N-12024 Load Forecast Report + Appendices - Redacted
106 passages
.................................................. 58 15 6.0 Commerical Sector ............................................................................................................ 64 16 6.1 Small General Service.......................
AI summary The text outlines a regulatory proceeding document's table of contents, detailing sections on commercial, industrial, and municipal sectors, system losses, net system requirements, peak demand, and sensitivity analysis. It structures rate classes and system performance metrics for regulatory review.
..................................................................................................... 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.
Page 5 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The document is a redacted 2024 Load Forecast Report, with no specific details or arguments disclosed due to confidentiality. The title indicates the report's focus on load forecasting for the year 2024.
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.
the NSUARB initiated a paper hearing process to review the 2023 Load Forecast 11 Report. 1 The Consumer Advocate (CA), the Small Business Advocate (SBA), the 0F 12 Industrial Group (IG), EfficiencyOne (E1), and Eastward Energy (EE) registe...
AI summary The NSUARB reviewed NS Power’s 2023 Load Forecast Report through a paper hearing, with intervenors including the Consumer Advocate, Small Business Advocate, and EfficiencyOne. Synapse Energy Economics provided analysis. The Board directed NS Power to implement agreed-upon recommendations, including IRP outcomes, carbon emission model reviews, and historical load assessments.
1 In addition to the above directives, the Board encourages NS Power to 2 include information on each of the following in future load forecasts: 3 4 • Evaluate the elasticity used in the SAE model with the elasticity 5 estimation from the...
AI summary The NSUARB directs NS Power to enhance load forecasts by evaluating model elasticity, re-evaluating residential input variables, incorporating demographic factors, aligning economic data with major banks, verifying EV adoption rates against Statistics Canada data, and maintaining communication with large infrastructure project customers to ensure system adequacy.
Page 12 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Summary of Stakeholder Consultations 2 3 On April 11, 2024, NS Power conducted a stakeholder session by videoconference with 4 representatives...
AI summary NS Power held a stakeholder session on April 11, 2024, to discuss updates to the 2024 Load Forecast, including EV forecasts, renewable-to-retail impacts, and forecasting methodologies. The session involved NSUARB, the Consumer Advocate, and other stakeholders, with a focus on revised assumptions and class-level trends.
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.
2024 Load Forecast Report REDACTED 1 Figure 11: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 Regarding household size, population is currently used in combination with customer count 6 to estimate average househo...
AI summary The 2024 Load Forecast Report discusses methodology for estimating household size in the SAE model using population and customer count data, impacting Heat Use, Cool Use, and Other Use variables. Figure 12 illustrates temporal changes in Household Size, influencing utilization rates of these variables.
ACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 12: Household Size 2 3 Household Size decreased steadily from 2000 to 2016 as population growth stagnated 4 while new housing continued to increase. From...
AI summary The 2024 Load Forecast Report analyzes household size trends from 2000 to 2025, noting stabilization until 2022 followed by growth due to population increases. It details econometric models for commercial and industrial sectors, emphasizing longer regression timescales for improved economic variable relevance and model fit.
2,660 -8.2 23,124 1.4 14‐23 7.4 1.4 24‐34 -9.5 1.4 3 4 DATE: April 30, 2024 Page 26 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 14: Commercial Economic Drivers 2
AI summary The 2024 Load Forecast Report includes a figure on commercial economic drivers, though the specific content has been redacted due to confidentiality. The report is dated April 30, 2024, and is page 26 of 100.
NFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 15: Industrial Economic Drivers 2
AI summary The text references a redacted figure from the 2024 Load Forecast Report, specifically Figure 15, which discusses industrial economic drivers.
Page 29 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The 2024 Load Forecast Report provides an analysis of expected electricity demand for the year 2024, including factors such as weather patterns, economic activity, and program impacts. The report is redacted and contains confidential information.
As with the 2023 Load Forecast, the forecasts developed by third party consultant E3 for 26 space heating and EV load shapes are used. The space heating forecast uses the uptake 27 required to meet stated emission goals over the next 20 ye...
AI summary The 2024 Load Forecast Report uses third-party consultant E3's forecasts for space heating and EV load shapes, based on emission goals over the next 20 years, without assuming specific regulatory or incentive changes within the current 10-year forecast period.
TION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 17: E3 Residential Space Heating Saturation 2 3 4 On the commercial side, the E3 electric heating stock model has a similar trajectory to that 5 of the residential model (see Figure...
AI summary The document discusses load forecast reports, focusing on residential and commercial space heating saturation models. It notes differences in adoption rates between E3 models and NS Power models, with adjustments made to align trajectories by 2040.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 24: EV Forecast 2 3 4 The forecast includes both light-duty vehicles (LDV) as well as medium-duty vehicles 5 (MDV) such as delivery trucks and other me...
AI summary The 2024 Load Forecast Report revises previous estimates, projecting over 150,000 electric vehicles (EVs) on Nova Scotia roads by 2034, primarily light-duty vehicles. The report highlights the impact of EVs on energy sales and peak demand, influenced by factors such as vehicle type, charging capacity, and driving patterns. E3’s EV Load Shaping Tool provides load shapes based on a bottom-up modeling approach.
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.
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.
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.
21 • Vent: ventilation DATE: April 30, 2024 Page 47 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 • EWHeat: electric water heaters 2 • Cooking: electric stoves 3 • Refrig: refrigerators and freezer...
AI summary The document provides a 2024 Load Forecast Report focusing on small general commercial end-use intensities, including categories such as ventilation, electric water heaters, cooking, refrigeration, lighting, office equipment, and miscellaneous loads. Historical and projected data is presented, with supporting data included in Attachment 2.
Page 49 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 evaluated on a case-by-case basis to try to enable the use of electricity while providing 2 benefits to the system (such as through the interru...
AI summary The 2024 Load Forecast Report outlines electrification forecasts for commercial and industrial classes, showing cumulative electricity usage by class and peak demand over the years 2024 to 2034. These forecasts are evaluated on a case-by-case basis to enable electricity use while providing system benefits.
lasticity estimation from the 8 TVP Pilot EM&V in matter M11267 and assess if the results provide a more robust model.” 9 The TVP Pilot estimates two elasticities: 2827F 10 • Own/daily price elasticity captures the change in the level of o...
AI summary The text discusses elasticity estimates from the TVP Pilot EM&V in matter M11267, focusing on own/daily price elasticity and substitution price elasticity. These estimates are based on subsets of customers enrolled in the TVP pilot and are not directly comparable to the overall population or long-term load forecasts.
Page 54 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The 2024 Load Forecast Report provides an analysis of projected electricity demand, though key details have been redacted due to confidentiality. The report is part of a regulatory proceeding and likely includes forecasts related to load management and resource planning.
Page 57 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 5.0 RESIDENTIAL SECTOR 2 3 The Residential sales forecast is generated as the product of a residential average use 4 forecast and a customer co...
AI summary The 2024 Load Forecast Report discusses the Residential sector's sales forecast, which is based on average use and customer count forecasts. Factors influencing growth include work-from-home activity, new customers, and increased heat pump usage. A figure compares forecast to actuals and weather normalized totals for 2021 to 2024.
fferent 13 components for 2021, 2022 and 2023 actuals vs forecast and weather normalized totals, 14 and the 2024 forecast. 15 16 Figure 37: Comparison of Forecast to Actuals 17 Year 2021 2022 2023 2024 Forecast Sales 4718 4715 4830 5175 We...
AI summary The text provides a comparison of forecasted and actual sales for the years 2021 to 2024, including weather and other variances. It discusses the 'Other variance' in 2022 and 2023, which is attributed to large variances in winter months, likely related to heating. The Load Forecast Report (LFR) is referenced, with a focus on the residential SAE model and statistical comparisons.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 electrification). The 2024 updates to heating intensity (see Figure 22) reduce the variance 2 significantly as shown in Figure 38: 3 4 Figure 38: Comparison o...
AI summary The 2024 Load Forecast Report discusses updates to heating intensity and compares forecasted load data with actuals, highlighting a reduction in variance. The report includes metrics such as MAD, MAPE, and variance percentages to evaluate forecast accuracy.
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.
sales, while DSM and naturally occurring efficiency improvements will decrease sales over 32 Mean absolute deviation. 33 Mean absolute percentage error. DATE: April 30, 2024 Page 59 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 L...
AI summary The 2024 Load Forecast Report discusses residential electricity sales trends, predicting a 0.2% annual increase in sales from 2024 to 2034. It highlights population growth, new housing construction, and efficiency improvements impacting demand. Single-family homes are expected to use more electricity than multi-unit residences.
18 on calibration done in 2013, improvements are expected to be lower in Nova Scotia than 19 in the EIA forecast for New England. While efficiency is expected to increase, house size DATE: April 30, 2024 Page 60 of 100 REDACTED (CONFIDENTI...
AI summary The 2024 Load Forecast Report discusses residential energy use trends, noting that while efficiency is expected to improve, house size will also increase, leading to a steady average use per new residential customer. Building shell efficiency, floor area, and the structural index are highlighted in Figure 40.
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.
2024 5181 62 0 -44 10 0 -30 5180 -55 -24 2025 5220 120 0 -71 23 -18 -63 5211 -114 -51 2026 5240 173 -26 -99 31 -75 -98 5146 -177 -79 2027 5271 220 -53 -131 41 -75 -133 5140 -241 -108 2028 5349 262 -79 -165 54 -75 -170 5176 -307 -137 2029 5...
