N-12023 Load Forecast Report + Appendecies - Redacted
69 passages
........................................................ 66 18 7.0 Industrial and Municipal Sectors ....................................................................................... 68 19 7.1 Small Industrial............................
AI summary Redacted sections of the 2023 Load Forecast Report discuss industrial/municipal sectors, system losses, unbilled sales, net system requirements, peak demand, and sensitivity analysis. The document is part of a regulatory proceeding, focusing on energy system planning and forecasting.
57: Hourly Peak Temperature Regression Model Results ................................................ 80 16 Figure 58: 12hr Avg Lag Peak Temperature Regression Model Results ..................................... 80 17 Figure 59: 24hr Avg L...
AI summary The text lists figures analyzing peak temperature regression models, load research data, and forecast accuracy for energy systems. It includes historical and projected system peaks, demand response impacts, and residential/commercial end-use contributions, relevant to energy load forecasting and system reliability analysis.
Page 5 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report addresses electricity demand projections for Nova Scotia, likely involving analysis of heating/cooling degree days (HDD/CDD) and regulatory considerations under the Regulations of Nova Scotia (R.S.N.S.). The redacted content suggests technical energy planning discussions.
1 1.0 EXECUTIVE SUMMARY 2 3 In accordance with the Nova Scotia Wholesale Electricity and Renewable to Retail Market 4 Rules, Nova Scotia Power Incorporated (NS Power, the Company) is required to provide 5 the Nova Scotia Utility and Review...
AI summary NS Power must submit a 10-year load forecast to NSUARB, covering 2023-2033. The forecast considers sales history, weather, economic factors, and uses SAE models. Uncertainties from variables like weather and policy changes are acknowledged.
erage annual increase of 0.7 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 28, 2023 Page 7 of 98 REDACTED (CONFIDENTIAL IN...
AI summary NS Power's 2023 Load Forecast Report projects a 0.7% annual increase in Net System Requirement (NSR) and a 2.3% annual rise in system peak demand, driven by customer growth, electrification, and EV adoption. Demand Side Management (DSM) and Demand Response (DR) programs are expected to mitigate some of this growth.
1 The Board directs NS Power to evaluate the model’s economic inputs, 2 including the COVID-19 variable and the assumptions and calculations 3 used to assess the impact of DSM, Solar PV, EVs, battery storage, and 4 weather. 5 6 The Board a...
AI summary The NSUARB directs NS Power to evaluate economic inputs in its load forecast model, including COVID-19 impacts, DSM, Solar PV, EVs, battery storage, and weather. It emphasizes stakeholder engagement and recommends refining elasticity assumptions, residential model variables, and housing completion data to improve forecast accuracy.
. 26 • A summary of the Smart Grid and Demand Response projects is provided in 27 Sections 4.4 and 10. 28 • A discussion of the economic inputs, including housing completions and median 29 income, is provided in Section 4.3. 30 4 Ibid, pag...
AI summary NS Power held a stakeholder session on March 31, 2023, discussing updates to the 2023 Load Forecast, including impacts of COVID-19, EV growth, space heating, peak temperature assumptions, renewable-to-retail effects, and small-scale solar trends. The session addressed stakeholder questions about a new system peak set in February 2023.
ng forecasting work. 19 20 In addition to the foregoing, NS Power also responded to questions from stakeholders 21 regarding the new all-time system peak set in February of 2023. 22 DATE: April 28, 2023 Page 13 of 98 REDACTED (CONFIDENTIAL...
AI summary NS Power addressed stakeholder inquiries about the new all-time system peak recorded in February 2023 and provided updates on load forecasting efforts. The 2023 Load Forecast Report, though redacted, is referenced as part of the proceedings.
e 16 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 of energy through wind production, with 10 percent serving residential customers, 50 2 percent serving commercial customers, 30 percent serving ind...
AI summary The 2023 Load Forecast Report details energy distribution by customer type (10% residential, 50% commercial, 30% industrial, 10% losses) and explains weather data's impact on electricity sales via HDD/CDD metrics. Peak forecasts remain unchanged as NS Power must serve full peak demand regardless of energy source allocation.
L INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 9: Annual HDD and CDD Over Time 2 3 4 5 Based on stakeholder feedback received in 2022, both a lagged average temperature and 6 wind speed were assessed for their impact on...
AI summary The 2023 Load Forecast Report assesses the impact of lagged average temperature and wind speed on peak loads using linear regression models. Analysis of 10 years of winter load data (November-March) incorporates variables like day of week, wind speed, and temperature averages, with results showing a negative temperature coefficient indicating increased load during colder periods.
9 https://climateatlas.ca/map/canada 10 Overall Warming with Reduced Seasonality: Temperature Change in New England, USA, 1900-2020, Stephen S. Young and Joshua S. Young, 2001 DATE: April 28, 2023 Page 23 of 98 REDACTED (CONFIDENTIAL INFOR...
AI summary The 2023 Load Forecast Report discusses the impact of multiple weather stations on peak load forecasting in Nova Scotia. Historically, the Shearwater RCS station was used, but the report now combines regional load data with appropriate weather stations across four zones: West, Northeast, Metro, and Cape Breton, weighted by their proportion of total system load.
ONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 17: Residential Economic Drivers 2
AI summary The document presents a redacted section of the 2023 Load Forecast Report, specifically Figure 17, which outlines residential economic drivers. Key details have been removed due to confidentiality.
2,898 -8.2 21,381 0.9 13‐22 -0.2 1.2 23‐33 -7.3 1.1 3 4 DATE: April 28, 2023 Page 29 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 18: Commercial Economic Drivers 2
AI summary The document contains a redacted section of the 2023 Load Forecast Report, which includes a figure related to commercial economic drivers. The content appears to be part of a regulatory proceeding and includes redacted confidential information.
42,201 1.3 475 0.5 2033 42,756 1.3 479 0.7 13‐22 1.6 0.7 23‐33 1.4 0.5 3 4 DATE: April 28, 2023 Page 30 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 19: Industrial Economic Drivers 2
AI summary The text includes a redacted section from the 2023 Load Forecast Report, which contains confidential information. It references Figure 19, which discusses industrial economic drivers, but the details are not visible due to redaction.
NS Power E3 HP NS Power Additional Peak E3 HP Year HP Energy Energy HP Peak in Forecast Peak (MW) (GWh) (GWh) (MW) (MW) 2025 100 26 47 47 - 2030 292 142 139 241 102 3 4 E3 used its in-house building electrification model, RESHAPE, to simul...
AI summary The text discusses the use of E3's RESHAPE model to forecast the impact of heat pumps on peak load in Nova Scotia, noting that heat pump efficiency declines at lower temperatures, leading to higher peak demand. Data from Itron in 2021/2022 is referenced to support these findings.
compared to a forecast of 97,000 vehicles by 2032 in the 2022 Load Forecast, which had 3 an estimate of 30 percent of vehicle sales by 2030. 4 5 Figure 26: EV Sales Forecast 6 7 8 9 The impact of EVs on energy sales and peak demand depends...
AI summary The text discusses the forecast of electric vehicle (EV) sales and their impact on energy sales and peak demand. It references E3's EV Load Shaping Tool, which models EV driving and charging behavior in Nova Scotia to estimate load shapes and peak demand contributions.
news articles, or by the default language in the ChargePoint app that customers use, 23 that suggests all customers are eligible to save from off-peak rates. 24 16 M09985 – CI C0010788 – Smart Grid Nova Scotia Project – Semi-Annual Report,...
AI summary The document discusses the impact of electric vehicle (EV) charging on energy and peak demand, referencing figures that illustrate seasonal charging energy delivery, peak demand from EV charging, and estimated energy and peak impacts based on the number of EVs. It also mentions managed charging measures and their potential effect on reducing peak demand.
elow. The end uses listed include: DATE: April 28, 2023 Page 48 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 • Heat: electric heating 2 • Cool: air conditioning 3 • Vent: ventilation 4 • EWHeat: el...
AI summary The text provides a list of end uses for electricity in the Small General Commercial category, including heating, cooling, ventilation, electric water heaters, cooking, refrigeration, lighting, office equipment, and miscellaneous loads. It references historical and projected end-use intensities in Figure 34 and mentions that supporting data is included in Attachment 2.
Page 57 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Apart from the shift related to increased work from home, the long-term trend is higher 2 than the previous forecast, with higher EV penetration...
AI summary The 2023 Load Forecast Report indicates an upward trend in residential electricity demand, driven by increased work from home, higher EV penetration, and new customer growth. Efficiency improvements and solar generation will reduce sales, but overall residential sector loads are expected to increase by 1.1% annually from 2023 to 2033. Population growth and new housing construction are also key factors in the forecast.
of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 42: Residential Sales Components by Year 2
AI summary The document presents a redacted section of the 2023 Load Forecast Report, specifically Figure 42, which outlines residential sales components by year. The content is confidential and has been redacted.
2023 Load Forecast Report REDACTED 1 Figure 42: Residential Sales Components by Year 2
AI summary The text refers to a 2023 Load Forecast Report, which includes a figure titled 'Residential Sales Components by Year.' However, the content is redacted, and no detailed information is provided.
-14 -213 5153 -501 -288 2031 5011 379 -348 433 -14 -241 5220 -568 -327 2032 5055 406 -413 544 -14 -269 5309 -634 -365 2033 5058 431 -485 666 -14 -297 5360 -698 -402 3 4 Figure 43 provides an approximation of the heat pump heating, heat pum...
AI summary The text discusses the methodology used to approximate heat pump and electric load levels at the system level, referencing the Regression Model Output and the response to NSUARB IR-12 (e) from the 2020 Load Forecast. It also notes that total DSM is adjusted for losses and allocated to Municipal class customers.
Page 61 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of projected electricity demand for the year 2023, though specific details have been redacted due to confidentiality.
to the commercial class. In prior 24 forecasts EV load was modeled in the Residential class only, but the data provided by E3 25 breaks out the charging between home, workplace and public charging infrastructure. 26 Approximately 35 percen...
AI summary The text discusses the reclassification of electric vehicle (EV) load from the residential to the commercial class, noting that 35% of EV energy and 30% of peak load are now attributed to the commercial class, resulting in an additional 340 GWh of load by 2033. It also mentions the impact of the pandemic on commercial energy sales, with a specific adjustment made in the General rate class model to account for continued declines.
TION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 45 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 46. Small General 8 service...
AI summary The 2023 Load Forecast Report discusses historical and forecasted Small General Service loads, noting a 2.2% annual increase driven by EV load additions, with commercial electrification of heating offset by DSM and decreased intensity forecasts for ventilation, lighting, and miscellaneous end uses.
ED) 2023 Load Forecast Report REDACTED 1 Figure 46: 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 2023 6 to 2033. Total change between 2023 an...
AI summary The 2023 Load Forecast Report indicates a 24% increase in total load between 2023 and 2033. General class load is expected to rise by 0.4% annually, influenced by EV load additions, space heating trends, DSM programs, and increased efficiency of lighting and miscellaneous end uses. A decrease in sales in 2024 is attributed to RTR participant shifts and higher solar generation.
Page 67 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 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 mode...
AI summary The 2023 Load Forecast Report discusses the Small Industrial class forecast, noting that sales have been flat over the last 10 years but are expected to grow at 1.0 percent annually due to underlying economic growth. The forecast model uses provincial manufacturing GDP as the primary economic variable.
2023 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 document discusses the forecasting of load for various industrial rate classes, including Large Industrial and Extra Large Industrial Active Demand Control. Customer surveys and historical data are used to forecast load, with some customers expecting increased energy consumption due to new facilities and expansions in sectors like mining and manufacturing.
Page 71 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document is a redacted section of the 2023 Load Forecast Report, which likely contains confidential information related to energy demand projections for Nova Scotia.
m 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. 25 DATE: April 28, 2023 Page 72 of 98 REDACTED (CONFIDENTIAL...
AI summary The document discusses the 2023 Load Forecast Report, highlighting system losses and unbilled sales, with system losses averaging 6.4% of NSR over the past five years and projected to remain between 6.0% and 7.0% over the 10-year forecast period. It also defines Net System Requirement (NSR) as the energy required to supply residential, commercial, and industrial sales, plus system losses, excluding certain factors like industrial self-generation and exports.
Page 76 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document is a redacted version of the 2023 Load Forecast Report, which provides an analysis of electricity demand trends and projections for the year 2023. Due to confidentiality, key details have been removed.
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 discusses the definition of total system peak demand in Nova Scotia, focusing on the period from December through February. It outlines NS Power's method of using an end-use approach to forecast peak demand, incorporating factors like heating, cooling, and demand response activities. EVs and DR programs are included in the 2023 Load Forecast, referencing studies and models from E3 and E1.
itical Peak Pricing (CPP) and BNI Curtailment. 25 The achievable potential of these programs was used in the Load Forecast. NS Power's 26 IRP Action Plan has targeted 75 MW of capacity for DR deployment by 2025. The 27 estimates in the Loa...
AI summary The document discusses the use of Demand Response (DR) programs, including Critical Peak Pricing (CPP) and BNI Curtailment, in the Load Forecast. NS Power's IRP Action Plan targets 75 MW of DR capacity by 2025, with forecasts adjusted to align with program development. An effective load carrying capacity (ELCC) of 48% is used to account for the intermittent availability of DR capacity.
Page 78 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The 2023 Load Forecast Report provides an analysis of expected electricity demand in Nova Scotia, though specific details have been redacted due to confidentiality.
1 control program offering in 2023, providing a total available peak savings of approximately 2 1 MW by early 2024. 3 4 NS Power is also working with E1 on a two-phase pilot project to investigate automatic 5 and manual control of various...
AI summary NS Power is implementing a demand response (DR) program with E1, aiming for 1 MW of peak savings by early 2024. A two-phase pilot project with commercial and industrial customers is underway, targeting 6.8 MW of peak mitigation. Data from these initiatives will be used to refine load forecasts and is expected to impact the 10-year forecast within the sensitivity analysis.
