N-12022 Load Forecast Report - Redacted
93 passages
1 TABLE OF CONTENTS 2 3 1.0 Executive Summary ............................................................................................................. 7 4 2.0 Introduction .................................................................
AI summary The text is a table of contents from a regulatory proceeding document, outlining sections such as forecasting approach, historical energy data, weather, economic information, price data, and sector-specific analyses (residential, commercial). It structures the report's content without discussing specific claims or arguments.
............................................ 67 16 Figure 44: Historical and Forecast Annual Small General Sales .................................................. 68 17 Figure 45: Historical and Forecast Annual General Demand Sales .........
AI summary The text lists figures related to historical and forecasted energy sales, demand, peak contributions, and system reliability metrics, including demand response, peak temperatures, and system/firm peak forecasts. No explicit arguments or claims are presented in the excerpt.
k (including DR) ..................................................... 84 30 Figure 58: Peak Contribution Components (MW)........................................................................ 85 DATE: April 29, 2022 Page 4 of 98 REDACTED...
AI summary The 2022 Load Forecast Report includes figures analyzing peak demand contributions, forecast accuracy, weather-normalized firm peak data, residential and commercial end-use peak shares, load research data comparisons, energy/peak sensitivity, and Integrated Resource Plan (IRP) scenario comparisons, focusing on load forecasting methodologies and demand response integration.
residential 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 comb...
AI summary The 2022 Load Forecast Report projects a 0.3% annual increase in Net System Requirement (NSR), driven by near-term customer growth, EV adoption, and hospital expansions, offset by long-term DSM initiatives and solar installations. Forecasts incorporate SAE models for residential/commercial end-use and econometric industrial projections.
ecast annual increase of 0.3 percent. 14 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 29, 2022 Page 8 of 98 REDACTED (CONFIDENTIAL IN...
AI summary NS Power forecasts a 0.3% annual increase in net system requirement and 1.6% annual growth in system peak demand, driven by customer growth and electric heating, partially offset by demand-side management (DSM) activities. Historical and projected data are visualized in Figures 1-3.
may increase the complexity of the model without improving accuracy. However, 24 given the ranges in weather event occurrences throughout the province, the Board 25 directs NS Power to incorporate more weather station data into future load...
AI summary The Board directs NS Power to enhance load forecasts by incorporating more weather station data using a load-weighted approach. NS Power acknowledges intervenors' requests but cites preliminary results from Smart Grid and Water Heating Demand Response projects. The Board mandates reporting these project impacts on load in the 2022 Load Forecast and encourages inclusion of literature sources in future forecasts.
were evaluated and what was incorporated into the 23 forecast early in the process. 24 25 In accordance with the Board’s direction, NS Power revised and enhanced the 2022 Load 26 Forecast in the following manner: 27 28 • The peak design te...
AI summary NS Power revised the 2022 Load Forecast per the Board’s direction, incorporating historic temperature data, warming trends, population-weighted weather analysis, and summaries of Smart Grid/Demand Response projects. System peak accuracy was added to Appendix C, with references to Sections 4.2, 10, and other documentation.
Page 13 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 • A discussion of the economic inputs to the residential model is provided in Section 2 4.3. 3 4 Summary of Stakeholder Consultations 5 6 On Apr...
AI summary NS Power conducted a stakeholder session on April 13, 2022, discussing updates to the 2022 Load Forecast, including impacts of COVID-19, EV forecasts, space heating, peak savings assumptions, and methodology from Energy and Environmental Economics, Inc. Stakeholders included NSUARB, Synapse, EfficiencyOne, and others.
21, pages 6 (M10109). DATE: April 29, 2022 Page 29 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Both retail sales and disposable income show distinct impacts from COVID, while 2 household compensat...
AI summary The document discusses the impact of economic factors like retail sales, disposable income, and work-from-home activity on residential load forecasts. It highlights the use of housing completions as a key indicator for residential customer forecasts, referencing a decision by the NSUARB to re-evaluate this approach due to population growth and housing shortages.
2022 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 In the commercial models, non-manufacturing gross domestic product (GDP) and non- 6 manufacturing employment continue t...
AI summary The 2022 Load Forecast Report discusses the use of economic drivers in forecasting load demand, including GDP and employment data for residential, commercial, and industrial sectors. The industrial models use longer regression timescales to improve the relevance of economic variables and model fit.
CTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 18: Commercial Economic Drivers 2
AI summary The document presents a redacted section of the 2022 Load Forecast Report, focusing on commercial economic drivers as illustrated in Figure 18. The content is partially redacted, with confidential information removed.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 24: Heat Pump Forecast 2 Total Overall % Install Overall % Overall % Overall Cooling Cumulative % Install Heating Year Non-Elec. Sat. for Sat. for Inte...
AI summary The 2022 Load Forecast Report includes a table forecasting heat pump installations from 2022 to 2032, detailing cumulative installations, percentages of non-electric and electric heat, and energy consumption metrics such as heating and cooling intensity.
2,204 81 324 3 4 DATE: April 29, 2022 Page 40 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Water Heaters 2 3 NS Power anticipates that some customers who convert their oil heating systems to heat 4...
AI summary NS Power expects increased adoption of electric water heaters due to conversions from oil heating systems to heat pumps. The 2019 survey indicates 63% of respondents use electricity for water heating, with saturation expected to rise to 82% by 2032. A pilot project with E1 is exploring benefits of direct control of water heaters.
benefits to the system (see Section 10). Figure 25 shows the expected changes in 11 saturation and overall intensity over the forecast period. 12 13 Figure 25: Water Heater Forecast 14 Overall % Overall Intensity Year Saturation (kWh/house...
AI summary The text discusses the forecasted changes in water heater saturation and intensity over the forecast period, as well as the existing federal and provincial incentives for electric vehicles (EVs) in 2022. It provides data on saturation percentages and overall intensity in kWh per household from 2022 to 2032.
Page 41 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 used plug-in hybrid vehicles. It is estimated that there were approximately 950 EVs in the 2 province as of the end of 2021. The EV forecast has...
AI summary The 2022 Load Forecast Report estimates that Nova Scotia will have over 75,000 EVs on the road by 2031, driven by provincial and federal targets. This includes light-duty, medium-duty, and heavy-duty vehicles, with the forecast updated to reflect a 30% EV sales target by 2030 and a 100% target by 2035.
e 42 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 charging. E3 provided estimates of total load and normalized per-vehicle electric vehicle 2 load shapes over the course of the year based on E3’s E...
AI summary The 2022 Load Forecast Report discusses E3's estimation of total load and normalized per-vehicle electric vehicle load shapes in Nova Scotia using a bottom-up modeling approach. The report includes a figure showing EV load and peak demand contribution based on these estimates.
urrently in the pilot stage, 12 Based on the NRCan 2009 Canadian Vehicle Survey Summary Report, https://oee.nrcan.gc.ca/publications/statistics/cvs/2009/appendix-1.cfm?graph=11 DATE: April 29, 2022 Page 43 of 98 REDACTED (CONFIDENTIAL INFO...
AI summary The document discusses the impact of electric vehicles (EVs) on Nova Scotia's grid, noting that data is being collected through the Smart Grid Nova Scotia (SGNS) Project. The project includes the installation of EV smart chargers and vehicle-to-grid smart chargers to study the effects of EV charging and utility-managed charging events.
thout mitigation measures, which 16 assumes an average of 1.3 kW/vehicle, or around 20-30 percent charging on peak 17 depending on the mix of vehicle and charger types. 18 13 Smart Grid Nova Scotia Project, NS Power Application, December 5...
AI summary The text discusses the impact of electric vehicles (EVs) on energy and peak load forecasts, assuming an average of 1.3 kW per vehicle and 20-30% charging during peak hours, depending on vehicle and charger types. It references a 2019 application by NS Power related to the Smart Grid Nova Scotia Project.
Peak @ Peak @ Load Year EVs 0.9kW/vehicle 1.3kW/vehicle (GWh) (MW) (MW) 2022 2,864 12 2 4 2023 5,978 26 5 8 2024 10,258 48 9 14 2025 15,680 76 14 21 2026 22,232 110 20 30 2027 29,908 153 27 40 2028 38,671 204 35 52 2029 48,465 259 45 66 20...
AI summary The text provides a forecast of peak load and electric vehicle (EV) growth from 2022 to 2032, along with information on solar generation in Nova Scotia. It highlights the discrepancy between forecasted and actual solar installations and their impact on residential load reduction.
Total New Year Load (GWh) Peak (MW) Installs 2022 2,610 -24 0 2023 3,947 -36 0 2024 5,351 -49 0 2025 6,825 -63 0 2026 8,505 -78 0 2027 10,420 -95 0 2028 12,604 -115 0 2029 15,093 -138 0 2030 17,931 -164 0 2031 20,724 -185 0 2032 23,069 -20...
AI summary The document discusses the projected load growth from 2022 to 2032, noting that distributed solar and battery storage combinations are not significantly assumed in the 2022 Load Forecast. It highlights the high cost of home batteries compared to gas generators, with the latter being more cost-effective for backup power. New pricing mechanisms like CPP and TOU may encourage battery use, but current costs remain high.
1 The impact of battery storage is being explored through the SGNS project, which will 2 include data collection from both stand-alone battery backup and PV/battery combinations. 3 EV smart charging and using EV batteries (vehicle-to-grid,...
AI summary The SGNS project is exploring the impact of battery storage, including stand-alone and PV/battery combinations, as well as EV smart charging and V2G technology. While V2G is still in development, some vehicles already offer the capability. Early data from the project is being used to estimate potential peak impact mitigation from battery uptake.
oject. 19 These are illustrative estimates based on limited data sets and will be refined as the project 20 continues. 21 22 Figure 30: Potential Peak Impacts from Batteries 23 Residential Share (%) Technology 50% 25% 10% 5% Battery Peak I...
