N-12023 Load Forecast Report + Appendecies - Redacted
93 passages
..................................................... 14 6 4.0 Discussion of Major Inputs ................................................................................................ 16 7 4.1 Historical Class Sales and Energy Data .......
AI summary The document outlines sections discussing historical energy data, weather impacts, economic factors, end-use trends, price data, demand-side management, and sector-specific analyses for residential and commercial sectors in a regulatory proceeding.
red by Hour per EV ............................................... 42 31 Figure 29: Peak Demand of ChargePoint EV Charging Fleet ...................................................... 43 32 Figure 30: EV Impact to Energy and Peak Forecasts...
AI summary The document contains a list of figures related to energy demand forecasting, EV charging impacts, solar PV effects, battery potential, residential and commercial electrification trends, and historical vs projected electricity prices. The text is redacted, with confidential information removed, and spans multiple pages of analysis.
1 Figure 43: Illustrative Contribution of Specific End Uses............................................................ 61 2 Figure 44: Commercial Class Sales ...................................................................................
AI summary The text lists figures illustrating energy sales, demand forecasts, temperature regression models, and economic indicators. It includes historical and projected data for residential, commercial, and industrial sectors, along with demand response and peak temperature analysis.
residential and commercial rate classes. The SAE models explicitly 27 incorporate end-use energy intensity projections into the Load Forecast. End-use energy 28 forecasts derived from the residential and commercial SAE models are then comb...
AI summary The 2023 Load Forecast Report projects a 0.7% annual increase in Net System Requirement (NSR), driven by new customer additions, EV adoption, and RTR market reductions. Long-term growth is tempered by DSM initiatives, efficiency gains, and solar installations.
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.
1 - Revisit the short-term economic inputs provided by the Conference 2 Board of Canada to ensure data are close to those used in the 3 forecasts of Canada’s major Banks. 4 5 - Revisit the model’s EV adoption rates and examine EV rebates t...
AI summary NS Power revised the 2023 Load Forecast by updating peak temperature models, incorporating EV adoption data aligned with federal ZEV mandates, and including hybrid electrification scenarios. The Board directed revisiting economic inputs from the Conference Board of Canada and EV rebate data from Statistics Canada and Nova Scotia Open Data.
1 Figure 10: Results of Temperature Regression Models 2 Temperature Variable Adjusted Coefficient R Squared Peak Hour 0.47 -24.2 12 Hour Lag Average 0.58 -28.0 24 Hour Lag Average 0.53 -28.0 3 4 5 Of the three regressions, the 12-hour lagg...
AI summary The document discusses the results of temperature regression models used in peak demand forecasting. The 12-hour lagged average temperature provides the best fit, and wind speed and weekday variables also influence demand. The 2023 peak model uses a 12-hour lagged temperature and average daily wind speed, with 10-year averages as forecast variables. A temperature trend similar to HDD calculations is also included.
FORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 15: Median Income and Employment Income 2 3 4 5 Substituting the median income variable in the forecast does not have a significant change 6 on the model statistics, with the s...
AI summary The 2023 Load Forecast Report discusses the use of household income as the residential economic indicator for forecasting, despite the availability of median income data. It notes that median income lacks recent data and has no forecast. The report also highlights the use of new housing completions as a key indicator for residential customer growth, which is expected to remain positive but decline over time.
2023 Load Forecast Report REDACTED 1 Figure 16: Yearly Change in Customers, Population, and Housing Completions 2 3 4 5 In the commercial models, non-manufacturing gross domestic product (GDP) and non- 6 manufacturing employment continue t...
AI summary The 2023 Load Forecast Report discusses forecasting methods for different customer sectors, including the use of economic drivers such as GDP and employment data. The report highlights the use of econometric models and the importance of adjusting variables to constant dollars to remove inflation effects.
2,357 81 270 2032 195,656 68 32 69 2,459 84 279 2033 211,636 68 32 72 2,552 87 288 6 7 Water Heaters 8 9 NS Power anticipates that some customers who convert their oil heating systems to heat 10 pumps will also convert their hot water supp...
AI summary NS Power anticipates increased adoption of heat pump water heaters due to operating savings, with saturation expected to reach 87% by 2033. A pilot program with E1 is exploring direct control of water heaters by different vendors. Efficiency improvements have not yet been incorporated into the forecast due to low uptake.
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.
nd contribution estimated from the load shapes provided by E3 combined 2 with the updated sales targets. 3 4 Figure 27: E3 EV Mileage Assumptions and Load/Peak Modeling Results 5
AI summary The text refers to load shapes provided by E3 and updated sales targets used to estimate contributions, with a figure illustrating EV mileage assumptions and load/peak modeling results.
Vehicle Avg Avg kW/vehicle Avg kWh/year Type km/year 15 on Peak LDV 17,427 4,323 0.9 MDV 22,779 8,205 1.6 HDV 62,888 113,890 7.3 6 7 The peak impact assumes that 70 percent of charging is managed by NS Power (including 8 smoothing through...
AI summary The text discusses the impact of electric vehicles (EVs) on the grid, focusing on peak demand and energy consumption. It notes that managed charging (70% by NS Power) reduces peak demand to 0.9 kW/vehicle, compared to 1.6 kW/vehicle in unmanaged scenarios. The Smart Grid Nova Scotia (SGNS) Project is collecting data on EV impact, with 100 smart chargers installed for testing.
1 events. Two bi-directional chargers have been installed as of December 2022 with 2 additional chargers expected to be installed through 2023. The current installations have 3 been installed at non-residential locations with utility contr...
AI summary The document discusses the installation and performance of bi-directional EV chargers in Nova Scotia, noting that two have been installed by December 2022 with more expected in 2023. These chargers are located at non-residential sites and are used to offset building load. The availability of fleet vehicles is a key factor in determining the capacity factor of these chargers. Charging patterns show higher load during summer and peak usage around 11:00 pm on weeknights, influenced by factors like TOU rates and customer perceptions.
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.
measures; this sensitivity assumes an average peak demand of 1.6 7 kW/vehicle based on the E3 unmanaged model. 8 9 Figure 30: EV Impact to Energy and Peak Forecasts (cumulative) 10 Peak @ Peak @ Load Year EVs 0.9kW/vehicle 1.6kW/vehicle (G...
AI summary The text discusses the impact of electric vehicles (EVs) on energy and peak demand forecasts, using an average peak demand of 1.6 kW/vehicle based on the E3 unmanaged model. The forecast data shows a cumulative increase in EVs and corresponding energy and peak demand growth from 2023 to 2033.
Total New Year Load (GWh) Peak (MW) Installs 2023 2,268 -24 0 2024 4,876 -51 0 2025 7,875 -82 0 2026 11,324 -112 0 2027 15,290 -152 0 2028 19,852 -197 0 2029 25,097 -249 0 2030 31,130 -309 0 2031 37,777 -376 0 2032 45,120 -449 0 2033 53,23...
AI summary The document outlines projected load growth and peak demand from 2023 to 2033, noting minimal impact from distributed solar and battery storage due to high battery costs. It highlights that gas generators are currently more cost-effective than batteries for residential use, though new pricing mechanisms may encourage battery adoption.
1 The impact of battery storage is being explored through the SGNS project, which will 2 include data collection from both stand-alone battery backup and PV/battery combinations. 3 EV smart charging and using EV batteries (vehicle-to-grid...
AI summary The SGNS project is exploring the impact of battery storage, including stand-alone and PV/battery combinations, as well as EV smart charging and V2G technology. Early data on peak mitigation from residential battery uptake scenarios is being analyzed, with refinements expected as the project progresses.
stimates 19 based on the 5 kW batteries utilized in the SGNS project, and will be refined as the project 20 continues. 21 22 Figure 32: Potential Peak Impacts from Batteries 23 Residential Share (%) Technology 50% 25% 10% 5% Battery Peak I...
AI summary The text discusses potential peak impacts from batteries in the SGNS project, showing significant reductions in peak demand when optimal demand response (DR) control is applied. The analysis is based on 5 kW batteries and is part of a 2023 Load Forecast Report that has been redacted.
Page 46 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 The impacts of technologies related to direct load control (DLC) of heating and hot water 2 loads are discussed in Section 10. 3 4 Intensities 5...