2.68617 0.092519 29.03359 3.1E-183 2.504829187 2.867510616 2.504829187 2.867510616 3 24hrAvgLag -28.0236 0.140797 -199.035 0 -28.299554 -27.74762015 -28.299554 -27.74762015 4 5 The 12-hour average lagged temperature provides the best model...
AI summary The text discusses a regression model used to predict peak electricity demand, highlighting the 12-hour average lagged temperature as the best fit based on R-squared metrics. Key coefficients include impacts from weekdays, wind speed, and temperature changes on peak demand.
Figure 61 below. DATE: April 28, 2023 Page 81 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 61: 24hr Avg Lag Peak Temperature Regression Model Results 2 3 4 The two estimations produce simila...
AI summary The document discusses load forecasting for the period 2023 to 2033, highlighting a 2.3 percent annual increase in the forecast system peak and a 2.2 percent annual increase in the firm peak, which accounts for demand response and interruptible load.
OVED) 2023 Load Forecast Report REDACTED 1 Figure 63: 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. 7 8 Normalizing the firm peak f...
AI summary The document discusses historical and forecasted firm peak load data, including demand response (DR), and references a weather-normalized analysis of firm peak load trends as shown in Figure 64. The report includes appendices with detailed peak load information.
REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 64: Weather-Normalized Firm Peak (including DR) 2 3 4 5 Figure 65 below shows the breakdown of the peak forecast by the various components. 6 7 Figure 65: Peak Contribution Components (M...
AI summary The 2023 Load Forecast Report provides a breakdown of peak contribution components, including modeled peak, residential heating, EV impact, demand response, commercial and industrial loads, large customers, DSM programs, and system peak. The report compares forecasted values for 2023 and 2033, with and without EV mitigation.
Page 85 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document presents the 2023 Load Forecast Report, which includes redacted confidential information. It outlines projections and analysis related to electricity demand for the year 2023.
re of -22.8°C and a daily average windspeed of 8 28.4 km/h. A breakdown of the actual peak compared to the forecast peak from the 2023 9 Load Forecast is provided in Figure 67. 10 DATE: April 28, 2023 Page 87 of 98 REDACTED (CONFIDENTIAL I...
AI summary The document discusses a 2023 Load Forecast Report, highlighting a forecast peak variance compared to the actual peak in February 2023, with specific weather conditions noted, including a temperature of -22.8°C and a daily average windspeed of 28.4 km/h.
ED) 2023 Load Forecast Report REDACTED 1 Figure 67: 2023 Forecast Peak Variance vs 2023 February Peak 2
AI summary The document presents a redacted section of the 2023 Load Forecast Report, specifically highlighting Figure 67, which compares the 2023 forecast peak variance with the February 2023 peak. This figure is likely used to analyze load forecasting accuracy and variability.
the heating, cooling and other variables across the Residential and 20 Commercial classes. To illustrate the impact of the individual end uses on the peak for the 21 individual classes, the coincident peak contribution of each class (Resid...
AI summary The document discusses the analysis of peak load contributions from different end uses in residential and commercial classes. It highlights the stable contributions over the forecast period, with a notable increase in electric vehicle (EV) contribution from 2.4% to 7.5% by 2032.
Page 89 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document is a redacted section of the 2023 Load Forecast Report, which likely contains confidential information related to energy demand projections and analysis for the year 2023.
1 The trend in the Commercial classes shows that the heating component of the peak is 2 expected to increase significantly over the forecast period, driven by the increased space 3 heating electrification and EVs. All other categories decr...
AI summary The text discusses trends in Commercial class peak demand, noting an expected increase due to electrification and EVs, while other categories decline. NS Power uses interval data and load research to forecast peak demand at the class level, with ongoing research using AMI data and load research data.
active 22 number of customers in that month (extracted from NS Power’s billing system). When 23 aggregated, the load research data follows system generation (blue line) closely. 24 DATE: April 28, 2023 Page 90 of 98 REDACTED (CONFIDENTIAL...
AI summary The document discusses load research data from 2022, comparing monthly load data with system generation, and references figures that illustrate the alignment between load research data and system generation, including an annual peak.
Page 91 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Losses can be estimated by comparing the sum of load research sales to system generation 2 and deriving hourly, monthly, and yearly system losse...
AI summary The 2023 Load Forecast Report discusses methods for estimating system losses by comparing load research sales to system generation, emphasizing the potential benefits of a load research approach over the current top-down method. It also highlights the use of load research data post-COVID-19 and the impact of 2022's warm weather on residential peak demand forecasts.
2023 Load Forecast Report REDACTED 1 Figure 72: Monthly historical Residential LRS load at peak and forecasts 2 3 4 5 Both the current residential peak demand forecast (green line) and the LRS peak 6 experimental model (black line) are des...
AI summary The 2023 Load Forecast Report discusses residential peak demand forecasts and experimental models, comparing them with actual AMI data. AMI meter coverage reached nearly 90% by the end of 2022, enabling more accurate forecasting methods to be tested.
D (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 73: AMI Peak Estimates 2 2023 Residential System Coincident Peak Forecast Evaluation AMI AMI LRS based % diff w AMI Top-down % diff w AMI adjusted to Weather...
AI summary The document presents a comparison of AMI peak estimates with other forecasting methods, including LRS-based bottom-up models and the top-down weather-normalized system load factor approach, highlighting differences in residential load forecasts for January.
ast, including sensitivity analysis. DATE: April 28, 2023 Page 94 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED
AI summary The document provides a 2023 Load Forecast Report, which includes a sensitivity analysis. The report is redacted and contains confidential information, with the date April 28, 2023, and is on page 94 of 98.
,142 1.7% 2,436 -1.8% 138 99.0% 742 11,288 1.4% 2024 4,847 0.4% 3,074 -2.2% 2,414 -0.9% 99 -27.8% 734 11,168 -1.1% 2025 4,846 0.0% 3,074 0.0% 2,445 1.3% 99 0.0% 734 11,199 0.3% 2026 4,901 1.1% 3,098 0.8% 2,489 1.8% 99 -0.3% 741 11,327 1.1%...
AI summary The text presents a table with forecasted values for various metrics across years 2023 to 2033, including demand and load forecasts. The table is part of the 2023 Load Forecast Report Appendix A, which provides detailed data for analysis.
Interruptible Demand Firm Net System Temp at 12hr Lag Contribution to Response Contribution Growth Peak Peak Temp Year Peak (reduction in to Peak Notes Firm Peak only, (%) MW) (MW) (deg C) (deg C) (MW) (MW) - January 24 weekday 2013 136 1,...
AI summary The table presents data on interruptible demand, firm peak contributions, and net system peak growth over several years, including temperature measurements and notes on specific dates and times. It highlights the relationship between demand response and peak load management.
XOther = OtherUse × OtherIndex Where OtherUse = f(Seasonal Use Pattern, Household Income, Household Size, and Price) OtherIndex = g(Other Appliance Saturation and Efficiency Trends) The AvgEESavings term captures E1’s DSM past reported sav...
AI summary The document discusses the inclusion of DSM activity in load forecasts, using regression coefficients to quantify the impact of demand-side management on load reduction. It also introduces a COVID variable to model the effects of the pandemic on residential load, including changes in work patterns and long-term impacts.
to explain the increase in residential load from people working from home during the pandemic and stays in the forecast at a reduced level to recognize permanent changes related to working patterns. Binary shift variables are added to the...
AI summary The text discusses modeling residential load changes, including factors like remote work during the pandemic, and the use of binary shift variables and a moving average to improve model accuracy and address autocorrelation in the data.
y the coefficients to calculate the overall impact. For example, the contribution of Efurn is calculated as [(Efurn2031-Efurn2021) x HeatUse x Coeff]/WtXHeat2021 Residential Input Variables – XCool Intensities Econ + Struct Regression Cent...
AI summary The text discusses the calculation of residential input variables, specifically XCool and XOther, using coefficients and intensity factors to forecast load changes between 2023 and 2033. It outlines the methodology for estimating the impact of cooling-related variables on overall load.
2023 Load Forecast Report Appendix B Page 8 of 33 Appendix B – Forecast Model Details Residential Input Variables – XOther Intensities Econ + Reg Struct Water Cook Ref/Frz Wash/ TV Light Misc Other Coeff Total Heat Dry Use Xother Var 2023...
AI summary The document provides details on residential and commercial load forecasting models, including input variables and their projected changes from 2023 to 2033. The residential model includes variables like water heating, cooking, and lighting, while the commercial model uses an SAE average use model and customer forecasts.
23 Load Forecast Report Appendix B Page 11 of 33 Appendix B – Forecast Model Details Small General Model Statistics Model Statistics Iterations 16 Adjusted Observations 120 Deg. of Freedom for Error 110 R-Squared 0.947 Adjusted R-Squared 0...
AI summary This section provides statistical details of a small general load forecast model, including metrics such as R-squared, AIC, and BIC. The model is used for forecasting demand from Small General customers, and adjustments are made outside the regression for factors like commercial and industrial growth, PV, EV, and DSM programs.
Bin.Feb18 + MBin.May20 + MBin.Jun20 + MBin.Oct22 + MBin.Sep22 + MBin.CovidVariable + SMA(1) Variable Coefficient StdErr T-Stat P-Value MStructGen.WtXHeat 0.748 0.033 22.453 0.00% MStructGen.WtXCool 0.714 0.070 10.199 0.00% MStructGen.WtXOt...
AI summary The text provides statistical data from a load forecasting model, including coefficients, standard errors, t-statistics, and p-values for various variables such as MBin.Feb18, MBin.May20, and SMA(1). This data is part of the 2023 Load Forecast Report Appendix B, detailing model statistics for general service.
rial Model MedInd_Salesm = MBin.Janm + MBin.Febm + MBin.Marm + MBin.Aprm + MBin.Maym + MBin.Junm + MBin.Julm + MBin.Augm + MBin.Sepm + MBin.Octm + MBin.Novm + MBin.Decm + MBin.Aft16 + b1×MEcon.ManEmp A binary variable for 2016 and subseque...
AI summary The text presents a statistical model for Medium Industrial sales, incorporating monthly binary variables and a binary variable for years after 2016 to improve model fit. The model uses data from 2006–2022 and includes coefficients, standard errors, T-Statistics, and P-Values for each variable.
3 Load Forecast Report Appendix B Page 28 of 33 Appendix B – Forecast Model Details Combined Model Statistics Model Statistics Iterations 1 Adjusted Observations 108 Deg. of Freedom for Error 101 R-Squared 0.835 Adjusted R-Squared 0.825 AI...
AI summary This section provides statistical details of a load forecast model, including model statistics such as R-squared, AIC, BIC, and error metrics. It also outlines the methodology used for the long-term system peak forecast, which is derived from a monthly peak linear regression model incorporating heating, cooling, base load requirements, and average daily wind.
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 linear regression model used to estimate monthly peak demand based on heating, cooling, base load, and average daily wind. It outlines how heating and cooling load requirements are calculated using coefficients from sales forecast models and normalized on an average MW load basis.
ACTED 2023 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 a...
AI summary The document discusses the sensitivity of energy sales and peak demand forecasts to various input variables. In the near term, weather has the strongest impact, while in the long term, economic factors become more dominant. Demand-side management (DSM), electric vehicles (EVs), and hybrid heating peak mitigation are identified as key drivers of forecast sensitivity.
les in the residential class at the end of 2022, continued use of a COVID variable helps model the change in sales from pre to post COVID until “new normal” is established in data set. • Previous model relied on economic variables to accou...
AI summary The document discusses the impact of the COVID-19 pandemic on residential and commercial electricity sales, the use of a COVID variable in modeling sales changes, and the forecasted increase in electric vehicle (EV) load due to federal ZEV sales targets. The 2023 forecast shows higher EV adoption and increased energy and peak demand compared to the 2022 forecast.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 8 of 21 Electrification of Heating • Using E3 “Current Trends” scenario (same as 2022). • A hybrid scenario was modeled by E3 to show potential reduction...
AI summary The document discusses the electrification of heating, modeling scenarios to assess peak load impacts, and updates to the load forecast model based on feedback from the 2022 review. Wind speed and lagged temperature were identified as significant factors in predicting peak load conditions.
ACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 10 of 21 Peak Conditions, cont. • Regressions were produced for 10 years of hourly winter data (Nov- Mar). • Independent variables were: day of week, windsp...
AI summary The document discusses the regression analysis of peak load conditions using temperature, wind speed, and day of week as variables. The 12-hour prior average temperature showed the strongest relationship with load, as indicated by the highest adjusted R squared value.
riable Coefficient Weekday 30.5 12hr Prior Avg Temp -28.0 Wind 2.7 • The weekday variable coefficient represents an additional 30.5MW of load on weekdays vs weekends, the 12 hr prior average temp variable represents a change of 28MW for ev...
AI summary The document discusses load forecasting variables, including weekday load differences, temperature effects on load, and wind impact. It also mentions the analysis of weighted average temperature for peak load forecasting, which was evaluated at the request of intervenors but not adopted due to no significant differences compared to simpler methods.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 13 of 21 Other • RTR participation is expected to start in 2024 with wind farm being built in 2023 totaling 33.6MW or approximately 130 GWh of annual g...
AI summary The document discusses the expected increase in renewable energy generation, particularly from wind and solar sources, and highlights changes in legislation affecting solar adoption. It also notes the impact of weather and pandemic-related factors on residential load forecasts and customer growth.
age 18 of 21 Forecast Comparison – Peak The peak forecast is similar to the 2022 forecast, and slightly higher by 2032 due to higher EV penetration and increased new customer count. 18 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Loa...
AI summary The peak forecast remains similar to the 2022 forecast but is expected to increase slightly by 2032 due to higher EV penetration and more new customers. The 2022 forecast showed variances compared to actuals, including impacts from weather, residential usage, and wind generation.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 20 of 21 2023 Peak The 2023 peak was unique in that it occurred at noon on a weekend in conditions that haven’t been experienced in Nova Scotia in year...
AI summary The 2023 peak load was significantly higher than forecasted due to extreme cold, a weekend occurrence, and lack of lighting load. Adjustments were made for temperature, wind, and weekend factors. Ongoing work includes integrating AMI data, evaluating electrification impacts, and residential demographic factors.