AI summary The text includes numerical data spanning from 2024 to 2034, potentially representing energy load forecasts or related metrics. It references Figure 42 and mentions the use of a regression model output and methodology from a 2020 Load Forecast response to NSUARB IR-12 (e).
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.
Page 63 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The document presents the 2024 Load Forecast Report, which contains confidential information that has been redacted. The report is likely related to energy demand projections for the upcoming year.
GWh in the 2023 23 forecast), and the EV load has decreased (+244 GWh by 2034 compared to +403 GWH in 24 the 2023 forecast), resulting in lower growth than the previous forecast. 25 DATE: April 30, 2024 Page 64 of 100 REDACTED (CONFIDENTIA...
AI summary The 2024 Load Forecast Report discusses changes in energy demand, noting a decrease in EV load and the impact of the COVID-19 pandemic on commercial energy sales. A specific COVID variable was added to the General rate class model in 2023 to account for the lag in sales.
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.
Page 70 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 7.0 INDUSTRIAL AND MUNICIPAL SECTORS 2 3 The forecast models for the Small Industrial and Medium Industrial classes are 4 econometric-based mod...
AI summary The Small Industrial class forecast uses econometric models based on provincial manufacturing GDP. Sales have been flat over the last 10 years but are expected to grow at 0.6% annually due to economic growth, offset by a migration of load to RTR (4 GWh).
2024 Load Forecast Report REDACTED 1 Figure 49: Historical and Forecast Annual Small Industrial Sales 2 3 4 5 7.2 Medium Industrial 6 7 Figure 50 depicts historical and projected sales for the Medium Industrial class. Load in 8 this class...
AI summary The 2024 Load Forecast Report discusses historical and projected sales for the Medium Industrial class, noting flat load since 2014, a slight increase from 2019 to 2022, and a projected decline in 2026 due to load migration to the RTR market.
2024 Load Forecast Report REDACTED 1 Figure 50: Historical and Forecast Annual Medium Industrial Sales 2 3 4 5 7.3 Other Industrial Rate Classes 6 7 Other Industrial rate classes include Large Industrial, Large Industrial Interruptible, 8...
AI summary The 2024 Load Forecast Report discusses the forecasting methodology for Other Industrial rate classes, including Large Industrial, Generation Replacement, and Load Following. Surveys of customers are used to predict load changes, with most expecting stable consumption, while one major customer is forecast to increase usage.
Page 73 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 forecast. Another Large Industrial customer has temporarily reduced load to the point that 2 they have migrated to the Medium Industrial class,...
AI summary The 2024 Load Forecast Report discusses changes in customer load, including a Large Industrial customer temporarily reducing load and migrating to the Medium Industrial class, with expected ramp-up in 2026. There is uncertainty around new facilities and expansions, and past forecasts have been adjusted due to overestimations.
Page 75 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The 2024 Load Forecast Report is mentioned, though the content is redacted. It likely contains information related to electricity demand projections for the year 2024.
1 7.4 Municipal 2 3 The Municipal class comprises municipal electric utilities that purchase wholesale 4 electricity from NS Power and distribute it within their own service territories. Utility loads 5 within these municipalities include...
AI summary The Municipal class includes municipal electric utilities that purchase electricity from NS Power and distribute it within their service areas. Since 2007, these utilities can source electricity from other providers via OATT. Some utilities now source 100% of their energy from third parties, reducing municipal load. NS Power must still provide backup capacity for these utilities, and their full peak demand is included in the Load Forecast.
term and must continue 23 to plan for serving these customers in the long term, the full amount of the municipal 24 electric utilities’ peak demand is included in the Load Forecast. DATE: April 30, 2024 Page 76 of 100 REDACTED (CONFIDENTIA...
AI summary The document discusses the 2024 Load Forecast Report, highlighting the inclusion of municipal electric utilities’ peak demand in long-term planning. It also addresses system losses and unbilled sales, noting that system losses averaged 6.5% of NSR over the past five years and are expected to remain between 6.0% and 7.0% over the 10-year forecast period.
TR 23 migration offsetting sales. Annual NSR is shown below in Figure 54. Forecast NSR values 24 and the contribution to NSR from the different sectors can be found in Appendix A. 25 DATE: April 30, 2024 Page 78 of 100 REDACTED (CONFIDENTI...
AI summary The document discusses the 2024 Load Forecast Report, including historical and forecast annual Net System Requirement (NSR) values, and a breakdown of forecast components from 2024 to 2034. Data for all classes is referenced in Attachment 4.
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.
Page 80 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The document presents the 2024 Load Forecast Report, which includes confidential information that has been redacted. The report provides an analysis of projected electricity demand for the year 2024.
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.
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.
Page 83 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED
AI summary The 2024 Load Forecast Report has been redacted, with confidential information removed. The report likely contains projections and analysis related to electricity demand in Nova Scotia for the year 2024.
1 normalization are the same as those used in the 2023 forecast and are described in Figure 2 57 below: 3 4 Figure 57: Peak Regression Coefficients 5 Coefficient Value Description Weekdays 30.5 Peaks that occur on weekdays will be 30.5MW h...
AI summary The document discusses peak load forecasting, including factors such as weekday vs. weekend differences, wind speed, and temperature lag. It notes that the forecast system peak is expected to increase by 1.4% annually, with near-term increases due to heating load and updated temperature averages, and long-term decreases due to reduced EV impact and hybrid heating scenarios.
4 degrees Celsius. In the long term the peak forecast 19 has decreased from the 2023 forecast due to less impact from EVs as well as the impact of 20 the hybrid heating scenario. 21 DATE: April 30, 2024 Page 84 of 100 REDACTED (CONFIDENTIA...
AI summary The 2024 Load Forecast Report discusses historical and forecasted system peak demand, noting a long-term decrease in peak forecasts due to reduced impact from EVs and hybrid heating scenarios. The firm peak is expected to increase by 1.4 percent annually, with normalized data showing improved alignment between historical trends and forecasts.
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 87 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Section 4.4, the EV contribution to peak is expected to be partially mitigated via utility 2 managed charging. The firm peak without EV peak mi...
AI summary The 2024 Load Forecast Report discusses the impact of electric vehicles (EVs) on peak demand, noting that utility-managed charging could mitigate some of the increase. It also highlights the effect of space heating on reducing peak demand. The 2023 system peak was the highest recorded, occurring during extreme cold weather with significant wind speeds, and was partially reduced due to customer interruptions.
firm peak is estimated to be 2,302 MW. Figure 62 provides a breakdown of actual system 16 peak compared to the forecast for 2022. 17 18 Figure 62: Forecast Peak Variance vs Actuals 19 MW 2023 Forecast Peak 2,256 Interruptible -88 Weather (...
AI summary The document discusses the forecast peak variance for 2023, comparing it to actual system peak demand of 2,455 MW. The peak was influenced by factors such as weather, wind, weekends, lighting, and unexplained variables. The forecast model struggled to predict this peak accurately, even with adjustments for known variables.
of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 historic data on which these are modeled would be close to average, which is why the 2 “Unexplained” portion is so large compared to a peak set under m...
AI summary The 2024 Load Forecast Report discusses the use of P90 values to evaluate extreme weather scenarios and corrects for unexplained variance in energy and peak load forecasts. The report highlights adjustments made to heating intensity and the impact on forecasted peak loads.
residential energy actuals versus forecast, as a test, the modeled peak 38 can be run, holding 37F 12 off the last 12 months of actuals of each of the last 3 LFRs. The end-use assumptions vary 13 between forecasts, and Figure 63 below show...
AI summary The text discusses residential energy actuals versus forecasts, highlighting how updated assumptions for 2024 have reduced uncertainty in peak load forecasting. The comparison of forecasted and actual peak loads shows improvements in accuracy, particularly in 2023 despite a rare mid-day peak occurrence.
22 2024 2023 52.0 3.6% 2,266.5 2,302.1 -35.6 -1.5% 23 24 38 Modeled peak does not include all components, it is based on a regression of the system load less large customers. 39 The 2023 Predicted accrued Peak was adjusted by subtracting a...
AI summary The text discusses the 2024 Load Forecast Report, which includes modeled peak load data and adjustments made to the 2023 predicted peak, such as subtracting 150 MW of lighting from the normalized peak. The report is redacted and confidential.
Page 89 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Solar Impact to Peak 2 3 One question raised in the 2023 forecast proceeding was for quantitative information on 4 the impact of solar producti...
AI summary The 2024 Load Forecast Report discusses the impact of solar production on peak demand, analyzing data from six community solar farms. It found that solar contributes more to peak demand in summer months than previously assumed, with coincidence factors ranging from 21% to 39%. Winter months showed no contribution due to system peaks occurring in the evening after sunset.
cold January evening after sunset, the coincidence factor 15 impacting the maximum demand is unlikely to change in the near term. 16 17 Figure 64: Peak Contribution Components (MW) 18 Previous Coincident Factor Updated Coincidence Factor E...
AI summary The document discusses the updated coincidence factor estimates for each month, showing a significant increase in some months, such as June and July, which may impact peak demand contributions. The text references the 2024 Load Forecast Report, which has redacted confidential information.
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.