AI summary The document provides illustrative estimates of potential peak impacts from battery technologies under different control scenarios, including no control and optimal demand response control, as part of the 2022 Load Forecast Report.
1 Battery peak estimates are based on the 5 kW batteries utilized in the SGNS project. 2 3 The impacts of technologies related to direct load control (DLC) of heating and hot water 4 loads are discussed in Section 10. 5 6 Intensities 7 8 F...
AI summary The text discusses battery peak estimates from the SGNS project and outlines residential end-use intensities, including categories like heating, cooling, and lighting. It also mentions the modeling of PV and EV outside of regression analysis and references sections and appendices for further details.
ell as smaller appliances such as computers, dehumidifiers, 28 microwaves, etc. This category also includes solar generation (photovoltaic or PV) 29 and EV forecasts. 30 DATE: April 29, 2022 Page 49 of 98 REDACTED (CONFIDENTIAL INFORMATION...
AI summary The document discusses residential and commercial end-use intensities, including trends in heating, cooling, and appliance usage. It highlights the increasing use of heat pumps and the impact on energy demand, as well as the slow decline in lighting and refrigeration due to improved efficiency. Supporting data is referenced in an attachment.
. The end uses listed include: 19 DATE: April 29, 2022 Page 50 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 • Heat: electric heating 2 • Cool: air conditioning 3 • Vent: ventilation 4 • EWHeat: ele...
AI summary The document provides a list of end uses for electricity in commercial settings, including heating, cooling, ventilation, and various other loads. It also references figures and attachments that include historical and projected end-use intensities for small general and general commercial sectors.
2022 Load Forecast Report REDACTED 1 Figure 33: Historical and Projected General Commercial End-Use Intensity 2 (kWh/m2) 3 4 5 Supporting data for General commercial end-use intensities is included in Attachment 3. 6 7 For the 2022 Load Fo...
AI summary The 2022 Load Forecast Report discusses historical and projected general commercial end-use intensity, noting increased heat pump penetration. It highlights growth in the commercial and industrial sectors due to electrification programs aimed at reducing emissions and energy usage.
of 2 electricity while providing benefits to the system (such as through the interruptible rider). 3 4 Figure 34: Commercial and Industrial Electrification Forecasts (cumulative) 5 Coinc. SmGen GenDemand LrgGen SmInd MedInd LrgInd Year Pea...
AI summary The text presents a table showing forecasts for commercial and industrial electrification from 2022 to 2032, with data on electricity generation and demand across different sectors. The data includes cumulative values for various categories such as small generation, generation demand, and large industrial consumption.
e 55 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The 2022 Load Forecast Report provides an analysis of electricity demand trends, incorporating factors such as heating and cooling degree days, and outlines the projected load requirements for the year.
1 4.6 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the 4 use of electricity in Nova Scotia, and the forecast takes the projected energy and demand 5 savings into account. NS Powe...
AI summary The document discusses Demand Side Management (DSM) in Nova Scotia, highlighting the use of DSM targets approved by the Board in matter M09096. It explains the challenges of double-counting DSM savings in forecasting models and the approach to address this issue by incorporating historical DSM savings.
To address the issue of double counting, the approach used is the same as that used in prior 24 forecasts: to introduce cumulative historical DSM savings as reported by E1 to the 25 regression model as a load modifying variable, and allow...
AI summary The text discusses the approach to address double counting in load forecasts by using cumulative historical DSM savings as a load modifying variable in a regression model. It references prior regulatory decisions and filings related to DSM resource plans and efficiency programs.
Page 56 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The 2022 Load Forecast Report provides an analysis of projected electricity demand, including factors such as heating and cooling degree days, and includes redacted confidential information.
1 forecast DSM. This does not imply that a portion of DSM activities is not taking place or 2 that forecast DSM is overstated; rather, it is a way of accounting for DSM that is captured 3 elsewhere in the forecast. The methodology is not s...
AI summary The text discusses the methodology for forecasting Demand Side Management (DSM) and its impact on residential and commercial/industrial load models. It explains how historical DSM data improves model accuracy and highlights the percentage of DSM savings captured by other variables in the forecast.
ured by other variables. DSM impacts are not provided for 22 Commercial and Industrial customers by rate class or by month, so by creating a combined 23 model for these classes, the level of uncertainty around allocating historical DSM sav...
AI summary The 2022 Load Forecast Report discusses the impact of Demand Side Management (DSM) on load forecasting, noting a reduction in uncertainty by combining Commercial and Industrial customer data. The DSM variable coefficient is -0.38, indicating future load forecasts should be adjusted by 38% of forecast DSM amounts.
REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 36: Annual Forecast DSM Savings (incremental) 2
AI summary The text references Figure 36 from the 2022 Load Forecast Report, which presents annual forecasted DSM (Demand Side Management) savings in an incremental format. The figure is redacted, so specific details about the savings or methodology are not visible.
Year Forecast Forecast DSM captured DSM captured DSM DSM Residential Commercial by Residential by Comm/Ind Adjustment for Adjustment for DSM and Industrial end use end use Residential with Comm/Ind with savings DSM savings forecast forecas...
AI summary The text presents a table showing DSM captured savings and adjustments for residential and commercial/industrial sectors across multiple years. It also notes that the methodology for determining the DSM coefficient is limited to consistent levels of DSM in historical data.
used to determine the DSM coefficient only works for levels of DSM 5 that have been relatively consistent throughout the historical data set and does not imply 6 that only a portion of future DSM will impact sales. If forecasts for DSM cha...
AI summary The text discusses the methodology used to determine the DSM coefficient, noting that it works only for consistent historical levels and may require revision if future DSM forecasts change significantly. It suggests treating incremental DSM levels above historical norms as not embedded in forecast variables.
Page 59 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 forecast is that there will be a certain amount of continued work-from-home load, likely 2 through hybrid work models. 3 4 Apart from the shift...
AI summary The 2022 Load Forecast Report discusses the impact of increased work-from-home trends, higher EV penetration, and electric space heating on long-term load forecasts. These factors are expected to influence load patterns starting around 2025, with new customer growth offsetting some efficiency gains and solar generation.
Year Regression New Solar EV RTR DSM Total Total DSM Model Customers Impact Impact Sales Adjustment Sales Res. captured by Output (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) DSM end uses 24 25 (GWh) (GWh) (GWH) 2022 4,704 56 -31 12 0 -25 4,715 -50...
AI summary The text presents a table showing energy-related metrics over the years, including regression model outputs, new customers, solar and EV impacts, RTR sales, DSM adjustments, and total sales. The data reflects trends in energy usage and demand-side management from 2022 to 2032.
-281 4,862 -549 -268 2031 4,730 268 -160 403 -3 -315 4,922 -616 -300 2032 4,764 282 -176 510 -3 -349 5,027 -681 -332 3 4 Figure 41 provides an approximation of the heat pump heating, heat pump cooling, electric 5 baseboard, and water heate...
AI summary The text discusses the methodology used to approximate system-level loads from heat pumps, electric baseboard heating, and water heaters using regression models and data from the 2020 Load Forecast. It notes that these numbers are illustrative and do not include DSM amounts or account for potential differences in how X variables apply to various end uses.
RMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 41: Illustrative Contribution of Specific End Uses 2 Year HP Heat HP Cool Baseboard Heat Water Heat (GWh) (GWh) (GWh) (GWh) 2022 607 74 1096 710 2023 656 81 1059 725 2024 705 87...
AI summary The 2022 Load Forecast Report provides an illustrative breakdown of energy consumption by specific end uses, including heat pump heating and cooling, baseboard heating, and water heating, across the years 2022 to 2032. The data shows projected trends in energy usage for these categories over time.
1 6.0 COMMERICAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General 4 rate classes. Like the residential model, the commercial SAE models express monthly sales 5 as a function of heating, coolin...
AI summary The Commercial SAE model forecasts energy use for the Small General and General rate classes in Nova Scotia, factoring in heating, cooling, and other loads. The model uses end-use intensity projections, GDP, employment, and weather data. The impact of the pandemic on commercial sales was moderate, with a rebound expected in 2022.
TION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 43 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 44. Small General 8 service...
AI summary The 2022 Load Forecast Report discusses historical and forecasted Small General Service loads, noting an average annual increase of 0.5 percent. Commercial electrification is expected to add 18 GWh by 2032, but this will be offset by demand-side management (DSM) and decreased intensity forecasts for ventilation, lighting, and miscellaneous end uses.
ED) 2022 Load Forecast Report REDACTED 1 Figure 44: 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 2022 6 to 2032. Total change between 2022 an...
AI summary The 2022 Load Forecast Report indicates a 5.5 percent increase in total load from 2022 to 2032. General class load is projected to decline by 0.4 percent annually over the 10-year forecast period, with increased space heating partially offset by demand-side management (DSM) programs and improved efficiency in lighting and miscellaneous end uses.
2022 Load Forecast Report REDACTED 1 Figure 45: 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 2022 6 to 2032. Total change between 2022 and 2...
AI summary The 2022 Load Forecast Report indicates a projected 3.7% decrease in total demand from 2022 to 2032. The Large General Service class remained unchanged from 2020 due to the impacts of the COVID-19 pandemic, with decreased sales in sectors like retail, office, university, and transportation. Customer surveys and historical data are used to forecast demand, with flat load levels assumed in the absence of survey data.
98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 annual survey: two indicated no change, four indicated a decrease, seven indicated an 2 increase. Growth in this class is expected to be driven by institut...