N-7NSPI (Synapse) RIR-1 to RIR-46 - Redacted
194 passages
41.9 42.4 42.8 41.9 39.0 37.1 38.4 40.2 40.3 40.1 41.3 42.2 39.3 36.2 34.8 34.6 31.7 31.8 32.6 32.6 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...
AI summary The text presents data on energy use and GHG emissions for the residential sector in Nova Scotia, including lighting energy use and heating and cooling degree-day indices over multiple years. It also references the Office of Energy Efficiency and a Load Forecast Report.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-7: 2 3 Residential and Commercial Heating and Heat Pumps (Section 4.4, pp 33-37) 4 5 (a) Please provide...
AI summary The document outlines a series of information requests related to the 2023 Load Forecast Report, focusing on residential and commercial heating technologies, heat pump usage, customer saturation, data sources, peak load impacts, energy consumption estimates, and modeling documentation. These requests aim to gather detailed insights into heating trends and forecast accuracy.
028 488,654 67,035 148,814 281,377 30,058 345,874 140,242 2029 488,654 63,521 152,027 296,880 32,479 363,291 128,253 2030 488,654 60,125 155,212 311,730 34,840 379,986 116,799 2031 488,654 56,841 158,364 325,973 37,139 396,007 105,840 2032...
AI summary The text provides a table with numerical data and references a Load Forecast Report (NSUARB M11108) and NSPI responses to Synapse Energy Economics Information Requests. It includes a note about supplementary heating for new customers, estimated at 35 percent.
on annual sales 4 numbers from heat pump installers in the province. 5 6 (d) The data sources are as follows: 7 8 • End use intensity data provided by NS Power; 9 • reports from Heritage Gas 1; and 10 • EIA Commercial Buildings Energy Cons...
AI summary The document provides data sources for heat pump installation numbers, including reports from NS Power, Heritage Gas, and the EIA Commercial Buildings Energy Consumption Survey. References are made to attachments and a redacted Load Forecast Report submitted to the NSUARB.
t all- 22 electric and all-electric and their space heating energy intensity (kWh/m2). Combined 23 with floor area estimates for a typical small and medium/large commercial customer 24 derived from CBECS, E3 estimated the space heating dem...
AI summary The document discusses the estimation of space heating energy intensity for commercial customers in Nova Scotia, using data from CBECS and NS Power, and scaling a sample of buildings from the New England region to represent the province's commercial heating service demand fuel mix. Load forecast data for 2025 and 2030 are also presented.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-7 Attachment 1 Page 2 of 7 Residential avgResidential Energy Diff E3 Peak 2013 9,737.40 9,737 2023 15.6 Peak impact that is already included 2014 9,597.49 9,5...
AI summary The document presents a load forecast report with residential energy consumption and peak demand data from 2013 to 2033, including historical and projected figures. It outlines trends in energy usage and peak impacts, particularly highlighting increases in peak demand over time.
Peak - Current Fcst Peak - no HP Growth Peak Diff 2023 1 2,010.69 2,010.88 -0.19 2023 2 1,941.02 1,941.18 -0.16 2023 3 1,757.42 1,757.54 -0.12 2023 4 1,346.06 1,346.09 -0.03 2023 5 1,106.07 1,106.07 0.00 2023 6 1,056.07 1,056.06 0.01 2023...
AI summary The text presents a table comparing peak load forecasts with and without heat pump growth for various months in 2023 and 2024. The data shows the differences between the two scenarios, highlighting the impact of heat pump adoption on projected peak demand.
2,127.44 1,948.37 179.07 2032 3 1,938.87 1,795.60 143.27 2032 4 1,433.26 1,373.87 59.39 2032 5 1,156.84 1,133.22 23.62 2032 6 1,081.49 1,079.45 2.04 2032 7 1,261.83 1,264.92 -3.09 2032 8 1,183.49 1,186.27 -2.78 2032 9 1,105.37 1,107.79 -2....
AI summary The text presents numerical data related to energy consumption or financial figures, followed by a reference to a redacted section of the 2023 Load Forecast Report Synapse IR-7 Attachment. The data appears to be part of a regulatory proceeding.
1,817.51 138.84 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-7 Attachment 1 Page 7 of 7 Overall Overall Total Overall Overall % Install % Install Heating Cooling Cumulati % Sat. % Sat. Year Non-Elec. Ele...
AI summary The document contains data and responses related to the 2023 Load Forecast Report, including installation statistics and energy consumption metrics. It also references a regulatory proceeding (NSUARB M11108) and NSPI's responses to Synapse Energy Economics.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-8: 2 3 Residential Water Heaters (WH) (Section 4.4, p 37-38) 4 5 (a) Are more efficient hot water end-u...
AI summary NSPI's response to Synapse Energy Economics' request regarding the 2023 Load Forecast Report discusses the consideration of efficient water heaters, efficiency standards, and the impact of heat pump (HP) adoption on water heating load. It notes that efficiency improvements are factored into the intensity equation based on EIA forecasts.
2027 2,120 Date Filed: June 20, 2023 NSPI (Synapse) IR-8 Page 2 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL Est...
AI summary The document contains a Load Forecast Report from 2023, submitted by NSPI to the NSUARB as part of a regulatory proceeding. It includes estimated electricity intensity values for water heaters from 2027 to 2033, with figures decreasing slightly each year.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests CONFIDENTIAL (Attachment Only) 1 Request IR-9: 2 3 Residential Electric Vehicles (EV) (Section 4.4, pp 38-43) 4 5 (a) Please provide...
AI summary The document outlines information requests from the NSUARB to NSPI regarding the 2023 Load Forecast Report, focusing on residential electric vehicle (EV) sales forecasts, load shaping tools, time of use tariffs, and EV load management assumptions. The requests aim to clarify data sources, methodologies, and assumptions used in the forecasting process.
t and Corresponding ev.energy Grid Signal ...............................24 Figure 22 – Early Example Results from the ev.energy Wind Following Use Case, for the period November 27 to December 1, 2022 .........................................
AI summary The document includes a redacted section from a 2023 Load Forecast Report by Synapse, an attachment from a Smart Grid Semi-Annual Report, and an update report on SGNS use case testing. These documents provide insights into load forecasting, smart grid initiatives, and use case testing related to energy systems.
1 Modification first reported in July 2021 Semi-Annual Report. 2 Modification first reported in February 2022 Semi-Annual Report. 3 Modification first reported in July 2022 Semi-Annual Report. Page 6 of 48 . . REDACTED (CONFIDENTIAL INFORM...
AI summary The document discusses modifications to use cases during the reporting period, including the addition of the Wind Following use case for ev.energy and the BMS10 – Critical Peak Reduction use case to the C&I BMS program. Baseline data collection for smart chargers began in January 2021 and excludes test events, providing insights into energy consumption patterns.
:00 hours. Figure 1 shows the comparison of previously reported average baseline data to the overall average baseline EV consumption for each hour of the day from January 2021 to December 31, 2022. Page 7 of 48 . . REDACTED (CONFIDENTIAL I...
AI summary The document discusses the average baseline EV consumption data over a 24-hour period from January 2021 to December 31, 2022, highlighting that the 22:00 to 23:00 period had the highest consumption. It also explains how ev.energy uses an algorithm to schedule smart charging and presents a counterfactual baseline for comparison.
inter, would result in higher energy requirements in the winter. This will continue to be monitored for analysis in future reporting. Figure 7 – ChargePoint Daily Average Seasonal Energy Delivered Page 11 of 48 . . REDACTED (CONFIDENTIAL I...
AI summary The document discusses seasonal energy delivery data from ChargePoint and baseline energy consumption patterns from ev.energy, noting that data is not yet sufficient for analysis. It also references a figure showing average baseline EV consumption by hour of the day and day of the week, consistent with previous reports.
leaves a gap during early morning hours. This will be improved through additional events across all 24 hours as additional use cases are executed, and further analysis is completed in future reports. As use case testing progresses, more co...
AI summary The text discusses the analysis of power shed opportunities during different event start times, highlighting the need for further testing and analysis to improve confidence in hourly shed potential. This data will be used to evaluate the value of shed capacity in relation to hourly system costs.
c Dispatch: curtailed to 25% of nominal or 50% of nominal charge. Estimated Shed per Event per Opt-In (kW) across all executed events in the ChargePoint fleet is currently 0.97 kW as shown in Table 1. ev.energy EVSE2 use case testing was i...
AI summary The document discusses the implementation and performance of demand response use cases involving EVSE1, EVSE2, and EVSE3, focusing on curtailment levels and load leveling during peak hours. Testing has been conducted with ChargePoint and ev.energy fleets, with specific curtailment percentages and time windows outlined.
as found to be 132.3 kW at 19:00 on February 27, 2022, with 31 chargers connected at the time shown in Figure 17. This peak is aligned to the end of an EVSE3 17:00 to 19:00 curtailment event, where Page 20 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The text discusses observed peak charging power levels during specific time periods, noting a peak of 132.3 kW on February 27, 2022, and 121.6 kW on September 23, 2022. These peaks occurred during Nova Scotia grid peak hours and were influenced by curtailment events and app messaging.
nced by other factors, including messaging within the ChargePoint app. Figure 17 – Screen Capture of the Peak Demand seen by ChargePoint EV Charging Fleet During the 17:00 to 19:00 Period, to Date Figure 18 – Screen Capture of the Peak Dem...
AI summary The document discusses the performance of the ChargePoint EV Charging Fleet, including peak demand patterns and the impact of the ev.energy scheduling algorithm, which created an unintended morning peak in energy delivery. Testing results and future reporting are also mentioned.
gorithm created a new, unintended, morning peak at approximately 05:00. Figure 19 – Actual Energy Delivered to Customers using ev.energy, June 14 to July 17, Before Any Use Case Events were Issued By iterating on the experiment, ev.energy...
AI summary The ev.energy scheduling algorithm initially created an unintended morning peak at 05:00. By introducing a proxy price signal, the algorithm was adjusted to smooth overnight charging, reducing the morning peak. Demand response events during 17:00 to 19:00 showed a 62% reduction in energy delivery during system peak times, with an estimated 1.01 kW shed per event.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 2 Page 30 of 48 Attachment 2 – SGNS Use Case Testing Update Report ‘Estimated Average Reduction per Event (kW)’ is the average actual discharge power observed for an event. The nominal...
AI summary The document discusses NS Power's progress in testing EVSE use cases for demand charge management and load leveling. Testing includes Fermata and Coritech units, with a focus on discharge levels and economic dispatch strategies. Testing for EVSE5 is anticipated to begin in March 2023 once full user control is achieved.
day Economic Dispatch: discharge during the two highest consecutive peak hours of the day. Introduced for Coritech on August 5, 2022. Fermata EVSE2 use case testing was introduced on December 5, 2022. This use case is based on economic dis...
AI summary The document discusses the implementation of EVSE3 and EVSE2 use cases for load leveling and time-varying curtailment, focusing on discharging during peak hours. Testing was introduced for Coritech and Fermata in late 2022, with technical capabilities demonstrated through bi-directional charging and scheduling algorithms.
ed to be a period of high demand for the building. Ideally, the testing period would align with the top monthly demand peaks which would then be reduced – resulting in a reduced monthly demand charge. An ESET student at NSCC has compiled d...
AI summary Testing of bi-directional EV chargers at NSCC showed potential for reducing monthly demand charges, but operational issues and scheduling conflicts hindered full implementation. Automation and integration with building management systems could improve effectiveness, especially in facilities with consistent and substantial peak demand periods.
gainst the highest 6 of 10 earlier baseline days (excluding event days, holidays, and weekends) and applies an adjustment factor to account for potential differences across baseline and event days. Page 44 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The text discusses the methodology for adjusting baseline days against the highest 6 of 10 earlier baseline days, excluding event days, holidays, and weekends. It also references a table summarizing events dispatched to C&I BMS systems since February 2022, including dates and periods.
12/6/2022 2/14/2022 12/12/2022 2/14/2022 Period End Date 12/31/2022 12/31/2022 12/31/2022 12/31/2022 Number of Events 4 14 2 20 Expected Event Participation 12 14 6 32 Received Event Notification 12 14 6 32 Opt-Outs 0 0 0 0 Opt in Ratio 10...
AI summary This document provides data on event participation and load reduction for a load leveling program introduced on December 6, 2022. It includes metrics like the number of events, participation rates, and estimated average reduction per event across multiple sites.
customer load during the highest consecutive peak hours of the day. Max. 4-hr duration. Introduced on December 6, 2022. Estimated Average Reduction per Event (kW) results to date are shown in Table 6. This use case is based on economic dis...
AI summary The document outlines three use cases for load leveling and demand response: BMS1, BMS2, and BMS3. BMS1 and BMS3 have been introduced and show estimated average reduction results. BMS2 testing is ongoing, with plans to proceed once customer notification periods are shortened. Testing and measurement verification have been completed for some cases.
integrated equipment for all three customer sites is ongoing. The use case will continue to be included in the schedule for execution and results and analysis will be presented in future reporting. B M S 5 – GENERATION C ONTINGENC Y A prio...
AI summary The document discusses ongoing integrated equipment implementation for customer sites and details the BMS5 use case for load reduction during generation contingency events. Testing for BMS5 is ongoing, with considerations for customer notification periods and project planning.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 2 Page 47 of 48 Attachment 2 – SGNS Use Case Testing Update Report B M S 10 – C RITIC AL PEAK RED UC TION Maximum 4-hour duration during 07:00 to 11:00 or 17:00 to 21:00 between Decemb...
AI summary The report discusses the implementation of the Critical Peak Reduction use case by NS Power, focusing on reducing load during grid critical peaks through direct control of equipment via the Event Signal Platform (ESP). Results show varying levels of kW savings across three sites, with no customer complaints to date. Future testing will explore more aggressive reductions.
h the aim of increasing load reduction at each customer site, and specifically for Site 1. Figure 36 – Summary of Critical Peak Reduction Event Dispatched to the BMS at Site 1 on December 19, 2022 Page 47 of 48 . . REDACTED (CONFIDENTIAL I...