AI summary The document discusses the modeling of residential end-use intensities, including electric heating, cooling, water heating, lighting, and other appliances. It also mentions the inclusion of photovoltaic (PV) and electric vehicle (EV) forecasts in the 'Other' category to illustrate their impact relative to other end uses.
l as smaller appliances such as computers, dehumidifiers, 26 microwaves, etc. This category also includes solar generation (photovoltaic or PV) 27 and EV forecasts. 28 DATE: April 28, 2023 Page 47 of 98 REDACTED (CONFIDENTIAL INFORMATION R...
AI summary The document discusses residential and commercial end-use intensities, highlighting trends such as increased use of heat pumps, changes in electric baseboard heating, and the impact of EV load and PV generation. Supporting data is referenced in Attachment 1.
2023 Load Forecast Report REDACTED 1 2 Figure 35: Historical and Projected General Commercial End-Use Intensity 3 (kWh/m2) 4 5 6 7 Supporting data for General commercial end-use intensities is included in Attachment 3. 8 9 For the 2023 Loa...
AI summary The 2023 Load Forecast Report discusses historical and projected general commercial end-use intensity, using baseline data from the EIA 2021 Annual Energy Outlook. Small scale solar and EV load are included in the Miscellaneous category of the General Commercial class. The report highlights growth in the commercial and industrial sectors driven by net-zero emissions goals and electrification programs.
Page 50 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, incorporating factors such as weather patterns, economic trends, and energy efficiency initiatives. The report includes detailed projections and assumptions related to future load requirements.
1 emissions. These programs involve converting heating loads to electricity (mainly from 2 oil), accelerating the uptake of electric cooling technologies, and examining opportunities 3 to power industrial processes through electrification....
AI summary The text discusses electrification programs targeting commercial and industrial sectors, focusing on converting heating loads to electricity, promoting electric cooling technologies, and electrifying industrial processes. Forecasts for electrification growth by customer class are provided, with specific data shown in Figure 36.
2030 22 13 13 8 7 2031 23 14 14 9 7 2032 24 14 14 10 8 2033 26 15 14 11 8 DATE: April 28, 2023 Page 51 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 4.5 Price Data 2 3 Price data is an input to the...
AI summary The document discusses the methodology for calculating price data used in load forecasts, including the use of a 12-month moving average of real revenue per kWh. It also references a 6.9% annual increase in electricity prices for 2023-2024, as outlined in Schedule 'B' of the GRA Settlement Agreement, followed by an average 2% annual increase thereafter.
1 4.6 Demand Side Management 2 3 Demand Side Management (DSM) and conservation plans continue to play a role in the 4 use of electricity in Nova Scotia, and the forecast takes the projected energy and demand 5 savings into account. Between...
AI summary The document discusses the role of Demand Side Management (DSM) in Nova Scotia's electricity use, noting that DSM plans are based on E1’s proposed supply agreement and 2019 Potential Study. It highlights the challenge of double-counting DSM savings in forecasting models and explains the approach used to address this issue by incorporating historical DSM savings into regression models.
1 that forecast DSM is overstated; rather, it is a way of accounting for DSM that is captured 2 elsewhere in the forecast. The methodology is not specific to DSM and could be applied 3 to other variables that need to be highlighted in the...
AI summary The text discusses the methodology used to forecast DSM (Demand Side Management) savings, explaining that DSM is not uniquely accounted for in the forecast but is captured elsewhere. It highlights the impact of including historical DSM data in the Residential model, which improves the model's fit. A coefficient of -0.425 indicates that 57.5% of DSM savings are already accounted for in other variables, while 42.5% need to be included separately. A combined model for Commercial and Industrial classes is used due to the lack of detailed DSM data by rate class or month.
ers by rate class or by month, so by creating a combined 22 model for these classes, the level of uncertainty around allocating historical DSM savings 23 across rate classes and months of the year is reduced. The DSM variable coefficient i...
AI summary The document discusses the use of a combined model to reduce uncertainty in allocating historical DSM savings across rate classes and months. The DSM variable coefficient is similar to 2022, and the model shows a good fit with an adjusted R-squared of 0.825 and a MAPE of 2.77.
) 2023 Load Forecast Report REDACTED 1 Figure 38: Annual Forecast Residential DSM Savings (incremental) 2
AI summary The 2023 Load Forecast Report includes a figure showing annual forecast residential DSM savings, highlighting incremental savings expected from demand-side management programs.
Year Forecast Forecast Forecast DSM DSM DSM DSM Residential Commercial Industrial captured by captured by Adjustment Adjustment DSM DSM savings DSM savings Residential Comm/Ind for for savings (GWh) (GWh) end use end use Residential Comm/I...
AI summary The text presents a table with forecasted energy savings from demand-side management (DSM) programs across multiple years, categorizing savings by residential, commercial, and industrial sectors, along with adjustments and coefficients for residential and commercial/industrial end use.
8.5 42.6 34.1 31.5 22.4 2032 73.2 46.8 8.3 42.1 33.2 31.1 21.8 2033 71.3 43.4 7.7 41.0 30.8 30.3 20.3 3 4 The methodology used to determine the DSM coefficient only works for levels of DSM 5 that have been relatively consistent throughout...
AI summary The text discusses the methodology for determining the DSM coefficient, noting that it works best with consistent historical data and may need revision if future DSM forecasts change significantly. It also mentions a breakdown of DSM impact by class in Section 9.
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.
Year Regression New Solar EV RTR DSM Total Total DSM Model Customers Impact Impact Sales Adjustment Sales Res. captured Output (GWh) (GWh) (GWh) (GWh) (GWh) (GWh) DSM by end (GWh) 23 (GWh) uses 24 (GWH) 2023 4834 54 -39 7 0 -27 4830 -62 -3...
AI summary The text presents a table showing the impact of various factors such as new customers, solar, EV, RTR, and DSM on sales and total adjustments from 2023 to 2032. The table includes data on regression models, new customers, and DSM adjustments over time.
-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.
d by the DSM coefficient. DATE: April 28, 2023 Page 60 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 43: Illustrative Contribution of Specific End Uses 2 Year HP Heat HP Cool Baseboard Heat W...
AI summary The document presents a load forecast report with data on energy consumption by end use, including heat pumps for heating and cooling, baseboard heat, and water heating from 2023 to 2033. The data is illustrative and shows trends in energy use over time.
1 6.0 COMMERICAL SECTOR 2 3 The Commercial SAE model creates a unique forecast for the Small General and General 4 rate classes. Like the residential model, the commercial SAE models express monthly sales 5 as a function of heating, coolin...
AI summary The Commercial SAE model forecasts electricity use for Small General and General rate classes based on heating, cooling, and other loads, incorporating factors like GDP, employment, and HDD/CDD. The model was updated in 2023 to include EV load in the commercial class, previously modeled only in the residential class. The model reflects a rebound in commercial sales post-pandemic.
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.
D) 2023 Load Forecast Report REDACTED 1 Figure 47: Historical and Forecast Annual General Demand Sales 2 3 4 5 Please refer to Appendix B for tables with a detailed breakdown of the changes from 2023 6 to 2033. Total change between 2023 an...
AI summary The 2023 Load Forecast Report discusses historical and projected annual general demand sales, noting a 4.4% increase from 2023 to 2033. The Large General Service class shows slower growth due to revised estimates of large project completion, with customer surveys and historical data informing the forecast.
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.
REDACTED 1 coming years. Based on discussions with customers, these are expected to add the amounts 2 shown in Figure 51. 3 4 Figure 51: New Large Industrial Projects 5 Year New Large Industrial Projects (Cumulative GWh) 2023 3 2024 12 202...
AI summary The document discusses new large industrial projects and their cumulative energy consumption from 2023 to 2027, as well as historical and forecasted sales for the Other Industrial sector. These figures are based on customer discussions and are part of a load forecast report.
energy exports are not included. Figure 53 provides a breakdown of the significant 7 variances between forecast and actuals for 2022. 8 9 Figure 53: 2022 Variance to Actual 10 Res Comm Ind Other Losses NSR 2022 Forecast 4,715 3,091 2,542 7...
AI summary The text discusses energy usage variances in 2022, highlighting the impact of weather, unexplained residential load increases, and factors like continued pandemic restrictions and higher-than-expected heat pump installations. It also forecasts an annual increase in NSR from 2023 to 2033, driven by new customers, space heating, and EV adoption, with some offset from solar, DSM, and RTR.
sectors can be found in Appendix A. 22 DATE: April 28, 2023 Page 74 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 54: Historical and Forecast Annual NSR 2 3 4 5 Figure 55 provides a breakdown...