AI summary The 2022 Load Forecast Report discusses the expected increase in electricity demand, driven by institutional facilities, particularly hospital expansions in Halifax and Sydney. The forecast projects an increase of approximately 50 GWh by 2032.
2022 Load Forecast Report REDACTED 1 Figure 48: 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 report discusses the forecasting of load for various industrial rate classes, including Large Industrial and Extra Large Industrial, using customer surveys and historical sales data. Survey responses indicate mixed expectations for energy consumption changes, with some customers expecting increases, decreases, or no change.
Page 74 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The document presents the 2022 Load Forecast Report, which includes confidential information that has been redacted. The report likely discusses electricity demand projections for the year 2022.
ith energy exports are not included. Figure 51 provides a breakdown of the 7 significant variances between forecast and actuals for 2021. 8 9 Figure 51: 2021 Variance to Actual 10 Res Comm Ind Other Losses NSR 2021 Forecast 4,718 3,070 2,4...
AI summary The document discusses the 2021 variance between forecast and actual energy usage, noting significant differences driven by weather impacts and unexplained variances, particularly in residential and commercial classes due to ongoing effects of COVID-19. It also forecasts an annual increase in NSR from 2022 to 2032, driven by new customers, space heating, and EV adoption, with solar and DSM offsetting some sales.
AL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 52: Historical and Forecast Annual NSR 2 3 4 5 Figure 53 provides a breakdown of the various components of the change in the forecast 6 from 2022 to 2032. Data for all cla...
AI summary The 2022 Load Forecast Report provides historical and forecast data on Net System Requirement (NSR) and its components, including residential, commercial, industrial, and other load categories, from 2022 to 2032. It details factors influencing the forecast, such as new customers, solar adoption, electric vehicle growth, and demand-side management (DSM) impacts.
1,519 Total DSM forecast -681 -574 -102 -7 -124 -1,487 DSM captured in underlying -358 -377 -46 -4 -68 -853 models 3 DATE: April 29, 2022 Page 79 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED
AI summary The text provides a portion of a 2022 Load Forecast Report, including a table with DSM forecast data and a redacted section. It highlights the difference between total DSM forecasts and those captured in underlying models, with figures showing a decrease over time.
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 text defines total system peak demand and explains how NS Power forecasts peak demand using an end-use approach. It includes peak mitigation strategies such as EVs and demand response (DR) activities, referencing the 2020 Integrated Resource Plan (IRP) and DSM Potential Study. DR programs like Direct Load Control and Critical Peak Pricing are highlighted.
nt. The achievable potential of these programs was used in the 25 Load Forecast. NS Power's IRP action plan has targeted 75 MW of capacity for DR 26 deployment by 2025. The estimates in the Load Forecast have been moved back by one 27 year...
AI summary The Load Forecast Report discusses the achievable potential of demand response (DR) programs, aligning with NS Power's Integrated Resource Plan (IRP) target of 75 MW of capacity by 2025. The forecast uses an effective load carrying capacity (ELCC) of 48% to account for intermittency, with reassessment planned as more data is gathered.
2022 Load Forecast Report REDACTED 1 Annual DR totals by program are provided in Figure 54. 2 3 Figure 54: Demand Response 4
AI summary The text references Figure 54, which provides annual Demand Response (DR) totals by program from the 2022 Load Forecast Report. The figure is redacted, so no further details are available.
Year Direct Critical Business, Total Total Load Peak Non-Profit (MW) with Control Pricing & Industrial ELCC (MW) (MW) Curtailment (MW) (MW) 2022 0 1 0 1 0 2023 4 4 1 9 4 2024 12 12 2 26 12 2025 24 22 4 50 24 2026 36 32 6 74 36 2027 39 36 7...
AI summary The text presents a table showing the implementation of Direct Load Control (DLC) and Critical Peak Pricing (DR) programs across various years, highlighting the growth in capacity and participation. A pilot project with E1 is underway to test water heater controls, with early results indicating potential peak savings.
m one group of pilot participants indicate that an average 10 reduction of 0.5 kW of peak savings per unit is achievable. 11 12 NS Power is also working with E1 on a two-phased pilot project to investigate automatic 13 and manual control o...
AI summary NS Power is conducting pilot projects with E1 to explore automatic and manual load control for commercial and industrial customers, aiming to achieve peak savings and develop demand response (DR) capacity. Data from these projects will be used to improve forecast assumptions.
Page 81 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Load Forecasts. The impact of these initiatives, at least within the 10-year timeframe of 2 this forecast, is expected to fall within the sensit...
AI summary The 2022 Load Forecast Report discusses the impact of demand response (DR) programs on load forecasts, noting that DR programs do not inherently reduce demand but can be used as a resource during peak times. The report also explains the change in assumed peak temperature from -15 to -13.7 degrees Celsius based on a 10-year average of coldest evening temperatures.
l as shown in Figure 55, but the 10 year period aligns 12 with the annual HDD estimate and provides a better reflection of current weather trends. 13 14 Figure 55: Peak Temperatures 15 Time Avg Evening Avg Avg Annual Avg Daily Period Peak...
AI summary The text discusses the alignment of a 10-year period with annual heating degree day (HDD) estimates and highlights trends in minimum temperatures over the past 30 years. It also outlines the method for calculating peak contributions from large customer classes and presents a forecast for system peak demand from 2022 to 2032.
N REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 56: Historical and Forecast System Peak (no DR) 2 3 4 5 Figure 56 also shows the January 2022 peak, which occurred at a temperature of -14.6 6 degrees C, and was very close to the fore...
AI summary The 2022 Load Forecast Report discusses historical and forecast system peak demand, noting the January 2022 peak at -14.6°C and a 1.5% annual increase in firm peak demand, which accounts for interruptible and demand response (DR) loads.
OVED) 2022 Load Forecast Report REDACTED 1 Figure 57: Historical and Forecast Firm Peak (including DR) 2 3 4 5 Forecast peak values, firm peak and interruptible peak information can be found in 6 Appendix A. As discussed in Section 4.4, th...
AI summary The 2022 Load Forecast Report discusses the projected growth in peak demand, including contributions from electric vehicles, space heating, and various customer classes. The report highlights the impact of managed charging on EV peak demand and the expected increase in residential and commercial heating demand by 2032.
(including their associated losses), as well as large 2 customer and interruptible customer contributions, and finally DSM. 3 4 Figure 58: Peak Contribution Components (MW) 5
AI summary The text discusses peak contribution components, including large customer and interruptible customer contributions, as well as Demand Side Management (DSM). It references Figure 58, which illustrates these components in MW.
Modeled Res EV DR C&I Large DSM Firm Inter. System Peak Heat (MW) (MW) Elect. Cust. (MW) Peak Cust. Peak (MW) Peak (MW) (MW) (MW) (MW) (MW) (MW) 2022 1,920 7 3 -0 10 99 -18 2021 144 2,165 2032 1,993 120 103 -37 193 112 -141 2342 152 2,532...
AI summary The document discusses system peak demand in Nova Scotia, highlighting the 2021 system peak of 1,968 MW and the factors influencing peak demand, such as temperature changes and weather conditions. It also provides modeled data for 2022 and 2032, including the impact of EV adoption and demand response programs.
a combination of day of week, time of day, temperature, and 20 weather conditions at both an hourly and daily level; as a result, the peak compared to 21 forecast will be more variable than energy (which considers longer time frames). Figu...
AI summary The 2022 Load Forecast Report discusses the variance between forecasted and actual system peak loads in 2021, highlighting factors such as interruptible load, weather, and unexplained differences. It also mentions the normalization of firm peak for weather and lighting load to align with historical trends.
INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 62: Commercial End-Use Peak Shares 2 3 4 The trend in the Commercial classes shows that the heating component of the peak is 5 expected to increase significantly over the for...
AI summary The 2022 Load Forecast Report discusses trends in commercial end-use peak demand, noting an increase in heating demand due to electrification. NS Power uses interval data and advanced metering infrastructure to refine peak demand modeling and improve forecasting accuracy.
1 11.0 SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, 4 including economics, weather, adoption of distributed generation, electricity rates and 5 DSM. Although each of these...
AI summary The text discusses the uncertainty in sales and peak load forecasts, influenced by factors like economics, weather, and DSM. A P10/P90 probability analysis using Monte Carlo simulations was developed in 2017 to estimate future load distribution, with sensitivity bands shown in Figure 66, highlighting a range of 360-630 GWh over 10 years due to weather and economic variations.
present actual system totals. 25 26 DATE: April 29, 2022 Page 95 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report REDACTED 1 Figure 66: System Energy Sensitivity 2 3 4 Similarly, a P10/P90 scenario was created fo...
AI summary The text discusses the creation of a P10/P90 scenario for peak demand using random sampling of weather and economic drivers, highlighting that peak variance is mainly driven by weather variation, with DSM scenarios falling mostly within the bands.
Interruptible Demand Firm Net System Temp at Contribution to Response Contribution to Growth Peak Peak Year Peak (reduction in Peak Notes Firm Peak only, (%) MW) (MW) (deg C) (MW) (MW) - February 13 2012 141 1,740 1,882 -13.2 -7 weekday ev...
AI summary The table provides data on interruptible demand, firm peak contributions, and net system peak growth for various years, including reductions in firm peak and temperature at peak times. It outlines the contribution of demand response to peak load management and system growth over time.
ends) The AvgEESavings term captures E1’s DSM past reported savings for the residential class. The associated regression coefficient, b4, assesses the portion of embedded DSM activity that is already Page 1 of 31 REDACTED (CONFIDENTIAL INF...