AI summary The text discusses the dispatch of Critical Peak Reduction Events to Building Management Systems (BMS) at multiple sites on December 19, 2022. It includes figures summarizing these events and a legend indicating the status of data collection for the Smart Grid Semi-Annual Report.
• Day-ahead generation planning • Connection Status & Alarm Status - The availability of EVSE for control during each test (including Economic Dispatch (EVSE1) communication to the chargers, and the customer participation level) • Aggregat...
AI summary The text outlines aspects of day-ahead and intra-day generation planning, focusing on load leveling and demand reduction. It includes details on EVSE monitoring, control signal latency, and the curtailment of EVSE charge power. Economic dispatch and marginal generation costs are also mentioned.
EV charge • Utility event statistics • Energy delivered (kWh) • Value of load curtailed ($/kW, convenient (charging times will Avoided Generation & Demand Reduction) • Intra-day generation planning EV charging patterns influenced by • Day-...
AI summary The text discusses EV charging statistics, energy delivery, load curtailment value, and the influence of EV charging patterns on generation planning, including intra-day and day-ahead planning, as well as unmanaged energy consumption.
• Target reactive power setpoint versus actual reactive power setpoint measured (%) . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 70 of 205 CI C0010788 – Smart Grid Semi-Ann...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and a Smart Grid Semi-Annual Report, focusing on reactive power setpoints and other technical details related to grid management and load forecasting.
• Curtailment of controlled • Connection Status & Alarm Status - The availability of BMS for control during each test (including Customer runs equipment as Building Generation Contingency (10 • Aggregated controlled load (kW) Curtailment o...
AI summary The text discusses aspects of building management systems (BMS) and their role in controlling customer equipment during contingency events, including communication latency, customer participation, and load management. It references controlled load curtailment, marginal generation cost, and net customer load.
(e.g. decreased • BMS monitoring signal latency (seconds) • Value of load curtailed ($/kW, compared with system peaks None Pilot 2/R3C • System load (MW) Peak event being called in order to Capacity BMS10 temperature or dP setpoints, • BMS...
AI summary The text discusses BMS monitoring and control signal latency, response time to curtailment, and the value of load curtailed during peak demand events, with a focus on understanding the impact of load management strategies.
(2) No SIF, but the development of the new product was not part of the SIF iii) Other, please describe below: 5) Between Jan 2021 and Dec 2021, did your business or institution realise any cost savings (2) No from new or significantly impr...
AI summary The text includes redacted sections from a regulatory proceeding, including a Load Forecast Report and a Smart Grid Semi-Annual Report. It references cost savings and processes developed between January 2021 and December 2021, though no specific details are provided due to redaction.
(YYYY-MM-DD) (include prefixes e.g. US, CA, PCT, etc.) Treaty filing? CA, etc.) (YYYY-MM-DD) characters or provide IP? WO, etc.) hyperlink) . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-9 Attachment 3 P...
AI summary The document is a redacted page from the 2023 Load Forecast Report, specifically Attachment 3, which is part of a Synapse IR-9 submission. The content has been confidentially removed, and no specific details are visible.
45,120 2033 437 479 47 52 531 485 50,860 2,372 53,233 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-11: 2...
AI summary NSPI did not consider projected cost declines of home batteries in their load forecast study. They provided an attachment with assumptions behind Figure 32 but did not evaluate electric thermal storage (ETS) for peak load moderation.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-12: 2 3 End-Use Intensities (Section 4.4, pp. 47-50) 4 5 (a) Please provide the underlying calculations...
AI summary The document outlines responses from NSPI to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. The requests pertain to end-use intensities in residential and commercial sectors, with NSPI directing the requestor to specific attachments for detailed calculations.
tions and specific end uses in scope. Date Filed: June 20, 2023 NSPI (Synapse) IR-12 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information R...
AI summary The document discusses the historic trend in electric heating shares and efficiency, noting that slow growth in electric heating has been offset by increased efficiency. However, in the forecast period, electric heating shares are expected to outpace efficiency gains, leading to an increase in overall intensity.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-13: 2 3 Commercial and Industrial Growth (Section 4.4, pp 52-53) 4 5 (a) Please provide the detailed ca...
AI summary NSPI responded to Synapse Energy Economics' request regarding the 2023 Load Forecast Report, providing detailed calculations for commercial and industrial demand growth, including heat pump installations and customer growth factors.
30 motors, pumps). Date Filed: June 20, 2023 NSPI (Synapse) IR-13 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDE...
AI summary The 2023 Load Forecast Report discusses NSPI's engagement with industrial customers, the removal of small and medium commercial growth from the forecast, and the factors influencing growth numbers, including new accounts and project expansions.
at Pump Water Heater (Air-Water) 976.65 976.65 - Calculator V5 - (October 26, 2018) Old vs. new space/water heating system Fossil Fuel Displacement or Brand New System Is there an existing space cooling system OR were t None Customer Rate...
AI summary The text presents a calculation related to energy usage for a centralized heat pump system (Air-Air) under the R12 customer rate class, including assumptions about energy demand and peak load. It also references a 2023 Load Forecast Report and mentions confidential information that has been redacted.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-14: 2 3 Introduction (Section 2.0, citing to Board Decision concerning 2022 Load Forecast) 4 5 (a) How...
AI summary The 2023 Load Forecast Report (NSUARB M11108) addresses the Board’s directives regarding the evaluation of economic inputs, elasticity adjustments, and demographic factors in the load forecast model. NSPI refers to Synapse IR-45 and IR-46 for detailed responses.
136.3 25.0 2027 137.1 25.2 2028 147.0 26.6 2029 135.1 24.5 Date Filed: June 20, 2023 NSPI (Synapse) IR-16 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Ec...
AI summary The document presents load forecast data for various years, including energy consumption in GWh and demand in MW. It references the 2023 Load Forecast Report (NSUARB M11108) and NSPI responses to Synapse Energy Economics information requests. The DSM methodology in the 2023 forecast is noted to be the same as in the 2022 forecast.
29.6 26.5 0.425 0.397 2027 71.6 55.7 9.8 41.2 39.5 30.4 26.0 0.425 0.397 2028 73 62.9 11.1 42.0 44.6 31.0 29.4 0.425 0.397 2029 73.7 52.2 9.2 42.4 37.0 31.3 24.4 0.425 0.397 2030 73.3 51 9 42.1 36.2 31.2 23.8 0.425 0.397 2031 74.1 48 8.5 4...
AI summary The text provides a table with numerical data and references a 2023 Load Forecast Report (NSUARB M11108) and NSPI responses to Synapse Energy Economics information requests. The data appears to relate to load forecasting and energy planning.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-17: 2 3 Demand Side Management Adjustment (Section 4.6, pp 54-56) 4 5 (a) Please provide the details of...
AI summary The document outlines a request for information regarding the Demand Side Management (DSM) adjustment in the 2023 Load Forecast Report. It asks for details on the data and statistical analysis used to develop the DSM coefficient for residential and commercial/industrial sectors, as well as any changes compared to the 2022 report and statistical measures associated with the coefficients.
(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 20, 2023 NSPI (Synapse) IR-17 Page 1...
AI summary The document refers to section 4.6 of the Report for the process used to develop the DSM variable coefficient, and provides details on model fit, statistics, and methodology changes from 2022 to 2023, including the removal of 2020 data and the addition of a binary for October 22 due to billing anomalies from Hurricane Fiona.
tachment 5 Residential Model, 20 filed electronically, in column G of the Inputs tab. Commercial and industrial class 21 amounts are in Attachment 1 in column D of the Inputs tab. Date Filed: June 20, 2023 NSPI (Synapse) IR-17 Page 2 of 2...
AI summary The document references the submission of a residential model and commercial and industrial class amounts in specific tabs and columns of an Excel file, as part of a load forecast report. The filing date is June 20, 2023, and the document is from NSPI (Synapse) IR-17.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 1 of 9
AI summary The document is a redacted version of the 2023 Load Forecast Report, specifically Attachment 1, which is part of Synapse IR-17. The content has been removed due to confidentiality.
0.00 2019 1 373,071.51 57,266.26 0.00 112,180.64 142,159.89 38,806.00 0.00 0.00 0.00 0.00 0.00 2019 2 352,964.42 55,508.64 0.00 113,712.53 135,331.90 38,813.00 0.00 0.00 0.00 0.00 0.00 2019 3 355,810.95 53,744.36 0.00 108,482.34 132,702.32...
AI summary The text includes a table with numerical data for the year 2019, followed by a reference to a redacted section of the 2023 Load Forecast Report by Synapse, Attachment 1, Page 2 of 9.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 2 of 9
AI summary The text is a redacted section of the 2023 Load Forecast Report, specifically Attachment 1, Page 2 of 9, submitted by Synapse. Due to confidentiality, the content is not fully visible.
2.55 128,630.43 39,609.00 0.00 0.00 0.00 0.00 1.00 2025 4 68,827.42 0 98,247.50 135,081.59 39,609.00 0.00 0.00 0.00 0.00 1.00 2025 5 63,208.91 536.4 68,480.38 134,215.99 39,609.00 0.00 0.00 0.00 0.00 1.00 2025 6 56,560.29 5,275.16 41,497.5...
AI summary The text contains a table with numerical data and a reference to the 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 3 of 9, which has been redacted due to confidentiality.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 3 of 9
AI summary The text is a redacted page from the 2023 Load Forecast Report, specifically Attachment 1, page 3 of 9. No substantive content is visible due to redaction, so no summary of key arguments or facts can be provided.
399.28 130,021.29 39,609.00 0.00 0.00 0.00 0.00 1.00 2031 8 56,185.70 84,026.59 3,203.93 132,755.90 39,609.00 0.00 0.00 0.00 0.00 1.00 2031 9 60,462.04 61,305.59 5,784.71 131,210.85 39,609.00 0.00 0.00 0.00 0.00 1.00 REDACTED (CONFIDENTIAL...
AI summary The provided text includes a table with numerical data and a reference to the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, Page 4 of 9. The table contains values related to financial and operational metrics, but the content is partially redacted due to confidentiality.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 4 of 9
AI summary The text is a redacted portion of the 2023 Load Forecast Report, specifically Attachment 1, Page 4 of 9, from Synapse IR-17. It contains confidential information that has been removed.
.36 130,023.47 39,609.00 0.00 0.00 0.00 0.00 1.00 2033 10 66,533.17 14,081.58 27,236.99 129,220.29 39,609.00 0.00 0.00 0.00 0.00 1.00 2033 11 72,729.97 293.69 63,811.87 130,283.99 39,609.00 0.00 0.00 0.00 0.00 1.00 2033 12 76,546.78 0 104,...
AI summary The text contains a table with numerical data and a reference to the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, Page 5 of 9. The content is partially redacted, indicating the presence of confidential information.
1.00 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 5 of 9
AI summary The document is a redacted page from the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, and appears to be part of a regulatory proceeding in Nova Scotia.
49.601 51,174.019 123,531.380 150,601.931 0.000 0.000 0.000 2017 6 296,797.412 -13,110.703 2,535.060 32,161.561 124,566.433 150,645.062 0.000 0.000 0.000 2017 7 287,339.111 -12,696.831 13,158.080 12,185.247 123,851.502 150,841.112 0.000 0....
AI summary The text contains a table of numerical data spanning multiple months and years, likely related to financial or operational metrics. It includes a reference to the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, Page 6 of 9, with a note that certain information has been redacted due to confidentiality.
0.000 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 6 of 9
AI summary The document is a redacted section of the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, page 6 of 9. It contains confidential information that has been removed.
Year Month Pred ESavingsProfile WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct X-Missing 2018 4 329,556.223 -17,848.172 0.000 72,600.702 123,441.088 151,362.606 0.000 0.000 0.000 2018 5 310,352.406 -16,587.919 0.000 52,673.375 122,727....
AI summary The document presents a table containing data from 2018 to 2019, including metrics such as energy savings, cooling and heating weights, and customer numbers. It appears to be related to energy efficiency or demand-side management programs, possibly involving forecasting and load management.
7 0.000 0.000 0.000 2022 9 300,718.111 -23,516.960 42,648.144 2,983.357 123,265.064 155,338.507 0.000 0.000 0.000 2022 10 313,826.220 -26,048.462 7,911.523 14,741.485 122,540.146 155,220.876 0.000 39,460.651 0.000 2022 11 285,290.550 -28,6...
AI summary The text provides numerical data spanning multiple months and years, likely related to financial or operational metrics. It includes values such as load forecasts, costs, and other figures, with the mention of the Load Forecast Report (LFR) and Synapse IR-17 Attachment 1. The data appears to be part of a larger analysis or report.
0.000 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 7 of 9
AI summary The text is a redacted page from the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, page 7 of 9. The content has been removed due to confidentiality.
630.02 -30,360.32 0 73,637.41 119,045.79 155,307.139 0.000 0.000 0.000 2028 1 343,854.99 -31,253.27 0 98,994.32 120,806.81 155,307.139 0.000 0.000 0.000 2028 2 350,603.91 -30,056.25 0 108,601.34 116,751.69 155,307.139 0.000 0.000 0.000 202...
AI summary The text presents a series of numerical entries, likely representing financial or operational data, with some values redacted due to confidentiality. It also references the 2023 Load Forecast Report from Synapse IR-17, Attachment 1, Page 8 of 9.
0.000 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 8 of 9
AI summary The document is a redacted page from the 2023 Load Forecast Report, specifically Synapse IR-17 Attachment 1, and contains confidential information that has been removed.
080.99 -28,846.48 0 118,112.92 109,507.42 155,307.139 0.000 0.000 0.000 2033 4 348,486.32 -27,298.64 0 105,454.60 115,023.22 155,307.139 0.000 0.000 0.000 2033 5 318,384.66 -25,070.20 317.262 73,519.43 114,311.03 155,307.139 0.000 0.000 0....
AI summary The text presents a series of numerical entries, likely related to financial data or load forecasts, with a mention of the 2023 Load Forecast Report, Synapse IR-17, Attachment 1, Page 9 of 9, which has been redacted due to confidentiality.