AI summary The 2023 Load Forecast Report presents historical and forecast annual NSR data, along with a breakdown of forecast components from 2023 to 2033, including contributions from residential, commercial, industrial, and other sectors, as well as factors like solar, EV, and DSM.
DSM -269 -205 -57 -3 -51 -586 2033 Forecast 5,360 3,355 2,501 101 795 12,113 Total DSM forecast -695 -558 -98 -7 -124 -1,483 DSM captured in underlying -426 -353 -41 -4 -73 -897 models 3 4 DATE: April 28, 2023 Page 76 of 98 REDACTED (CONFI...
AI summary The document provides a forecast of Demand Side Management (DSM) metrics and load data for 2033, including total DSM forecast and captured DSM in underlying models. The information is part of a 2023 Load Forecast Report, with some content redacted.
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.
ssed as more 2 information is gathered through the implementation of DR programs. 3 Annual DR totals by program are provided in Figure 56. 4 5 Figure 56: Demand Response 6
AI summary The document discusses the collection of information through the implementation of Demand Response (DR) programs, with annual DR totals by program provided in Figure 56.
Year Direct Critical Business, Total Total Load Peak Non-Profit (MW) with Control Pricing & Industrial ELCC (MW) (MW) Curtailment (MW) (MW) 2023 4 4 1 9 4 2024 12 12 2 26 12 2025 24 22 4 50 24 2026 36 32 6 74 36 2027 39 36 7 82 39 2028 39...
AI summary A pilot project from November 2021 to February 2023, in conjunction with E1, tested water heater controls by installing 201 controllers in residential homes. Preliminary results showed load reductions during managed load shift events starting at 5:00 p.m.
load shift events. Preliminary results from the project indicate 12 that average load reductions for the first and second hour of events that started at 5:00 p.m. 13 between December 2021 and February 2022 were 0.56 kW and 0.40 kW for Shif...
AI summary The text discusses preliminary results from a load shifting project, showing average load reductions during the first and second hours of events, with plans to install additional controllers as part of a water heater initiative. Results from the 2022/2023 winter season are currently being analyzed.
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.
1 customer and interruptible customer contributions, and finally DSM. As discussed in 2 Section 4.4, the EV contribution to peak is expected to be mitigated via utility managed 3 charging. The firm peak without EV peak mitigation (assuming...
AI summary The text discusses the impact of electric vehicles (EVs) and heat pumps on peak electricity demand in Nova Scotia, estimating their contributions to peak load by 2033. It references various models and studies, including those by E3, Itron, and the SAE model, and notes differences in estimates based on efficiency assumptions.
k occurred on Tuesday, January 11, 2022 in the evening at a 23 temperature of -14.6°C and was 2,216 MW with a firm peak of 2,061 MW. The 2022 24 forecast system peak was 2,165 MW with a firm peak of 2,021 MW. Normalized firm 25 peak using...
AI summary The document discusses the 2022 and 2023 system peak load forecasts and actuals, highlighting discrepancies between forecasted and actual peak loads. It notes that the peak on February 4, 2023, was the highest recorded, influenced by extreme cold and strong winds.
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.
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.
DSM (MW) January 982 1,184 1,232 -3.8% 1,166 1.6% February 1,392 1,146 1,166 -1.7% 1,122 2.1% 3 4 Differences between the forecast green (top-down) and black lines (bottom-up) highlight 5 the different approaches and cover a range from nea...
AI summary The text discusses the comparison between top-down and bottom-up forecasting methods for demand-side management (DSM) in Nova Scotia, highlighting the limitations of the top-down approach and the advantages of the bottom-up method, which uses class-level load research to capture customer behavior more accurately. The use of AMI smart meter data is proposed to improve forecast accuracy and analyze the impact of factors like heat pump penetration and building shell assumptions.
1 11.0 SENSITIVITY ANALYSIS 2 3 The sales and peak forecasts are fundamentally uncertain and depend on many variables, 4 including economics, weather, adoption of distributed generation, electricity rates and 5 DSM. Although each of these...
AI summary The text discusses the uncertainty in sales and peak forecasts due to factors such as economics, weather, distributed generation, electricity rates, and demand-side management (DSM). A P10/P90 probability analysis using Monte Carlo simulation was conducted in 2017 to estimate the probable distribution of future load, with sensitivity bands shown in Figure 74 representing a range of approximately 434-558 GWh over a 10-year period.
s represent actual system totals. 25 DATE: April 28, 2023 Page 95 of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 74: System Energy Sensitivity 2 3 4 Similarly, a P10/P90 scenario was created f...
AI summary The document discusses the creation of a P10/P90 scenario for peak demand, using random sampling of weather and economic drivers. The variation in peak demand is approximately 376-448 MW, and Figure 75 shows the peak forecast with the latest adjustments to the peak end-use model.
DENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 Figure 75: System Peak Sensitivity 2 3 4 This analysis provides a potential range of outcomes for the 2023 Load Forecast. Energy 5 is most sensitive to economics over the lo...
AI summary The 2023 Load Forecast Report discusses the sensitivity of energy demand and peak load to factors like economics and temperature. It compares the 2023 forecast with the 2020 IRP cases, noting that the 2023 forecast lies between Mid and High Electrification scenarios. The report also highlights the slow ramp-up of EV and heat pump adoption to meet decarbonization targets.
Residential Commercial Industrial Municipal Total Year Sector Growth Sector Growth Sector Growth and Other Growth Losses Energy Growth GWh % GWh % GWh % GWh % GWh GWh % 2013 4,362 4.8 3,244 1.5 2,604 20.3 201 4.8 784 11,194 6.9 2014 4,404...
AI summary The table presents energy consumption data across residential, commercial, industrial, municipal, and other sectors from 2013 to 2024, showing varying growth rates and energy losses over time.
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.
2,627 2,819 3.0 -13 Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix B Page 1 of 33 Appendix B – Forecast Model Details 2023 NS Power Load Forecast REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Loa...
AI summary This section of the 2023 Load Forecast Report Appendix B details the residential average use SAE model, which incorporates variables for heating, cooling, and other end uses, as well as factors like efficiency, saturation trends, and seasonal patterns to forecast residential electricity demand.
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.
2023 Load Forecast Report Appendix B Page 4 of 33 Appendix B – Forecast Model Details Variable Coefficient StdErr T-Stat P-Value MA(1) 0.490 0.096 5.108 0.00% Residential Model Statistics Model Statistics Iterations 21 Adjusted Observation...
AI summary This section of the 2023 Load Forecast Report provides statistical details of the residential load forecasting model, including coefficients, standard errors, t-statistics, and p-values for variables such as MA(1). It also includes model statistics such as R-squared, AIC, BIC, and other diagnostic measures for evaluating model performance.
t Appendix B Page 5 of 33 Appendix B – Forecast Model Details Residential SAE Model Fit Residential Model 2023-2033 Reconciliation The following tables provide details reflecting the changes between 2023 and 2033 forecast years. Some of th...
AI summary This section provides a reconciliation of residential load forecasts from 2023 to 2033, showing changes in key metrics such as existing and new customer load, EV load, solar load, RTR, and DSM. The data highlights increases in customer load and EVs, while solar load decreases slightly.
(698) (402) Change 4.7% 7.8% 13.6% -9.2% -0.3% -5.6% 11.0% to load Res Sales = Existing Customer Load + New Customer Load + EV Load + Solar Load + RTR + DSM Existing customer load is calculated as Res Average Use (9,845 kWh/customer in 202...
AI summary The document discusses residential load forecasting, including factors like existing customer load, new customer load, EV load, solar load, and DSM. It provides data on residential average use, including calculations for 2023 and 2033, and highlights changes in load factors such as heating, cooling, and other variables.
2023 Load Forecast Report Appendix B Page 7 of 33 Appendix B – Forecast Model Details Residential Input Variables – XHeat Intensities Econ + Regression Struct Efurn HP Heat Secondary Furnace Fans HeatUse Coeff Total XHeat Heat Variable 202...
AI summary The document provides a detailed breakdown of residential input variables for heating (XHeat) and cooling (XCool) in the 2023 Load Forecast Report. It outlines specific factors such as Efurn, HP Heat, and HeatUseVariable, along with their associated coefficients and changes over time.
ercial Model Detail Small General Service Small General Service is projected using an SAE average use model and a sales forecast is generated as the product of the average use and customer forecast. Like the residential model, monthly Smal...