Page 91 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 66: Commercial End-Use Peak Shares 2 3 4 5 The trend in the Commercial classes shows that the heating component of the peak is 6 expecte...
AI summary The 2024 Load Forecast Report discusses the increasing impact of heating and commercial EVs on commercial peak demand. It highlights NS Power's shift from using Load Research Samples (LRS) to Advanced Metering Infrastructure (AMI) data for more accurate forecasting.
res, the sum of all the available AMI DATE: April 30, 2024 Page 92 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 meters, per hour, per class, is later adjusted so the monthly totals correspond to r...
AI summary The text discusses the 2024 Load Forecast Report, highlighting how the use of Advanced Metering Infrastructure (AMI) improves the accuracy and smoothness of load shape data compared to previous years, which relied on statistical estimates from Licensed Retail Suppliers (LRS).
2024 Load Forecast Report REDACTED 1 Figure 68: 2023 Monthly Load Research Data vs System Generation 2 3 4 5 Class coincident peak demand forecast using LRS and the AMI future 6 7 The 2024 class contribution to peak analysis is still focus...
AI summary The 2024 Load Forecast Report discusses the use of Advanced Metering Infrastructure (AMI) in forecasting residential peak demand. The report highlights the shift to using AMI data for more accurate load forecasting, focusing on residential class demand due to its weather dependency and reduced noise compared to other classes.
-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.
-use disaggregation may be used to update/validate end-use 20 assumptions (depending on accuracy per appliance type) and then used to evaluate the 21 impact to the Residential peak. DATE: April 30, 2024 Page 96 of 100 REDACTED (CONFIDENTIA...
AI summary The text discusses the use of disaggregation to update and validate end-use assumptions for residential peak load, referencing the 2024 Load Forecast Report, which contains redacted confidential information.
represent actual system totals. 25 DATE: April 30, 2024 Page 97 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 Figure 70: System Energy Sensitivity 2 3 4 Similarly, a P10/P90 scenario was created fo...
AI summary The document discusses the creation of a P10/P90 scenario for peak demand using random sampling of weather and economic drivers, with a width of 430-530 MW. Adjustments to the peak end-use model, such as wind and 12-hour temperature averages, are included in the forecast.
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.
Page 99 of 100 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report REDACTED 1 10-year timeframe of this forecast, but the scenario developed represents the likely 2 trajectories of these technologies. 3 4 Figure 72: 2022...
AI summary The document presents the 2024 Load Forecast Report, including comparisons of different Integrated Resource Plan (IRP) scenarios such as Evergreen IRP E1, E2, and E3, and includes forecast values for energy and peak demand for various years.
weekday 2017 67 1,951 2,018 -4.4 -13 -13 evening (between holidays) - January 7 weekend 2018 80 1,993 2,073 2.7 -12 -13 evening - February 27 weekday 2019 111 1,949 2,060 -0.6 -15 -14 morning (min lighting load) - January 17 weekday 2020 9...
AI summary The text presents data on weekday and holiday load forecast reports (LFR) over several years, including metrics such as demand, load forecast, and variations in load. The data includes specific dates and times, such as evenings and mornings, and highlights fluctuations in demand across different years.
� � ×� � 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.
sEcon is Employment Compensation divided by House Hold population, Price is the price of electricity for the specific customer class. Each accompanied by its own elasticities. Base line year is 2015. OtherIndex is defined as: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑆𝑆𝑆𝑆...
AI summary The text defines 'OtherIndex' using a formula involving variables like Type, SatyType, EffyType, and EI15Type, which are related to end-use saturation, efficiency, and calibration weights. The baseline year is 2015, and the formula is part of the 2024 Load Forecast Report.
140.062 22.657 6.182 0.00% MA(1) 0.506 0.094 5.412 0.00% Residential Model Statistics Model Statistics Iterations 21 Adjusted Observations 120 Deg. of Freedom for Error 106 R-Squared 0.989 Adjusted R-Squared 0.987 AIC 6.481 BIC 6.806 F-Sta...
AI summary The document presents statistical model outputs and reconciliation data for the 2024 Load Forecast Report, including model statistics, error metrics, and reconciliation details for residential energy demand forecasting from 2024 to 2034.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 9 of 35 Residential Input Variables – XHeat Intensities Econ + Regression Struct Efurn HP Heat Secondary Furnace Fans HeatUse Coeff Total XHeat Heat Vari...
AI summary The text presents residential input variables for heating and cooling, including intensity values and calculations for 2024 and 2034. It outlines the formula for XHeat, which is a combination of various heating components multiplied by a heat use variable and coefficient. The growth rate for intensities is multiplied by coefficients to determine the overall impact.
y the coefficients to calculate the overall impact. For example, the contribution of Efurn is calculated as [(Efurn2034-Efurn2024) x HeatUse x Coeff]/WtXHeat2024 Residential Input Variables – XCool Intensities Econ + Struct Regression Cent...
AI summary The text discusses residential input variables related to cooling (XCool) and other residential factors (XOther), including calculations based on intensity values, usage variables, and coefficients. It outlines the methodology for calculating contributions from different cooling technologies and their impact on load forecasts.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 10 of 35 Residential Input Variables – XOther Intensities Econ + Reg Struct Water Cook Ref/Frz Wash/ TV Light Misc Other Coeff Total Heat Dry Use Xother...
AI summary The document provides details on the 2024 Load Forecast Report, focusing on residential and commercial input variables used in modeling energy consumption. It outlines formulas for calculating XOther and XHeatm, which are used to forecast load based on factors like heating and cooling requirements, GDP, employment, and price trends.
th GDP and employment (SmlGenVarm), real price (Pricem), monthly HDD and CDD and a variable accounting for the number of days in a given month: XHeatm = EIheat × Pricem -.15× SmlGenVarm× HDDm XCoolm = EIcool × Pricem -.15× SmlGenVarm× CDDm...
AI summary The text describes a statistical model used to forecast monthly electricity use, incorporating variables such as price, GDP, employment, HDD, CDD, and days in a month. It also includes binary shift variables to account for specific events and an ARMA process to improve model accuracy.
INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 12 of 35 Variable Coefficient StdErr T-Stat P-Value MStructSmlGen.WtXHeat 0.856 0.037 23.347 0.00% MStructSmlGen.WtXCool 0.330 0.042 7.797 0.00% MStructSmlGen.WtXOther 0.727 0....
AI summary This section presents statistical data from the 2024 Load Forecast Report, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. The data includes information on heating, cooling, and other factors, as well as monthly and yearly bin variables.
(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.
(CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 16 of 35 Small General Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total XCool (kWh) 2024 36,...
AI summary The text presents input variables for forecasting cooling and other energy usage in the 2024 Load Forecast Report. It includes values for 2024 and 2034, along with percentage changes, and provides a formula for calculating XCool.
-3.8% 28.3% 0.0% 0.0% 24.5% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor Small General Input Variables – XOther Intensities Econ + Reg Struct Vent Water Cook Refrig Light Office Misc OtherUse Coeff Scaling Total Heat Var Fact...
AI summary The text provides a formula for estimating the General Service rate class model based on monthly heating and cooling requirements, as well as other use variables. The model incorporates end-use intensity projections, GDP, employment, real price, and monthly heating and cooling degree days.
, with GDP and employment (GenVarm), real price (Pricem), monthly HDD and CDD and a variable accounting for the number of days in a given month: XHeatm = EIheat × Pricem -.15× GenVarm× HDDm XCoolm = EIcool × Pricem -.15× GenVarm× CDDm XOth...
AI summary The text describes a statistical model used for forecasting monthly sales, incorporating variables like GDP, employment, price, HDD, CDD, and days in a month. It also includes adjustments for specific events such as the pandemic and Hurricane Fiona, as well as an ARMA process for model improvement.
FORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 18 of 35 Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.743 0.031 23.702 0.00% MStructGen.WtXCool 0.698 0.063 11.070 0.00% MStructGen.WtXOther 1.080 0.019 57.501...
AI summary This section presents statistical data from the 2024 Load Forecast Report, including coefficients, standard errors, t-statistics, and p-values for various variables related to load forecasting. These statistics are used to analyze the impact of different factors on load demand.
NTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 19 of 35 General Service Model Statistics Model Statistics Iterations 12 Adjusted Observations 120 Deg. of Freedom for Error 109 R-Squared 0.920 Adjusted R-Squared 0.912...
AI summary This section presents statistical details from the 2024 Load Forecast Report, including model statistics such as R-squared, AIC, BIC, and error metrics. It also discusses the reconciliation of general demand forecasts for the commercial sector, noting that it is forecast as gross total sales rather than average use.
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.
(241) Change 13.3% -4.1% 8.0% -8.0% -2.7% -7.3% -0.8% Gen Sales = Sales + RTR + EV Load + Solar Load + Hybrid + DSM General Demand Sales –Regression XHeat XCool XOther Binaries ARMA Sales (GWh) 2024 543,197 117,739 1,767,734 (56,741) (6,99...
AI summary The document provides a forecast of general demand sales and input variables for 2024 and 2034, including changes in heating, cooling, and other demand factors. It outlines the calculation methods and the impact of various variables on overall demand.
IAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 22 of 35 General Demand Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total Xcool 2024 317,164 1.44 0.698...