0.000 0.000 0.000 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-17 Attachment 1 Page 9 of 9 Variable Coefficient StdErr T-Stat P-Value MSales.EESavingsProfiled -0.397 0.129 -3.076 0.27% MStructGen.WtXCool...
AI summary The document presents statistical data from the 2023 Load Forecast Report, including coefficients and p-values for various factors affecting load forecasting. It is part of NSPI's responses to Synapse Energy Economics' information requests in the NSUARB M11108 proceeding.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-18: 2 3 Residential Sector (Section 5) 4 5 (a) Please provide the inputs and calculations used to produ...
AI summary The document outlines a series of information requests from the NSUARB to NSPI regarding the 2023 Load Forecast Report, focusing on residential sector data, calculations, and assumptions related to solar adoption, new customers, and the impact of the COVID-19 pandemic on residential electricity usage.
lculations used to create Figure 43. 28 Date Filed: June 20, 2023 NSPI (Synapse) IR-18 Page 1 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information...
AI summary NSPI responds to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. NSPI notes that there are no specific proposed regulations for improved building shell efficiency and relies on the EIA forecast for efficiency improvements, which would impact heating and cooling loads.
loads would be expected to decline. Date Filed: June 20, 2023 NSPI (Synapse) IR-18 Page 2 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Req...
AI summary The document refers to the 2023 Load Forecast Report (NSUARB M11108) and provides details on how estimated floor space is calculated based on historic data and forecasted additions for residential properties.
NSPI (Synapse) IR-18 Page 3 of 3 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 1 of 6
AI summary The document contains a redacted section of the 2023 Load Forecast Report by Synapse, submitted as Attachment 1 of IR-18, with confidential information removed.
Annual New Cumulative Cumulative Single Family Multi Family Customer Load New Single New Multi Annual Usage Annual Usage Structrual (GWh, before Family Family (kWh) (kWh) change index DSM) 2023 2192 3985 16000 4860 1.000 54 2024 4209 7750...
AI summary The document presents a table outlining annual and cumulative data for new single and multi-family units, their annual energy usage, structural change indices, and customer load in gigawatt-hours (GWh) before DSM. The data spans from 2023 to 2033, showing increasing trends in new units and customer load.
INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 2 of 6 2020 2021 2022 2023 Forecast Sales 4540 4718 4715 4830 Residential Weather variance -91 -141 -127 - Non-weather variance 226 42 263 - Actual Sales 4675 4...
AI summary The 2023 Load Forecast Report provides a comparison of forecasted and actual electricity sales from 2020 to 2023, including adjustments for weather and non-weather factors, highlighting variations in residential demand.
226 42 263 - Actual Sales 4675 4619 4851 - Weather-adjusted Sales 4766 4760 4978 4830 Month Jan-20 Feb-20 Mar-20 Apr-20 May-20 Jun-20 Jul-20 Aug-20 Sep-20 Oct-20 Nov-20 Year 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 Residentia...
AI summary The text presents data on actual and weather-adjusted sales in kilowatt-hours (kWh) for different months in 2020, categorized by residential, commercial, and other sectors, along with losses. The data is part of the Load Forecast Report, which is referenced as Attachment 1 of Synapse IR-18.
FORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 3 of 6 Dec-20 Jan-21 Feb-21 Mar-21 Apr-21 May-21 Jun-21 Jul-21 Aug-21 Sep-21 Oct-21 Nov-21 Dec-21 2020 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021...
AI summary The text presents a portion of the 2023 Load Forecast Report, containing numerical data across multiple months, likely related to energy load forecasting. Due to redaction, specific details and context are not fully available.
INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 4 of 6 Jan-22 Feb-22 Mar-22 Apr-22 May-22 Jun-22 Jul-22 Aug-22 Sep-22 Oct-22 Nov-22 Dec-22 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 2022 5,611,240...
AI summary The document presents data from the 2023 Load Forecast Report, including monthly load figures for the period from January to December 2022. The data includes various load-related metrics and is part of a regulatory proceeding in Nova Scotia.
2,035,725) (4,037,108) REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-18 Attachment 1 Page 5 of 6
AI summary The text includes a redacted section from the 2023 Load Forecast Report, specifically Synapse IR-18 Attachment 1, Page 5 of 6. The content appears to be part of a regulatory proceeding in Nova Scotia, likely related to energy forecasting or load management.
1746 2029 4292 339 5621 2140 251 1496 1738 656 88 3883 1112 169 778 775 341 59 1731 2030 4334 350 5621 2250 261 1414 1753 670 89 3868 1165 179 732 781 347 61 1725 2031 4375 360 5621 2357 270 1335 1768 683 90 3854 1217 187 689 788 353 62 17...
AI summary The text includes a table with numerical data and references to the 2023 Load Forecast Report (NSUARB M11108) and NSPI Responses to Synapse Energy Economics Information Requests. It also contains a note indicating that certain information has been redacted due to confidentiality.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-19: 2 3 End-Use Intensity Trends (Section 4.4) 4 5 (a) The report references critical peak pricing (CPP...
AI summary NSPI responds to Synapse Energy Economics' request regarding the impact of critical peak pricing (CPP) and time-of-use (TOU) rates on EV charging behavior and load forecasts. NSPI assumes 70% of EV charging will be managed through TOU rates and direct control. NSPI plans to file a recommendation on CPP and TOU deployment as part of matter M09777 by June 30, 2023. The load forecast assumes TVP rates will help meet peak savings goals.
al Peak 29 Pricing column). Date Filed: June 20, 2023 NSPI (Synapse) IR-19 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NO...
AI summary NSPI explains the impact of COVID-19 on commercial sector load forecasts, noting reduced energy use in 2021 and 2022. The forecast shows an increase through 2033 due to the shift of EV load to the commercial sector, despite factors like solar adoption and demand-side management reducing sales.
t the EV load, sales would decrease 23 due to increased solar, RTR sales, and DSM. Please refer to Appendix B pages 12 and 18 24 for a breakdown of the components of the forecast. Date Filed: June 20, 2023 NSPI (Synapse) IR-20 Page 1 of 1...
AI summary The 2023 Load Forecast Report indicates that the addition of EV load to the Small General Service class has significantly increased load growth, contributing 21% to the class load and 3.9% overall growth in 2023 compared to 1.5% in 2022. The increase is primarily driven by the heating component and is expected to continue through 2027.
tor to the increase starting in 2027. Date Filed: June 20, 2023 NSPI (Synapse) IR-21 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information R...
AI summary The document is a non-confidential response from NSPI to information requests by Synapse Energy Economics regarding the 2023 Load Forecast Report (NSUARB M11108), filed on June 20, 2023.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-22: 2 3 General Service (Section 6.2). 4 5 (a) Please explain and quantify the specific reasons for the...
AI summary The 2023 Load Forecast Report (NSUARB M11108) outlines responses from NSPI to Synapse Energy Economics' information requests. Key factors affecting load changes include EV load, RTR participation, DSM programs, and increased efficiency. The report notes EV load added 11.4% to class load, while RTR reduced load by 2.7%. DSM program effects decreased slightly compared to the 2022 forecast.
XOther) also show increased growth over the 10 year period, contributing 4.3 percent 29 compared to 2 percent in the 2022 forecast. The increase in the model is mainly due to the Date Filed: June 20, 2023 NSPI (Synapse) IR-22 Page 1 of 2 R...
AI summary The 2023 Load Forecast Report indicates increased growth in heating, EV load, and DSM, with the XHeat variable contributing 5.8% growth over 10 years. The report also mentions a 117 GWh load application by an RTR participant and changes to the commercial net metering program affecting solar forecasts.
in 14 the general class. 15 16 (d) The higher solar forecast in the commercial class is driven by changes to the commercial 17 net metering program. Please refer to E1 IR-07. Date Filed: June 20, 2023 NSPI (Synapse) IR-22 Page 2 of 2 REDAC...
AI summary The document references a higher solar forecast in the commercial class, attributed to changes in the commercial net metering program, with a reference to E1 IR-07. The text also includes a Load Forecast Report (LFR) submitted by NSPI (Synapse) on June 20, 2023.
2023 Load Forecast Report Synapse IR-22 Attachment 1 Page 1 of 1
AI summary The document refers to the 2023 Load Forecast Report by Synapse, attached as IR-22, and is on page 1 of 1. It does not provide further details or discussion of the report's content.
100% 65% 25% 46% 100% 100% 100% 0.747 1.257 0.698 2.949 109.990 1.000 1.000 0.746 1.252 0.698 2.953 109.730 1.000 1.000 2033 100% 65% 25% 46% 100% 100% 100% 0.768 1.265 0.698 2.965 114.416 1.000 1.000 REDACTED (CONFIDENTIAL INFORMATION REM...
AI summary NSPI responded to Synapse Energy Economics' information request regarding the 2023 Load Forecast Report, explaining that changes from 2022 include project delays and revised load estimates. The increase in load is attributed to new institutional facilities, with hospitals accounting for 20 GWh, government buildings for 6 GWh, universities for 5 GWh, and other customers for 3 GWh.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-24: 2 3 Small Industrial (Section 7.1). 4 5 (a) Please expl...
AI summary NSPI explains minor differences between the 2023 and 2022 load forecasts, attributing them to a higher growth rate in manufacturing GDP. The sales forecast is expected to increase steadily through 2026 and remain level thereafter, aligning with economic indicators.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-25: 2 3 Medium Industrial (Section 7.2). 4 5 (a) Please explain and quantify the specific reasons for t...
AI summary NSPI responded to Synapse Energy Economics' request regarding differences in the 2023 Load Forecast Report. The changes are attributed to a decline in manufacturing employment growth and a 38 GWh reduction due to customer load migration to the RTR participant in 2024, resulting in an overall growth rate of -0.7 percent.
the proposed timing and likelihood of Date Filed: June 20, 2023 NSPI (Synapse) IR-26 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information R...
AI summary The document relates to NSPI's responses to Synapse Energy Economics information requests regarding the 2023 Load Forecast Report (NSUARB M11108), including updates to scaling factors as projects progress toward completion.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-27: 2 3 Municipal (Section 7.4). 4 5 (a) Please provide the total municipal loads in GWh and MW over th...
AI summary NSPI provided a response to Synapse Energy Economics' information request regarding municipal load forecasts from 2023 to 2033. The response includes total estimated loads in GWh and peak demand in MW, and notes that load served by NS Power is expected to decrease in 2024 due to customers returning to 100% third-party supply under OATT.
rn to 100 percent third party supply. Date Filed: June 20, 2023 NSPI (Synapse) IR-27 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information R...
AI summary NSPI provides responses to Synapse Energy Economics' information requests regarding system losses and unbilled sales, including historical data, seasonal variations, and assumptions about unmetered sales in the 2023 Load Forecast Report.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-30: 2 3 Peak Demand (Section 10) 4 5 (a) Please provide details about the Demand Response (DR) resource...
AI summary The document outlines information requests and responses related to the 2023 Load Forecast Report by NSPI, focusing on demand response resources, ELCC calculations, peak demand modeling, and data calibration. NSPI refers to a 2019 DSM Potential Study for details on demand response sectors and potentials.
1 (b) The basis for the ELCC of 48 percent is described in the 2023 Load Forecast Report on 2 pages 77 and 78, as follows: “DR forecasts continue to use an effective load carrying 3 capacity (ELCC) of 48 percent to account for the fact tha...
AI summary The text references the 2023 Load Forecast Report and various attachments, discussing the Effective Load Carrying Capacity (ELCC) of 48 percent for demand response (DR), the calibration period for peak models, and how DSM potential is calculated using a blended average coefficient of 49 percent.
ulative DSM amount using a blended average coefficient of 49 percent. Please 25 refer to the table below which has been updated with intervening years between 2022 and 26 2033: 27 Date Filed: June 20, 2023 NSPI (Synapse) IR-30 Page 2 of 3...
AI summary The document references the 2023 Load Forecast Report (NSUARB M11108) and mentions NSPI's response to Synapse Energy Economics' information requests, including a blended average coefficient of 49 percent for DSM.
sults Across Demand Response Scenarios ..........................113 11.6.2 Comparison of Potential Results Across Scenarios ....................................................114 11.7 Demand Response Snapback Effects .......................
AI summary The document outlines a study on energy efficiency and demand response potential in Nova Scotia for the period 2021-2045. It includes sections on demand response scenarios, snapback effects, and concludes with findings on energy efficiency and demand response strategies.
Savings by Sector (MW, gross at generator) .................................................................................................................................................. 43 Figure 5-5. EE Economic Potential, Electricity...
AI summary The document provides a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period from 2021 to 2045. It includes figures related to energy savings by sector and economic potential.
ure 8-18. EE Base Case Market Potential, 2021 Top 40 BNI Measures for Winter Peak Demand Savings (MW, net at generator) ................................................................................................................ 78 Fig...
AI summary The document includes figures related to energy efficiency (EE) market potential, savings, and investment for the period 2021-2045, including residential cumulative achievable potential sensitivity analysis. It references the 2023 Load Forecast Report and is part of a study conducted by Synapse IR-30.
tial Electricity Savings (GWh, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 4 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 13 of 355 Nova Scot...
AI summary The document presents cumulative electricity savings potential from energy efficiency and demand response programs in Nova Scotia over a 25-year period, ranging from more than 2,000 GWh to just under 3,500 GWh (net at generator). It distinguishes between technical and economic potential savings (gross) and market potential (net of freeridership).
Cumulative Savings as a Percent of Total Sales (%) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 7 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 16 of 355 Nova Scot...
AI summary The document presents cumulative savings as a percentage of total sales and discusses the technical and economic potential of energy efficiency and demand response in Nova Scotia over a 25-year period, showing flat savings potential of 2,100 to 2,200 MW (gross at generator) and market potential ranging from 375 MW to 600 MW (net at generator).
arios; (1) maximum achievable, (2) mid case, (3) base case, and (4) low case, and ranges between 375 MW and 600 MW (net at generator) over the 25-year period. Market potential is net of freeridership. Figure ES-8. EE Market Potential Winte...
