
AI summary The text discusses the use of a regression coefficient (b4) to assess the impact of embedded DSM activity on billed sales, noting that DSM activity reduces load. It also describes the inclusion of a binary variable to account for the effects of the COVID-19 pandemic on load, as well as the use of binary shift variables to address anomalies in billing data and improve model fit.
ON REMOVED) 2024 Load Forecast Report Appendix B Page 7 of 35 Residential SAE Model Fit Residential Model 2024-2034 Reconciliation The following tables provide details reflecting the changes between 2024 and 2034 forecast years. Some of th...
AI summary The 2024 Load Forecast Report Appendix B discusses the reconciliation of residential load forecasts from 2024 to 2034, showing changes in existing and new customers, EVs, solar, RTR, hybrid, and DSM. The report highlights discrepancies between model-level monthly data and system-level annual data.
(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.
(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.
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.
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.
.ManGDP A binary variable was added for October 2022 to account for billing delays after hurricane Fiona. Variable Coefficient StdErr T-Stat P-Value MBin.Jan 20630.178 1697.352 12.154 0.00% MBin.Feb 17243.849 1698.709 10.151 0.00% MBin.Mar...
AI summary A binary variable was added for October 2022 to account for billing delays caused by Hurricane Fiona. The statistical analysis shows significant coefficients for monthly billing variables, with the October 2022 variable having a lower coefficient compared to other months.
rial Model MedInd_Salesm = MBin.Janm + MBin.Febm + MBin.Marm + MBin.Aprm + MBin.Maym + MBin.Junm + MBin.Julm + MBin.Augm + MBin.Sepm + MBin.Octm + MBin.Novm + MBin.Decm + MBin.Aft16 + b1×MEcon.ManEmp A binary variable for 2016 and subseque...
AI summary This text presents a statistical model used for forecasting medium industrial sales, incorporating monthly binary variables and an economic indicator for manufacturing employment. A binary variable for 2016 and subsequent years was added to improve model fit, reflecting a transition from declining to flat sales trends. The model uses data from 2006 to 2023.
INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 27 of 35 Medium Industrial Model Statistics Model Statistics Iterations 1 Adjusted Observations 216 Deg. of Freedom for Error 202 R-Squared 0.703 Adjusted R-Squared 0.684 AIC 1...
AI summary This section presents statistical details and model fit information from the 2024 Load Forecast Report, focusing on the Medium Industrial Model and a Combined Model for Commercial and Industrial DSM Coefficient. Key metrics include R-squared, AIC, BIC, and other statistical indicators.
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.
0.7584 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 35 of 35 Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. A...
AI summary The document discusses the Peak Model Fit and its alignment with historical data, noting that while an explicit peak DSM variable could not be included due to insignificant parameters, indirect effects of energy-related DSM are still reflected in the peak model. The forecast comparison and accuracy are also covered in the appendix.
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.
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.
N-6NSPI (Synapse) RIR-1 to RIR-54 - Redacted
71 passages
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.
customers using natural gas for primary heating 27 (viii) Number of customers using propane for primary heating 28 (ix) Number of customers using wood for primary heating. 29 Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 1 of 12 REDAC...
AI summary The text lists customer heating fuel usage statistics and references a 2024 Load Forecast Report submitted by NSPI in response to Synapse Information Requests under NSUARB M11689.
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.
the 28 corresponding average peak load impacts in kW per residential customer and 29 per commercial customer by the following technology type: electric resistance Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 3 of 12 REDACTED (CONFIDE...
AI summary The document is part of a regulatory proceeding involving Nova Scotia Power Inc. (NSPI) and includes responses to information requests related to the 2024 Load Forecast Report. The report discusses load impacts by technology type, including residential and commercial customers.
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.
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.
the names of such jurisdictions and Date Filed: June 19, 2024 NSPI (Synapse) IR-8 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDEN...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, addressing electric water heater standards, rebate programs, and load control initiatives. The report notes that heat pump water heaters are not explicitly modeled due to low uptake, and load control programs are described but not yet evaluated for impact.
2 3 4 The driving population is characterized by drivers’ EV type and access to charging. 5 For LDVs, there are 4 EV types (short and long-range plug-in hybrid and short and 6 long-rang battery electric vehicle) and 6 charging access types...
AI summary The text discusses the characterization of EV drivers in Nova Scotia based on EV type and charging access, leading to 24 customer types. It outlines unmanaged and managed charging scenarios, highlighting how drivers choose charging locations based on electric rates but not time-varying prices in unmanaged scenarios.
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.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (f) 2 (i-ii) No, Figure 26 is for all EVs types. 3 4 (g) NS Power is currently undertaking engagement with Time-Varying Pricing (TV...
AI summary NSPI is engaged in developing a long-term plan for Time-Varying Pricing (TVP) Tariffs through stakeholder engagement sessions, with upcoming sessions discussing tariff design, marketing, and recommendations for future deployment as part of matter M09777.
kWh/year kW/vehicle kW/vehicle on peak (unmanaged) Home L1 Home L2 Workplace Public L2 Public DCFC Peak by Charging LDV 3485 0.85 1.48 type 0.20 0.45 0.07 0.02 0.11 MDV 8205 1.62 1.06 Transit Bus 113890 7.33 27.70 PHEV 1743 0.85 1.48 Energ...
AI summary The text presents data on energy consumption and charging demand for various vehicle types, including light-duty vehicles (LDV), medium-duty vehicles (MDV), transit buses, and plug-in hybrid electric vehicles (PHEV). It includes metrics such as kWh/year, kW/vehicle, and peak demand by charging type, as well as cumulative energy usage and peak demand forecasts from 2023 to 2024.
and impact to peak loads for example), it will be 27 incorporated into the forecast. 28 29 (d) Residential Share in Figure 28 represents the percentage of residentials with batteries. Date Filed: June 19, 2024 NSPI (Synapse) IR-11 Page 1 o...
AI summary The text discusses the impact of residential battery energy storage (BES) on peak loads, including estimates of peak impact under different scenarios. It also includes customer forecast data for residential customers in 2034.
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.
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-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.
1F 5 National Energy Research Lab 3 showed that the results had not changed significantly since 2F 6 those earlier reports. The literature has not been reviewed for a more recent study. 7 8 (c) As stated on page 52 of the report, daily pri...
AI summary The text discusses the estimation of price elasticity based on Itron's experience across multiple jurisdictions, noting that NS Power relies on this data for load forecasting. The elasticity values are derived from the TVP pilot and are used to model changes in consumption behavior in response to price changes.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-18: 2 3 Demand Side Management (Section 4.6, pp 54-56). 4 5 (a) Please provide the source data for the DSM values used i...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, specifically addressing Demand Side Management (DSM) values and their sources, including references to past filings and studies.