AI summary The document discusses the AvgEESavings term, which captures past reported demand-side management (DSM) savings for the residential class, and the regression coefficient b4, which indicates that DSM activity reduces load. It also explains the use of the COVID variable to account for pandemic-related changes in residential load patterns.
he dependent variable (in this case, sales). To help eliminate this autocorrelation, a moving average, MA, of period 1, MA(1) was added, which estimates the autocorrelation with its the predecessor. Variable Coefficient StdErr T-Stat P-Val...
AI summary The text discusses the use of a moving average (MA(1)) to address autocorrelation in a statistical model where the dependent variable is sales. The model includes various coefficients and statistical values for different variables, such as heating, cooling, and energy efficiency savings, as well as seasonal and event-specific factors.
t Appendix B Page 5 of 32 Appendix B – Forecast Model Details Residential SAE Model Fit Residential Model 2022-2032 Reconciliation The following tables provide details reflecting the changes between 2022 and 2032 forecast years. Some of th...
AI summary The document provides a reconciliation of residential load forecasts between 2022 and 2032, showing changes in customer load, EV load, solar load, and DSM captured. It includes a table with data on existing and new customer usage, energy efficiency savings, and load adjustments.
84) (335) Change 1.3% 4.9% 10.5% -3.1% -0.1% -6.8% 6.6% to load Res Sales = Existing Customer Load + New Customer Load + EV Load + Solar Load + RTR + DSM Existing customer load is calculated as Res Average Use (9,748 kWh/customer in 2022,...
AI summary The text discusses residential load forecasting, including the calculation of existing customer load based on average usage and the inclusion of variables such as heating, cooling, and energy efficiency savings in the forecast model. It also outlines the regression analysis used to estimate residential average use.
2022 Load Forecast Report Appendix B Page 7 of 32 Appendix B – Forecast Model Details Residential Input Variables – XHeat Intensities Econ + Regression Struct Efurn HP Heat Secondary Furnace Fans HeatUse Coeff Total XHeat Heat Variable 202...
AI summary The text provides details on residential input variables for heating and cooling loads in the 2022 Load Forecast Report. It outlines the components and their respective intensities, coefficients, and changes from 2022 to 2032, including the calculation method for XHeat and XCool.
Report Appendix B Page 12 of 32 Appendix B – Forecast Model Details Small General Model Fit Small General 2022-2032 Reconciliation The Small General Demand customer forecast model is constructed like the residential model (including heat p...
AI summary This section of the report discusses the Small General Demand customer forecast model, which is structured similarly to the residential model and includes heat pump programs within the SAE model. Adjustments for commercial and industrial growth programs, PV, and DSM are made outside the regression. The XHeat, XCool, and XOther variables use a flat scaling factor for easier comparison of regression coefficients.
2022 Load Forecast Report Appendix B Page 13 of 32 Appendix B – Forecast Model Details Small General Load – Post Regression (GWh) Load from NS Power C&I Solar SG DSM Small Gen Total DSM Regression Electrification adjustment Sales (with SG...
AI summary The 2022 Load Forecast Report Appendix B outlines the calculation of small general load using a regression model. It includes inputs such as average use per customer, customer count forecasts, and adjustments for programs like DSM. The model projects changes in load from 2022 to 2032, including variables like XHeat, XCool, and XOther.
vicem = b1× XHeatm + b2× XCoolm + b3× XOtherm + MBin.Feb18 + MBin.May20 + MBin.Jun20 + SMA(1) Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.641 0.035 18.178 0.00% MStructGen.WtXCool 0.325 0.048 6.807 0.00% MStructGen.WtXO...
AI summary The text presents a statistical model equation and its coefficients, including variables related to heat, cooling, and other factors, along with their standard errors, t-statistics, and p-values. The model is part of a load forecast report and includes a seasonal moving average component.
Appendix B Page 17 of 32 Appendix B – Forecast Model Details General Service Model Fit General Demand 2022-2032 Reconciliation The general demand class, which makes up the largest portion of the commercial sector, is forecast as gross tota...
AI summary This section discusses the General Demand 2022-2032 Reconciliation, focusing on forecasting methods for the general demand class in the commercial sector. It mentions the use of a flat scaling factor and adjustments for NS Power commercial growth programs, including heat pumps, PV, and DSM.
22 Load Forecast Report Appendix B Page 18 of 32 Appendix B – Forecast Model Details General Demand Load – Post Regression (GWh) Load NS Power Solar GD DSM Gen Sales Total DSM Regression C&I Adjustment (with DSM) GD captured Model Electrif...
AI summary The document presents a load forecast report focusing on general demand load and sales, with detailed regression models and input variables. It includes data for 2022 and 2032, highlighting changes in load, sales, and various demand-side management (DSM) factors.
2,375,439 Change 4.9% 0.4% -4.4% 0.0% 1.0% 2.0% Sales = XHeat + XCool + XOther + Binaries + ARMA General Demand Input Variables – WtXHeat Intensities Econ + Struct Regression Heating HeatUse Coeff Total XHeat Variable 2022 493,867 1.26 0.6...
AI summary The text discusses general demand input variables for heating and cooling, including intensities, economic and structural factors, regression coefficients, and scaling factors. It outlines how these variables are used in the load forecast model to calculate XHeat and XCool, with examples of their contributions to overall demand.
0.370 75,451 Change -4.3% 20.4% 0.0% 0.0% 16.1% 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 T...
AI summary The document presents data and formulas related to energy demand forecasting, including variables such as XCool and XOther, which are calculated using intensity values, coefficients, and scaling factors. It outlines input variables for general demand and provides details on an industrial econometric model used for load forecasting.
-0.379 Kurtosis 3.652 Jarque-Bera 8.004 Prob (Jarque-Bera) 0.0183 Page 23 of 31 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix B Page 25 of 32 Appendix B – Forecast Model Details Medium Industrial Model Fit...
AI summary This section presents statistical analysis of a load forecast model, including metrics like kurtosis and the Jarque-Bera test, as well as a detailed description of a combined model for commercial and industrial demand-side management (DSM) coefficients. The model uses weighted variables and binary indicators to explain sales trends.
es trends over time. Binary variables were added similar to those used in the underlying commercial and industrial models. Variable Coefficient StdErr T-Stat P-Value MSales.EESavingsProfiled -0.380 0.121 -3.153 0.21% MStructGen.WtXCool 0.5...
AI summary The text presents statistical results from a load forecasting model, including coefficients and significance levels for various variables related to energy efficiency savings, structural generation weights, and seasonal binaries. The EESavings variable is highlighted as representing the amount of demand-side management needed to explain historical sales trends.
eather sensitive load drivers in each month of the year. OtherLoadm is comprised of: OtherLoadm=ResOtherm + SmlGSOtherm + GSOtherm + SmIndSalesm + MedIndSalesm + UnMSalesm Where ResOtherm, SmlOtherm and GSOtherm are the non-weather depende...
AI summary The text describes the decomposition of load drivers into weather-sensitive and non-weather-dependent components, including the use of a sales model with regression coefficients to isolate non-weather factors such as DSM activities. It also mentions normalization of load requirements and the use of a binary variable to account for the impact of the COVID-19 pandemic starting in 2020.
2022 Load Forecast Report Appendix B Page 32 of 32 Appendix B – Forecast Model Details Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. Although it was...
AI summary This document discusses the 2022 Load Forecast Report, focusing on the peak model fit and forecast comparisons. It highlights the relationship between demand-side management (DSM) and peak demand forecasting, as well as the accuracy of the forecast models used for total energy requirements and system peak demand.
Figure D2 for energy and Figure D3 for peak (both before the impact of DSM). 10th (10%) and 90th (90%) percentiles can easily be obtained from Normal distributions and so they are highlighted in D2. Figure D2: Distribution of Energy (Befor...
AI summary The text discusses probabilistic load forecasting, focusing on energy and peak demand distributions before the impact of demand-side management (DSM). It references figures showing percentile ranges and sensitivity analysis for forecast accuracy.
Appendix D – Forecast Sensitivity Analysis Figure D4: Peak Forecast (Residential, Commercial and Small and Medium Industrial) The asymmetry in this figure, seen as the off-centre median, is explained by the bias introduced by plotting the...
AI summary The document discusses the asymmetry in peak forecast data, attributing it to the use of the MAX function in selecting the highest monthly Peak HDD. It notes that the Monthly HDD has become more influential than Peak HDD in 2022 due to year-round residential heating impacts, leading to a steeper peak demand curve influenced by E3 electrification scenarios.
022 Load Forecast Report Appendix D Page 7 of 9 Appendix D – Forecast Sensitivity Analysis Figure D5: Relative Sensitivity of Peak In terms of the sensitivity of the energy sales forecast to the various input variables, Figures D6 and D7 s...
AI summary The document discusses the sensitivity of energy sales forecasts to various input variables, noting that weather has the strongest impact in the near term while economics becomes dominant in the long term. Demand-side management (DSM) and other factors significantly outweigh the impact of economic, weather, or end-use changes.
REMOVED) REDACTED 2022 Load Forecast Report Appendix D Page 9 of 9 Appendix D – Forecast Sensitivity Analysis Figure D8: Relative Impact of Inputs 2023 Energy 2023 Peak 2032 Energy 2032 Peak Item (GWh (MW) (GWh) (MW) Included in Forecast D...
AI summary The document presents a sensitivity analysis from the 2022 Load Forecast Report, highlighting the impact of various factors such as demand-side management (DSM), solar PV, electric vehicles (EV), and battery storage on energy and peak load forecasts for 2023 and 2032. It includes different scenarios for EV adoption and the effects of weather and economic factors.