AI summary The document outlines energy efficiency (EE) and demand response (DR) potential in Nova Scotia, showing winter peak demand savings ranging from 375 MW to 600 MW over 25 years. It also notes that technical and economic savings could reach 85% to 99% annually, largely due to fuel switching in HVAC systems. Line loss differences between studies are highlighted, affecting demand forecasts.
than the base scenario costs due to lower participation levels and lower per participant incentives and marketing costs. The annual portfolio costs (in nominal dollars) are expected to increase from: • $3.3 million in 2021 to $21.4 million...
AI summary The document outlines projected annual portfolio costs for demand response (DR) scenarios from 2021 to 2045, showing increasing costs across base, high, and low scenarios. It also highlights the achievable MW potential and percent of peak load reduction for cost-effective DR options under each scenario, with the base scenario representing the highest potential.
h must be applied to dozens or in some cases hundreds of energy efficiency measures) are limited in their ability to accurately predict adoption for specific measures or in specific customer segments. ©2019 Navigant Consulting, Ltd. Page 1...
AI summary The text discusses the limitations of forecasting models in predicting the adoption of energy efficiency measures, especially at the individual measure or customer segment level. It highlights that while aggregate results can be more reliable, forecasting inaccuracies can occur at the measure-level and may offset each other when aggregated. More detailed techniques exist but are not typically warranted due to increased costs.
Study for 2021-2045 potential results are presented for DR options, sub-options, customer class, and building type for cost- effective DR options. Section 12 – presents the Conclusion of the study. The report also includes the following el...
AI summary The document outlines a study on energy efficiency and demand response potential from 2021 to 2045, including appendices with modeling plans, baseline studies, and model inputs and outputs for residential and commercial sectors.
Electronics & IT Other Source: Navigant 2.3 Fuel Shares Navigant developed fuel share and equipment data for each end use based on the segmentations defined in the previous sections, using the 2019 Nova Scotia Baseline Study results for sp...
AI summary The document outlines the methodology used by Navigant to develop fuel share and end use allocation data based on the 2019 Nova Scotia Baseline Study and the 2018 End Use Intensity Model. This data is used to understand energy consumption patterns and is essential for forecasting and planning purposes.
o EE Definition: Describes the efficient technology set to replace the baseline technology. o Unit Basis: The normalizing unit for energy, demand, cost, and density estimates. ©2019 Navigant Consulting, Ltd. Page 25 . REDACTED (CONFIDENTIA...
AI summary The document discusses the definition of efficient technology and the unit basis for energy, demand, cost, and density estimates in the context of a load forecast report for Nova Scotia's energy efficiency and demand response potential study from 2021 to 2045.
same baseline technology density into a single competition group to avoid the double-counting of savings. (Appendix A provides further explanation on competition groups). 7 See the accompanying model input workbook for density and saturati...
AI summary The document discusses energy efficiency measure characterization approaches, focusing on residential and BNI measures, and outlines methods for analyzing energy and demand savings. It references the Load Forecast Report and mentions the use of a model input workbook for density and saturation sources.
Electric Furnace 22 Residential Control of electric loads by a thermostat Heat Pump 23 DCL Direct Load Control Small Commercial and/or load control switch. HVAC 24 Small Industrial Hot Water Firm capacity reduction commitment. HVAC Large C...
AI summary The text outlines various methods for managing and controlling electric loads, including direct load control, battery control, and EV charging control, across different customer classes such as residential, commercial, and industrial.
BNI Curtailment- Water Heating Control Water Heating BNI Curtailment- Industrial Total Facility BTM Battery Control BTM Battery Control Batteries EV Charging Control EV Charging Control EV CPP with enabling technology Critical Peak Pricing...
AI summary The document outlines various demand response (DR) and energy efficiency strategies, including direct load control (DLC) for residential and small BNI customers, BNI curtailment for large BNI customers, BTM battery control for all customer classes, and EV charging control during peak demand periods. These strategies aim to reduce demand and manage load during peak times.
ive DR Options (Percent of Peak Load) Source: Navigant analysis 11.4.2 Achievable Potential by DR Sub-Option for Cost-Effective DR Options This section presents the breakdown of cost-effective potential by DR sub-option. Each sub-option is...
AI summary This section discusses the achievable potential of demand response (DR) by sub-option, highlighting that customers with enabling technology have nearly double the potential compared to those without. Direct load control of water heaters is the second-highest potential, followed by residential direct load control of heat pumps and electric furnaces.
rnaces and heat pumps, the snapback magnitude during evening peak hours may be the same or even greater than the DR evening peak demand reduction as customer demand increases during the evening hours.
AI summary The text discusses the potential for snapback magnitude during evening peak hours to be equal to or greater than the demand response (DR) evening peak demand reduction as customer demand increases during the evening.
There are differences in snapback estimates between different types of space heating equipment. For example, for heat pumps, the snapback may be much more pronounced than for central furnaces. For a two-stage heat pump, the compressor oper...
AI summary The text discusses differences in snapback estimates for space heating equipment, particularly heat pumps. It explains that heat pumps may experience more pronounced snapback effects compared to central furnaces due to the use of heat strips when the compressor cannot operate at lower temperatures. However, evaluations suggest that the net increase in electricity use is likely minimal over time, and supplemental fuels can mitigate the effect.
ions from Nova Scotia Power and calibrate the bottom-up derived estimates to match the utility’s demand projections. 2.2.3 Step 3: Define Demand Response Options and Characterize Figure 21 presents Navigant’s proposed list of DR options by...
AI summary The document outlines a three-step process for calibrating demand projections and defining demand response (DR) options. It references a proposed list of DR options by market segment, developed by Navigant and approved by E1, with finalization pending stakeholder feedback. It also cites a publicly available dataset from Open EI for load profiles.
water heating. control switch. • Load Control Demand Switch C&I Curtailment Firm capacity reduction commitment. Various load types • Manual General including- heating, $/kW payment based on Demand, • Auto-DR ventilation, lighting, contract...
AI summary The text outlines various load control and demand response mechanisms, including C&I curtailment, electric vehicle charging control, and load control switches, detailing payment structures and the types of loads managed during demand reduction events.
he DR potential study. Figure 22. Key Inputs and Outputs for DR Potential Model Inputs Model Outputs • System load • Customer count by market segment • Load profiles by market segment • Retail sales by rate schedule and by business type (i...
AI summary The text outlines key inputs and outputs for a DR potential study, including system load, customer count, load profiles, retail sales, and participation forecasts. Outputs include the number of DR program participants and winter demand reductions and energy savings by market segment in Nova Scotia.
collects heat from tubes in the ground outside of your building) 7 Other (Please Specify: ) 98 Don’t Know/Not Sure © Narrative Research, 2019 15 . Date Filed: August 14, 2019 Page 15 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 L...
AI summary The text includes a survey response option related to heat collection systems and references a 2019 commercial survey as part of a load forecast report and energy efficiency study. It also contains a redacted section and a page reference from a legal document.
3.5 3.5 3.0 3.4 3.8 2.9 3.7 3.8 2.9 3.5 3.5 3.8 3.5 3.4 3.6 3.4 4.1 3.8 3.5 3.7 Responses of 'Don't know' were excluded from calculation of the mean. 14 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 14 of 54 REDACTED (C...
AI summary The text includes a series of numerical values and a reference to a load forecast report and energy efficiency study. It mentions the exclusion of 'Don't know' responses from a mean calculation and references a confidential document from Synapse IR-30 and a study by Navigant.
83 438 150 269 404 209 535 580 243 368 467 222 409 414 200 623 135 145 133 135 15 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 15 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-3...
AI summary The document includes a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, covering the period 2021-2045. It is part of a regulatory proceeding and includes confidential information that has been redacted.
.5 .7 .6 .5 .6 .5 .6 .6 .8 .7 Responses of 'Don't know' were excluded from calculation of the mean. 18 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 18 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecas...
AI summary The document contains a portion of a load forecast report and an energy efficiency and demand response potential study for Nova Scotia, dated August 14, 2019. It includes statistical data and a reference to a redacted attachment from Synapse IR-30.
1.7 28.1 24.2 26.8 25.8 32.3 32.1 31.4 28.6 Responses of 'Don't know' or above 85 were excluded from calculation of the mean. 20 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 20 of 54 REDACTED (CONFIDENTIAL INFORMATION...
AI summary The document contains statistical data and references to a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study. It includes a narrative research section filed on August 14, 2019, and mentions a redacted confidential attachment from Synapse IR-30.
7.4 8.3 9.2 4.1 8.7 7.2 7.3 6.7 8.4 7.8 This question was randomly posed to approximately one in nine respondents. 35 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 35 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a set of numbers, a statement about a question posed to respondents, and references to a load forecast report and energy efficiency study. It also references a redacted document and a report by Navigant.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 43 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 43 of 54 REDACT...
AI summary The text includes a table of numerical data and a reference to a load forecast report and energy efficiency study. It also mentions a redacted confidential document and a page number from an attachment. The context suggests a regulatory proceeding involving energy forecasting and efficiency analysis.
68 202 244 258 118 164 222 122 342 346 158 215 294 149 243 261 133 371 102 90 59 80 This table excludes those who responded 'Don't know' to any of Q34a-i. 46 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 46 of 54 REDACT...
AI summary The text includes a table of numbers and a reference to a load forecast report and an energy efficiency and demand response potential study, both related to Nova Scotia. The document is redacted and contains information filed on August 14, 2019.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 28 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 28 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 323 of 355 No...
AI summary The text includes a table from a 2019 electricity usage survey for businesses, part of a load forecast report and energy efficiency study for Nova Scotia. The data is redacted and appears in a legal or regulatory proceeding context.
183 112 20 51 104 71 51 48 45 44 61 38 59 108 42 51 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 51 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 346 of 355...
AI summary The document contains pages from a 2023 Load Forecast Report, specifically Appendix C and D, which discuss demand response model inputs and outputs. These pages are part of a regulatory proceeding and were filed electronically. Some pages are intentionally left blank, and the content is partially redacted.
2023 38.3% 15.5% 24.2% 4.9% 8.4% 5.8% 1.1% 1.7% 0.0 2032 52.2% 11.7% 13.8% 3.0% 6.1% 4.8% 0.8% 1.3% 0.1 Please note that all values are in MW. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Respo...
AI summary This document outlines a series of information requests related to the 2023 Load Forecast Report, focusing on peak demand, AMI coverage, data collection timelines, and the impact of DSM on residential load. The requests include details on interval data, loss levels, and the use of smart meter data in future forecasts.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Response IR-31: 2 3 (a) The Load Research analysis presented in Section 10 is based on the premise that the hourly...
AI summary NSPI's response to Synapse Energy Economics' information request discusses the methodology for load forecasting, including the use of load research data and the integration of interval data. It outlines the current SAE model, the use of AMI data, and the planned evaluation of peak demand forecasting methods starting in 2024.
1 (d) The names of the classes are: 2 3 (a) 02: Domestic Non-All-Electric 4 (b) 03: Domestic All-Electric 5 (c) 06: Domestic Time-of-Day 6 (d) 10: Small General 7 (e) 11: General Demand 8 (f) 21: Small Industrial 9 (g) 22: Medium Industria...
AI summary The document outlines various customer classes and provides data on system generation losses compared to scaled load research classes for a peak day in 2022. The analysis of these losses is ongoing, with percentages and magnitudes of losses listed for different times of the day.
9.52% 2022-01-11 18:00:00 208.64 9.42% 2022-01-11 19:00:00 365.22 16.76% 2022-01-11 20:00:00 304.22 14.17% 2022-01-11 21:00:00 381.88 18.11% 2022-01-11 22:00:00 286.57 14.08% 2022-01-11 23:00:00 243.87 12.57% Date Filed: June 20, 2023 NSPI...
AI summary The document presents data from the 2023 Load Forecast Report (NSUARB M11108), including load values and percentages for specific dates and times in January 2022. It is part of NSPI's responses to Synapse Energy Economics information requests and is labeled as non-confidential.
experiment is to compare top-down (current) and bottom-up class peak estimation 10 (experiment), therefore, to compare apples to apples DSM can be removed from both 11 models. 12 Date Filed: June 20, 2023 NSPI (Synapse) IR-31 Page 4 of 5 R...
AI summary The text discusses a comparison between top-down and bottom-up methods for estimating class peak demand, suggesting that removing DSM from both models allows for a more accurate comparison. It also references the 2023 Load Forecast Report and mentions the expectation of having sufficient AMI data for rate classes by the 2024 load forecast report.
Attachment 2 for the inputs used. 30 Date Filed: June 20, 2023 NSPI (Synapse) IR-32 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Re...
AI summary The document discusses challenges in modeling DSM and end uses due to limited data sets and difficulties with the probabilistic approach in the 2023 Load Forecast Report. These issues are under study for inclusion in future models.
IR-32 Page 2 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-32 Attachment 1 Page 1 of 2 Year Actual_Sales p10 p50 p90 2013 11,193.6 2014 11,037.4 2015 11,099.1 2016 10,809.0 2017 10,872.7 2018 11,248....
AI summary This document provides a table showing historical and projected electricity sales data from 2013 to 2033, including actual sales and forecast percentiles (p10, p50, p90) for each year. The data is part of the 2023 Load Forecast Report from Synapse.
14.3 12,306.2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-32 Attachment 1 Page 2 of 2 Year Actual_Peak p10 p50 p90 2013 2,032.7 2014 2,118.2 2015 2,015.0 2016 2,111.0 2017 2,017.6 2018 2,072.5 2019 2,06...
AI summary The document presents a load forecast report from 2023, including actual peak load data for the years 2013 to 2022 and projected values for 2023 to 2033. It also references a regulatory proceeding (NSUARB M11108) and includes responses from NSPI to information requests by Synapse Energy Economics.
1 Request IR-33: 2 3 Sensitivity Analysis (Section 11, 2020 IRP Comparison, pp 97-98) 4 5 (a) Please provide information about how the evergreen IRP analysis might affect future 6 loads. 7 8 (b) For comparison with the previous forecast pl...