N-1 in M08929 (NS Power’s IRP and M08059 Generation 9 Utilization and Optimization), August 14, 2019. The four DSM scenarios are shown in the 10 following graph from the report: Date Filed: June 19, 2024 NSPI (Synapse) IR-18 Page 2 of 3 RE...
AI summary The text references a 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It mentions the base case in the IRP forecast being aligned with current DSM levels, and refers to data in Attachment 4 of the 2024 LFR report.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-19: 2 3 Demand Side Management Adjustment (Section 4.6, pp 54-56) 4 5 (a) Please provide the details of the data and sta...
AI summary NSPI responded to Synapse's information requests regarding the Demand Side Management (DSM) adjustment in the 2024 Load Forecast Report, specifically addressing the data and statistical analysis used for developing DSM coefficients in residential and commercial/industrial sectors, as well as changes compared to the 2023 report.
(a) Please refer to section 4.6 (Demand Side Management) of the Report for a description of 30 the process used to develop the coefficient for the DSM variable. The model fit and model Date Filed: June 19, 2024 NSPI (Synapse) IR-19 Page 1...
AI summary The document refers to section 4.6 of the Report for details on the development of the DSM variable coefficient, with model fit statistics provided in Appendix B and Attachment 5. The methodology for the 2024 forecast is the same as in the 2023 forecast for residential, commercial, and industrial variables. DSM amounts are located in specific columns of electronic attachments.
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.
1 the hybrid heating scenario, not featured for this class in 2023, is expected to reduce load 2 in this class by a further 2.7 percent. These changes offset the larger increase in load from 3 components that make up the regression model (...
AI summary The text discusses load forecasting changes, including the impact of hybrid heating scenarios, EV load, DSM, and solar generation on electricity sales. It highlights a shift in load factors, the absence of a 2024 sales drop due to RTR participation, and an increased solar generation effect due to higher solar penetration and legislative changes.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-25: 2 3 Large General Service (Section 6.3). 4 5 (a) Please explain and quantify the specific reasons for the difference...
AI summary NSPI responded to Synapse's information request regarding the 2024 Load Forecast Report, explaining that the forecast for Large General Service class shows a decline of 12 GWh by 2034, primarily due to revised estimates of large project completion and migration of load to the RTR market. Institutional facilities account for 13 GWh of growth, while other customers account for 7 GWh.
as 28 revised accordingly. Date Filed: June 19, 2024 NSPI (Synapse) IR-28 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 R...
AI summary The document provides the 2024 Load Forecast Report for municipal loads, including total estimated load in GWh and peak demand in MW for the years 2024 through 2034. It notes that the load served by NS Power is expected to decrease in 2025 due to customers switching to third-party suppliers through the OATT and BUTU programs.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-32: 2 3 Peak Demand and Demand Response (Section 10, pp 81-83) 4 5 (a) Please provide details about the Demand Response...
AI summary NSPI is responding to Synapse's information requests regarding the 2024 Load Forecast Report, specifically on demand response (DR) resources, ELCC factors, and DR peak reduction calculations. The requests focus on DR modeling, data sources, and assumptions used in the forecast.
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.
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.
n DR resources was explained in the 2021 25 load forecast as follows 1: 26 1 Nova Scotia Power Inc. 2021 Load Forecast Report, April 30, 2021, page 72 (M10109). Date Filed: June 19, 2024 NSPI (Synapse) IR-32 Page 3 of 8 REDACTED (CONFIDENT...
AI summary The document discusses the estimation of demand response (DR) resources based on the 2021 load forecast, referencing a 48% estimate (52% with planning reserve margin) derived from E3’s Capacity Value Study. Specifics of DR programs are still being determined, with values to be reassessed as more information is gathered. The document also notes that other jurisdictions apply ELCC values to DR programs for resource adequacy assessments.
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.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (iv) The number of program participants required for NS Power’s peak reduction 2 estimates will depend on type of end use and enabl...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report. Key points include the dependency of peak reduction estimates on program type and technology, exclusion of DR from EVs and batteries in Figure 56, and the impact of energy efficiency measures on demand response potential.
1 (x) The 2022 Evergreen IRP DR values are the same as those provided by E1’s 2 Demand Response Potential Study but shifted by 2 years. 3 4 (d) Please refer to Attachment 1. 5 6 (e) The requested details are available in Section 10.1 C&I B...
AI summary The text references demand response (DR) values from the 2022 Evergreen IRP, citing studies by E1 and NS Power's Smart Grid Nova Scotia Project. It provides specific document locations and matter numbers for detailed information on DR programs and their results.
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.
2023 Program Program Component Pathway Process Market Impact Demand Response Residential Demand Response DHW Direct Load Control Comprehensive BNI Demand Response DR Aggregator Comprehensive For the DR program, the Evaluator has prepared a...
AI summary The document discusses the Demand Response (DR) program, focusing on Residential Demand Response and BNI Demand Response components. It outlines the distinction between new and total available DR capacity and explains how available DR capacity differs from peak demand savings, emphasizing that EOne does not control the use of resources to reduce peak demand.
1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-32 Attachment 1 Page 2 of 12 Available DR capacity will be evaluated based on events called from December to February excluding weekends and holidays. Event...
AI summary The document discusses the evaluation of available demand response (DR) capacity based on events from December to February, excluding weekends and holidays. It explains that available DR capacity is measured over the first two hours of a DR event and is evaluated on a per-participant basis. The report also clarifies how available DR capacity is calculated and reported for the winter period.
2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-32 Attachment 1 Page 3 of 12 1 Residential DR Overview This section describes the Residential Demand Response (DR) program component, follows up on past eva...
AI summary The Residential Demand Response (DR) program was officially launched in 2023 by EOne. It includes a pathway from a pilot initiative, the Domestic Hot Water Direct Load Control Pilot, which was implemented in 2020. During the 2022-23 winter peak season, NS Power called 35 DR events to reduce demand during peak periods.
Table 5: 2023 Residential DR Evaluation Approach Evaluation Objectives Research Questions Methodology Establish available DR › Are the data in the tracking sheet complete, › Tracking sheet audit capacity results for the accurate, and consi...
AI summary This section outlines the evaluation approach for the 2023 Residential Direct Load Control pathway, focusing on assessing data completeness, accuracy, and available DR capacity through a tracking sheet audit and water heater controller meter data analysis.
tor audited the final 2023 tracking sheet to ensure it was complete and the entered data were consistent. The results obtained are presented in Appendix I. Water Heater Controller Meter Data Analysis One of the main objectives of the 2023...