AI summary The document discusses the Small General Service load forecasting model, which uses an SAE average use model and incorporates factors such as heating and cooling requirements, GDP, employment, and price elasticities. It also includes adjustments for seasonal and event-related factors like the pandemic and Hurricane Fiona.
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.
he residential model (including heat pump programs inside the SAE model). Adjustments done outside the regression include estimates for other commercial and industrial growth programs, PV, EV and DSM. Historically the XHeat, XCool and XOth...
AI summary The document discusses the residential load model, including adjustments for EV, solar, and DSM programs. It outlines the regression model used to calculate load from existing average use and customer count forecasts, highlighting changes from 2023 to 2033.
ated as Small Gen Average Use (12,376 kWh/customer in 2023, 12,855 kWh/customer in 2033) x number of customers (26,341 in 2023, increasing to 29,096 in 2033). Small General Average Use –Regression XHeat XCool XOther Binaries ARMA Avg Sales...
AI summary The document provides a forecast model for small general average use in electricity consumption, detailing variables such as heating, cooling, and other factors, along with their respective intensities, coefficients, and scaling factors for 2023 and 2033. It outlines the regression-based approach used to predict changes in energy use over time.
-4.3% 22.4% 0.0% 0.0% 18.0% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor Small General Input Variables – XOther Intensities Econ + Reg Struct Vent Water Cook Refrig Light Office Misc OtherUse Coeff Scaling Total Heat Var Fact...
AI summary The text provides a table and formula related to energy consumption forecasting for general service, including variables such as cooling, ventilation, and lighting, with data for the years 2023 and 2033. It includes calculations for XCool and XOther, and highlights changes in energy use intensities and coefficients over time.
general demand class, which makes up the largest portion of the commercial sector, is forecast as gross total sales rather than average use as is the case in the small general and residential classes. Like the small general model, a flat s...
AI summary The document discusses the forecast for general demand load in the commercial sector, using a regression model that includes factors such as EV load, solar, and DSM. Adjustments are made to the model to account for these factors, and the forecast shows a projected increase in demand by 2033.
(437) (264) Change 4.3% -2.7% 11.4% -1.9% -6.8% 4.4% Gen Sales = Sales + RTR + EV Load + Solar Load + DSM General Demand Sales –Regression XHeat XCool XOther Binaries ARMA Sales (GWh) 2023 533,784 114,190 1,746,561 (13,042) (3,141) 2,378,3...
AI summary The text provides a forecast model for general demand, including variables such as XHeat, XCool, and XOther, along with their respective intensities and coefficients. It outlines the regression model and how changes in factors like heating and cooling demand are calculated using multiplicative growth rates.
ad Forecast Report Appendix B Page 20 of 33 Appendix B – Forecast Model Details General Demand Input Variables – XCool Intensities Econ + Struct Regression Cooling CoolUse Variable Coefficient Scaling Factor Total Xcool 2023 312,580 1.38 0...
AI summary The document provides a forecast model detailing input variables for XCool and XOther, including cooling intensities, economic and structural factors, regression coefficients, and scaling factors for the years 2023 and 2033. It highlights changes in these variables over time.
-4.4% 27.4% 0.0% 0.0% 23.0% XCool = Cooling x CoolUseVariable x Coeff x Scaling Factor General Demand Input Variables – XOther Intensities Econ Reg + Struct Vent Water Cook Refrig Light Office Misc Other Coeff Scaling Total Heat Use Factor...
AI summary The text presents demand input variables and their changes over time, including calculations for cooling and other demand factors. It also describes an industrial econometric model with variables for monthly sales and economic factors, noting the inclusion of a binary variable for October 2022 due to billing delays from Hurricane Fiona.
MBin.Julm + MBin.Augm + MBin.Sepm + MBin.Octm + MBin.Novm + MBin.Decm + MBin.Oct22 + b1×MEcon.ManGDP A binary variable was added for October 2022 to account for billing delays after hurricane Fiona. Variable Coefficient StdErr T-Stat P-Val...
AI summary The text discusses a statistical model incorporating binary variables for months and a binary variable for October 2022 to account for billing delays caused by Hurricane Fiona. Coefficients, standard errors, T-Statistics, and P-Values are provided for each variable, indicating their significance in the model.
2023 Load Forecast Report Appendix B Page 27 of 33 Appendix B – Forecast Model Details Combined Model for Commercial and Industrial DSM Coefficient NonResSalesm = b1×NonResEESavingsProfiledm + b2×GenWtXHeatm + b3×GenWtXCoolm + b3×GenWtXOth...
AI summary This section of the 2023 Load Forecast Report Appendix B presents a combined model for commercial and industrial demand-side management (DSM) coefficients, including variables such as non-residential energy efficiency savings, weighted end-uses, and binary variables for billing issues in February 2018 and October 2022. It also provides statistical details of the model.
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.
energy sales model can be written as: ResSales = b1×ResXHeat+b2×ResXCool+ResOther Where b1 and b2 are regression coefficients found after running the sales model. ResOther can be written as: ResOtherm =ResSalesm- b1×ResXHeatm-b2×ResXCoolm...
AI summary The text describes an energy sales model that separates weather-dependent and non-weather-dependent variables, including the impact of past demand-side management (DSM) activities. It also accounts for factors like the average daily wind speed on peak days and the effects of events such as the COVID-19 pandemic and billing delays related to Hurricane Fiona.
2023 Load Forecast Report Appendix B Page 33 of 33 Appendix B – Forecast Model Details Peak Model Fit As seen in the figure below (and in the model statistics above), this approach produces a good fit with historical data. Although it was...
AI summary The document provides details on the peak model fit and forecast comparison and accuracy for the 2023 Load Forecast Report. The peak model is noted to have a good fit with historical data, though it lacks an explicit peak DSM variable due to insignificant parameters. The forecast comparison includes figures on total energy requirement, system peak demand, and firm peak demand.
D 2023 Load Forecast Report Appendix D Page 3 of 9 Appendix D – Forecast Sensitivity Analysis Figure D1: Distribution of January HDD 5. Oracle’s Crystal Ball runs about 10,000 trials, taking a random set of numbers from the relevant variab...
AI summary The document discusses the use of Oracle’s Crystal Ball for running 10,000 trials in a Monte Carlo simulation to analyze load forecast sensitivity. It highlights the inclusion of heat pumps in the SAE models and the distribution of energy and peak load forecasts before the impact of demand-side management (DSM).
before the impact of DSM). 10th (10%) and 90th (90%) percentiles can easily be obtained from Normal distributions and so they are highlighted in D2. Figure D2: Distribution of Energy (Before DSM) Page 3 of 8 REDACTED (CONFIDENTIAL INFORMAT...
AI summary The text discusses probabilistic load forecasting, focusing on the distribution of energy and peak demand before demand-side management (DSM) is applied. It references figures showing percentiles and sensitivity analysis, including the impact of variables on system peak forecasts.
D Page 6 of 9 Appendix D – Forecast Sensitivity Analysis Figure D4: Peak Forecast (Residential, Commercial and Small and Medium Industrial) The asymmetry in this figure, seen as the off-centre median, is explained by the bias introduced by...
AI summary The document discusses a forecast sensitivity analysis, highlighting the asymmetry in peak demand forecasts due to the use of the MAX function on monthly peak heating degree days (HDD). It notes that monthly HDD has become more influential than peak HDD in recent years, particularly in 2024, due to the impact of year-long residential heating on sales and the proposed E3 electrification scenarios.
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.
ivers, while on the peak side DSM, EVs, hybrid heating peak mitigation and weather/economics are all similar. Figure D8 shows the relative impact of these items. Figure D8: Relative Impact of Inputs 2023 Energy 2023 Peak 2033 Energy 2033 P...
AI summary The document discusses the impact of various factors on energy and peak demand forecasts for 2023 and 2033, including demand-side management (DSM), solar PV, electric vehicles (EVs), hybrid heating systems, and weather/economics. It outlines scenarios such as the E3 hybrid scenario and mentions the potential for large-scale hydrogen production.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Appendix E Page 5 of 21 Changes from 2022 Input Data Source COVID adjustments Residential class still has a WFH variable that reduces over time. Commercial class varia...
AI summary The 2023 Load Forecast Report discusses updates to the load forecast, including changes from the 2022 report. Key updates include adjustments for the impact of the COVID-19 pandemic, electrification of heating, and the inclusion of EV sales mandates. The report also addresses peak design conditions and the expected increase in solar generation.