AI summary The response to Request IR-33 discusses the impact of the Evergreen IRP analysis on future loads and provides a comparison of load forecasts for 2023 and 2032. The Evergreen IRP results will help NS Power update its IRP Action Plan and inform future load forecasts, particularly as electrification and peak reduction programs progress.
2,106 Electrification 20 Date Filed: June 20, 2023 NSPI (Synapse) IR-33 Page 1 of 1 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests PARTI...
AI summary The document refers to the 2023 Load Forecast Report (NSUARB M11108) and includes NSPI's responses to information requests from Synapse Energy Economics. It is marked as partially confidential and redacted.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests PARTIALLY CONFIDENTIAL
AI summary The 2023 Load Forecast Report (NSUARB M11108) includes NSPI's responses to information requests from Synapse Energy Economics. The document is marked as partially confidential.
1 Request IR-34: 2 3 Appendix A: Forecast 4 5 (a) Please provide in electronic format the specific calculations used to create the values 6 in Tables A1, and A2. 7 8 (b) Please explain the historical changes in the Interruptible Contributi...
AI summary The request asks for the calculations behind Tables A1 and A2 and an explanation of changes in the Interruptible Contribution to Peak values. The response directs to the 2023 Load Forecast Report and provides a table showing historical and forecasted interruptible peak values, noting an expected slow increase from 146-156 MW.
76 2019 111 163 52 2020 96 152 56 2021 94 158 64 2022 155 146 -9 Date Filed: June 20, 2023 NSPI (Synapse) IR-34 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Ene...
AI summary NSPI has been requested by Synapse Energy Economics to provide detailed data, models, and calculations related to the 2023 Load Forecast Report, including residential model parameters, end-use intensity data, and factors influencing heat and cooling usage.
centage for Other. 26 27 (h) Has NSPI collaborated with E1 in developing common assumptions for the end-use 28 components? If so, please describe the results of that collaboration. 29 Date Filed: June 20, 2023 NSPI (Synapse) IR-35 Page 1 o...
AI summary The document contains a portion of NSPI's responses to information requests related to the 2023 Load Forecast Report, specifically addressing collaboration with E1 on common assumptions for end-use components.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Response IR-35: 2 3 (a) Please refer to 2023 Load Forecast Report Attachment 5 - Residential Model, filed 4 electr...
AI summary NSPI provides responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. Key details include heat and cool share forecasts for residential use, references to specific report attachments, and a note on no collaboration in 2022.
Patterns and the economic indicator for that forecast report (Employment Compensation 27 being this year’s). 28 29 (h) No collaboration on this topic was undertaken in 2022. Date Filed: June 20, 2023 NSPI (Synapse) IR-35 Page 2 of 2 REDACT...
AI summary The text mentions the 2023 Load Forecast Report and notes that no collaboration on the topic occurred in 2022. It includes a filing date and a reference to a confidential document.
9 2,249.83 607.97 61.95 1.01 29.78 260.86 0.00 58.87 1.21 2031 1,335.35 2,356.85 621.76 61.11 1.01 30.32 270.40 0.00 59.23 1.22 2032 1,260.74 2,459.29 635.04 60.20 1.01 30.85 279.41 0.00 59.65 1.23 2033 1,190.05 2,552.41 647.75 59.18 1.00...
AI summary The text presents a table with numerical data and a reference to the 2023 Load Forecast Report Synapse IR-35 Attachment 1 Page 2 of 8, which has been redacted due to confidentiality.
354.78 379.60 0.00 1,186.38 0.97 2028 1,709.65 529.47 374.75 45.88 185.08 51.06 48.98 761.21 343.01 376.75 0.00 1,187.15 0.97 2029 1,737.98 529.42 370.21 45.51 183.19 50.93 49.21 761.69 331.54 373.92 0.00 1,186.90 0.97 2030 1,752.69 529.36...
AI summary The text presents numerical data spanning multiple years, likely related to financial or operational metrics. It includes values such as costs, revenues, and other figures, but the content is partially redacted. The mention of the '2023 Load Forecast Report' suggests the data may be related to energy forecasting or planning.
.01 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 2031 1,401.01 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 2032 1,401.01 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 2033 1,401.01 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 REDACTED (CONFIDENTIAL INFORMATIO...
AI summary The text appears to be a portion of a Load Forecast Report from 2023, specifically Attachment 1, Page 4 of 8. The content is partially redacted, indicating that it contains confidential information. The document includes numerical data spanning from 2031 to 2033, likely related to energy load forecasts.
349.5 5,620.7 4,392.0 422.6 5,454.3 1401.00923 0 0 1 1 1 1 0 0 0 0 307.59972 4,594.02 490.69 5,121.63 2031 4,375.1 360.0 5,621.2 4,418.7 439.8 5,455.8 1401.00923 0 0 1 1 1 1 0 0 0 0 307.59972 4,622.00 510.61 5,123.01 2032 4,415.3 369.9 5,6...
AI summary The text presents a table with numerical data and references to a 2023 Load Forecast Report (Synapse IR-35 Attachment 1 Page 6 of 8), which has been redacted due to confidentiality. The table includes figures related to load forecasts and other metrics.
333 (290) 29.3 5,366.2 2031 10,208.5 488,654.0 950.8 13,871.8 2,492.9 29,574.5 532,100.3 16000 4860 1.037016 379.2 5,390.6 (14.0) 433 (348) 70.9 5,461.5 2032 10,297.5 488,654.0 845.1 14,716.9 2,313.3 31,887.8 535,258.8 16000 4860 1.040964...
AI summary The document presents data on residential load forecasts, including customer numbers, EVs, solar installations, and demand-side management (DSM) impacts from 2023 to 2033. It highlights changes in these metrics over the decade, with notable increases in customer numbers and solar installations, and decreases in residential sales due to DSM efforts.
182 57 1.1 1.161 343 2033 31 288 60 1.2 1.161 549 Change to 1.6% 30.9% 0.9% 11.8% 0.0% 60.1% Xother Inputs EWHeat ECook Ref/Frz Washer/Dr TV Light Misc OtherUse VCoeff Total Xother 2023 1,536 530 645 808 407 504 1,236 0.98 0.939 5,198 2033...
AI summary The document presents data from the 2023 Load Forecast Report (NSUARB M11108), including energy usage projections and changes between 2023 and 2033. It includes energy consumption data for various categories and percentage changes, as well as NSPI's responses to Synapse Energy Economics information requests.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-36: 2 3 Appendix B: Small General Service Model (pp 9-14) 4 5 (a) Please provide in electronic spreadsh...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. The responses include references to attachments and methodology used in forecasting software, such as Metrix ND, for calculating variables like XHeat, XCool, and XOther.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 is outlined in Appendix B, Forecast Model Details in the Load Forecast report. The inputs 2 to XHeat, XCool, and X...
AI summary The 2023 Load Forecast Report (NSUARB M11108) outlines the methodology and data inputs used by NSPI in their responses to Synapse Energy Economics information requests. The report includes attachments detailing model components, heat pump growth, and assumptions for PV, EV, and DSM forecasts.
TIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 1 of 11
AI summary The text references the 2023 Load Forecast Report, specifically Synapse IR-36 Attachment 1, which is part of a regulatory proceeding in Nova Scotia. The document appears to be a technical attachment related to load forecasting and may be used in a regulatory process.
35,888.06 1.400 2026 64,913.84 1.270 35,772.68 1.430 2027 65,800.60 1.280 35,659.92 1.460 2028 67,031.99 1.290 35,457.38 1.490 2029 68,185.27 1.290 35,350.68 1.520 2030 68,838.53 1.300 35,246.40 1.550 2031 69,852.87 1.300 35,144.50 1.580 2...
AI summary The text presents numerical data related to load forecasts and associated rates for various years from 2026 to 2033. It includes values and percentages, likely representing projections for energy usage and costs. The document is part of a Load Forecast Report and contains confidential information that has been redacted.
NTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 2 of 11
AI summary The text references the 2023 Load Forecast Report (LFR) and mentions Synapse IR-36 Attachment 1, which is part of a document from a regulatory proceeding.
40,148.04 13,911.61 32,698.85 2033 9,056.70 2,266.10 1,814.22 21,069.96 38,593.63 14,025.55 32,648.56 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 3 of 11
AI summary The text includes a table with numerical data and a reference to the 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 3 of 11, which has been redacted due to confidentiality.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 3 of 11 Year SmlGenIndices.PV AContrib2Sales.SmlGenOtherUse 2013 0.00 12.310 2014 0.00 12.290 2015 0.00 12.310 2016 0.00 12.500 2017 0.00...
AI summary The document presents a table of data from the 2023 Load Forecast Report, showing values for SmlGenIndices.PV and AContrib2Sales.SmlGenOtherUse from 2013 to 2033. The data shows trends over time but does not include any analysis, discussion, or arguments.
DENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 4 of 11
AI summary The text refers to the 2023 Load Forecast Report, specifically Synapse IR-36 Attachment 1, which is on page 4 of 11. The content is not fully visible due to the redaction of sensitive information.
0.00 2032 1.00 1.00 1.00 0.00 0.00 0.00 0.00 2033 1.00 1.00 1.00 0.00 0.00 0.00 0.00 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 5 of 11
AI summary The document contains a redacted section from the 2023 Load Forecast Report, specifically Attachment 1, Page 5 of 11. It includes numerical data and is part of a regulatory proceeding in Nova Scotia.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-36 Attachment 1 Page 5 of 11 Regression Sales Results Out of Monthly Model (kWh / HH) Year AContrib2Sales.AnnualAvgUse 2013 10,865.32 2014 10,579.12 2015 11,0...
AI summary The document presents a table showing annual average electricity use per household (kWh/HH) from 2013 to 2033, based on the 2023 Load Forecast Report. The data shows fluctuations in usage over time, with a general upward trend from 2023 to 2033.
9,577.1 2029 68,185.3 35,350.7 128,543.5 6,157.1 1,880.7 9,505.8 2030 68,838.5 35,246.4 125,917.3 6,264.3 1,912.1 9,393.4 2031 69,852.9 35,144.5 123,414.4 6,356.6 1,943.5 9,293.1 2032 70,794.3 35,133.4 121,297.7 6,541.4 1,979.8 9,242.9 203...
AI summary The text includes numerical data related to load forecasting, with years ranging from 2029 to 2033 and values for various metrics. It also references a confidential section of the 2023 Load Forecast Report, specifically Synapse IR-36 Attachment 1 Page 7 of 11, and includes terms such as 'ARMA', 'XHeatNew', and 'XCoolNew'.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Attachment 7 - General Model, filed electronically. The components of the calculations 2 are provided in Attachmen...
AI summary The 2023 Load Forecast Report discusses factors influencing load growth, including economic growth, non-manufacturing GDP, and annual cooling degree days (CDD). The report references Attachment 1 for detailed calculations and sources of data.
2023 Load Forecast Report Synapse IR-37 Attachment 1 Page 2 of 9
AI summary The text references the 2023 Load Forecast Report by Synapse, specifically Attachment 1, Page 2 of 9. It does not provide further details or discussion on the content of the report.
0.00 0.00 0.00 2.25 0.00 2025 0.00 0.00 0.00 0.00 0.00 2.25 0.00 2026 0.00 0.00 0.00 0.00 0.00 2.25 0.00 2027 0.00 0.00 0.00 0.00 0.00 2.25 0.00 2028 0.00 0.00 0.00 0.00 0.00 2.25 0.00 2029 0.00 0.00 0.00 0.00 0.00 2.25 0.00 2030 0.00 0.00...
AI summary The text presents a table with numerical data spanning years from 2025 to 2033, likely related to energy load forecasts or financial figures. A mention of 'REDACTED (CONFIDENTIAL INFORMATION REMOVED)' and a reference to the '2023 Load Forecast Report Synapse IR-37 Attachment 1 Page 4 of 9' indicate the content is part of a regulatory proceeding involving energy forecasting.
- - - - - (13,041.87) - 2,441,389.34 2031 0 2.25 0 649,050.18 137,710.94 1,691,228.92 - - - - - (13,041.87) - 2,464,948.18 2032 0 2.25 0 672,713.22 140,418.07 1,681,597.39 - - - - - (13,041.87) - 2,481,686.81 REDACTED (CONFIDENTIAL INFORMA...
AI summary The text contains a table with numerical data and a redacted section from a 2023 Load Forecast Report by Synapse, including regression coefficients and statistical analysis related to load forecasting variables.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-39: 2 3 Appendix B: Medium Industrial Model (pp 24-26) 4 5 (a) Please provide in electronic spreadsheet...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. The response explains that the Medium Industrial forecast model uses Manufacturing Employment as a long-term driver due to its satisfactory predictive performance, as determined by model statistics.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-40: 2 3 Appendix B: Combined Model for Commercial and Industrial DSM Coefficient (pp 27-28) 4 5 (a) Ple...
AI summary NSPI responded to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report, explaining that the General Service model was used for commercial and industrial DSM coefficient calculations due to its dominance in the commercial class and lack of alternative considerations.
FIDENTIAL ELECTRONIC 30 ONLY”. Date Filed: June 20, 2023 NSPI (Synapse) IR-41 Page 1 of 2 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests...
AI summary The document contains a redacted 2023 Load Forecast Report submitted by NSPI in response to Synapse Energy Economics information requests. It includes load factors for various large classes of customers, such as Large General, Large Industrial, and Municipal.
6 5 129,564.0 5,766.5 34,398.1 169,728.6 31.0 228.1 0.4 101.0 2016 6 55,834.8 2,484.8 14,846.5 73,166.0 30.0 101.6 0.3 25.1 2016 7 8,902.9 396.7 2,370.0 11,669.6 31.0 15.7 - - 2016 8 3,204.1 142.9 853.5 4,200.6 31.0 5.7 - - 2016 9 31,942.7...
AI summary The text presents a table with numerical data and references a redacted confidential document titled '2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 2 of 19'. The data includes various metrics, potentially related to energy or financial reporting.