AI summary The text provides data on general demand input variables for XCool and XOther from the 2024 Load Forecast Report. It includes values for cooling, CoolUseVariable, coefficients, scaling factors, and total Xcool for the years 2024 and 2034, along with percentage changes.
30.2% 0.0% 0.0% 26.4% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor General Demand Input Variables – XOther Intensities Econ Reg + Struct Vent Water Cook Refrig Light Office Misc Other Coeff Scaling Total Heat Use Factor Xothe...
AI summary The text presents data and models related to load forecasting, including demand input variables and an industrial econometric model. It includes percentages, coefficients, and variables used in forecasting energy demand for residential and industrial sectors, with specific references to factors like hurricane Fiona impacting billing delays.
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.
through a monthly peak linear regression model that relates monthly peak demand (excluding large customer contribution) to heating, cooling, and base load requirements, as well as average daily wind: Peakm = b1×HeatVarm + b2×CoolVarm + b3×...
AI summary The document describes a statistical model used to estimate monthly peak demand based on heating, cooling, base load, and wind variables. The model normalizes heating and cooling load requirements to an average MW load basis by dividing by the number of days and hours in the month.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix B Page 32 of 35 CoolAvgMWm = CoolLoadm/ Daysm /24 The impact of peak-day weather conditions are then captured by interacting peak-day HDD and CDD with average m...
AI summary This section describes the methodology for calculating peak heating and cooling loads in the 2024 Load Forecast Report. It uses average monthly heating and cooling load requirements, interacts them with peak-day HDD and CDD indexes, and calculates base load variables to account for non-weather sensitive loads.
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.
2,062 2,065 2022 2,035 Actual Firm Peak: 2,036 1861.3 2013.6 1,951 1,993 1,949 1954 1,875 2,061 2,397 Percent Error 2013 -4.7% 4.8% -3.3% -0.4% -2.9% -0.3% -0.8% 3.2% -6.6% -19.8% 2014 4.2% -4.1% -1.8% -4.1% -2.2% -2.3% 1.3% -8.0% -21.0% 2...
AI summary The text presents actual firm peak values and percent error for various years from 2013 to 2022. The data shows fluctuations in actual firm peak values and corresponding percent errors, indicating variations in forecasting accuracy over time.
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.
in the forecast. 6 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 7 of 18 EVs • 2024 forecast has been updated to account for the fact that the federal government’s zero emissions vehicle (ZEV) sales...
AI summary The 2024 Load Forecast Report updates EV and electrification of heating projections. EV load forecasts have been reduced due to lagging Nova Scotia sales compared to federal targets. Electrification of heating uses a hybrid scenario to reduce peak load impact by leveraging existing non-electric backup heat.
Impact of Electrification (MW) 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 9 of 18 Renewable to Retail • RTR participation is expected to start in late 2025 (340 GWh of wind production), with a t...
AI summary The 2024 Load Forecast Report Appendix E discusses the impact of Renewable to Retail (RTR) participation, which is expected to start in late 2025, reducing customer sales by 246 GWh compared to the 2023 forecast. The report outlines expected reductions by customer class from 2025 to 2026.
ad Forecast Report Appendix E Page 16 of 18 2023 Forecast to Actuals Below is an estimate of the major variances between the 2023 forecast and 2023 actuals for both energy and peak. Item GWh Item MW 2023 Forecast NSR 11,288 2023 Forecast P...
AI summary The document compares 2023 forecast and actuals for energy and peak demand, highlighting variances due to weather, customer behavior, and wind generation. It also outlines updates on pilot projects, solar generation, hydrogen production, AMI integration, and assumptions related to customer growth and economic inputs.
w of price elasticity estimate 17 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Appendix E Page 18 of 18 Ongoing work for future reports • Integrating AMI data into the sales and peak forecast • Impact of electrific...
AI summary The document outlines ongoing work for future load forecast reports, including integrating AMI data, evaluating the impact of electrification and emissions targets, and assessing new technologies like time variable pricing and direct load control.
N-6NSPI (Synapse) RIR-1 to RIR-54 - Redacted
108 passages
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Report Tables 4 5 (a) Please provide in electronic spreadsheet format...
AI summary The document outlines NSPI's response to Synapse's information requests regarding the 2024 Load Forecast Report, specifying locations of figures in various attachments. It includes references to multiple Synapse IR attachments and the 2024 LFR Attachment 4.
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 (g) Please provide the hourly peak loads and temperature data for ten years as referenced 2 on page 21. 3 4 (h) Please identify whi...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, explaining HDD/CDD calculations and providing data in attachments. They note that building heat gains are not considered in these metrics.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (g) What consideration if any was given to labour shortages or increased automation for 2 the industrial sector? 3 4 (h) What would...
AI summary NSPI's response to Synapse Information Requests discusses economic forecast methods, including use of Conference Board data and comparison with major banks for 2024-2025. The analysis includes pseudo-binary variables for work-from-home effects in residential and commercial sectors, with differing impacts on load. Housing completions data was validated against customer additions.
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.
s using propane for primary heating 26 Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 2 of 12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document is a non-confidential portion of NSPI's responses to Synapse Information Requests related to the 2024 Load Forecast Report (NSUARB M11689), filed on June 19, 2024.
ng, heat pumps, and hybrid heat pumps. Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 4 of 12 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document is a non-confidential portion of NSPI's responses to Synapse's information requests related to the 2024 Load Forecast Report under NSUARB matter M11689. It includes data on load forecasting and energy efficiency programs such as heat pumps and hybrid heat pumps.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (vii) Please provide NSPI’s estimates of the average of the maximum winter peak 2 load impacts per building (kW/building) for 2024...
AI summary NSPI is responding to Synapse's information requests regarding load forecasts for commercial electric heating systems from 2024 to 2034, including peak load impacts and heat pump efficiencies. NSPI also discusses adjustments made to heating component intensities based on weather-dependent variances.
res tab). Because only 26 the electric heating component is forecast the E3 graphs cannot be reproduced, but 27 graphical representations of the heating components are as follows: Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 5 of 12...
AI summary The text includes a reference to the 2024 Load Forecast Report (NSUARB M11689) and mentions NSPI's responses to Synapse Information Requests. It also notes that the E3 graphs cannot be reproduced due to the electric heating component forecast.
779 116,394 2031 495,055 57,839 164,347 332,105 35,322 403,197 105,111 2032 495,055 54,587 167,510 346,064 37,590 418,912 94,404 2033 495,055 51,501 170,560 359,242 39,756 433,753 84,312 2034 495,055 48,779 173,270 371,695 41,693 447,782 7...
AI summary The text provides numerical data related to load forecasting and refers to the 2024 Load Forecast Report (NSUARB M11689), along with a mention of the E3 numbers in Attachment 1 (HP Stock tab). It also references a table estimating heat sources for new customers and notes that supplementary heating is estimated to be approximately 35 percent.
New New New Customers New Customers New Customers Customers Residential with Heat Pump with Electric with Non Electric with Customers Heat Baseboard Heat Heating Supplementary Year Electric Heat 2024 7,021 4,213 1,755 1,053 2,457 2025 13,6...
AI summary The text provides a table showing the number of new residential customers with various heating types from 2024 to 2034. It also references the 2024 Load Forecast Report (LFR) and Attachment 1 Residential Intensities for calculations related to end-use saturation, particularly heat pump saturation based on annual sales data from installers.
provided in 2024 LFR 4 Attachment 1 Residential Intensities. The heat pump saturation is estimated based 5 on annual sales numbers from heat pump installers in the province. 6 7 (ii) This refers to residential customers. 8 9 (iii) The 44 p...
AI summary The text discusses heat pump adoption rates in Nova Scotia, referencing the 2024 Load Forecast Report and noting that actual adoption has exceeded estimates from the Energy Efficiency and Conservation Act (E3). The report includes data on residential heat pump saturation based on installer sales.
1 (f) 2 (i-ix) The E3 numbers in Figure 18 are provided in Attachment 1 (HP Stock tab). The 3 commercial numbers in the forecast are not produced on the same basis as the 4 residential numbers, the intensities are based on kWh per area rat...
AI summary The document discusses the methodology used in forecasting commercial energy usage, highlighting differences between residential and commercial forecasting approaches. It references the E3 model, heat pump saturation impacts, and the hybrid scenario, while noting limitations in forecasting peak values at the individual class or end-use level.
end use level. 26 27 (iv) The incremental impact to peak for each of the heating sources modeled by E3 is 28 provided in Attachment 1 on the Annual Load Summary tab. 29 Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 9 of 12 REDACTED (C...
AI summary The text references the 2024 Load Forecast Report (NSUARB M11689) and mentions NSPI's responses to Synapse Information Requests. It includes a reference to the Energy Efficiency and Conservation Act (E3) and the Annual Load Summary tab in Attachment 1, which details the incremental impact to peak for heating sources.
and intensities can be found in 2024 LFR Attachment 2 and 3, but are 26 applicable to heating and cooling end uses as a whole and not specific to heat 27 pumps. 28 Date Filed: June 19, 2024 NSPI (Synapse) IR-7 Page 10 of 12 REDACTED (CONFI...
AI summary The document discusses the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It references attachments 2 and 3 of the LFR, which provide intensity and efficiency values for heating and cooling, though not specific to heat pumps or individual end uses. Efficiency data is sourced from the EIA and uses Btu out/Btu in, not COP.