N-7NSPI (Synapse) RIR-1 to RIR-46 - Redacted
366 passages
1.9 2.4 2.5 2.1 1.7 1.7 1.8 1.5 1.6 1.6 0.6 0.5 0.9 0.7 0.6 0.8 1.2 1.3 1.2 1.3 Activity 2 Floor Space (million m ) 1.92 1.95 1.99 2.01 2.09 2.15 2.25 2.28 2.30 2.33 2.34 2.36 2.38 2.40 2.40 2.45 2.48 2.55 2.56 2.58 Energy Intensity (MJ/m...
AI summary The text presents data on floor space, energy intensity, heating and cooling degree-day indexes for the commercial/institutional sector in the Atlantic region, including a table of energy use by end use and energy source. The data includes figures for various years and is part of a load forecast report.
1 2 (h) Please document the data that the RESHAPE model used for this forecast and its 3 impacts on the forecast. 4 5 Response IR-7: 6 7 (a) Please see the following table estimating the number of customers for each category: 8 Customers C...
AI summary The response provides a table detailing the estimated number of customers in various categories, including residential, electric resistance, and heat pump users, from 2023 to 2029. This data is part of the RESHAPE model's forecast and its impact analysis.
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.
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.
ide the confidential version of the January 2023 Smart Grid Nova Scotia 28 Semi-Annual Report as an attachment. 29 30 (h) Please provide the source data and calculations for Figure 28. Date Filed: June 20, 2023 NSPI (Synapse) IR-9 Page 1 o...
AI summary The document includes requests for data and calculations related to the 2023 Load Forecast Report, specifically Figure 28 and Figure 30. NSPI provides responses referencing attachments and outlines the methodology for applying federal sales targets to annual sales forecasts.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests CONFIDENTIAL (Attachment Only) 1 2 3 4 (d) E3 modelled differences in LDV driving behaviours between summer months and winter 5 month...
AI summary The text discusses the modeling of EV charging loads based on driving behavior, including differences in LDV driving patterns between summer and winter months, and the use of the E3 EV Load Shape Tool to forecast EV charging loads.
CONFIDENTIAL (Attachment Only) 1 of LDV weekly driving patterns expressed as the probability that a driver is at a given 2 location or is driving. 3 4 5 6 The driving population is characterized by drivers’ EV type and access to charging....
AI summary The text discusses modeling LDV weekly driving patterns and the impact of EV types and charging access on load shapes. It contrasts unmanaged and managed charging scenarios, highlighting how drivers respond to electric rates and the lack of consideration for time-varying prices in unmanaged scenarios.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests CONFIDENTIAL (Attachment Only) 1 A third charge management type, charge management with Vehicle-Grid Integration 2 (VGI), still featu...
AI summary The document discusses charge management strategies for EV owners, including Vehicle-Grid Integration (VGI) and the use of aggregators to reduce peak demand. It outlines assumptions about EV customer responsiveness to time-of-use (TOU) rates and the expected impact on load forecasting.
kWh/year kW/vehicle on peak LDV 4,323 0.85 MDV 8,205 1.62 Transit Bus 113,890 7.33 2022 Energy 2022 Peak Year BEV tot PHEV tot MDV HDV Total GWh Peak MW Avg kW per car 2021 709 238 947 4 0.8 0.9 2022 1,768 1,096 2,864 12 2.4 0.85 2023 3,50...
AI summary The document presents data on energy consumption and peak demand for different vehicle types (LDV, MDV, Transit Bus) and provides projections for BEV and PHEV totals, energy usage in GWh, peak demand in MW, and average kW per car from 2021 to 2032.
trics, details regarding business case development, general updates on broader data and learnings, EfficiencyOne’s role and corresponding outcomes regarding demand response programs. The Board acknowledges that information such as that not...
AI summary The Board requires detailed project reports, including financial breakdowns and program implementation status, for the Smart Grid Nova Scotia Project. Reports must include expenditures categorized as presented in the application and allocations against funding partner contributions.
w functionality to support use cases. • Data extract report designs and functionality for assets and events were developed during the reporting period and will be delivered in Q1 2023. EV SMART CHARGING • Use case testing continued through...
AI summary The document discusses the development of data extract report designs and functionality for assets and events, set to be delivered in Q1 2023. It also covers ongoing use case testing for EV smart charging, including issues with ChargePoint Home Flex units due to a Wi-Fi module defect, and the extension of testing into 2023 to analyze data from replaced/updated chargers.
ries to make sure the issue is resolved. • Tesla installations continue to be onboarded into Tesla Powerhub for full utility control and visibility. Tesla integration with ESP is ongoing. COMMERCIAL AND INDUSTRIAL SOLAR + BATTERY • All thr...
AI summary The document outlines progress on Tesla Powerhub integration, the commissioning of commercial and industrial solar + battery sites, and the implementation of building management systems (BMS) for energy optimization. Integration with the Energy System Platform (ESP) is ongoing, with testing and as-built drawings completed for multiple sites.
be coordinating program design and learnings with Phase 2 of the E1/NS Power C&I 8 M09985, Nova Scotia Power - Smart Grid Nova Scotia Project - Semi-Annual Report, July 29, 2022, page 5 of 111. Page 8 of 17 . REDACTED (CONFIDENTIAL INFORMA...
AI summary The document mentions coordination of program design with Phase 2 of the E1/NS Power C&I and references a DR pilot involving the procurement of a DR Aggregator by E1. Two BMS integrations have been completed, with testing ongoing at three sites.
Customer Programs Section 3 of NS Power’s Compliance Filing 9 provides an overview of the customer programs for each asset class under the Project. As of December 31, 2022, the structure of those customer programs is the same as outlined i...
AI summary This section outlines updates to NS Power's customer programs, including the extension of the EV Smart Charging program, adjustments due to vehicle compatibility issues, and the inclusion of Powerwalls in the 10 Intelligent Feeder project. These updates affect program eligibility and incentives.
r year would be provided to those customers who agree to the use of their Powerwalls in the SGNS Project. 10 An update on customer recruitment is set out below. CUSTOMER CRITERIA AND SELECTION EV Smart Charging • The original EV Smart Char...
AI summary The document discusses the EV Smart Charging program extension, including customer recruitment criteria and selection processes. It mentions the end of the original program and the offer extended to non-Tesla vehicle owners to continue participation with SGNS-provided ChargePoint Home Flex chargers.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 10 of 205 January 31, 2023 C. Henwood • At the end of the reporting period, 17 of an eligible 73 customers had enrolled in the...
AI summary The EV Smart Charging program with ev.energy has enrolled 108 customers as of December 31, 2022, including 18 Tesla customers. NS Power continues to encourage participation and will keep the program open until the budgeted participant limit is reached. The program offers incentives to participants who transition from the original ChargePoint program.
2023, all drivers who transitioned from the original ChargePoint program would be offered the $20 per month incentive payment, as the original ChargePoint program ended December 31, 2022. Community Solar G arden • Recruitment of applicants...
AI summary The ChargePoint program transitioned in 2023, offering a $20 monthly incentive to drivers. The Community Solar Garden reached full residential capacity, with a waitlist for new subscriptions. NS Power continued managing battery storage enrollment and tested commercial subscriptions in 2023.
battery enrollment when participating customers sell their homes. In 2022, four homes with SGNS battery storage were sold and the new homeowners were successfully onboarded to the program. Page 10 of 17 . REDACTED (CONFIDENTIAL INFORMATION...
AI summary The document discusses the enrollment of homes with battery storage when sold, the status of commercial and industrial solar and battery projects, and data collection efforts related to DER use cases. Customer feedback and survey results are also being collected to inform program improvements.
. REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 14 of 205 January 31, 2023 C. Henwood Phase 2 progress during the reporting period included: • Refinement of pilot design eleme...
AI summary Phase 2 of a pilot project is ongoing, with efforts focused on refining customer agreements, DR event criteria, incentives, and baseline methodology. NS Power is submitting reports to the Strategic Innovation Fund (SIF) as part of its funding commitments, including several confidential and non-confidential appendices.
under ‘electric vehicle (EV) vehicle-to-grid (V2G) Chargers,’ as highlighted in Attachment 3. 1 0 F • Customer Tesla vehicles continue to be excluded from use case events to protect from undue risks placed on Tesla protection systems. The...