AI summary The document discusses the evaluation of demand response (DR) capacity for water heater controllers in 2023, including an update to the baseline methodology and the analysis of data from DHW Direct Load Control participants. The Evaluator used an in-house tool and updated the unitary available DR capacity for Shifted controllers, as Aquanta controllers are no longer being installed.
e rates and applied these to the 2023 participation level. The detailed methodology and results obtained are presented in Subsection 3.2.2. The 2020-2022 Measure Assessment4 was updated accordingly. 3 Herein, systems refer to water heaters...
AI summary The document discusses the methodology used to calculate demand response (DR) capacity based on evaluation results from the 2020-2022 Measure Assessment. It references the detailed calculation methodology presented in Section 3 and applies these to the 2023 participation level.
sults Building on all the above methods and collected data, the Evaluator calculated the new and total amount of available DR capacity as per the calculation methodology presented in Section 3 below. Note on Margins of Error For evaluation...
AI summary The Evaluator calculated the new and total available demand response (DR) capacity using a defined methodology. A 10% margin of error at a 90% confidence level was aimed for, acknowledging that this reflects precision rather than accuracy. Margins of error for unitary available DR capacity and in-service rates are included in the evaluation, with calculation examples provided in Appendix II of the 2023 DSM Programs Evaluation Executive Summary.
at were multiplied by the number of controllers to obtain the evaluated available DR capacity. This subsection presents both the new and total available DR capacities generated through Residential DR. 3.2.1 In-service Rates For the DHW Dir...
AI summary This section discusses the calculation of available demand response (DR) capacity through residential DR programs, focusing on the in-service rate (ISR) for the DHW Direct Load Control pathway. It compares ISR values between Shifted and Aquanta, highlighting a stable rate compared to previous years.
In-service Rate 81% 82% Margin of Error 4% 2% 3.2.2 Unitary Available DR Capacity For the DHW Direct Load Control pathway, EOne and the Evaluator rely on a unitary available DR capacity value, more precisely the average available DR capaci...
AI summary The document discusses the unitary available DR capacity for the DHW Direct Load Control pathway, focusing on updating baseline methodology and improving accuracy of estimates using 2022 and 2023 controller meter data. It also references the 2020-2022 Measure Assessment and mentions the Residential Demand Response Final Report.
DACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-32 Attachment 1 Page 8 of 12 Baseline Definition Selection Baselines for DR events are often established based on an average of similar days prior to the actual...
AI summary The document discusses the selection of baseline definitions for Demand Response (DR) events, focusing on the analysis of different scenarios and their mean percent error (MPE). The approach used by EOne and the Evaluator's refinement of this method is highlighted, with a preference for non-event days due to the lack of correlation between DHW load and outdoor temperature.
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%.
Events First Two Hours Morning 497 363 356 245 407 430 Evening 424 294 336 217 344 359 Overall 451 319 343 224 367 385 ± 3% Residential Demand Response Final Report 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Sy...
AI summary The table shows a decrease in available DR capacity over time, primarily due to varying baseline loads throughout the day and increased hot water usage. The 2023 unitary available DR capacity is 31% lower than in 2022, with 17% of this decrease attributed to changes in baseline methodology.
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.
the Nova Scotia Utility and Review Board (NSUARB) as part of the 2014 Cost of Service Study Progress Update.8 Table 8: Evaluated 2023 Residential DR New Available DR Capacity Shifted Aquanta Total Number of Units 22 41 63 In-service Rate (...
AI summary The document references a 2014 Cost of Service Study Progress Update by the Nova Scotia Utility and Review Board (NSUARB), and includes tables evaluating residential demand response (DR) capacity for 2023, detailing metrics such as number of units, in-service rates, and available DR capacity at the meter and generator levels.
0.00 0.02 0.04 0.06 0.08 Targets Evaluated 8 Nova Scotia Utility and Review Board, Matter M06555, 2014 Cost of Service Study Progress Update, Exhibit N-2.09, Appendix I1. Residential Demand Response Final Report 10 REDACTED (CONFIDENTIAL I...
AI summary The document discusses the realization rates for new and total available demand response (DR) capacities in Nova Scotia, comparing tracked and evaluated capacities. The realization rates were established at 85% for new DR capacity and 79% for total available DR capacity.
EOne 0.027 MW 85% Evaluation Results 0.023 MW Total Available DR Capacity Tracked Available DR Capacity by EOne 0.073 MW 79% Evaluation Results 0.058 MW Evaluated new and total available DR capacities were 15% to 21% lower than the values...
AI summary The 2023 Residential Demand Response evaluation found that new and total available DR capacities exceeded targets, with new capacity at 0.023 MW and total at 0.058 MW. The evaluation also noted that the new baselining approach improved accuracy in estimating DR capacity.
generator, thus exceeding the planned new and available DR capacity of 0 MW. 2023 Res DR Finding: The new baselining approach selected through the 2023 evaluation resulted in more accurate estimates. The residential DHW load during a DR ev...
AI summary The 2023 residential demand response (DR) findings indicate that the new baselining approach improved accuracy but resulted in lower available DR capacity. Available DR capacity varies by time of day, with mornings having 20% more capacity than evenings. Connectivity issues caused about 20% of controllers to fail to respond to DR events. The evaluated DR capacity was lower than what EOne tracked, prompting a recommendation to include in-service rates and unitary values in EOne's tracking.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-33: 2 3 Peak Demand (Section 10 Coincident Peak Demand Research, pp 92-96) 4 5 (a) Please provide more details about the...
AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report, addressing topics such as the use of interval data, AMI coverage, loss levels on peak days, and the robustness of the load forecasting model. The response highlights differences in peak load modeling approaches and the impact of demand-side management.
rison 7 between top-down (current) and bottom-up (experimental, and main part of that section) 8 peak residential forecast an apples-to-apples comparison. A no-DSM adjustment would Date Filed: June 19, 2024 NSPI (Synapse) IR-33 Page 3 of 5...
AI summary The document discusses the 2024 Load Forecast Report and NSPI's responses to Synapse Information Requests. It mentions the assessment of model robustness using statistical metrics and the comparison of peak residential forecasts with and without DSM adjustments.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (d) Inputs for DSM and end uses are being studied for inclusion in future models. At present, 2 they are more difficult to model, a...
AI summary NSPI is addressing challenges in modeling DSM and end-use inputs for future load forecasts, citing limited data sets and difficulties with the probabilistic approach. The 2024 Load Forecast Report includes historical and projected electricity sales data from 2010 to 2034.
1 Request IR-36: 2 3 Appendix A: Forecast 4 5 (a) Please provide in electronic format the specific calculations used to create the values 6 in Tables A1, and A2. If this information has already been provided in electronic 7 format in one o...
AI summary The response to Request IR-36 provides references to the 2024 Load Forecast Report for the calculations used in Tables A1 and A2. It also explains that the Interruptible Contribution to Peak (PHP) forecast is expected to remain steady at 147MW, with historical data showing significant differences between actual and forecast values.