2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 2 3 The largest variances were predominantly in the winter months (Nov-Mar), 4 indica...
AI summary The 2024 Load Forecast Report (NSUARB M11689) indicates significant variances in winter months (Nov-Mar), likely due to unaccounted heating load. NSPI provided responses to Synapse Information Requests, and the report includes updated forecast models with increased heating intensities and reduced variance.
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.
Yukon Energy’s Peak Smart program: About Peak Smart Yukon Energy 26 27 Evaluations have not been completed for these programs therefore impacts are not yet 28 available. Date Filed: June 19, 2024 NSPI (Synapse) IR-8 Page 2 of 2 REDACTED (C...
AI summary The document references Yukon Energy’s Peak Smart program and mentions that evaluations for these programs have not been completed, so their impacts are not yet available. It also references the 2024 Load Forecast Report (NSUARB M11689) and NSPI responses to Synapse Information Requests.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-9: 2 3 Residential Electric Vehicles (EV) (Section 4.4, pp 36-41) 4 5 (a) Please provide NSPI’s rationale and all data t...
AI summary NSPI has been asked to provide detailed information regarding its assumptions and data supporting EV load forecasts in the 2024 Load Forecast Report. The request includes details on EV scenarios, underlying data for figures, and explanations of load shaping tools and methodologies used.
peak load shapes for the winter season. Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 1 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document is a non-confidential response by NSPI to information requests from Synapse related to the 2024 Load Forecast Report (NSUARB M11689), focusing on peak load shapes for the winter season.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (v) For LDV, MDV, and HDV, please provide peak load reductions in kW per 2 vehicle for each vehicle category that NSPI expect and a...
AI summary The document outlines a series of information requests from the NSUARB to NSPI regarding the impacts of managed charging programs and time of use rates on peak load reductions for different vehicle categories, as well as the status of time of use tariffs in pilot stages. NSPI is asked to provide supporting evidence for assumptions about managed and unmanaged EV charging.
and rationales for these assumptions. Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 2 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFID...
AI summary The document discusses questions raised about assumptions in the 2024 Load Forecast Report, specifically regarding vehicle types and peak load impacts from electric vehicles. NSPI provides context about aligning with federal targets and references the Dunsky report as a source for forecast assumptions.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 In the near term, growth in EV adoption in recent years has exceeded that of the low 2 scenario, but overall sales are expected to...
AI summary The 2024 Load Forecast Report discusses EV adoption growth exceeding the low scenario in the near term, but aligning with it over 10 years. It references differences in driving behavior between summer and winter months and the use of the E3 EV Load Shape Tool for forecasting EV charging loads.
22 method. The graph below shows an example of LDV weekly driving patterns 23 expressed as the probability that a driver is at a given location or is driving. Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 4 of 8 REDACTED (CONFIDENTIAL...
AI summary The document discusses the 2024 Load Forecast Report (NSUARB M11689) and includes NSPI's responses to Synapse Information Requests. It references a graph illustrating LDV weekly driving patterns and mentions the date filed as June 19, 2024.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1
AI summary This document is the 2024 Load Forecast Report (NSUARB M11689) and includes NSPI's responses to Synapse Information Requests. It is marked as non-confidential.
varying electric rates. 24 Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 5 of 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The document relates to the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It mentions the filing date and page reference, but the content is redacted due to confidentiality.
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.
and demonstrative of the potential of specific use cases for managed charging as 29 detailed in M11621. They were not used to develop peak load impacts of EVs. 30 Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 6 of 8 REDACTED (CONFIDEN...
AI summary The document references the 2024 Load Forecast Report (NSUARB M11689) and NSPI's responses to Synapse Information Requests. It mentions managed charging use cases detailed in M11621 but notes that they were not used to assess EV peak load impacts.
). 26 27 (ii) Managed charging applies to all vehicle types, but the nature of the management 28 and impact to the charging shapes is specific to the vehicle type. 29 Date Filed: June 19, 2024 NSPI (Synapse) IR-9 Page 7 of 8 REDACTED (CONF...
AI summary The text refers to the 2024 Load Forecast Report and NSPI responses to Synapse Information Requests, with attachments and references to specific sections. It also mentions managed charging and its impact based on vehicle type.
2024 Load Forecast Report Synapse IR-9 Attachment 1 Page 1 of 3
AI summary The text references the 2024 Load Forecast Report, specifically Synapse IR-9 Attachment 1, which is part of a regulatory proceeding. The document appears to be a technical attachment related to load forecasting, but no further details are provided.
18.1 40.2 17.0 5.4 3.2 2029 20,159 14,322 729 428 35,639 33,586 150 144 33.7 0.95 32 63.7 61 71.4 23.8 54.8 22.3 7.1 4.3 2030 26,451 18,517 900 550 46,419 44,365 195 189 43.9 0.94 42 82.7 80 93.3 31.1 70.1 29.1 9.3 5.5 2031 35,961 24,858 1...
AI summary The text presents a series of numerical data points spanning from 2029 to 2034, likely related to energy forecasting or planning. It includes values for various metrics, but the content is partially redacted, indicating the presence of confidential information. The mention of the '2024 Load Forecast Report Synapse IR-9' suggests a connection to energy load forecasting.
95.9 30.5 10.9 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-9 Attachment 1 Page 2 of 3 New EV Sales Incremental EV Sales Dunsky Year Sales Values Total 2020 705 2021 1,226 2022 2,220 2023 4,032 2024 7,32...
AI summary The document provides a load forecast report with projected electric vehicle (EV) sales from 2020 to 2035, including both total sales and incremental sales values. The data shows a significant increase in EV sales over time, with high and low case scenarios for certain years.
50,694 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-9 Attachment 1 Page 3 of 3 2024 EV Forec2023 EV ForecDunsky EV LoDunsky EV High 2023 4,032 3,619 4,032 4,032 2024 7,323 6,769 5,166 8,666 2025 13,300 1...
AI summary The document presents electric vehicle (EV) load forecasts from 2023 to 2035, with various scenarios including low, high, and baseline projections. These forecasts are part of the 2024 Load Forecast Report and include data from Synapse IR-9 Attachment 1.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-9 Attachment 2 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Info...
AI summary The NSPI responded to IR-10 requests regarding PV generation data, summer peak impacts, and Load (GWh) definitions. They referenced the Net Metering Report and provided attachments for supporting data and calculations.
r to Attachment 1, Solar Peak Impact tab. 25 26 (c) Please refer to Attachment 1, 2024 tab. 27 1 M11553, Exhibit N-1, NS Power, 2023 Net Metering Report, January 31, 2024. Date Filed: June 19, 2024 NSPI (Synapse) IR-10 Page 1 of 2 REDACTED...
AI summary The document discusses the 2024 Load Forecast Report, specifically the Solar Peak Impact tab and the 2024 tab, which include data on solar production and its impact on load forecasting. It also references the 2023 Net Metering Report and mentions the Load column representing total solar production over a year, though it does not account for the timing mismatch between energy injection and consumption offset.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-11: 2 3 New Technologies (Section 4.4, pp 43-46) 4 5 (a) Were projected cost declines of home batteries considered in th...
AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report. NSPI stated that projected cost declines of home batteries were not considered, V2G technology is not yet incorporated due to its developmental stage, and the residential share in Figure 28 refers to the percentage of residential customers with batteries.
Customer Forecast Data: Year Customer Count Residential Total (December 31) 2034 546,929 Best estimates based on data available as of April 12, 2021. Still many uknowns and much uncertainty with values presented. REDACTED (CONFIDENTIAL INF...
AI summary The document provides customer forecast data and load forecast scenarios for residential customers in Nova Scotia, including DER peak impact and DR program impacts. The data is based on estimates as of April 12, 2021, with significant uncertainty noted.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-11 Attachment 1 Page 3 of 3 Residential Uptake 50% 25% 10% 5% Battery Peak Impact - No Control (MW) 0 0 0 0 Battery Peak Impact - Optimal DR Control (MW) (1,3...
AI summary The document includes a request and response related to the 2024 Load Forecast Report. The request pertains to residential end-use intensities and appliance efficiency data, and the response indicates that the data is sourced from Itron and is available in the provided attachments.
original EIA data or documentation. Date Filed: June 19, 2024 NSPI (Synapse) IR-12 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDE...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report. The response includes data sources for commercial and industrial growth calculations, including customer outreach, heat pump installations, and electrification efforts. The focus is on demand growth, particularly from heat pumps and customer expansion.
VAC systems, manufacturing/process Date Filed: June 19, 2024 NSPI (Synapse) IR-13 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDEN...
AI summary The document outlines NSPI's approach to forecasting load growth, including engagement with industrial customers, use of annual surveys, and contributions from new accounts and project expansions. It notes that small and medium commercial growth is excluded from Figure 32 and that growth numbers are based on historical data and known projects.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Introduction (Section 2.0, citing to Board Decision concerning 2023 Load Forecast). Please 4 explain in detail h...
AI summary NSPI responds to Synapse Information Requests regarding the 2024 Load Forecast Report, addressing the Board's direction to include the IRP, SGNS, AMI, and TVP outcomes; reviewing carbon emission reduction assumptions; and assessing historical load data compared to survey results. Citations are provided for each section in the report.
al targets (page 36). 27 28 (c) The estimates for large customer growth based on survey results was revised and is 29 discussed in Section 6.3 (pages 68-69) and Section 7.3 (page 74). Date Filed: June 19, 2024 NSPI (Synapse) IR-14 Page 1 o...