2022 Load Forecast Report Appendix E Page 1 of 8 Nova Scotia Power Electrification Support Load Forecast Inputs – Overview April 2022 Liz Mettetal, PhD Sierra Spencer Michaela Levine Arne Olson Dan Aas REDACTED (CONFIDENTIAL INFORMATION RE...
AI summary This document is part of the 2022 Load Forecast Report Appendix E, prepared by Nova Scotia Power with contributions from E3, a consulting firm specializing in engineering, economics, and public policy. The report provides input for load forecasting related to electrification support.
r-complete electrification of transportation and most buildings is a “safe bet” • Measures are lower cost and commercially available to support economy-wide decarbonization Electrification must be pursued in parallel to aggressive power...
AI summary The document discusses the electrification of transportation and buildings as a cost-effective strategy for decarbonization, supported by the PATHWAYS model. It also outlines the forecast for electric vehicle (EV) adoption, assuming 100% electric LDV sales by 2035 and a slow ramp-up to 30% by 2030.
tocks ▪ Light-duty vehicle (LDV) forecast assumes 100% electric LDV sales by 2035, with the base forecasts relying on a “slow sales ramp” scenario that achieves 30% electric LDV sales by 2030 Light-Duty Vehicle Stocks Parcel Truck/MDV Stoc...
AI summary The document discusses light-duty vehicle (LDV) and parcel truck/medium-duty vehicle (MDV) stock forecasts, assuming a transition to electric vehicles (EVs) by 2035 and 2040, respectively. It highlights the impact of these transitions on transportation load shaping processes.
E3 generates forecast of Trip data Charger & EV Demographics Driver Charging attributes Costs & Tariffs transportation load shape based on simulations of EV driving and charging behavior, using travel 1. EV Driving & Charging Simulation...
AI summary The text describes a process for forecasting transportation load shape based on simulations of EV driving and charging behavior using travel survey data. It includes inputs such as vehicle type, charging access, and cost, and outputs like normalized load shapes and charging session statistics.
r vehicle segment X, year Y driving statistics 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2022 Load Forecast Report Appendix E Page 6 of 8 Profiles for light-duty vehicle drivers were developed to inform future charging patterns The d...
AI summary The document outlines the development of light-duty vehicle (LDV) driving profiles based on National Household Travel Survey data, assuming representative driving patterns in Nova Scotia. These profiles are used to inform future EV charging patterns and are input into the EV Load Shape Tool. The average annual mileage is based on historical Nova Scotia VMT statistics.
RMATION REMOVED) 2022 Load Forecast Report Appendix E Page 7 of 8 LDV Charging Profiles Charging profiles represent population-level charging scaled down to one vehicle In unmanaged charging, drivers begin charging immediately upon arr...
AI summary The document discusses LDV charging profiles, distinguishing between unmanaged and managed charging. Unmanaged charging occurs immediately upon arrival, while managed charging shifts timing to reduce costs and flatten peak loads. It also mentions the role of aggregators in managing EV charging and references a heating equipment stock rollover in the appendix.
2022 Load Forecast Report Appendix E Page 8 of 8 Heating Equipment Stock Rollover E3’s electrification study scenarios ultimately yield near-complete electrification of residential and commercial buildings by 2050; to achieve policy targ...
AI summary The 2022 Load Forecast Report Appendix E discusses E3’s electrification study scenarios, which predict near-complete electrification of residential and commercial buildings by 2050, driven largely by heat pump adoption in the 2030s. The analysis assumes rapid growth in heat pump usage, tempered by stock rollover, and notes alignment with NSP forecasts despite data limitations.
N-7Refiled NSPI (CA) RIR 1 to RIR-17 - Redacted
21 passages
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-1: 2 3 Reference Report p. 7: “As with any forecast, there is a degree of uncertainty arou...
AI summary The requestor is asking NS Power to confirm whether Appendix D includes scenario analysis for uncertainties in the 2022 Load Forecast Report, specifically related to weather, economics, and the general warming trend. NS Power confirms that only weather (normal) and economics are considered in the scenario analysis using Monte Carlo simulations.
1 Request IR-4: 2 3 Reference Report pp. 43-44: “The management of charging in this scenario was based on 4 minimizing the cost of electricity to charge with the electric rates referenced being the 5 existing time of use tariffs that are c...
AI summary Request IR-4 seeks clarification on NS Power's assumptions about managed EV charging demand reductions, weather impacts on EV loads, and traffic data reviews. NS Power responds by contrasting previous unmanaged charging estimates with E3's models but does not directly address weather or traffic data impacts.
29 coincident peak time of a weekday evening in January at hour ending 1800, so the 30 difference between the E3 models would be 0.6 kW/vehicle. Not all of the charging will Date Filed: July 8, 2022 NSPI (CA) IR-4 Page 1 of 2 REDACTED (CON...
AI summary NSPI discusses challenges in managing EV charging demand during peak winter hours, noting 30% of vehicles remain unmanaged. Temperature impacts EV efficiency and battery performance, though traffic data analysis during peak periods has not been conducted.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-5: 2 3 Reference Report pp. 57-58 regarding the DSM adjustments, please provide workpapers i...
AI summary NSPI responds to a Consumer Advocate request for workpapers on DSM adjustment coefficients, explaining they are derived from regression models in the 2022 Load Forecast Report. NSPI notes an inadvertent error in Figure 36 of the report, correcting the adjustment factor used in the last column while confirming the underlying model remains accurate.
1 REVISED Figure 36: Annual Forecast DSM Savings (incremental) 2 Forecast Forecast DSM captured DSM captured DSM Adjustment DSM Adjustment Residential Commercial by Residential by Comm/Ind for Residential for Comm/Ind Year DSM and Industri...
AI summary The document presents a revised forecast of annual Demand Side Management (DSM) savings for residential and commercial/industrial sectors from 2022 to 2032, including captured DSM and adjustments with coefficients. The data highlights incremental savings and adjustments over time, reflecting NSPI's DSM planning under NSUARB oversight.
56.5 36.2 27.6 37.9 21.5 2032 73.2 55.0 35.7 26.9 37.5 20.9 3 Date Filed: July 8, 2022 NSPI (CA) IR-5 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569)...
AI summary NSPI confirms that the 'DSM captured by end uses' column in the 2022 Load Forecast Report is calculated by subtracting the 'Res DSM Adjustment' from the 'Total Res DSM' column. The forecast uses a regression model incorporating past DSM activity, price, appliance efficiency, and economic variables. NS Power lacks historical appliance-level DSM data, relying instead on class-level annual savings.
appliance level or building shell level DSM data, only annually reported savings at the 25 class level. The treatment of DSM in the forecast is outlined in section 4.6 of the Report. Date Filed: July 8, 2022 NSPI (CA) IR-6 Page 1 of 1 REDA...
AI summary The document references the treatment of demand-side management (DSM) data in the 10-Year Energy and Demand Forecast, noting annual class-level savings reporting. It cites Section 4.6 of the Report and mentions NSPI's responses to the Consumer Advocate's information requests, with the matter numbered NSUARB M10569.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-7: 2 3 Reference Appendix B, p. 32: “Although it was not possible to produce a peak model wit...
AI summary NSPI confirms that the DSM adjustment coefficients (pp. 57-58) relate to the peak model analysis. The regression model shows DSM as increasing peak load due to non-negative coefficients, contradicting expectations. Coefficients for cooling and heating variables are provided with statistical details.
mVarsNew.Jan_Other 1.290 0.093 13.799 0.00% mVarsNew.Feb_Other 1.236 0.101 12.266 0.00% mVarsNew.Mar_Other 1.377 0.084 16.369 0.00% mVarsNew.Apr_Other 1.570 0.047 33.165 0.00% mVarsNew.May_Other 1.543 0.035 43.547 0.00% mVarsNew.Jun_Other...
AI summary The text presents statistical model results, including variables (e.g., monthly 'Other' costs) and model metrics (R-squared: 0.976, AIC: 7.87). It references 'AnnualSavings.DSMDemSavings' with a 0.50% value, suggesting analysis of demand-side management savings.
nd Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL Model Statistics Mean Abs. % Err. (MAPE) 2.69% Durbin-Watson Statistic 1.831 Durbin-H Statistic #NA Lj...
AI summary The document discusses statistical metrics of a 10-year energy and demand forecast model (NSUARB M10569), noting a 2.69% MAPE and consistent DSM savings (27 MW/year). NSPI asserts that DSM alignment between energy and peak models ensures the model's validity, citing historical consistency and alignment with energy DSM savings.
Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Reference Report p. 59: “The non-weather variance in 2021 is mainly related to an inc...
AI summary NSPI responds to a consumer advocate's inquiry about factors influencing demand forecasts, noting population growth since 2016, no pandemic-driven migration analysis, and lack of concrete housing policies. The response addresses residential and commercial model assumptions, efficiency in new construction, and forecasted customer growth.
(b) No. Both provincial and municipal governments have discussed targets related to 29 affordable housing and population growth, but no concrete policies or programs have been Date Filed: July 8, 2022 NSPI (CA) IR-8 Page 1 of 2 REDACTED (C...
AI summary NSPI states no concrete policies exist for affordable housing, with housing forecasts relying on Conference Board data. New customer load estimates consider electric heating and building efficiency. Population impacts are modeled via economic variables, not explicitly.
Date Daily Avg Temp Daily Load 10/11/2021 14.02083 25914.76 10/12/2021 15.75417 26505.84 10/13/2021 14.37917 26463.92 10/14/2021 15.03333 26970.68 10/15/2021 12.23333 26588.97 10/16/2021 12.8625 26187.48 10/17/2021 16.49167 26604.07 10/18/...