AI summary The document outlines modifications to an EV charging and demand response project, including the exclusion of Tesla vehicles from use case events, the addition of managed EV charging via the ev.energy platform, and the inclusion of Commercial and Industrial Building Management System (BMS) controls for Demand Response (DR). These changes were first reported in various semi-annual reports.
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.
is based on energy used at the charger level. Figure 1 accounts for data collected from January 1, 2021, to December 31, 2022. Baseline data is considered for days without events, excluding holidays. To date, aggregated charging data was e...
AI summary The text discusses the analysis of EV charging data collected from January 2021 to December 2022, highlighting baseline energy consumption patterns by hour of the day. It notes a shift in charging activity from the evening to early morning hours, with peak consumption remaining between 23:00 and 00:00. Data was captured using the ChargePoint portal and analyzed in Excel, with some Tesla vehicles transitioning from the ChargePoint user group.
: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.
ndicates that the 22:00 to 23:00 period is the highest consumption throughout the reporting period. Figure 2 – ev.energy Data Baseline Plot – June 14 to December 31, 2022, by Hour (Counterfactual) Page 8 of 48 . . REDACTED (CONFIDENTIAL IN...
AI summary The text discusses data from ev.energy, showing peak electricity consumption during late evening hours and the timing of vehicle charging sessions. It highlights managed and unmanaged charging patterns, including 'boosted' charging, and provides insights into EV charging behavior.
ed’ charging, which is when the driver opts out of both demand response events and smart charging in order to receive energy to their vehicle immediately. It is observed that smart charging sessions Page 9 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The document discusses patterns in EV charging behavior, noting that smart charging sessions are typically longer and consume more energy, often occurring overnight. Charging away from home is shorter, and boosted charging events are not high energy consumption events. Seasonal variations in baseline energy consumption from ChargePoint devices are also analyzed.
on to the average between summer and winter has occurred. Understanding these types of patterns and metrics will support building an accurate business model where metrics can be applied seasonally. Page 10 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The document discusses seasonal variations in EV charging energy consumption, noting higher usage in summer than winter, which contradicts the initial hypothesis that colder temperatures would increase energy needs. This pattern is being monitored for future analysis.
two semi-annual reports, with the 23:00 peak occurring during the weekdays and not during the weekends. Figure 8 – Average Baseline EV Consumption for Each Hour of the Day on Each Day of the Week The 23:00 charging trend does not appear on...
AI summary The document discusses patterns in EV charging behavior, noting a 23:00 peak on weekdays but not on weekends, possibly due to differences in rate arbitrage and charging behavior influenced by time-of-use rates and app defaults.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 2 Page 13 of 48 Attachment 2 – SGNS Use Case Testing Update Report PEAK EV C HARGER D EMAND Understanding peak EV charger demand from the fleet of chargers under study in the project p...
AI summary This report discusses peak EV charger demand observed in the SGNS program, noting a peak of 171.1 kW on March 1, 2022, and a more recent peak of 145.8 kW on December 10. The report highlights the fluctuation in charger availability and the potential curtailment capacity of the program.
id not surpass the peak from the previous period, as noted above and in the July 2022 report. Figure 9 – Screen Capture of the Peak Demand (171.1 kW) seen by ChargePoint EV Charging Fleet, to Date Page 13 of 48 . . REDACTED (CONFIDENTIAL I...
AI summary The document discusses peak demand data from the ChargePoint EV Charging Fleet, including a peak of 171.1 kW and a later peak of 145.81 kW, with the latter influenced by the ev.energy algorithm shifting charging times. Testing events involving ChargePoint chargers are also described.
sting on ev.energy included the entire registered fleet. The number of vehicles receiving events fluctuated with user acquisition growth, which totaled 108 vehicles at the end of the reporting period. Figure 12 and Table 1 summarize all Ch...
AI summary The report details the performance of the ev.energy platform, including the number of vehicles in the registered fleet and the use of ChargePoint events for curtailment. The majority of events were for the EVSE3 use case, with metrics like 'Received Event Ratio' and 'Estimated Average Shed per Event' providing insights into participation and curtailment effectiveness.
ese figures would remain relatively unchanged for all chargers that are participating in a smart charging program and receiving an event. 2 Detailed in the July 2021 Semi-Annual Report. Page 15 of 48 . . REDACTED (CONFIDENTIAL INFORMATION...
AI summary The text discusses the use of ChargePoint EV chargers in a smart charging program, referencing event counts and dispatches since January 2021. It includes a figure and table summarizing event data for various EVSE units and their use-case testing.
5/19/2021 5/25/2021 1/25/2021 7/13/2021 1/25/2021 Period End Date 12/22/2022 12/22/2022 12/22/2022 12/22/2022 12/22/2022 Number of Events 88 100 149 14 351 Expected Event Participation (Chargers) 4618 5372 5734 791 16515 Received Event Not...
AI summary The text presents data on event participation and performance metrics for electric vehicle charging events between January 2021 and December 2022. It includes statistics on the number of events, charger participation rates, opt-in and opt-out rates, and average energy shedding during events.
(kW) 0.99 0.97 1.04 0.83 1.00 Estimated Average Shed per Event per Opt-in and Charged (kW) 5.3 5.2 4.0 5.4 4.7 Page 16 of 48 . . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page...
AI summary The text presents data on load shedding and EVSE use-case summaries, including event dates and periods, from a Smart Grid Semi-Annual Report and a Load Forecast Report. It includes statistical data on kW and average shed per event, along with details on EVSE use cases.
8/30/2022 8/26/2022 7/12/2022 1/25/2021 Period End Date 11/14/2022 11/14/2022 11/14/2022 6/30/2022 Number of Events 9 7 49 65 Opted in and Charging during event 178 138 875 1191 Chargers Available 857 718 4510 6085 Opt-Outs 16 12 32 60 Ave...
AI summary The data shows the highest power shedding occurs at 23:00 and 07:00, with some variability due to small sample sizes. More events across all 24 hours are needed for improved analysis and to fill gaps in early morning data.
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.
t opportunity that a level 2 charger provides during periods where a vehicle is actively charging. Figure 14 – Power Shed Opportunity of Vehicles that are Actively Charging versus Event Start Time E V S E1 – LOAD LEVELING, D AY AHEAD GENER...
AI summary The document discusses load leveling use cases for EVSE1 and EVSE2, focusing on power shedding opportunities during vehicle charging events. EVSE1 curtails charging to 25% or 50% of nominal charge, with an estimated shed of 0.99 kW per event. EVSE2, introduced later, also curtails charging during intra-day economic dispatch events, with a shed of 0.97 kW per event.
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.
int Results Figure 15 depicts the technical capability to curtail EV charging load during the Nova Scotia grid peak, between 17:00 and 19:00, by curtailing electric vehicle supply equipment (EVSE). Page 19 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The text discusses the technical capability to curtail EV charging load during grid peak hours in Nova Scotia, using EVSE and ChargePoint dashboards. It highlights opportunities to shift EV charging peaks away from grid peaks to alleviate system impacts, with specific data from February 27, 2022, showing a peak charging power of 132.3 kW.
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.
icult to positively correlate the results to the specific action. This concept, and how the relative signal change influences the charging will be explored further as this use case testing is ongoing. In addition to further data collection...
AI summary The text discusses planned iterations in testing EVSE5, including changing the wind signal to an absolute signal and squaring the wind signal for better approximation of on/off scenarios. It also mentions the introduction of DR events to examine value-stacking effects if correlated wind following is established.
dence for this use case but demonstrate the theoretical opportunity to build such functionality should it prove to be useful. Further consideration and analysis will be presented in future reporting. 3.1.3 . C USTOMER EXPERIENC E OB SERVAT...
AI summary The document discusses customer experience observations from the ChargePoint pilot program, highlighting high satisfaction with cost-savings and environmental benefits. NS Power plans to survey ev.energy participants in 2023, and the report mentions ongoing use case testing for bi-directional charging.
NS Power will issue a customer survey to ev.energy project participants in 2023. Customer feedback data will be included in the final SGNS report. 3.2. BI-DIRECTIONAL CHARGING 3.2.1 . OB SERVATIONS AVAILAB ILITY In both residential and com...
AI summary NS Power plans to survey participants in the ev.energy project in 2023, with feedback included in the final SGNS report. The document discusses the importance of EV availability for bi-directional charging, using data from the Coritech charger at NSCC Annapolis Valley Campus, showing that the Nissan Leaf is typically available for testing outside of working hours.