0 2 316,304.2 15,503.3 78,443.5 410,250.9 29.0 589.4 1.2 710.8 2020 3 300,081.9 14,635.8 74,155.8 388,873.4 31.0 522.7 0.9 472.7 2020 4 212,626.6 10,331.5 52,346.6 275,304.6 30.0 382.4 0.7 250.4 2020 5 140,904.2 6,833.3 34,576.1 182,313.6...
AI summary The text presents a table with numerical data, including figures related to load forecasts and other metrics, but the content is partially redacted. The table appears to be from a 2023 Load Forecast Report by Synapse, and it includes data from 2020 and 2023.
NTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 3 of 19
AI summary The text refers to the 2023 Load Forecast Report by Synapse, Attachment 1, Page 3 of 19. It indicates that some information has been redacted or removed, but the document is part of a regulatory proceeding in Nova Scotia.
1 213,681.1 12,017.8 53,096.1 278,795.0 30.0 387.2 0.8 302.2 2023 12 309,560.1 17,440.6 77,017.1 404,017.8 31.0 543.0 1.0 537.5 2024 1 370,936.3 21,011.0 91,192.4 483,139.7 31.0 649.4 1.2 804.3 2024 2 344,440.5 19,527.8 84,713.8 448,682.1...
AI summary The text presents numerical data from a 2023 Load Forecast Report, including values such as 213,681.1, 12,017.8, and 53,096.1, followed by a redacted section indicating confidential information has been removed. The report is labeled as 'Synapse IR-41 Attachment 1 Page 4 of 19'.
492.83 0.64 314.99 2033 5 176,515.04 11,550.68 47,166.38 235,232.10 31.0 316.17 0.42 132.28 2033 6 76,121.14 4,987.02 20,351.32 101,459.49 30.0 140.92 0.16 23 2033 7 12,138.98 796.21 3,247.17 16,182.36 31.0 21.75 0 0 2033 8 4,365.95 286.72...
AI summary The text presents numerical data related to load forecasting, including values for different years and months, along with associated metrics. The data appears to be part of a load forecast report, and a section is redacted due to confidentiality.
- - 2016 1 - - - - 31.0 - - - 2016 2 - - - - 29.0 - - - 2016 3 - - - - 31.0 - - - 2016 4 2.7 0.3 3.0 6.0 30.0 0.0 - - 2016 5 979.7 119.6 1,069.7 2,168.9 31.0 2.9 - - REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Syn...
AI summary The document contains a table with data from 2016, including numbers and percentages, followed by a mention of a redacted confidential section from the 2023 Load Forecast Report, specifically Synapse IR-41 Attachment 1, Page 8 of 19.
- - 2019 6 10,733.9 1,023.0 9,003.8 20,760.7 30.0 28.8 - - 2019 7 43,351.0 4,145.5 36,453.7 83,950.3 31.0 112.8 1.0 113.5 2019 8 51,203.5 4,876.9 42,884.6 98,965.0 31.0 133.0 1.2 159.6 2019 9 22,412.8 2,124.6 18,705.8 43,243.2 30.0 60.1 0....
AI summary The text contains a table with numerical data spanning multiple years and months, likely related to financial or operational metrics. It also references a redacted confidential document titled '2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 9 of 19', suggesting it is part of a regulatory proceeding involving load forecasting.
- - 2022 11 214.8 18.0 152.7 385.5 30.0 0.5 - - 2022 12 - - - - 31.0 - - - 2023 1 - - - - 31.0 - - - 2023 2 - - - - 28.0 - - - 2023 3 - - - - 31.0 - - - REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 At...
AI summary The document contains a table with data spanning from 2022 to 2023, likely related to energy or financial metrics. The content is partially redacted, and the page is part of a 2023 Load Forecast Report from Synapse, specifically Attachment 1, Page 10 of 19.
- - 2026 4 5.7 0.4 3.5 9.6 30.0 0.0 - - 2026 5 2,052.0 150.4 1,250.6 3,453.1 31.0 4.6 - - 2026 6 16,337.0 1,199.1 9,968.1 27,504.1 30.0 38.2 0.2 7.7 2026 7 65,968.8 4,849.4 40,296.3 111,114.5 31.0 149.4 1.1 169.8 2026 8 77,833.7 5,730.1 47...
AI summary The document includes numerical data related to 2026, potentially representing financial or operational metrics, followed by a redacted section from the 2023 Load Forecast Report by Synapse, Attachment 1, Page 11 of 19.
0.8 161.7 2029 9 39,376.3 2,716.4 22,151.0 64,243.7 30.0 89.2 0.7 57.7 2029 10 4,029.7 278.4 2,269.2 6,577.3 31.0 8.8 - - 2029 11 309.0 21.4 174.2 504.5 30.0 0.7 - - 2029 12 - - - - 31.0 - - - 2030 1 - - - - 31.0 - - - REDACTED (CONFIDENTI...
AI summary The text presents numerical data related to load forecasts and includes a redacted section from the 2023 Load Forecast Report, specifically Attachment 1, Page 12 of 19. The data appears to be part of a regulatory proceeding in Nova Scotia.
- - 2033 2 - - - - 28.0 - - - 2033 3 - - - - 31.0 - - - 2033 4 7.7 0.5 4.0 12.2 30.0 0.0 - - 2033 5 2,767.9 181.2 1,437.3 4,386.3 31.0 5.9 - - 2033 6 22,036.6 1,444.9 11,454.1 34,935.6 30.0 48.5 0.2 9.8 REDACTED (CONFIDENTIAL INFORMATION R...
AI summary The text contains a table with numerical data and a reference to a redacted confidential document titled '2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 13 of 19'. The table likely represents load forecasting data, but the content is partially redacted.
154,701.4 16,853.6 37,784.8 8,787.8 450,103.7 31.0 605.0 2015 11 196,318.9 14,435.1 149,104.3 19,539.7 38,308.3 6,911.5 424,617.7 30.0 589.8 2015 12 206,907.1 12,157.4 152,069.7 19,597.5 38,534.0 8,824.5 438,090.2 31.0 588.8 2016 1 237,864...
AI summary The document contains a series of numerical data entries spanning multiple years and months, likely representing financial or operational metrics. A portion of the text is redacted, indicating the presence of confidential information. The remaining text refers to a 2023 Load Forecast Report by Synapse, specifically Attachment 1, Page 15 of 19.
151,348.8 21,594.1 38,506.7 6,693.8 417,880.6 31.0 561.7 2019 6 188,285.5 14,946.1 147,297.2 20,182.1 39,833.2 6,786.8 417,330.8 30.0 579.6 2019 7 189,467.6 15,191.5 150,763.5 22,939.6 40,644.8 6,850.8 425,857.7 31.0 572.4 2019 8 205,941.2...
AI summary The text contains numerical data, likely representing financial or operational metrics over time, followed by a redacted section from a 2023 Load Forecast Report by Synapse Energy Economics. The data may relate to energy usage, costs, or other operational indicators.
129,170.6 23,910.6 40,233.6 6,426.4 416,547.7 28.0 619.9 2030 3 223,442.9 18,617.0 143,271.1 24,593.0 38,277.9 6,470.3 454,672.3 31.0 611.1 2030 4 210,408.5 15,500.4 138,754.1 22,355.5 39,605.0 6,512.0 433,135.5 30.0 601.6 2030 5 216,753.1...
AI summary The text presents a table with numerical data spanning multiple years and categories, followed by a redacted section from the 2023 Load Forecast Report by Synapse Energy Economics. The table likely contains forecasted load data, though specific details are not visible due to redaction.
137,429.7 23,026.6 41,390.6 6,621.1 475,461.5 30.0 660.4 2033 10 240,209.8 16,267.5 142,196.8 19,457.2 39,861.3 6,509.5 464,502.0 31.0 624.3 2033 11 214,681.8 15,769.3 137,719.9 22,157.3 40,365.8 6,434.8 437,128.8 30.0 607.1 2033 12 231,74...
AI summary The response to Request IR-42 indicates that the data in Appendix C of the 2023 Load Forecast Report is not weather normalized. The response does not provide raw data or normalization factors, as the data is not normalized.
† Monthly HDD † Monthly CDD † Economics REDACTED (CONFIDENTIAL INFORMATION REMOVED) M11108 2023 Load Forecast Synapse IR-43 Attachment 2 has been filed electronically. REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast...
AI summary The document outlines the 2023 load forecast report, including demand-side management (DSM) projections, solar PV impact, electric vehicle (EV) forecasts, and various scenarios for energy demand. It also references responses from NSPI to Synapse Energy Economics' information requests.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-44: 2 3 Nova Scotia Power Electrification Support Overview by E3 4 5 (a) Please provide the full report...
AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report. The report, titled 'Nova Scotia Power Electrification Support Load Forecast Inputs – Overview,' was included as Appendix E in the 2022 Load Forecast Report. E3 contributed data on EV load shape and space heating.
Aggregate load shape for vehicle segment X, year Y driving statistics 5 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-44 Attachment 1 Page 6 of 8 Profiles for light-duty vehicle drivers were developed to...
AI summary The document discusses the development of load profiles for light-duty vehicle (LDV) drivers to inform future electric vehicle charging patterns. It references data from the National Household Travel Survey and uses historical Nova Scotia VMT statistics to model driving behavior, which is then input into the EV Load Shape Tool.
ast given lack of data and reporting differences (e.g., primary heating source) – however, growth in HPs is aligned with NSP 8 REDACTED (CONFIDENTIAL INFORMATION REMOVED) M11108 2023 Load Forecast Synapse IR-44 Attachment 2 has been filed...
AI summary The document relates to the 2023 Load Forecast Report submitted as part of the NSUARB M11108 proceeding. It includes responses from NSPI to information requests by Synapse Energy Economics, with attachments filed electronically. The content is related to load forecasting and energy planning.
their existing non-electric heating as backup, through the 2022 IRP Evergreen 28 process, as well as a scenario analysis for ETS to moderate peak load. It will 29 monitor growth in heating load for commercial customers and work with 30 EOn...
AI summary The text discusses the use of heat pumps with a COP of 2.3 at -15°C in the 2022 IRP Evergreen process, as well as DSM allocation between commercial and industrial classes. NS Power is working with EOne on DR implementation and peak load mitigation strategies.
1 NS Power’s reply submission did not agree with a few of the intervenors’ 2 requests. NS Power does not consider the line loss determination model as a 3 tool for load planning and said a report on this topic is not required. NS Power 4 c...
AI summary NS Power disagrees with some intervenors' requests, including the need for a line loss determination model report and an ELCC factor for the LIIR. However, it agrees to consider an ELCC adjustment for EV load shapes. The response includes incorporating agreed changes into the report, such as analyzing multi-hour temperature and windspeed impacts on peak load forecasting and incorporating water heating load control with a peak sensitivity analysis.
Please refer to Section 10 at page 78. Date Filed: June 20, 2023 NSPI (Synapse) IR-45 Page 2 of 4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information...
AI summary The document outlines recommendations and updates related to the 2023 Load Forecast Report, including refining electrification impact analysis, incorporating commercial and industrial electrification impacts post-2027, evaluating heat pump impacts, and updating EV adoption rates. Some items are deferred for future study.
Please refer to Section 10 at page 77. Date Filed: June 20, 2023 NSPI (Synapse) IR-45 Page 3 of 4 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information...
AI summary The document discusses recommendations related to the 2023 Load Forecast Report, including hybrid scenarios for heating and strategies to monitor and mitigate commercial heating load growth. It references Section 10 on page 79 of the document.
forecast early in the 28 process. 29 30 In addition to the above directives, the Board encourages NS Power to include 31 information on each of the following in future load forecasts: 32 33 • Examine the elasticity used in the SAE model to...
AI summary The NSUARB encourages NS Power to improve future load forecasts by examining the elasticity used in the SAE model and evaluating input variables in the residential model to enhance model robustness.
1 • Given the continued population growth in Nova Scotia and 2 ongoing housing shortage in the province, re-evaluate the use of 3 housing completions for the near-term; 4 5 • Given the current inflationary environment, evaluate the use of...
AI summary The text outlines directives related to housing completions, income metrics, and household characteristics for energy demand analysis. It requests clarification on how these directives are incorporated into a report and references a response and table for further details.
and peak from electrification as well as from new technologies, especially findings Please refer to NSUARB Please refer to NSUARB IR- from pilot projects. IR-45. 45. NSUARB NS Power is directed to continue Direction evaluating and implemen...
AI summary The NSUARB has directed NS Power to continue evaluating and implementing improvements to the calculation of weather-normalized values for energy and peak, as recommended by intervenors. This is part of the 2023 Load Forecast Report (NSUARB M11108), which also includes an assessment of the impact of DSM, Solar PV, EVs, and battery storage.
gory UARB Comments Status Notes assess the impact of DSM, Solar PV, EVs, battery storage, and weather. The Board directs NS Power to continue NS Power held a to actively engage with intervenors as stakeholder conference on well as other st...
AI summary The NSUARB has directed NS Power to engage stakeholders and assess the impact of DSM, Solar PV, EVs, and battery storage on load forecasting. The Board also requested an evaluation of elasticity inputs in the SAE model and residential model robustness.
- Given the continued population growth in Nova Scotia and ongoing housing shortage in the province, re-evaluate the use of housing completions for the near term; NSUARB - Given the current inflationary Suggestions environment, evaluate th...
AI summary The document suggests re-evaluating the use of housing completions as a metric for demand forecasting in light of population growth and housing shortages. It also proposes using median household income instead of total household income to better reflect economic conditions, and suggests incorporating demographic factors for more accurate load forecasting.
achieved. Not included forecast. Revisit the short-term economic inputs These metrics are updated provided by the Conference Board of Included. annually and included in Date Filed: June 20, 2023 NSPI (Synapse) IR-46 Page 3 of 4 REDACTED (C...
AI summary The document discusses the 2023 Load Forecast Report and NSPI's responses to Synapse Energy Economics' information requests. It highlights the need to revisit economic inputs from the Conference Board of Canada and EV adoption rates in alignment with Statistics Canada data. NS Power is encouraged to maintain communication with customers regarding large infrastructure projects.