AI summary The document presents a table showing daily average temperatures and corresponding daily load values from October 11 to December 11, 2021. The data reflects the relationship between temperature and electricity demand over this period.
d Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Reference Report p. 86 Figure 60 “Weather-Normalized Firm Peak.” For items a-d bel...
AI summary The document outlines a request (IR-14) made to NSPI regarding the weather-normalized sales, requirements, and peak load data from the 2022 Load Forecast Report. The request includes detailed inquiries about methodology, calculations, and supporting workpapers for the weather normalization process.
and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 DailyEnergy = Constant + b1×HDD13 + b2×HDD0 + b3×Lag1HDD13 + 2 b4×Lag2HDD13 + b5×JanHDD13 + b6×FebH...
AI summary The text describes a model used to calculate daily energy demand based on temperature variables, including HDD and CDD factors. It references attachments containing model inputs, outputs, and coefficients, and explains the allocation of weather impact across residential, commercial, and municipal sectors. The normalization factor for weather adjustments was updated from 20 MW/degree to 25 MW/degree in 2016, with a revised figure provided for 2019.
2022 LFR CA IR-14 Attachment 2 Page 6 of 15 Variable Coefficients Month HDD13 HDD 13 HDD0 lag1 lag2 CDD18 0 232.686 -97.305 358.84 Summary Jun 2021 heating load 7323 MWh Normal heating load 6995 MWh 2021 Varinace to Normal 328 MWh
AI summary The document provides data on heating load for June 2021, showing a total of 7323 MWh, compared to a normal heating load of 6995 MWh, resulting in a variance of 328 MWh. It also includes variable coefficients and other metrics related to heating degree days and load forecasting.
and Demand Forecast (2020 Load Forecast Report) (NSUARB M09707) NSPI Responses to NSUARB Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 On page 9 of 137, NS Power reports the 2020 growth in system peak at 8.9% and the prior 4 y...
AI summary This text discusses a request for clarification regarding discrepancies in load forecasts and actual demand in Nova Scotia, specifically focusing on the 2019 and 2020 system peak growth rates. The request seeks explanations for the difference between forecast and actual values, including the impact of lighting load and daylight hours.
end lines, so for the February/March morning peak the sensitivity is around 30 MW 16 per degree Celcius, or around 6 MW. 17 18 (v) The variance is discussed in part (i) above. Date Filed: July 2, 2020 NSPI (NSUARB) IR-13 Page 3 of 3 REDACT...
AI summary The text discusses temperature sensitivity during peak demand periods, noting a sensitivity of approximately 30 MW per degree Celsius for February/March morning peaks, with a variance referenced in an earlier section.
n billed sales and NSR. The loss Date Filed: July 8, 2022 NSPI (CA) IR-15 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 10 - Year Energy and Demand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer A...
AI summary The text discusses the use of load research data to estimate peak losses and allocate system peak to different classes, noting that the methodology has a 10% precision target. It also highlights the degradation of sample quality since 2018 due to legacy meters and the potential for AMI data to improve accuracy.
emand Forecast (2022 Load Forecast Report) (NSUARB M10569) NSPI Responses to Consumer Advocate Information Requests NON-CONFIDENTIAL 1 Request IR-16: 2 3 Reference Report p. 77. We understand NS Power’s argument to be as follows: Using the...
AI summary The Consumer Advocate requests clarification on NSPI's use of the 25 MW/°C demand change estimate in weather normalizing load forecasts. NSPI confirms the estimate is not used in the forecast model, and clarifies that weather normalized values are used for explanation and comparison, not as inputs to the model.
1 Request IR-17: 2 3 Respecting the Board’s direction to “evaluate improvements to the weather normalization 4 estimate and examine the impact of incremental cold on loads in the temperature ranges 5 where peak loads occur,” (Report, p. 12...
AI summary The request asks NS Power to explain why it has not made an interim adjustment to its demand change metric following the Board's direction, referencing Wilson testimony from M10109. It also requests a list of tasks for updating the load forecast report. The response indicates that NS Power agreed with the Board's direction to re-evaluate weather normalization and peak load forecasting methods.
N-9Evidence - Synapse
25 passages
Memorandum TO: NOVA SCOTIA UTILITY AND REVIEW BOARD (NSUARB) FROM: DAVID WHITE DATE: JULY 29, 2022 RE: EVIDENCE RE THE NSPI 2022 LOAD FORECAST (M10569) Introduction For many years Nova Scotia Power, Inc. (NSPI) has filed a load forecast re...
AI summary The 2022 NSPI Load Forecast Report shows a 3.4% overall increase, contrasting with recent forecasts predicting modest declines. The forecast incorporates SAE model results, DSM adjustments, and factors like customer growth, with significant increases post-2024 linked to electrification.
Synapse Energy Economics, Inc. Evidence Regarding the NSPI 2022 Load Forecast 1 Figure 1. Net system requirements Source: Synapse from NSPI Figure C1. The historical trend for firm peak demand shows a general increase, as shown in Figure 2...
AI summary The NSPI 2022 Load Forecast predicts a 16% increase in peak demand over 2022–2032, driven by heating electrification, contrasting with prior forecasts of minimal change. DSM programs are credited with reducing energy growth from 15.4% to 3.4% over the same period, highlighting their role in mitigating demand increases.
t DSM programs reduce that increase to a more modest 3.4 percent. These results are consistent with the previous forecast report. Overall, DSM is playing a major role in limiting the energy growth. Synapse Energy Economics, Inc. Evidence R...
AI summary DSM programs reduce energy growth to 3.4% by 2032, with residential sector showing the largest increase. Synapse's analysis supports NSPI's load forecast, highlighting DSM's role in curbing growth.
30% -0.58% Industrial 25% +2.44% Total 100% +3.44% Source: Synapse from NSPI load forecast report. In general, the forecast seems reasonable, but there are significant increases in the energy and peak requirements from the previous forecas...
AI summary The NSPI 2022 load forecast shows significant increases in energy and peak requirements, driven by electrification and growth. Synapse recommends exploring DSM program impacts, stakeholder engagement, and technologies like battery storage to address forecast uncertainties and improve accuracy.
re more fully technologies to control peak space heating and water heating loads. • Explore whether battery storage and solar/battery storage combinations could modify the peak loads. • Provide updates on the water heating load control pro...
AI summary The text outlines initiatives to manage peak loads through battery storage, solar/battery combinations, and DSM program updates. It emphasizes revising pandemic impacts on commercial sales, monitoring DSM savings, and addressing design day temperature changes due to global warming. Heat pumps and water heaters are highlighted as critical for residential energy efficiency.
iven the need for general consistency within Canada as a whole. We note however the inherent uncertainty of all economic forecasts and also that the future may diverge significantly from the forecast. The forecast now gives more considerat...
AI summary NSPI updated its forecast to reflect climate change impacts, adjusting heating and cooling degree day trends. The residential sector, comprising 45% of load, is projected to grow 6.6% with DSM programs, versus 13% without. Additional weather data had minimal impact and was not incorporated.
stomer load. The residential forecast (which includes the effects of DSM programs) increases by 6.6 percent over the forecast period. Without DSM programs, the increase would be roughly twice as much. NSPI changed the economic drivers in t...
AI summary NSPI's residential load forecast shows a 6.6% increase over the forecast period, significantly reduced by DSM programs. The forecast model uses economic drivers like new construction and household compensation, with the SAE model capturing key load factors such as heat pumps and EVs. Historical DSM savings and other variables influence average customer use calculations.
trends. The primary change drivers for XOther are water heat (increased electric heater saturation), reductions in lighting use, and miscellaneous. The net effect is to increase XOther by 1.5 percent. From this one can see that there are m...
AI summary The document analyzes residential energy use factors, noting heating (42%), cooling (2%), and other uses (56%) drive average consumption. Forecasts show slight increases from XHeat (-0.4%), XCool (+1.6%), and XOther (+0.9%), with NSPI applying adjustments for new customers, EVs, solar, RTR markets, and DSM savings. Appendix B provides regression model results and adjustments.
-3.1% -0.1% -6.8% 6.6% load Note: Res Sales = Existing Customer Load + New Customer Load + EV Load + Solar Load + RTR + DSM. Source: NSPI load forecast report Appendix B. Heat pumps The heat pump section of the report discusses replacement...
AI summary The report forecasts heat pump saturation increasing from 35% (2022) to 66% (2032), with residential load changes offsetting due to fossil-to-electric heating replacements. Cooling demand (XCool) rises 78%, but overall residential load increases only 1.3% due to heating efficiency gains. Uncertainty remains about installation modes (sole heat source vs. hybrid systems) and actual saturation rates.
PI provide updates on the water heating load control project in the next load forecast report, including estimates of the impact of hot water heater device control initiatives on system peak demand.13 Electric vehicles Electric vehicles re...
AI summary The document discusses updates on water heating load control and electric vehicle (EV) load growth, noting EVs could contribute 12.5% of vehicle stock by 2032, with energy load estimates of 510 GWh and peak impacts of 89-131 MW. Uncertainty surrounds EV adoption due to supply chain issues and public goals. NSPI's SGNS project tests utility control of EV charging to shift demand to off-peak times, with a request for more SGNS results in future load forecasts.
harging, to shift electric vehicle charging to off-peak times.16 We ask that more complete results of the SGNS project regarding electric vehicle impacts be included in the next load forecast report. Solar generation (PV) Solar generation...