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.
2024 Load Forecast Report Synapse IR-37 Attachment 1 Page 6 of 6 Residential load post regression Existing Cus New Custo EV's Solar RTR Hybrid DSM Residential Sales (with DSM) Total DSM Captured by End Uses Residential DSM Coeff 2024 10,46...
AI summary The document presents residential load forecasts and regression analysis for 2024 and 2034, including factors like new customers, EVs, solar, DSM, and residential sales. It outlines changes in load, average use, and intensities, providing insights into residential energy consumption trends.
1.05 0.923 4,712 2034 1,263 3,680 734 61 1.00 0.923 5,300 Change to -19.0% 34.2% 3.2% -0.2% -4.4% 0.0% 12.5% Xcool Inputs Central AC HP Cool Room AC CoolUse VaCoefficient Total Xcool 2024 29 220 65 1.1 1.094 392 2034 35 337 68 1.3 1.094 61...
AI summary The text presents load forecast data for 2024 and 2034, including changes in various energy usage categories, and references a 2024 Load Forecast Report (NSUARB M11689) and NSPI Responses to Synapse Information Requests. The data includes inputs for cooling and other energy uses, with percentages of change between the two years.
NSPI and other Programs Before future DSM After DSM Year SAE model Forecasted Forecast RTR Solar PV EV (GWh) Total Total SAE + DSM Final Sales Regression Customer without (GWh) Programs adjsutments (GWh) Sales (kWh Count HP and (GWh) outsi...
AI summary The table presents data on energy consumption and demand-side management (DSM) programs over multiple years, including metrics like SAE model sales, forecasted customer counts, and DSM adjustments. It shows how DSM programs impact total energy sales and forecasts.
(19.2) 39.4 13.5 415.4 (25.8) 389.6 2034 13,894.5 29,466.2 409.4 -6.7 (22.0) 48.7 20.0 429.4 (27.8) 401.6 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-38 Attachment 1 Page 9 of 9 Small Gen load post regr...
AI summary The text presents load forecast data for Small Gen from 2024 to 2034, including metrics such as average customer load, EV load, solar generation, and DSM impacts. It also includes regression analysis results and intensity inputs, showing changes in various parameters over the forecast period.
NSPI and other Programs Before future DSM After DSM Year SAE model Forecast RTR EV Solar PV Hybrid Total Total SAE + DSM Final Sales Regression Sales without HP (GWh) Programs adjsutments (GWh) (kWh / HH) and (GWh) outside model without DS...
AI summary The table presents energy consumption data for NSPI, comparing forecasted and actual sales before and after DSM programs from 2014 to 2021. It includes metrics such as SAE model, RTR, EV, Solar PV, and Hybrid, highlighting the impact of DSM on total energy sales.
102.2 (120.7) (31.8) (147.2) 2,432.7 (152.3) 2,280.4 2032 2,617,567.0 2,617.6 (96.9) 127.3 (145.4) (42.2) (157.3) 2,460.3 (167.5) 2,292.8 2033 2,644,793.1 2,644.8 (96.9) 157.6 (172.6) (52.6) (164.6) 2,480.2 (181.6) 2,298.6 2034 2,679,690.8...
AI summary The text presents numerical data related to load forecasts, demand-side management (DSM), and energy generation, including changes in load from regression, EV, solar, and hybrid models for the years 2024 and 2034. It includes percentages of change and statistical coefficients for load forecasting.
41) (6,996) 2,364,934 2034 730,139 148,836 1,857,456 (56,741) - 2,679,691 Change to 7.9% 1.3% 3.8% 0.0% 0.3% 13.3% Gen Demand Intensities - Inputs to Regression Xheat Inputs Heating HeatUse VaCoefficient Total Xheat 2024 556,051 1.31 0.743...
AI summary The document presents load forecast data for 2024 and 2034, including demand intensity inputs for heating, cooling, and other uses. It also references a 2024 Load Forecast Report (NSUARB M11689) and NSPI responses to Synapse Information Requests, highlighting changes in energy use and coefficients over time.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-42: 2 3 Appendix B: Combined Model for Commercial and Industrial DSM Coefficient 4 5 (a) Please provide in electronic sp...
AI summary The NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report. The responses explain the use of the General Service model for commercial and industrial demand-side management (DSM) coefficients and note that no alternative models were considered.
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.
† Monthly HDD † Monthly CDD † Economics REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-45 Attachment 2 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2024 Load Forecast...
AI summary The 2024 Load Forecast Report includes projections for energy demand, peak load, and the impact of demand-side management (DSM), solar PV, and electric vehicle (EV) adoption. It also outlines possible scenarios for hydrogen production, battery storage, and the effects of weather and economics on load forecasts.
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-8Evidence of Synapse (BCC)
50 passages
Evidence Regarding Nova Scotia Power’s 2024 Load Forecast Evidence RE: M11689 Prepared for the Nova Scotia Utility and Review Board July 11, 2024 AUTHORS Ben Havumaki Kenji Takahashi Aidan Glaser Schoff 485 Massachusetts Avenue, Suite 3 Ca...
AI summary This document provides evidence on Nova Scotia Power’s 2024 load forecast, including forecast comparisons, sector and DSM program overviews, and references to Board directives from prior proceedings. It outlines the context for the forecast review and incorporates recommendations from previous evaluations.
..................................................................................7 1.5. Recommendations from the Previous Forecast Review .....................................................8 2. ENERGY FORECAST .............................
AI summary The document outlines energy and peak demand forecasting methodologies, analyzing residential, commercial, industrial, and municipal sectors. It discusses DSM effects, sensitivity analysis, and provides recommendations for improving forecast accuracy and alignment with demand-side management strategies.
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.
vince and has also raised its forecast for the Renewables to Retail (RTR) market, which together contribute to reducing the forecast increases in energy and peak relative to last year’s load forecast. Consistent with previous forecasts, NS...
AI summary Nova Scotia Power's 2024 load forecast incorporates Renewables to Retail (RTR) and Demand Side Management (DSM) adjustments, reflecting modest load growth due to climate-driven fossil fuel reductions and increased electrification. The forecast contrasts with prior years' varying projections, emphasizing updated factors influencing energy demand trends.
2034 Source: Synapse from Figure 1 in NSPI’s 2024 Load Forecast Report (2024 Load Forecast) and responses to Synapse IR-1 The current forecast predicts a 205 GWh increase in the net system requirement (NSR) from 2024 to 2034, which is equa...
AI summary The forecast predicts a 205 GWh increase in net system requirement (NSR) from 2024 to 2034, driven by electrification, cooling demand, new customers, and EVs. Offsetting factors include rooftop solar, demand-side management (DSM), Renewables to Retail (RTR) sales, and hybrid heating assumptions.