AI summary NSPI is not considering multiple load forecasts, stating that the load forecast provides the most likely outcome for planning purposes, while the IRP examines multiple potential paths. The response references Appendix D and Figure 72 for sensitivity analysis and comparison with IRP scenarios.
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.
30 system adequacy. Date Filed: June 19, 2024 NSPI (Synapse) IR-16 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Response...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report, addressing topics such as TVP elasticities, population growth, household size, economic indicators, EV inputs, and communication with large customers. These responses are part of the NSUARB M11689 proceeding.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL
AI summary The document outlines NSPI's responses to Synapse Energy Economics' information requests regarding the 2024 Load Forecast Report, which was part of the NSUARB M11689 proceeding. The report is non-confidential and provides insights into load forecasting for Nova Scotia.
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.
EDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-19 Attachment 1 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse...
AI summary The 2024 Load Forecast Report (NSUARB M11689) has been filed, along with NSPI's responses to Synapse Information Requests. The report is part of the regulatory process and includes non-confidential information related to load forecasting.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-20: 2 3 Residential Sector (Section 5) 4 5 (a) Please provide the inputs and calculations used to produce the adjustment...
AI summary The document outlines a series of information requests related to the 2024 Load Forecast Report, focusing on residential sector sales forecasts, the impact of the COVID-19 variable, and the methodology used in creating specific figures. The requests aim to clarify modeling approaches, data sources, and changes in estimates since the previous forecast.
so, please explain in detail. 27 28 (g) Please provide the inputs and calculations used to create Figure 39. 29 30 (h) Please provide the inputs and calculations used to create Figure 40. Date Filed: June 19, 2024 NSPI (Synapse) IR-20 Page...
AI summary The document contains a series of information requests related to the 2024 Load Forecast Report, including requests for inputs and calculations used in specific figures and discussions on the impact of proposed building efficiency regulations. NSPI provides partial responses, including references to attachments and explanations related to the impact of the COVID variable on load forecasts.
26 27 (e) No. 28 29 (f) No. 30 Date Filed: June 19, 2024 NSPI (Synapse) IR-20 Page 2 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL...
AI summary NSPI provides responses to Synapse's information requests regarding the 2024 Load Forecast Report, including data locations and assumptions related to building efficiency and floor space estimates.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-21: 2 3 End-Use Intensity Trends (Section 4.4) 4 5 (a) The report references...
AI summary NSPI responds to Synapse's information requests regarding the 2024 Load Forecast Report, addressing the impact of critical peak pricing (CPP) and time-varying pricing (TVP) on EV charging behavior and load forecasting assumptions. NSPI assumes 70% of EV charging will be managed through rate structures and direct control, and TVP rates are expected to meet peak savings goals.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-22: 2 3 Commercial Sector (Section 6.0). 4 5 (a) Please explain and quantify...
AI summary NSPI explains the impact of COVID-19 on commercial sector electricity loads in 2022 and 2023, estimating a reduction of -69 GWh in 2022 and -57 GWh in 2023. The current forecast shows a lesser increase in sales due to factors like commercial sales shifting to the RTR market, increased solar adoption, and reduced EV sales.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-23: 2 3 Small General Service (Section 6.1). 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 Small General Service has not changed significantly from 2023. Differences are due to higher-than-expected 2023 sales and slight declines through 2026, with shifts in sales to RTR and decreased EV sales. The EV load contribution decreased from 21% to 14%, and the XOther component showed increased growth in 2024 compared to 2023.
he 11.4 percent predicted in 2023, and Date Filed: June 19, 2024 NSPI (Synapse) IR-24 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary This document is part of a regulatory proceeding involving Nova Scotia Power Inc. (NSPI) and includes a 2024 Load Forecast Report submitted to the Nova Scotia Utility and Regulatory Board (NSUARB) under matter number M11689. It outlines NSPI's responses to information requests from Synapse.
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.
ON REMOVED) 2024 Load Forecast Report Synapse IR-24 Attachment 1 Page 1 of 3
AI summary The document is a page from the 2024 Load Forecast Report by Synapse IR-24, attached as Attachment 1. It appears to be part of a regulatory proceeding, though the content of the page is not visible due to being marked as 'ON REMOVED'.
100% 2031 100% 63% 25% 45% 100% 100% 100% 100% 65% 25% 46% 100% 100% 100% 2032 100% 63% 25% 44% 100% 100% 100% 100% 65% 25% 46% 100% 100% 100% 2033 100% 63% 25% 44% 100% 100% 100% 100% 65% 25% 46% 100% 100% 100% 2034 100% 62% 24% 44% 100%...
AI summary The document contains a table of percentages for various years, likely related to energy load forecasts, and a reference to a confidential 2024 Load Forecast Report by Synapse IR-24. The content is partially redacted.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 Small Industrial (Section 7.1). 4 5 (a) Please explain and quantify the specific reasons for the differences fro...
AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report. The responses explain that load migration to RTR and changes in electrification load growth are key factors affecting forecast differences. Sales are expected to remain flat until 2027 due to RTR migration, after which they will increase with electrification growth.
ation of load to the RTR participant. Date Filed: June 19, 2024 NSPI (Synapse) IR-27 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFI...
AI summary NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report. Key points include a decrease in forecasted new project load by 93 GWh, a 99% representation of sector load by survey responses, and a +1.8% aggregate load change. Past forecasts were found to have overstated new project growth.
51 23 24 25 (b) The load served by NS Power is expected to decrease in 2025 as the customers participating 26 in OATT have applied for BUTU service to enable third party supply. Date Filed: June 19, 2024 NSPI (Synapse) IR-29 Page 1 of 1 RE...
AI summary The document discusses load forecasts, noting that NS Power's load is expected to decrease in 2025 due to customers switching to BUTU service. It also provides information on system losses, which are higher in winter and typically range between 6 to 7 percent of net system requirement.
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.
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 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.
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.
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.
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.
egates to a system level peak. As more Date Filed: June 19, 2024 NSPI (Synapse) IR-33 Page 1 of 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document is a 2024 Load Forecast Report submitted by NSPI (Synapse) in response to information requests from the NSUARB under matter number M11689. The report includes non-confidential information related to load forecasting.
15.95 2023-02-04 14:00 385.46 15.94 2023-02-04 15:00 363.73 15.41 2023-02-04 16:00 365.88 15.60 Date Filed: June 19, 2024 NSPI (Synapse) IR-33 Page 2 of 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689...
AI summary The document contains a redacted section from NSPI's responses to Synapse Energy Economics' information requests in the context of the 2024 Load Forecast Report (NSUARB M11689). It includes time-stamped data and a reference to the regulatory proceeding.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-34: 2 3 Sensitivity Analysis (Section 11 and Appendix D) 4 5 (a) Please provide in electronic format the data and calcul...
AI summary The NSPI provided responses to Synapse's information requests regarding the 2024 Load Forecast Report, including details on the sensitivity analysis, variables selected, and the statistical distributions used in the Monte Carlo simulation.
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.
361.16 11672.09 11995.36 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-34 Attachment 1 Page 2 of 2 Year Actual System Peak p10 p50 p90 2010 2114.2 2011 2168.1 2012 1881.7 2013 2032.7 2014 2118.2 2015 2015...
AI summary The 2024 Load Forecast Report provides historical and projected system peak data from 2010 to 2034, with specific values for 2024 and beyond. The report is part of a regulatory proceeding (NSUARB M11689) and includes responses from NSPI to information requests from Synapse.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests REDACTED
AI summary The 2024 Load Forecast Report (NSUARB M11689) includes NSPI's responses to Synapse Information Requests. The content is partially redacted, limiting the availability of detailed information.
46 2017 67 156 89 2018 80 156 76 2019 111 163 52 2020 96 152 56 2021 94 158 64 Date Filed: June 19, 2024 NSPI (Synapse) IR-36 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses...
AI summary The document presents load forecast data for various years, including actual and forecasted interruptible peak values, along with differences and variances. The report is related to the 2024 Load Forecast Report (NSUARB M11689) and includes NSPI's responses to Synapse Information Requests.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-37: 2 3 Appendix B: Residential Model 4 5 (a) Please provide in electronic spreadsheet format the data and the statistic...
AI summary NSPI has received a request (IR-37) from Synapse for detailed data and model parameters related to the residential model in the 2024 Load Forecast Report. The request includes data on end-use intensity, electric space heat and hot water usage, and factors influencing variables such as XHeat, XCool, and XOther. NSPI is asked to disclose whether it collaborated with E1 on common assumptions.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 (i) Please note any changes in the model specification relative to the 2023 forecast 2 residential model, and please further quanti...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report, detailing changes in the residential model specification and the impact of these changes, particularly focusing on the growth of HP Heat and HP Cool shares over time.
(g) There are no significant changes for the OtherUse variable in a 10-year span. As shown in 29 Appendix B: page 2, the drivers within OtherUse are Household Size, Price, Seasonal Use Date Filed: June 19, 2024 NSPI (Synapse) IR-37 Page 2...
AI summary The text discusses load forecasting models, noting no significant changes in the OtherUse variable over a 10-year period and the removal of a statistically insignificant binary variable from the 2023 model in the 2024 model. Collaboration on the topic in 2023 is also noted as absent.