AI summary The text discusses load forecasting considerations for electric vehicles, solar PV, and battery storage, noting their potential impacts. It requests more comprehensive SGNS project data on EV and battery storage impacts, and highlights new customer contributions to residential load growth.
integrated the heat pump and hot water end-uses into the intensity calculations and regression model results rather than treating them separately. However, Figure 41 of the report does provide some 16 Id, p. 44 17 Id, pp. 45-47. 18 Id, pp....
AI summary The analysis integrates heat pump and hot water usage into load forecasting models, noting a net 541 GWh increase from heating/cooling but offset by 549 GWh reduction in baseboard heating. The commercial sector's load decreased by 0.58% over the forecast period, with subsector composition detailed. Electric vehicle adoption drives a 10.5% load increase. The SAE model's key drivers require reevaluation.
e. In 2022, the Small General Service group represented 10 percent of the commercial load, the General Service group represented 75 percent, and the Large General Service group represented 15 percent. Our comments focus on the General Serv...
AI summary The text analyzes commercial load distribution, focusing on the General Service group (75% of commercial load) and highlights a projected 3.7% sales decline by 2032, driven by DSM program reductions (-7.3%) and offsetting factors like electrification (+2.4%). It questions the cost-benefit analysis of commercial electrification programs and notes the absence of COVID-19 variables in models, requiring reevaluation.
e included in the commercial regression models. Results to date indicate that 2022 commercial sales are in line with those of 2019.22 This will need to be reevaluated and updated in the next forecast. Large general sales are expected to in...
AI summary The document discusses updated load forecasts for Nova Scotia's commercial and industrial sectors. Commercial sales in 2022 align with 2019 levels, requiring reevaluation. Industrial forecasts show a 2.4% growth rate, down from prior years, with methodology relying on surveys and estimates due to uncertainty around large customer demand.
egory is based on customer surveys and new customer inquiries. Thus, the methodology is different than for the other sectors and should be considered as an informed estimate rather than a calculation. We note too that the survey of the Lar...
AI summary The industrial energy sales forecast for 2022-2032 incorporates survey data and expansion projections, noting pandemic-driven load reductions and a 3.9% overall increase. The methodology is deemed an estimate due to reliance on customer surveys. Uncertainties include major customer operational changes and unclear DSM effects in the industrial sector, prompting a request for NSPI clarification.
clear in the report how much of the commercial and industrial demand savings presented in Figure 36 are contained in the industrial forecast. We ask NSPI to clarify the DSM effects for each sector. Synapse Energy Economics, Inc. Evidence R...
AI summary The text requests NSPI to clarify the breakdown of commercial and industrial demand savings by sector, question the leveling-off of electrification impacts post-2027, and highlight discrepancies in municipal sector energy data. It also notes that DSM adjustments in the SAE model are half of full savings due to historical data inclusion.
t half as much as the full DSM savings. This is because the SAE model already includes the effects of some of those savings in its statistical equations, which are based on historical data and trends. The residential statistical model incl...
AI summary The document discusses adjustments to residential and commercial/industrial DSM savings forecasts, noting methodological concerns due to significant coefficient changes between years. It highlights discrepancies in the C/I adjustment and requests clarification from NSPI on the methodology's robustness.
a reduction of 2 GWH in 2022 to 24 GWH in 2032. For the medium general load, it goes from 17 to 187 GWh, or 7.9 percent of the load in 2032. No explicit adjustments are indicated for other customers. The adjustments discussed in the foreca...
AI summary The forecast discusses load adjustments, noting a significant increase in system peak and the need to adjust DSM savings factors. Adjustments are deemed reasonable but with statistical uncertainties. Increased DSM savings may require upward adjustments.
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 5. Peak contribution components...
AI summary NSPI models peak demand using historical data and adjustments, with commercial/industrial electrification as the largest growth driver. The 2032 System Peak increases by 350 MW, driven by electrification, residential heating, and EV adoption, though demand response could mitigate some impacts.
end-use. The electrification of vehicles (identified as EV) is also a major growth factor that can be mitigated with time- of-charge controls. We also wonder if more can be done with demand response. The interruptible load representing pri...
AI summary The text discusses concerns about peak load growth driven by electric vehicle adoption and industrial demand, urging NSPI to explore time-of-use rates and expanded demand response measures. It highlights the need for updated forecasts incorporating post-2026 demand response programs from the IRP Action Plan and acknowledges adjustments in load forecasting methodology.
peak shares are shown in Figure 61 for the residential sector and in Figure 62 for the commercial sector. Our understanding is that these contributions are in the Modeled Peak values shown previously. In the NSPI response to E1 IR-9, it wa...
AI summary The text requests NSPI to clarify heat pump performance during peak loads, quantify ETS's role in reducing peak demand, and investigate water heating load control. It notes a shift from resistance heating to heat pumps in residential heating but highlights increased water heating contributions. Induction cooking's potential impact on energy use is also mentioned.
f induction cooking is more efficient than current stoves and its possible effects considered. Induction cooking is a new technology that should be evaluated for its effects on energy and peak loads. For the commercial sector, the heat end...
AI summary The text requests NSPI to evaluate commercial heat use impacts on peak loads and refine peak forecasting methods, citing discrepancies between forecasts and actual data. Sensitivity analyses highlight weather and economic factors as key uncertainties, with ongoing efforts to improve forecasting using class-specific and AMI data.
be biased by outliers; • Testing if Median Household Income provides a more accurate indicator of the level of income in the province is encouraged; and • Consider incorporating household size and age of household residents to determine if...
AI summary Synapse Energy Economics requests NSPI to enhance its 2022 load forecast by investigating heat pump effects, electric vehicle impacts, battery storage, and commercial electrification programs. Recommendations include incorporating household demographics, improving data on load control projects, and evaluating program cost-benefit analyses.
• We also raise a point about the appropriateness of the commercial electrification programs. We ask NSPI to provide further information about their relative benefits and costs (p.14). • It is not clear in the report how much of the commer...
AI summary The text outlines requests for clarification and further analysis from Synapse Energy Economics, Inc. regarding NSPI's 2022 load forecast, focusing on commercial electrification programs, demand savings, EV impacts, time-of-use rates, DR measures, thermal storage, and emerging technologies like induction cooking. Questions emphasize cost-benefit evaluation, sector-specific DSM effects, and load management strategies.
effects on energy and peak loads (p.20). • We ask NSPI to evaluate commercial heat use more fully as to what is driving it and how the peak impacts could be moderated (p.20). • We ask that NSPI review its peak forecasting methodology in li...
AI summary The text requests NSPI to improve peak load forecasting by evaluating commercial heat use, reviewing methodology, and conducting sensitivity analyses. It also supports NSPI's efforts to enhance forecast transparency. Synapse Energy Economics, Inc. provided evidence on the 2022 load forecast.
N-11E1(NSPI) RIR-1 to RIR-2
24 passages
EfficiencyOne (E1) – In the Matter of Nova Scotia Power Incorporated’s (NS Power) 10-Year Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL 1 Request IR-01: 2 3 R...
AI summary EfficiencyOne (E1) responds to Nova Scotia Power's (NS Power) request for data on heat pump installations in Nova Scotia, including forecast vs. actual installations, energy consumption, and savings from 2022 to 2025. E1 references UK and RAP reports highlighting gaps between heat pump deployment targets and actual outcomes.
s by year (kWh and kW demand associated with each heat pump installation 23 or overall). 24 25 (c) The forecast and actual energy and demand savings for heat pump installations by year. Date Filed: 12 September 2022 E1 (NS Power) IR-01 Pag...
AI summary The document outlines NS Power's 10-year energy and demand forecast, focusing on heat pump installations' energy savings by year. EfficiencyOne (E1) provided responses to NS Power's information requests as part of the regulatory proceeding M10569, which includes forecasts and actual energy/demand savings data.
1 (d) The forecast and actual incentive ($) totals associated with heat pump installations by 2 year. 3 4 (e) The average coefficient of performance (COP) of heat pumps installed by year. 5 6 Response IR-01: 7 EfficiencyOne has historicall...
AI summary EfficiencyOne (E1) provides data on heat pump installations under the Green Heat program, including forecast vs. actual installations and COP metrics. Data sources include DSM Evaluation Reports and specific matter numbers (e.g., M03669, M04819). The 2015 DSM Plan was not modeled.
ts’ • Rows 67, 68, 69, 70 • Column K 2015 n/a (2015 DSM Plan was not modelled) M07393, E-1, 2015 DSM Evaluation Reports, Green Heat, Table 13, page 29. 2016 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M07964, E-1, 2016 DSM Evaluation Reports...
AI summary EfficiencyOne (E1) provides responses to NS Power's information requests regarding the 2022 Load Forecast Report, comparing forecasted vs. actual installations from 2015–2017. References include past regulatory matters (e.g., M07393, M06733), DSM evaluation reports, and tables from Green Heat and residential program data.
2018 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M09096, E-5, 2018 DSM Evaluation Reports, • Row 243; (Column AY 1000)/(Column Existing Residential Program, Table 29, page E Column N) 46. • Row 244; (Column AY 1000)/(Column E Column N) 2019...
AI summary The document references multiple years (2018–2024) of DSM Evaluation Reports and technical tables related to Nova Scotia Power's Existing Residential Program. It cites matter numbers (e.g., M06733, M09096) and specific table rows/columns from these reports, indicating ongoing regulatory analysis of program performance and cost metrics.
2024 M10473 E-1(i) Appendix A Attachment 4 2023- n/a 2025 Settlement Plan Measure Level Energy Efficiency Technical Tables • Rows 254, 255, 256, 257 • Column Z 2025 M10473 E-1(i) Appendix A Attachment 4 2023- n/a 2025 Settlement Plan Measu...