Synapse Energy Economics, Inc. Evidence Regarding Nova Scotia Power’s 2024 Load Forecast 2 Table 1. Net system requirement components Mun. and Res. Comm. Ind. Losses NSR Other 2024 Forecast (GWh) 5,180 3,118 2,267 159 767 11,490 Model 466...
AI summary The table presents Nova Scotia Power’s 2024 load forecast, breaking down net system requirements by sector (residential, commercial, industrial) and adjustments from programs like DSM and RTR. Key components include energy use, losses, and program impacts, with a 2034 forecast also provided. Data highlights reductions from DSM (-699 GWh) and RTR (-246 GWh), alongside growth from EV adoption (548 GWh).
in terms of energy, driven by electric vehicles, new customers, and building electrification. The commercial forecast increases at a more modest level, and the industrial load shows a small decrease. Table 3. Sector energy requirements (GW...
AI summary The text outlines energy forecasts for residential, commercial, industrial, and municipal sectors, noting slight increases and decreases. It highlights DSM's projected role in reducing 2034 energy use by 699 GWh (5.6%) through demand-side management initiatives.
ducing energy use and to a lesser degree, peak loads. Specific effects appear in Figures 35 and 55 of the Report. Overall, NSPI projects DSM will reduce the 2034 load by 699 GWh, or about 5.6 percent. Note however that the DSM Program savi...
AI summary NSPI projects DSM programs will reduce 2034 load by 699 GWh (5.6%) but acknowledges statistical models may overcount historical DSM savings. Adjustments factor in 55.6% residential and 44.8% C&I sector savings already captured, reducing net additional savings to about half. NSPI suggests incremental savings above historical norms should be excluded from forecasts.
024 Load Forecast 5 embedded in forecast variables but instead to subtract these out at 100 percent over the relevant period of time. 3 Table 4. DSM Program savings versus forecast adjustments
AI summary The text discusses adjusting load forecasts by subtracting demand-side management (DSM) program savings at 100% over the relevant period, rather than embedding them in forecast variables. Table 4 compares DSM program savings against forecast adjustments, highlighting the methodology for incorporating program impacts into load forecasting.
DSM DSM DSM DSM Adjustment Adjustment Forecast Forecast Forecast Captured by Captured by for for Residential Commercial Industrial Residential Comm/Ind Residential Comm/Ind DSM DSM DSM end-use end-use with with Net DSM Savings Savings Savi...
AI summary The table presents forecasted DSM savings (residential, commercial, industrial) and net DSM savings percentages from 2024 to 2034, showing declining industrial savings and stable residential/communal trends, with net DSM savings consistently around 50%.
24.7 51% 2033 71.3 43.4 7.7 31.7 28.2 39.6 22.9 51% 2034 69.1 44.0 7.8 30.7 28.6 38.4 23.2 51% Source: Synapse from Figure 35 from 2024 Load Forecast Recommendations and Considerations We ask that NSPI explore the benefits of increasing DS...
AI summary The document references a 2024 load forecast and recommends NSPI increase DSM levels. It also outlines Board directives from Matter 11108, including implementing IRP, AMI, and reviewing carbon emission assumptions.
ng gross domestic product and manufacturing employment. A longer regression timescale was used for the industrial models as that produces better statistics. We consider these to be reasonable choices. The forecast now gives more considerat...
AI summary The load forecast incorporates climate change impacts via updated HDD/CDD trends (-17 HDD/year, +1.4 CDD/year) and adjusts peak temperature assumptions. The residential sector (44% of load) is projected to grow 2.3% (2024-2034) with DSM programs, versus 9.1% without them. Synapse Energy Economics provides analysis on these modeling choices.
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.
arious effects, we reproduce below a table from Page 6 of NSPI’s Appendix B. The first column shows the SAE regression model results, and the other columns reflect various adjustments to the forecast. Table 5. Residential load: post regres...
AI summary The document presents a table from NSPI's Appendix B showing residential load forecasts, highlighting the impact of EVs, new customers, and electrification (e.g., heat pumps) on consumption growth. Existing customer load is projected to rise 2.3% due to electrification, with DSM and RTR adjustments influencing outcomes.
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.
in the next load forecast. Electric vehicles NSPI forecasts new EV load separately from its SAE modeling. EVs represent another load growth area, similar to the expected load growth from heat pumps. This forecast predicts that there will b...
AI summary Nova Scotia Power Inc. (NSPI) forecasts significant load growth from electric vehicles (EVs) by 2034, estimating 150,000 EVs and a total energy load of 560 GWh with peak load impacts of 136 MW (base case) and 245 MW (sensitivity case). The forecast assumes 70% of EVs are on managed charging, though the basis for this assumption is not explained.
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.
ast, Figure 26 39 NSPI’s response to Synapse IR-9 (c). 40 2024 Load Forecast, page 37. 41 Saxifrage, Barry. 2024. “How your province rates in the global electric car race.” Canada’s National Observer. April 8. Available at: https://www.nat...
AI summary The text discusses the need for NSPI to monitor EV adoption, update load forecasts, and consider load management strategies. It recommends examining the reasonableness of Dunsky’s low scenario and developing rate designs to reduce on-peak EV charging as EV penetration increases.
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.
out 428 GWh (7.0 percent) to the residential load by 2034, a modest decrease in the growth rate relative to last year’s forecast. 46 New customer load is calculated outside of the regression model. 47 Synapse has expressed concerns about t...
AI summary The document discusses NSPI's load forecast and the concerns raised by Synapse regarding the correlation between new housing and customer growth. The NSUARB has directed NSPI to re-evaluate the use of housing completions in its forecast model and consider additional demographic factors. NSPI maintains that housing completions remain the best proxy for customer growth.
wth. Recommendations and Considerations NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. Other The value of price elasticity has...
AI summary NSPI is advised to validate the use of new home construction as a proxy for customer growth and to conduct a literature review on price elasticity. The Board has raised concerns about NSPI's modeling of work-from-home impacts due to the pandemic, and Synapse requests more empirical justification for NSPI's approach.
ngoing COVID consumption changes and the modeling results obtained as a result of this approach. Specifically, NSPI should explain why it has forecast a 49 Board Decision in Matter M10569, page 5. 50 Response to Synapse IR-18(d). 51 NSUARB...
AI summary The document critiques NSPI's approach to modeling the enduring effects of COVID-19 on residential electricity consumption, questioning the reduction in the impact of the pandemic on load forecasts and suggesting alternative econometric strategies for more accurate modeling.
at enduring COVID-related impacts from August 2022 and onward are 65 percent lower than those same effects from July 2020 through July 2022. However, this assumption appears to lack empirical support. Moreover, given that the load forecast...