.18 1.29 1,881.40 473.18 312.91 39.04 154.11 45.86 44.97 681.87 304.14 466.96 0.00 1,227.63 0.98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-37 Attachment 1 Page 2 of 6
AI summary The document contains a redacted portion of the 2024 Load Forecast Report, specifically Attachment 1, Page 2 of 6. It includes numerical data and is part of a regulatory proceeding, though the content is confidential and not fully visible.
199.76 11,409.55 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-37 Attachment 1 Page 3 of 6 Year Res.Indices Heating Res.Indices Cooling Res.Indices Others XHeat XCool XOther AvgEESavings 15-May 15-Apr Aug...
AI summary The text presents a table with various indices and factors related to load forecasting for the year, including residential heating, cooling, and other indices, as well as variables like average energy efficiency savings and contributions to sales. The data appears to be part of a 2024 Load Forecast Report by Synapse.
1 1 0 0 0 0 199.76247 5,096.25 532.12 5,632.84 2031 5,538.1 407.1 5,618.3 5,576.8 506.5 5,474.5 1145.45619 0 0 1 1 1 1 0 0 0 0 199.76247 5,147.43 554.09 5,638.74 2032 5,608.7 418.4 5,622.8 5,663.6 526.4 5,498.5 1145.45619 0 0 1 1 1 1 0 0 0...
AI summary The text presents a table with numerical data related to load forecasts and financial figures, including years, values, and other metrics. It also references a confidential document titled '2024 Load Forecast Report Synapse IR-37 Attachment 1 Page 4 of 6'.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-38: 2 3 Appendix B: Small General Service Model 4 5 (a) Please provide in electronic spreadsheet format the data and the...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report, specifically addressing the Small General Service Model and related calculations. The responses include references to attachments and electronic filings.
1.360 35,739.08 1.730 9,259.65 2,168.80 2034 63,121.32 1.370 35,681.71 1.770 9,018.15 2,179.20 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-38 Attachment 1 Page 2 of 9
AI summary The text contains a table with numerical data and a reference to the 2024 Load Forecast Report Synapse IR-38 Attachment 1, Page 2 of 9. The content is partially redacted due to confidentiality.
12.00 0.00 2031 1.00 0.00 1.00 0.00 0.00 0.00 12.00 0.00 2032 1.00 0.00 1.00 0.00 0.00 0.00 12.00 0.00 2033 1.00 0.00 1.00 0.00 0.00 0.00 12.00 0.00 2034 1.00 0.00 1.00 0.00 0.00 0.00 12.00 0.00 REDACTED (CONFIDENTIAL INFORMATION REMOVED)...
AI summary The text contains a table with numerical data spanning years 2031 to 2034, followed by a redacted section from the 2024 Load Forecast Report, specifically Synapse IR-38 Attachment 1, Page 4 of 9.
Regression Sales Results Out of Monthly Model (kWh / HH) Year AContrib2Sales.AnnualAvgUse 2014 10,826.21 2015 10,953.36 2016 11,043.57 2017 11,027.94 2018 11,393.38 2019 11,650.83 2020 10,739.14 2021 11,266.11 2022 12,405.73 2023 12,626.60...
AI summary The document presents a regression sales results table showing annual average usage in kWh per household from 2014 to 2034, followed by a redacted section of the 2024 Load Forecast Report, Synapse IR-38, Attachment 1, Page 5 of 9.
0 2032 61,767.9 35,810.4 130,186.0 5,880.3 2,118.2 10,278.2 1 0 1 0 0 0 12 0 2033 62,464.6 35,739.1 129,010.1 5,946.6 2,164.0 10,269.2 1 0 1 0 0 0 12 0 2034 63,121.3 35,681.7 127,794.0 6,053.3 2,210.5 10,287.4 1 0 1 0 0 0 12 0 REDACTED (CO...
AI summary The document provides a load forecast report with numerical data across multiple years, including details on energy consumption and related metrics. However, the content is partially redacted, and specific details about the forecast or its implications are not clearly outlined.
0 2,617,120.01 2033 16.87 0.00 0.00 0.00 0.00 0.00 7.80 0.00 0.00 2,644,332.48 2034 17.07 0.00 0.00 0.00 0.00 0.00 7.80 0.00 0.00 2,679,214.86 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-39 Attachment 1...
AI summary The text presents a table with financial data and load forecast information, including load indices for heating, cooling, and other factors, as well as dates and acronyms related to forecasting methods. The document is part of a regulatory proceeding and includes confidential information that has been redacted.
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.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-40: 2 3 Appendix B: Small Industrial Model 4 5 (a) Please provide in electronic spreadsheet format the data and the stat...
AI summary NSPI responded to Synapse's information request regarding the 2024 Load Forecast Report, explaining that the Small Industrial model calculations were done using Metrix ND and that the model specification has not changed since 2023, with Nova Scotia’s Manufacturing GDP still being the main driver.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-41: 2 3 Appendix B: Medium Industrial Model 4 5 (a) Please provide in electronic spreadsheet format the data and the sta...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report. The response indicates that the Medium Industrial model calculations were conducted using Metrix ND software and that the model specification has remained unchanged since 2023, with manufacturing employment being the primary driver.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-43: 2 3 Appendix B: Peak Forecast 4 5 (a) Please provide in electronic spreadsheet format the data and the statistical m...
AI summary The document outlines responses to information requests regarding the 2024 Load Forecast Report, including details on statistical models, load values, and coincident peak load factors used in forecasting. The responses refer to attachments and the Metrix ND software used by NS Power for calculations.
es. The values used are the historical Date Filed: June 19, 2024 NSPI (Synapse) IR-43 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONF...
AI summary The document discusses the 2024 Load Forecast Report, including load factors for various large customer classes and changes made to the binary variables used in the 2023 forecast. These changes improved the model fit with a higher adjusted R-squared value.
109,698.5 4,991.6 26,390.3 141,080.5 31.0 189.6 0.6 110.7 2020 11 203,426.1 9,293.0 49,088.4 261,807.6 30.0 363.6 0.8 272.9 2020 12 288,335.7 13,223.8 69,780.0 371,339.5 31.0 499.1 0.9 467.0 2021 1 342,782.1 16,031.4 82,118.9 440,932.4 31....
AI summary The text presents numerical data and references a 2024 Load Forecast Report, specifically Synapse IR-43 Attachment 1, Page 2 of 11. The data appears to be related to financial and operational metrics, though the content is partially redacted due to confidentiality.
361,781.7 17,758.5 81,940.4 461,480.7 31.0 620.3 1.0 646.7 2028 1 432,236.9 20,994.0 97,422.1 550,653.0 31.0 740.1 1.3 924.6 2028 2 414,217.3 20,142.8 93,420.1 527,780.3 29.0 758.3 1.3 948.8 2028 3 392,139.6 19,092.0 88,497.1 499,728.6 31....
AI summary The document contains numerical data related to load forecasts and financial figures, with a mention of the 2024 Load Forecast Report by Synapse IR-43 Attachment 1. The content appears to be part of a regulatory proceeding, though specific details are redacted.
70.19 73,591.53 383,912.94 30.0 533.21 0.77 411.14 2034 12 417,357.01 21,926.16 104,217.55 543,500.72 31.0 730.51 1.04 761.65 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-43 Attachment 1 Page 4 of 11
AI summary The text includes a table with numerical data and a redacted section from the 2024 Load Forecast Report, Synapse IR-43, Attachment 1, Page 4 of 11. The table contains values related to load forecasting, but the content is partially confidential and not fully accessible.
9,930.3 48,001.8 30.0 66.7 0.5 29.7 2019 10 2,675.5 263.6 2,080.3 5,019.5 31.0 6.8 - - 2019 11 178.8 17.6 138.4 334.8 30.0 0.5 - - 2019 12 - - - - 31.0 - - - 2020 1 - - - - 31.0 - - - 2020 2 - - - - 29.0 - - - 2020 3 - - - - 31.0 - - - 202...
AI summary The document contains numerical data spanning multiple years, likely related to financial or operational metrics, and references a confidential 2024 Load Forecast Report by Synapse with attachment details. The data includes figures for different months and years, possibly related to energy usage or financial performance.
- REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-43 Attachment 1 Page 8 of 11
AI summary The text is a redacted page from the 2024 Load Forecast Report, Attachment 1, page 8 of 11, submitted by Synapse. The content is confidential and not accessible.
233,966.2 14,723.8 135,673.7 20,501.3 40,263.7 6,797.5 451,926.1 31.0 607.4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-43 Attachment 1 Page 9 of 11
AI summary The document contains a redacted section of the 2024 Load Forecast Report, specifically Attachment 1, Page 9 of 11, which includes numerical data and confidential information that has been removed.
† 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.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-46: 2 3 Nova Scotia Power Electrification Support Overview by E3 4 5 (a) Please provide any supplemental materials produ...
AI summary NSPI provided responses to Synapse Information Requests regarding the 2024 Load Forecast Report. The responses included references to heat pump and EV load shapes, as well as modeling based on federal sales targets and net zero emission goals.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-48: 2 3 Peak Demand (Section 10.0, p. 94): 4 5 (a) Please provide the generation fuels used to supply system generation,...