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.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 2 Page 31 of 48 Attachment 2 – SGNS Use Case Testing Update Report maximum SOC setpoint of 100%. Note that power data for the charger is not available from the NSCC BMS during 00:00 to...
AI summary The report details an EVSE event where NS Power discharged 19.9 kWh from an EV battery back to a building during peak hours, reducing the vehicle's SOC from 100% to 66%. NS Power then set the charger to 3 kW to allow the EV to recharge overnight, reaching 90% SOC by 23:23. A similar event is shown for Fermata, where 5 kW was discharged during grid peak hours.
t course of business may provide additional insight for this use case to be presented in future reporting. The application of this use case will also be further evaluated throughout testing in 2023. E V S E8 – PARTIAL OR WHOLE HOME B AC KU...
AI summary The document discusses the evaluation of EVSE use cases, including partial or whole home backup and C&I customer demand reduction. Testing was conducted using Coritech and Fermata units, with further evaluation planned for future phases. The C&I use case involved discharging EVs during peak demand periods to reduce demand charges.
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.
-directional EV chargers would be more impactful at reducing monthly demand at facilities with consistent energy demand profiles and peak periods that are substantial and shorter in duration. Page 34 of 48 . . REDACTED (CONFIDENTIAL INFORM...
AI summary The document discusses the impact of bidirectional EV chargers on demand charge management, particularly for customers with consistent energy demand profiles and peak periods. It highlights the Fermata charger's capability to automatically discharge battery power to offset high building demand when a pre-set meter target is exceeded.
gure 33 depicts that, during on-peak hours, the Tesla time-based control mode discharges the battery through to 23:00, followed by an off-peak charging period. This configuration was managed by the Page 39 of 48 . . REDACTED (CONFIDENTIAL...
AI summary Figure 33 and 34 illustrate the operation of Tesla Powerwall batteries with time-based control during on-peak and off-peak hours. The configuration was initially managed through the Tesla app, but this functionality has been removed as utility integration continues.
patch through pool dispatch or manual events, based on day ahead generation planning, marginal cost-based forecast. ESP constraints maintain one charge/discharge cycle per day. No results to report. Page 40 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The document discusses battery dispatch through pool dispatch or manual events based on intra-day generation planning and system loading. It also outlines modified time-varying curtailment during specific hours, initially executed manually and later aligned with NS Power's time-of-day rate for automatic execution.
and lightning. Aggregated power of controlled equipment, which is calculated by the BMS based on equipment nameplates and trends and not measured directly, is available for each customer site in ESP. Table 6 summarizes all events that have...
AI summary The document discusses the calculation of aggregated power reduction for controlled equipment using Building Management System (BMS) data, and the methodology used to estimate average event savings for demand response events. It notes that the approach uses ESP-sourced power trend data instead of utility metering due to the small load reduction being lost in the noise.
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.
ts to minimize customer impact during events, which is indicated by ‘Pre-Peak Signal.’ Measurement and verification (M&V) of February testing was completed by the E1/NS Power Demand Response Working Page 45 of 48 . . REDACTED (CONFIDENTIAL...
AI summary The document discusses efforts to minimize customer impact during demand reduction events using the 'Pre-Peak Signal.' M&V of February testing was completed using AMI data. Investigations into increasing load reduction capacity are ongoing, focusing on existing integrated equipment at three customer sites.
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.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 3 Page 2 of 4 SGNS Customer Program Enrollment Value to be Tested Data and Metrics to Measure System Impact and Value Project Scenarios for Comparison Measuring Outcomes ESP
AI summary The document outlines a Smart Grid Semi-Annual Report Attachment 3, focusing on customer program enrollment, value to be tested, data and metrics for measuring system impact and value, project scenarios for comparison, and measuring outcomes, with ESP as a key component.
Project Scenarios for Comparison Measuring Outcomes ESP Processes Applicable to Value Reduced Upward Pressure on Revenue Baseline Scenario Baseline Scenario 2 Test Scenario Customer Benefit DER Class DER Group Value Streams DER Control Var...
AI summary The text outlines project scenarios for comparing outcomes related to DER (Distributed Energy Resources) control variables, value streams, and their impact on system reliability, grid stability, and customer benefits. It mentions different control strategies such as no utility influence, device-level control, and direct utility control.
• Connection Status & Alarm Status - The availability of EVSE for control during each test (including communication to the chargers, and the customer participation level) • Aggregated EV charger load (kW) Customer charges as Curtailment of...
AI summary The text outlines parameters related to electric vehicle supply equipment (EVSE) monitoring, including connection status, alarm status, load curtailment, and the value of curtailed load. It discusses aspects of EVSE control, customer participation, and the impact of load curtailment on generation contingency and residential load.
• EVSE control signal latency (seconds) to achieve a scheduled curtailment Pilot 2/R3A thresholds (EVSE7) target charge rate • EVSE Control Variable Input: Feeder Voltage (V), • Value of discharge capacity ($/kW, distribution congestion wi...
AI summary The text discusses technical aspects of EVSE control, including signal latency, response times, and curtailment performance. It also mentions V2G discharge and system upgrades related to distribution congestion and capacity valuation.
Customer charges as • Curtailment of EVSE charge • Value of load curtailed ($/kW, Further leverage EVSE out-of-the- Cold Load Pickup Relief • Net residential load (kW) convenient (document impact power (kW) by control of • Time delay (resp...
AI summary The text discusses customer charges related to EVSE (Electric Vehicle Supply Equipment) curtailment, including the value of load curtailed, net residential load, and restoration processes involving EVSE6. It also mentions leveraging EVSE out-of-the-box functionality for delayed restoration and deferred distribution.
• Connection Status & Alarm Status - The availability of EVSE for control during each test (including Utilize local EVSE settings, controls communication to the chargers, and the customer participation level) through vendor provided softwa...
AI summary The text discusses the monitoring and control of EVSE during testing, including connection status, alarm status, customer participation, and the monitoring of EVSE signals and latency. It also references customer demand charges, load curtailment values, and scheduled charging times.
• EV charger load (kW) $/kWh) convenient (charging times will Demand Charge • EVSE control signal latency (seconds) discharge (V2G) schedule N/A N/A management customer peak • EVSE Control Variable Input: Feeder Voltage (V), • Value of dis...
AI summary The text discusses various technical parameters related to electric vehicle supply equipment (EVSE), including load, control signal latency, response time, and demand charge management. It also touches on aspects such as feeder voltage, current, and load, as well as the value of discharge capacity in a vehicle-to-grid (V2G) context.
he sample of customers enrolled in the Smart Grid NS Project) Economic Dispatch • Day-ahead generation planning Customer charges as Load Leveling (e.g. Peak (EVSE1) EV Control based on: • Curtailment of EV charge • Utility event statistics...
AI summary The text discusses aspects of the Smart Grid NS Project, including economic dispatch, day-ahead generation planning, load leveling, and EV control based on energy delivered and the value of load curtailment.
EV charge • Utility event statistics • Energy delivered (kWh) • Value of load curtailed ($/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.
Intermittent Renewable Customer charges as Renewable Integration Generation Following • Curtailment of EVSE charge • Utility event statistics • Energy delivered (kWh) • Value of load curtailed ($/kW, convenient (charging times will Chargin...
AI summary The document discusses the integration of intermittent renewable energy with EV charging, focusing on load management, curtailment of EVSE charging, and the influence of EV charging patterns on energy delivery and load curtailment value.
Customer charges as Generation Contingency (10 Curtailment of EV in response to a • Curtailment of EV charge • Utility event statistics • Energy delivered (kWh) • Value of load curtailed ($/kW, convenient (charging times will Minute Operat...
AI summary The text discusses customer charges, energy curtailment related to EV charging during contingency events, and the impact of EV charging patterns on utility operations, including statistics on vehicle charging sessions and energy consumption.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 3 Page 3 of 4 SGNS Customer Program Enrollment Value to be Tested Data and Metrics to Measure System Impact and Value Project Scenarios for Comparison Measuring Outcomes ESP
AI summary The document outlines a Smart Grid Semi-Annual Report Attachment 3, focusing on customer program enrollment, value testing, data and metrics for measuring system impact and value, project scenarios for comparison, and outcomes measurement using the ESP platform.
Project Scenarios for Comparison Measuring Outcomes ESP Processes Applicable to Value Reduced Upward Pressure on Revenue Baseline Scenario Baseline Scenario 2 Test Scenario Customer Benefit DER Class DER Group Value Streams DER Control Var...