AI summary The document discusses the need for NSPI to reassess its load forecasting model for the residential sector, particularly regarding the impact of COVID-19 and demographic factors. It also highlights discrepancies between current and previous commercial sector load forecasts and notes that the statistical models for the General Service subsector are satisfactory.
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.
to Synapse IR-22. 53 Note that the reported DSM impacts are in addition to DSM effects embedded in the SAE model itself. Therefore, the actual savings from DSM programs are significantly greater. Synapse Energy Economics, Inc. Evidence Reg...
AI summary The document discusses the impact of DSM programs on electricity demand, highlighting that DSM effects are in addition to those embedded in the SAE model. It also outlines projected changes in small general service sales, including the influence of factors like renewable energy, electric vehicles, and solar. NSPI notes the long-term impact of the pandemic on commercial sales and revised forecasts for large general service loads.
fic solar or DSM projections. Overall, the forecast appears reasonable given the inherent uncertainties, but it does raise the following issues for consideration: Recommendations and Considerations Given that significant changes to the loa...
AI summary The 2024 load forecast for Nova Scotia Power (NSPI) is deemed reasonable but highlights the need for NSPI to address uncertainties related to EV and solar penetration, as well as the impacts of RTR and hybrid heating. The forecast also indicates that the industrial sector will remain relatively stable, with small and medium subsectors showing slight growth and decline, respectively.
0.4 percent annually as load migrates to RTR providers. The Large (also called Other) category represents approximately two-thirds of the industrial load, with load projected to remain essential flat. The forecast projects increased electr...
AI summary The industrial load forecast projects a 30 GWh increase by 2034 due to electrification, with DSM savings at 57 GWh and RTR resources at 59 GWh. The forecast assumes current major customer operations and raises questions about the potential for greater industrial electrification, savings, and the impacts of real time rates and increased RTR.
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.
and 0.587 in 2021. This means that the SAE model is currently embedding less DSM savings than before except in 2021. The net effect of DSM savings is -356 GWh in 2034 for the residential sector. 61 59 Impacts of electrification for the oth...
AI summary The document discusses the impact of Demand Side Management (DSM) savings on load forecasts, noting that adjustment factors have varied over time. The net load impacts of DSM in 2034 are estimated at -356 GWh for the residential sector, -221 GWh for the commercial sector, and -57 GWh for the industrial sector. The adjustments are considered reasonable but come with some uncertainty.
f DSM program savings are increased above historical levels, then the adjustment factors probably should be adjusted upward to reflect greater levels of incremental savings. 62 Id, pages 54-56. Synapse Energy Economics, Inc. Evidence Regar...
AI summary The document discusses the 2024 load forecast for Nova Scotia Power, noting a moderate increase in system peak despite lower projections compared to the 2023 forecast. The forecast includes adjustments similar to those used in 2023, based on statistical modeling and economic and demographic projections.
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.
means an increase in capacity requirements of about 434 MW (using a 20 percent planning reserve margin). This represents a significant increase and investment cost. Recommendations and Considerations We ask NSPI to investigate what can be...
AI summary The text discusses an increase in capacity requirements of 434 MW, emphasizing the need for investment and suggesting that NSPI explore time-of-use rates and other measures to mitigate peak load increases, particularly in the C&I sectors.
EVs are a substantial contributor to peak growth, though diminished in expected peak contribution relative to last year’s load forecast projections. We would like to see a more complete evaluation of the options to control this growth in t...
AI summary The document discusses the impact of electric vehicles (EVs) on peak load growth and critiques NSPI's use of an ELCC factor from a 2019 study for demand response forecasting. It highlights concerns about the methodology and suggests a need for more comprehensive analysis and updated projections.
0 calls/year and 12 hours/call, the second group with 10 calls/year and 4 hours/call). These types were not created for the existing or proposed DR 63 2024 Load Forecast, pages 81-82. Synapse Energy Economics, Inc. Evidence Regarding Nova...
AI summary The document highlights that NSPI did not create specific types of demand response (DR) programs for existing or proposed initiatives and did not conduct a detailed analysis of appropriate ELCC values for the proposed DR programs, as noted in the 2024 Load Forecast.
rding Nova Scotia Power’s 2024 Load Forecast 28 programs by NSPI. Further, NSPI did not conduct any detailed analysis of appropriate ELCC values for the proposed DR programs. 64
AI summary The document highlights that Nova Scotia Power Inc. (NSPI) did not conduct detailed analysis of appropriate ELCC values for the proposed DR programs, raising concerns about the accuracy and reliability of their 2024 Load Forecast.
• 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.
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.
cantly to peak growth. Recommendations and Considerations We ask NSPI to quantify specifically the electrification and EV impacts for the commercial sector and to consider how this can be moderated. Overall, the peak forecast seems plausib...
AI summary The text discusses concerns regarding peak growth forecasts, emphasizing the need for NSPI to quantify electrification and EV impacts in the commercial sector and to explore mitigation strategies. The forecast is deemed plausible but requires refinement and further discussion on underlying factors.
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.
end uses that pose uncertainties about their future adoption rates, in particular heat pumps, EVs, DSM, and demand response, we highly recommend that NSPI develop a few different scenarios (e.g., low Synapse Energy Economics, Inc. Evidence...
AI summary The text recommends that NSPI develop multiple load forecast scenarios, including low, reference, and high cases, to account for uncertainties in technologies like heat pumps, EVs, and DSM. It also highlights concerns about under-forecasting of firm peak load and suggests evaluating the possibility of higher-than-forecast peaks.
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.
tomer 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. 3. We recommend that NSPI model heat pump water heater...
AI summary The document outlines several recommendations for NSPI regarding load forecasting and load management strategies, including modeling hybrid electric heating, heat pump water heaters, EV adoption, solar generation coincidence factors, battery storage incentives, and validating customer growth proxies.
lly for peak management. 7. NSPI should validate the use of new home construction as a proxy for customer growth, addressing concerns about potential shortcomings of this proxy variable. 8. We ask that NSPI reassess its modeling approach f...
AI summary The document outlines various requests for NSPI to refine its load forecasting models, including validating proxies for customer growth, reassessing the impact of the pandemic on residential load, and exploring the effects of solar, DSM, and RTR on different sectors. It also suggests adjusting DSM adjustment factors if savings increase and investigating measures to mitigate peak load increases.
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.
the transparency and accuracy of the load forecast. There is still more to do; but overall, NSPI’s Report is very well done and satisfactorily explains the underlying factors driving the forecast. Synapse Energy Economics, Inc. Evidence Re...
AI summary The document provides feedback on NSPI’s 2024 load forecast, highlighting the need for increased DSM levels, further investigation into heat pump impacts on energy and peak load, and the use of data from a water heater demand response pilot in future forecasts.