AI summary NSPI responded to Synapse's information requests regarding the 2024 Load Forecast Report. NSPI stated that generation by fuel type was not an input to Figure 68 and that generation mix was not modeled for the load forecast. For electric vehicles, NSPI noted that total vehicle sales and stock were not modeled, but assumed 70% of vehicles use managed charging.
ario and New England, are 24 forecasting a shift of their peaks to winter with expected increases in electric space heating 25 and EV load in response to carbon reduction targets. Date Filed: June 19, 2024 NSPI (Synapse) IR-50 Page 1 of 1...
AI summary The document includes requests and responses related to load forecasting and municipal electric utility obligations. NSPI refers to Synapse IR-29 for municipal load forecasts and provides Attachment 1 for end-use peak share data.
d-Use Peak Shares (Section 10.0, pp. 91-92): 4 5 (a) Please provide the underlying data used to create Figure 65 and Figure 66. 6 7 Response IR-52: 8 9 (a) Please refer to Attachment 1. Date Filed: June 19, 2024 NSPI (Synapse) IR-52 Page 1...
AI summary The document requests the underlying data for Figures 65 and 66, which relate to peak load shares by category for 2024 and 2033. The response refers to Attachment 1, which includes a table showing the distribution of load by category and their contribution to peak demand.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2024 Load Forecast Report Synapse IR-52 Attachment 1 Page 2 of 2 MW Year Heat Misc Light Vent Refrig Office EWHeat Cooking EV Total 2024 177.93 96.11 149.96 30.84 48.95 36.68 6.68 10.50 1.48 559....
AI summary The document presents a 2024 Load Forecast Report with data on electricity demand across various sectors, including heat, lighting, and office usage, for the years 2024 and 2033. It also references a response by NSPI to Synapse Information Requests in the context of the NSUARB M11689 matter.
2024 Load Forecast Report (NSUARB M11689) NSPI Responses to Synapse Information Requests NON-CONFIDENTIAL 1 Request IR-53: 2 3 Forecast Sensitivities (Section 10.0, pp. 97-99): 4 5 (a) Please describe if and/or how the P10/P90 probability...
AI summary NSPI responds to Synapse's request regarding the use of P10/P90 probability analysis in load forecasting, explaining that it is used for ad-hoc analysis and not part of regular planning processes. The response also mentions that the methodology is similar to 90/10, with details provided in Appendix D of the 2024 Load Forecast Report.
N-8Evidence of Synapse (BCC)
34 passages
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.
-57 -3 -61 -699 2034 Forecast 5,298 3,151 2,258 213 775 11,695 Source: Figure 55 from 2024 Load Forecast The historical trend for firm peak demand shows a general increase, as shown in Figure 2 below. We plotted the actual rather than the...
AI summary The 2024 load forecast indicates a 14.6% increase in firm peak demand (323 MW), primarily driven by electrification. Electric vehicles (EVs) are identified as the largest contributor to this growth. The forecast shows slower growth rates post-2024, with historical trends reflecting increasing demand. Adjustments to modeled values from SAE are detailed in Figure 61/Table 2.
r’s 2024 Load Forecast 3 Figure 2. Firm peak demand 2,700 2,500 Firm Peak Demand (MW) 2,300 2020 2021 2,100 2022 2023 2024 1,900 Actual 1,700
AI summary The document presents a 2024 load forecast with firm peak demand data from 2020 to 2024, showing a decline from 2,700 MW in 2020 to 1,900 MW in 2024, with actual demand values plotted for each year.
2034 Source: Synapse from Figure 2 from 2024 Load Forecast and responses to Synapse IR-1 Table 2. Firm peak demand components Res. Modeled C&I Large Firm Inter. System Heat EV DR Hybrid DSM Peak Elect. Cust. Peak Cust. Peak Peak (MW) (MW)...
AI summary The text presents a load forecast table from Synapse, analyzing firm peak demand components for 2024 and 2034, including residential heat, EV, DR Hybrid, and DSM factors. It compares scenarios with and without EV mitigation, showing projected demand increases and reductions from DSM and EV factors.
ion that NSPI would continue to explore all realistic scenarios relating to hydrogen production and incorporate these into the load forecast modeling. 4 NSUARB Decision in Matter 11108, page 6. Synapse Energy Economics, Inc. Evidence Regar...
AI summary NSPI is directed to explore hydrogen production scenarios for load forecasting and address significant variance between 2023 forecasts and actual 2022 Net System Requirement. The NSUARB Decision in Matter 11108 emphasized examining factors like cooling demand, heat pump adoption, and residential electricity drivers. NSPI's responsiveness to these directives is noted with some exceptions.
tial sales estimate. Both these values are presented in the same row of the “Residential Load – Post Regression” table in NSPI’s Appendix B and should be calculated consistently or labeled clearly. 10 The residential statistical model also...
AI summary The text discusses NSPI's residential load forecasting models, including adjustments for post-pandemic work-from-home trends and regression coefficients for the 2023 and 2024 forecasts. It notes a decline in the pandemic's impact on residential load, with projected effects of 150 GWh (2023) and 100 GWh (2024). Commercial and industrial models use different economic indicators, with industrial models using longer regression timescales for better statistics.
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.
projected to increase by about 2.3 percent over the forecast period. This increase is largely the result of electrification, as NSPI projects growing adoption of heat pumps for heating and cooling 18 2024 Load Forecast, Appendix B, pages 2...
AI summary NSPI projects a 2.3% increase in load over the forecast period, driven by electrification, including heat pump adoption and electric water heating. Residential heat pump saturation is modeled to rise from 44% in 2024 to 64% by 2034, with 210,800 units expected by 2034. The analysis highlights NSPI's approach to residential and commercial heat pump modeling.
e for non-electric heating customers and roughly 32 percent are for electric heating customers. In comparison, the total number of heat pumps installed in 2023 were approximately 20,900 heat pumps. 22 For commercial customers, NSPI models...
AI summary The text discusses NSPI's 2024 load forecast, highlighting discrepancies in heat pump installation data (20,900 units in 2023) and modeling assumptions. NSPI's forecast assumes electric heating saturation at 70% by 2035 but does not explicitly account for hybrid heating systems, leading to a -68 MW adjustment in 2034. Synapse Energy Economics critiques the unclear methodology behind this adjustment.
ding Nova Scotia Power’s 2024 Load Forecast 13 is unclear how exactly NSPI estimated this value, and we cannot observe any connection between the savings value and any value presented in Figure 20. The forecast growth in heat pumps explain...
AI summary The document critiques NSPI's 2024 load forecast for unclear savings estimation and lack of connection to Figure 20. Heat pumps are identified as a major driver of residential electricity growth (9% increase by 2034), contributing 5.6% of load increases. However, fossil fuel displacement from heat pumps is noted as unreported savings, raising concerns about model accuracy.
there are additional unreported savings there. It is important to note in particular two potential issues with the accuracy of NSPI’s model in estimating energy and peak load impacts from heat pumps: • Heating intensities have increased by...
AI summary The text highlights two issues with NSPI’s model for estimating heat pump impacts: a 39% increase in heating intensities due to unallocated variance adjustments and discrepancies in peak load calculations compared to E3’s data. These inaccuracies may affect energy and load forecasts.
4 Load Forecast, Appendix B, page 8. 26 2024 Load Forecast, Appendix B, page 9. 27 2024 Load Forecast, Figure 41, Figure 41, 42. 28 2024 Load Forecast, page 34. 29 2024 Load Forecast, page 34. Synapse Energy Economics, Inc. Evidence Regard...
AI summary The text highlights discrepancies between NSPI's and E3's 2030 heat pump energy usage estimates (242 GWh vs. 74 GWh) due to differing assumptions about heating scenarios and saturation rates. It recommends validating assumptions about heat pump displacement of fossil-based heating and using AMI data for more accurate modeling.
ptions about customer usage of secondary heating equipment. Finally, NSPI should model hybrid electric heating within its model instead of making a simplified adjustment based on E3’s hybrid scenario. Water heaters For water heaters, the f...
AI summary The text discusses NSPI's modeling of hybrid electric heating and the forecasted increase in electric water heater adoption, which is expected to significantly impact energy and peak load growth. It also notes NSPI's collaboration with E1 on demand response projects and the use of pilot data for future forecasts.
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.
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.
as penetration increases. These load management strategies should be reflected in the next load forecast with greater detail, with all assumptions supported empirically to the maximum extent possible. Solar generation As noted previously,...
AI summary The document discusses the impact of increasing solar generation on load forecasting, noting that while small-scale solar has limited impact on winter peak loads, it may affect non-winter monthly peaks. NSPI has updated its coincidence factors based on data from community solar farms and forecasts a modest reduction in residential and commercial energy load.
ill continue to monitor 46 2023 Load Forecast, Appendix B, page 6 and 2024 Load Forecast, Appendix B, page 8. 47 2024 Load Forecast, page 58. 48 NSUARB Decision in Matter 11108, page 6. Synapse Energy Economics, Inc. Evidence Regarding Nov...
AI summary The document discusses concerns regarding Nova Scotia Power’s (NSPI) 2024 Load Forecast, particularly the reliance on housing completions as a proxy for customer growth. It highlights potential issues with this method, such as the ongoing housing shortage affecting the correlation between housing starts and customer growth. Recommendations are made for NSPI to validate this proxy and consider alternative forecasting methods.
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.
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.
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.
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.