AI summary EfficiencyOne (E1) provides data on heat pump energy savings from DSM Plans and technical tables for NS Power’s 2025 Settlement Plan. The document references measure-level energy efficiency data and a 10-year forecast proceeding (M10569).
Power) 10-Year Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL Year Forecast Incentive Level Actual Incentive Level 2013 M04819, E-7 (C ), ENSC (Avon) RIR-11 Ce...
AI summary The document outlines E1's responses to NS Power's information requests regarding energy and demand forecasts, including rebate details for heat pump programs from 2013–2015. It references specific matter numbers (M10569, M04819) and highlights rebate levels for Central Ducted Air Source and Ground Source Heat Pumps, with varying percentages and caps.
October 2, 2015 – December 31, 2015: Ductless Muni-Split Heat Pump: $300 rebate for First Head and $150 rebate for each subsequent head or system. 2016 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 Ductless Mini-Split Heat Pump: $300 rebate •...
AI summary The document outlines rebate amounts for various heat pump models from 2016 to 2020, including ductless mini-split, central ducted, air-to-water, and ground source systems. It references multiple regulatory matters (e.g., M06733, M08604) and evaluation reports related to residential programs and DSM (Demand Side Management) initiatives.
2020 DSM Evaluation Reports, Technical Tables Existing Residential Program, Table 24, page • Rows 248, 255, 256, 257; Column 32. J Column K 2021 M09096 E-1(i) Appendix A Attachment 1 M10473, E-2, 2021 DSM Evaluation Reports, Technical Tabl...
AI summary The document references 2020 and 2021 DSM Evaluation Reports, technical tables, and matter numbers (M09096, M10569) related to Nova Scotia Power's 10-Year Energy and Demand Forecast. EfficiencyOne (E1) provided responses to NS Power's information requests, including data from specific table rows and columns.
E1 (NS Power) IR-01 Page 7 of 7 EfficiencyOne (E1) – In the Matter of Nova Scotia Power Incorporated’s (NS Power) 10-Year Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CON...
AI summary EfficiencyOne (E1) recommends NS Power explore electrification scenarios using electric thermal storage (ETS), wood/pellet stoves, and gas heating systems. E1 requests data on installations, energy consumption, demand savings, and rebate totals for these technologies from rebate inception through the 2025 DSM plan, emphasizing grid flexibility and peak load management.
ETS and wood/pellet stove 24 installations by year. 25 26 (d) The forecast and actual incentive ($) totals associated for both ETS and wood/pellet stove 27 installations by year. Date Filed: 12 September 2022 E1 (NS Power) IR-02 Page 1 of...
AI summary EfficiencyOne (E1) responds to Nova Scotia Power's (NS Power) information requests regarding the 2022 Load Forecast Report, including data on ETS and wood/pellet stove installations, forecast vs. actual incentive totals by year. The proceeding is referenced as M10569.
1 Response IR-02: 2 EfficiencyOne has historically included residential wood/pellet stoves and ETS units (since 2020) 3 through the Home Energy Assessment, and New Home Construction program components, 4 however wood/pellet stove and ETS s...
AI summary EfficiencyOne's Green Heat program component includes data on wood/pellet stove and ETS unit installations, with forecast and actual numbers referenced in tables citing specific matter numbers and documents. Data availability is highlighted for this program compared to others.
Year Forecast Number of Wood/Pellet Stove Actual Number of Wood/Pellet Stove 2018 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M09096, E-5, 2018 DSM Evaluation Reports, • Row 238; (Column AY 1000)/(Column Existing Residential Program, Table 2...
AI summary A table linking forecast and actual numbers of wood/pellet stoves across 2018–2024, referencing DSM evaluation reports and matter numbers (e.g., M06733, M09096). Data sources include technical tables from settlement plans and annual reports.
Year Forecast Number of ETS Actual Number of ETS 2018 n/a n/a 2019 n/a n/a 2020 M09096 E-1(i) Appendix A Attachment 1 M10056, E-1, 2020 DSM Evaluation Reports, Technical Tables Existing Residential Program, Table 30, pages • Rows 157, 158...
AI summary The document presents a table comparing forecasted and actual Energy Efficiency Savings (ETS) across years 2018-2025, referencing specific regulatory matters (e.g., M09096, M10473) and technical tables from DSM evaluation reports. It highlights discrepancies between projected and actual savings for energy and demand reductions, with citations to evaluation reports and technical data.
• Rows 263, 264 • Column Z 1 2 (b) E1 has projected and actual total energy and demand savings for both ETS and wood/pellet 3 stove installations by year as provided in measure level technical tables associated with 4 DSM Plans and annual...
AI summary E1 (EfficiencyOne) provides projected and actual energy savings data for ETS and wood/pellet stove installations but does not track energy consumption in the Green Heat program. Tables in DSM Plans and annual evaluations are referenced for detailed savings data.
Green Heat, Table 14, page 32. 2016 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M07964, E-1, 2016 DSM Evaluation • Rows 238, 240 Reports, Existing Residential Program, Table 28, page 64. • Column AA • Column Z 2017 M06733, E-7 E1 (NSPI) RIR-...
AI summary The text lists references to Green Heat and DSM Evaluation Reports from 2016 to 2021, citing matter numbers (e.g., M06733, M07964) and specific table rows/columns in documents related to Nova Scotia Power Incorporated (NSPI) and EfficiencyOne (E1). It tracks DSM program evaluations across years.
, page 50. • Column AD • Column AC 2021 M09096 E-1(i) Appendix A Attachment 1 Technical M10473, E-2, 2021 DSM Evaluation Tables Reports, Existing Residential • Rows 248, 252 Program, Table 36, page 50. • Column AP • Column AO Date Filed: 1...
AI summary The document references EfficiencyOne's (E1) responses to Nova Scotia Power Incorporated's (NS Power) information requests regarding the 10-Year Energy and Demand Forecast (M10569). It includes technical tables and reports from the 2021 DSM Evaluation, specifically Table 36 on page 50.
Year Forecast Energy Forecast Demand Actual Energy Actual Demand Savings Wood/Pellet Savings Wood/Pellet Savings Savings Stove Stove Wood/Pellet Wood/Pellet Stove Stove 2022 M09096 E-1(i) Appendix A Attachment 1 Technical n/a Tables • Rows...
AI summary The text presents tables comparing forecast and actual energy/demand savings across years (2022-2025), referencing specific regulatory matters (M09096, M10473) and technical appendices. It includes row/column references from '2023-2025 Settlement Plan Measure Level Energy Efficiency Technical Tables' and mentions 'ETS Unit Installations' in energy savings contexts.
Installations Installations 2012 n/a n/a n/a n/a 2013 n/a n/a n/a n/a 2014 n/a n/a n/a n/a 2015 n/a n/a n/a n/a 2016 n/a n/a n/a n/a 2017 n/a n/a n/a n/a 2018 n/a n/a n/a n/a 2019 n/a n/a n/a n/a 2020 M09096 E-1(i) Appendix A Attachment 1...
AI summary EfficiencyOne (E1) responds to Nova Scotia Power Incorporated (NS Power) information requests regarding DSM evaluations and residential programs, referencing matters M09096, M10056, M10473, and M10569. Technical tables and reports from 2020 and 2021 are cited in the context of demand-side management program assessments.
Power) 10-Year Energy and Demand Forecast (2022 Load Forecast Report) – M10569 E1 Responses to NS Power Information Requests NON-CONFIDENTIAL Year Forecast Incentive Level Wood/Pellet Actual Incentive Level Wood/Pellet Stoves Stoves Wood/P...
AI summary The document references the 10-Year Energy and Demand Forecast (2022 Load Forecast Report) and includes responses from E1 to NS Power information requests. It outlines rebate levels for wood/pellet stoves and references various regulatory filings and evaluations related to demand-side management programs.
2, 2017 DSM Evaluation Reports, • Rows 238, 240; Column Existing Residential Program, Table 20, page G Column H 34.
AI summary The text references DSM Evaluation Reports from 2017 and mentions specific rows and columns in Table 20 on page 34, which relate to the Existing Residential Program.
2018 M06733, E-7 E1 (NSPI) RIR-10 Attachment 1 M09096, E-5, 2018 DSM Evaluation Reports, • Rows 238, 240; Column Existing Residential Program, Table 22, page G Column H 31. 2019 n/a (2019 DSM Plan was not modelled) M09651, E-1, 2019 DSM Ev...
AI summary The text lists various years with associated matter numbers and references to DSM Evaluation Reports and technical tables, specifically focusing on the Existing Residential Program and Energy Efficiency Technical Tables across multiple years.
t 4 n/a 2023-2025 Settlement Plan Measure Level Energy Efficiency Technical Tables • Rows 260, 261, 269, 270 • Column K Date Filed: 12 September 2022 E1 (NS Power) IR-02 Page 8 of 9 EfficiencyOne (E1) – In the Matter of Nova Scotia Power I...
AI summary This document is part of a regulatory proceeding involving Nova Scotia Power Incorporated's 10-year energy and demand forecast, specifically the 2022 Load Forecast Report. It includes EfficiencyOne's (E1) responses to information requests from NS Power, focusing on the 2023-2025 Settlement Plan Measure Level and Energy Efficiency Technical Tables.
Year Forecast Incentive Level Wood/Pellet Actual Incentive Level Wood/Pellet Stoves Stoves 2025 M10473 E-1(i) Appendix A Attachment 4 n/a 2023-2025 Settlement Plan Measure Level Energy Efficiency Technical Tables • Rows 260, 261, 269, 270...
AI summary The text presents tables and references to various regulatory matters and documents related to incentive levels for wood/pellet stoves and Energy Efficiency Technical Tables. It includes references to specific regulatory matters, evaluation reports, and program tables.