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.
cooking technologies (page 32). 22. We ask NSPI to quantify specifically the electrification and EV impacts for the commercial sector and to consider how this can be moderated (page 32). 23. We recommend that there be future sensitivity an...
AI summary The text requests NSPI to quantify electrification and EV impacts in the commercial sector and suggests future sensitivity analyses to mitigate projected peak increases using new technology.
N-9Rebuttal Evidence - NSPI
18 passages
ting it into the existing model or building a new or modified version to make use of this 22 data. 4 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2024, page 33. DATE FILED: September 6, 2024 Page 5 of 25 2024 Load Forecas...
AI summary Synapse, the Consumer Advocate (CA), and the Small Business Advocate (SBA) recommend exploring increased demand-side management (DSM) levels in future load forecasts. NS Power responds by referencing its Integrated Resource Planning (IRP) process, noting that the Base DSM profile was used in scenarios showing the lowest cost to customers.
SM profile was included in the scenarios that 19 demonstrated the lowest cost to customers. 6 Otherwise, development of Demand Side 5F 20 Management (DSM) investment and associated programs are no longer the responsibility of NS 21 Power....
AI summary Bill 228 amendments to the Nova Scotia Public Utilities Act removed the joint DSM application requirement between NS Power and EfficiencyOne (E1), shifting DSM responsibility to E1 with NS Power's advisory role through the DSMAG. E1 is developing DSM programs with stakeholders, pending NSUARB approval.
idence of solar generation with month 24 system peaks and make updates to coincidence factors as warranted. 10 25 26 NS Power Response: 27 28 NS power agrees with this recommendation. 29 30 2.1.6 Recommendation 6 31 32 NSPI should continue...
AI summary The document discusses a recommendation for NSPI to investigate incentives for battery storage deployment, including rate design. NS Power agrees with this recommendation. Synapse's 2024 Load Forecast Report (M11689) is cited as evidence. The analysis focuses on aligning battery storage opportunities with system needs and cost-effectiveness.
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.
asis for the planning and 4 overall operating activities to serve customer load.” 16 Despite year-over-year variances from load 15F 5 forecast to load actuals, the load forecast continues to provide a reliable basis on which to plan and 6...
AI summary The document discusses the reliability of load forecasts despite variances, emphasizing sensitivity analysis over scenario planning. It requests NSPI to assess solar and DSM impacts on the Large General Service forecast and industrial electrification effects. NS Power notes existing DSM inclusion and solar impact assumptions, with future updates if projects are identified.
es 11 -13. 17 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2024, page 33. 18 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2024, page 33. DATE FILED: September 6, 2024 Page 11 of 25 2024 Load Forecast Rep...
AI summary NS Power responds to recommendations regarding industrial electrification, stating DSM is already incorporated into forecasts and referring to prior responses for details on DSM programs and real-time rates. They acknowledge limited insight beyond customer surveys for industrial electrification magnitude.
29 NS Power notes Synapse's observations with respect to price signal-based interventions (time- 30 varying pricing, interruptibility rider, etc.) as tools to moderate and/or mitigate system peak 22 Synapse Evidence, 2024 Load Forecast Rep...
AI summary NS Power acknowledges Synapse's evidence on using price signal-based interventions like time-varying pricing and interruptibility riders to manage system peak demand. The discussion references Synapse's 2024 Load Forecast Report (M11689) and focuses on demand-side management strategies.
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.
1 The NSUARB recently acknowledged that the Project outcomes may play a role in 2 reaching 2030 environmental mandates. DR and associated programs are becoming 3 an increasingly bigger part of enabling DSM activities and achieving broader...
AI summary The NSUARB highlights the role of demand response (DR) programs in achieving 2030 environmental targets, citing E1's 2023-2025 DSM Plan and NS Power's alignment with provincial goals. The text recommends evaluating thermal storage and induction cooking technologies, with NS Power noting limited adoption of thermal storage despite its inclusion in residential usage.
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).
, page 33. 28 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2024, page 33. 29 Synapse Evidence, 2024 Load Forecast Report (M11689), July 11, 2024, page 33. DATE FILED: September 6, 2024 Page 16 of 25 2024 Load Forecast Rep...
AI summary NS Power responds to Synapse's 2024 Load Forecast Report, acknowledging sensitivity analyses for peak load projections and referencing ongoing initiatives like SGNS, IRP, and TVP. The Consumer Advocate recommends refining residential heating intensity adjustments using AMI data for better accuracy.
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.
y, 23 with engagement and participation from multiple organizations including NS 24 Power, will provide a balanced assessment of the cost impacts of the hybrid 25 approach and provide the necessary information to conduct a more refined 26...
AI summary The document discusses the 2030 Clean Power Plan, which includes Load Management activities aligned with the Hybrid Peak program from the Evergreen IRP. NS Power's updated IRP Action Plan supports electrification strategy progression, with plans to conduct a Hybrid Peak study in 2024 in partnership with other organizations in Nova Scotia.
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.
Synapse 27 Recommendation 4, NS Power agrees with further investigation of the peak impact of EVs. The 28 values referenced by Grid Strategies are from a single weekend day in 2023, which is not 35 Consumer Advocate Evidence, 2024 Load For...
AI summary The document discusses NS Power's agreement to further investigate the peak impact of electric vehicles (EVs), referencing evidence from the Consumer Advocate and the Time-Varying Pricing (TVP) Pilot Program report. It mentions that Grid Strategies' data is based on a single weekend day in 2023, which may not be representative.
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 2025 ACE Plan application, the Board directs NS Power to provide details on 2 changes to the D004, D061 and D062 Routine budget estimation methods that were 3 assessed and provide explanations as to why they were, or were not, applied. 4...
AI summary The Board directs NS Power to provide details on changes to budget estimation methods for D004, D061, and D062 routines and to examine material cost increases. The SBA recommends using insights from the Smart Grid Nova Scotia pilot project in load forecast models.
1 energy resources (DER). The learnings of SGNS project will be used as a foundational basis for 2 potential future orchestration of DERs and related technology. As the outcomes of these initiatives 3 are developed into more comprehensive...
AI summary The text discusses the integration of distributed energy resources (DER) and the potential impact of electric vehicles (EVs) on peak load, referencing a 2024 Load Forecast report. It also outlines a recommendation from the Small Business Advocate (SBA) to expand load shifting programs for small businesses to improve demand response forecasting. NS Power refers to a prior response to a Synapse recommendation.
94285BCC-Synapse (NSPI) IR-1 to IR-54
20 passages
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.
aters. 34 c. Please provide NSPI’s estimates of the average of the maximum winter peak load impacts 35 per household (kW/household) for 2024 through 2034, for all electric water heating
AI summary The document requests NSPI to provide estimates of the average maximum winter peak load impacts per household (in kW) for electric water heating from 2024 through 2034, focusing on demand-side management implications.
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.
kW impacts per vehicle on peak in Figure 25. 25 1. Please provide more details about E3’s EV Load Shaping Tool and how it was 26 used to produce the average kW impact for LDV, MDV, and HDV. 27 2. Please provide supporting evidence for the...
AI summary The text requests detailed information on E3's EV Load Shaping Tool, including its methodology for calculating average kW impacts per vehicle type (LDV, MDV, HDV), supporting evidence for peak load impacts, daily load shapes for summer/winter seasons, NSPI's winter peak load shapes, and expected peak load reductions from managed charging programs or time-of-use rates.
provide peak load reductions in kW per vehicle 34 for each vehicle category that NSPI expect and assumed through managed 35 charging programs or time of use rates.
AI summary The text requests NSPI to provide peak load reductions in kW per vehicle category, based on their expectations from managed charging programs or time-of-use rates.
Date Filed: May 29, 2024 Synapse (NSPI) Page 8 of 24 1 e. The SGNS project found average evening peak reductions of 0.24 kW/vehicle from one 2 EV pilot program participant cohort and 0.65 kW/vehicle from another cohort. NSPI states 3 that...
AI summary The text includes questions to NSPI about EV pilot program impacts on peak load, managed charging strategies, time-of-use tariffs, and assumptions regarding EV charging management percentages. It seeks clarification on SGNS project findings, peak load impact estimates, and the relevance of these findings to winter peak and different vehicle types.
unmanaged scenario is 1.6 kW/vehicle. 23 1. Please provide E3’s average peak impact estimate per vehicle for the managed 24 charging scenario. 25 2. Please explain how E3 developed the peak load impacts for the managed and 26 unmanaged cha...
AI summary The text contains requests for E3 to clarify their analysis of EV charging scenarios (managed vs. unmanaged) and solar PV generation impacts. Questions focus on peak load estimates, methodology for calculating impacts, and supporting data for PV capacity factors and summer peak effects.
Date Filed: May 29, 2024 Synapse (NSPI) Page 9 of 24 1 c. Please provide the source data and calculations for the values in Figure 27. 2 d. Please describe the meaning of the Load (GWh) column in Figure 27. Please elaborate 3 whether this...
AI summary The document outlines regulatory requests for detailed data and clarifications on load forecasting, new technologies (e.g., vehicle-to-grid), end-use intensities, and commercial/industrial growth from Nova Scotia Power Incorporated (NSPI). The Board seeks source data, assumptions, and explanations for figures and calculations in NSPI's filings.
Request IR-13: 28 Commercial and Industrial Growth (Section 4.4, pp 49-50) 29 a. Please provide the detailed calculations behind the values presented in Figure 32. 30 b. For the small and medium commercial customers, identify the component...
AI summary The text includes regulatory requests (IR-13 and IR-14) seeking detailed calculations for commercial/industrial demand growth in Figure 32, specifically asking about heat pump contributions, customer growth factors, and new customer counts in forecasts.
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.
literature for a more recent relevant study than the 2006 National Energy Research Lab 15 study provided in response to IR-15 in 2023. 16 c. Please provide the technical definitions of the daily price elasticity and inter-period 17 substit...
AI summary The text includes requests (IR-15, IR-18) seeking technical definitions, data sources, and explanations related to price elasticity and Demand Side Management (DSM) in energy forecasting, emphasizing the need for updated studies and detailed DSM scenarios.
l DSM Savings provide in Figure 35 33 relative to the same forecast provided in the 2023 Load Forecast Report, and please 34 provide a narrative explanation of any such changes. 35
AI summary The text requests a comparison of DSM Savings in Figure 35 to the 2023 Load Forecast Report, seeking a narrative explanation for any discrepancies. This focuses on demand-side management program outcomes relative to energy usage forecasts.
Date Filed: May 29, 2024 Synapse (NSPI) Page 12 of 24 1 Request IR-19: 2 Demand Side Management Adjustment (Section 4.6, pp 54-56) 3 a. Please provide the details of the data and statistical analysis that was used to develop the 4 coeffici...
AI summary The document outlines requests for detailed data and statistical analysis related to Demand Side Management (DSM) coefficients in residential and commercial/industrial sectors, as well as residential solar and new customer adjustments. It seeks clarification on methodological changes, statistical measures, and historical/forecasted DSM values for each sector.
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.
tify the specific reasons for the differences from the previous 33 forecast. 34 b. Please explain why the sales forecast appears to increase in its growth rate after about 35 2026. 36 Date Filed: May 29, 2024 Synapse (NSPI) Page 15 of 24 1...
AI summary The document outlines requests for clarification on forecast discrepancies, industrial load changes, municipal energy needs, system losses, and net system requirements from Nova Scotia Power Incorporated (NSPI), emphasizing the need for detailed explanations and data verification.
Date Filed: May 29, 2024 Synapse (NSPI) Page 16 of 24 1 2 Request IR-32: 3 Peak Demand and Demand Response (Section 10, pp 81-83) 4 a. Please provide details about the Demand Response (DR) resources modeled in this 5 analysis. What sectors...
AI summary The document requests detailed information on NSPI's demand response (DR) modeling, including sector representation, ELCC factor development, data sources, jurisdictional comparisons, DR savings calculations, assumptions about device quantities, participant numbers, and reasons for limited peak load reductions post-2029 despite electrification forecasts.
Date Filed: May 29, 2024 Synapse (NSPI) Page 17 of 24 1 j. How did the 2022 Evergreen IRP estimate peak demand reductions. How many 2 customers and which end uses or technologies does the IRP assume for 3 estimating the peak load reduction...
AI summary The text contains a series of requests related to demand-side management (DSM) programs, peak demand research, and data analysis, including requests for reports, evaluations, and detailed explanations of figures and calculations.
gure 69 between 24 the Residential coincidental peak load with and without DSM. Please also explain the 25 relationship between these differences and the DSM values in Figure 35 and the demand 26 response values in Figure 56. 27 f. How is...
AI summary The text includes several requests for information related to sensitivity analysis, forecasting, and modeling in an integrated resource plan. It asks for data and explanations regarding the impact of demand-side management, model robustness, and the variables used in forecasts and residential models.
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.
ntensity data and 28 cite its derivation. 29 c. Please provide in electronic spreadsheet format the calculation of the XHeat, XCool and 30 XOther variables. 31 d. Please provide in electronic spreadsheet format the source and calculations...
AI summary The text outlines several requests for detailed data and model specifications related to heat, cooling, and industrial models. These requests include providing spreadsheet formats for calculations, model parameters, and changes in model specifications relative to previous forecasts.