AI summary The text outlines project scenarios for comparison, focusing on DER (Distributed Energy Resource) classes and groups, value streams, control variables, and their impact on system reliability, grid stability, and customer benefits. It highlights different levels of utility control and software release requirements.
• Feeder Voltage (V), Current (A) and Load (MVA) • Connection Status & Alarm Status - The availability of batteries for control during each test (including • Battery Real Power (kW) Manage optimal control strategy for • Target Power Factor...
AI summary The text outlines technical parameters related to power factor management in a C&I context, including voltage, current, load, battery functionality, and monitoring signal latency, focusing on optimal control strategies for power factor correction.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 3 Page 4 of 4 SGNS Customer Program Enrollment Value to be Tested Data and Metrics to Measure System Impact and Value Project Scenarios for Comparison Measuring Outcomes ESP
AI summary The document outlines a section from a Smart Grid Semi-Annual Report, focusing on customer program enrollment, value to be tested, data and metrics for measuring system impact and value, project scenarios for comparison, measuring outcomes, and ESP.
Project Scenarios for Comparison Measuring Outcomes ESP Processes Applicable to Value Reduced Upward Pressure on Revenue Baseline Scenario Baseline Scenario 2 Test Scenario Customer Benefit DER Class DER Group Value Streams DER Control Var...
AI summary The text outlines project scenarios for comparison, focusing on measuring outcomes, value streams, and technical and functional data related to DER control variables. It includes baseline and test scenarios, emphasizing system reliability, grid stability, and customer benefits.
lue • Target PF setpoint versus actual PF measured (%) • Customer Demand (kVA)
AI summary The text presents two key metrics: the difference between the target power factor (PF) setpoint and the actual PF measured, expressed as a percentage, and the customer demand measured in kilovolt-amperes (kVA). These metrics are likely related to electrical system performance and load management.
Economic Dispatch • Day-ahead generation planning • Curtailment of controlled • Connection Status & Alarm Status - The availability of BMS for control during each test (including Customer runs equipment as Load Leveling (e.g. Peak (BMS1) •...
AI summary The text discusses economic dispatch, including day-ahead and intra-day generation planning, load leveling, and controlled load curtailment. It mentions the use of BMS for equipment control and the consideration of marginal generation costs and load profiles.
Avoided Generation & Demand Reduction) • Intra-day generation planning • Net customer load (kW) • Day-ahead generation planning actions (e.g. decreased • BMS monitoring signal latency (seconds) • Value of load curtailed ($/kW, compared wit...
AI summary The text outlines metrics and considerations related to generation planning, including intra-day and day-ahead planning, net customer load, system load, and the value of curtailed load. It also references BMS (Building Management System) monitoring and control signal latency, as well as capacity and pilot programs.
Wh) to understand impact of BMS 1, 2, 3 • Time varying curtailment or • Aggregated load curtailed (kW) • Time varying curtailment schedule turn off) • BMS response time to curtailment after receiving signal (seconds) uninfluenced operation...
AI summary The text discusses the impact of Building Management Systems (BMS) 1, 2, and 3 on time-varying curtailment, aggregated load curtailed in kW, and BMS response time to curtailment signals. It also mentions deferred transmission and distribution system upgrades.
• 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.
• Curtailment of controlled • Connection Status & Alarm Status - The availability of BMS for control during each test (including Customer runs equipment as • Aggregated controlled load (kW) Curtailment of equipment in Critical Peak Reducti...
AI summary The text discusses aspects of critical peak reduction, including curtailment of controlled load, connection status, alarm status, and the role of BMS in monitoring and controlling customer equipment during tests. It also references critical peak pricing and factors like marginal generation cost and net customer load.
utility dashboard used by the Project. This was rectified with a communication to the affected customers and support from ChargePoint to move all participant data to the Canadian ChargePoint server. 2. ChargePoint Customer App Def ault Lan...
AI summary The document discusses issues with the ChargePoint app's default language misleading users about time-of-day tariffs, Tesla EV behavior under power fluctuations, and data migration to a Canadian server. These issues were addressed through communication and support from ChargePoint.
sessions are required to optimize the time spent developing and testing the integrations. 2 The Sunverge batteries use an OpenADR integration, while the Tesla batteries use an API integration. Page 5 of 14 . . REDACTED (CONFIDENTIAL INFORM...
AI summary The document discusses issues with ChargePoint chargers, where 20% of installed units had defects, and highlights the need for future programs to include contractor labor in warranty considerations. NS Power and ChargePoint are collaborating to resolve the issues.
e utility-owned distributed equipment should, where possible, include warranty considerations that include the contractor labour to replace these units in addition to the standard parts-only warranty. In the July to December 2022 reporting...
AI summary The text discusses issues with ChargePoint's distributed equipment, including a rise in failure rates and warranty considerations. It also highlights challenges in managing the Energy System Platform (ESP) due to integration complexities and the need for administrative resources. ChargePoint has acknowledged a widespread defect in the Wi-Fi module and has issued software updates to address the issue.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 5 Page 7 of 14 Attachment 5 – SGNS Project Lessons Learned scheduled jobs to consume data from the SFTP site. The project team continues to explore deeper analysis methods to ensure in...
AI summary The SGNS project team is analyzing data using Tableau and integrating additional data sources to enhance reporting. Some residential battery customers installed solar systems without notifying NS Power, creating safety risks, and the team has implemented processes to identify and address such installations.
part on the consent of the EV manufacturer to operate without explicit contracts in place. This creates a ’platform risk’ that ev.energy may be prevented from connecting to certain manufacturers’ EVs. For example, in November 2022, some Ch...
AI summary The text discusses challenges with the ev.energy platform, including platform risks due to lack of explicit contracts with EV manufacturers, and issues with token expiration affecting user experience. It also covers the cancellation of EV Smart Charging DR events before weather events and the implementation of a 'storm mode' by NS Power to improve reliability during severe weather.
r will document the sample chosen for the torque checks to avoid picking the same samples. • NS Power will proactively consider retorque the whole site ahead of major wind and/or weather event. 18. Supporting New Assets through Severe Weat...
AI summary The text discusses operational considerations for smart grid platforms during severe weather, the prioritization of 'ready by' time in EV charging algorithms, and limitations in reactive power control for C&I battery energy storage systems under zero export mode. NS Power is proactively managing these challenges.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 5 Page 11 of 14 Attachment 5 – SGNS Project Lessons Learned The fire damaged the customer’s solar array, but did not damage other property or persons, as it was discovered and mitigate...
AI summary A fire damaged a customer's solar array, but the manufacturer provided compensation and improved equipment. An issue with a current transformer metering system during the installation of a Fermata bi-directional charger led to incomplete load data capture, requiring alternative solutions to ensure proper demand charge management.
properly, and any potential risks are addressed. Identifying and evaluating multiple solutions is important for pilot programs as the standard, or first approach, may not be the most effective one. 23. C&I Building Management System ( Desi...
AI summary The text discusses an issue with the ESP not sending the duration signal to the BMS during load reduction events, leading to the BMS remaining in non-standard operating modes. A permanent workaround was implemented by Siemens through the use of timers on the Desigo BMS server.
CI C0010788 – Smart Grid Semi-Annual Report Attachment 5 Page 13 of 14 Attachment 5 – SGNS Project Lessons Learned 26. Coritech Charger Commissioned w ith Coritech-ow ned Modem A Coritech-owned modem was initially installed to allow Corite...
AI summary The document discusses issues encountered during the commissioning of a Coritech-owned EV charger, including delays due to modem replacement and the need for better integration monitoring. It highlights the importance of improved event scheduling and error notification systems to support DERMS recommendations.
Report Date: 2022-08-10 Unrestricted Page 1 of 15 Document # PM-FM-011 Version 5 2022-07-22 . . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 96 of 205 REDACTED CI C0010788 –...
AI summary The document outlines project activities related to the Energy Services Platform (ESP), Microgrid Control Platform (MCP), and the Shediac Smart Energy Community Demonstration, as part of a Smart Grid Nova Scotia Demonstration. It includes progress percentages, status, and comments for each activity.
ormed • • • • o o • • • • • • • • • • • Activity #3: Smart Grid Nova Scotia Demonstration % Completion Status Comments 93% On Schedule Asset installations partially complete. Unrestricted Page 3 of 15 Document # PM-FM-011 Version 5 2022-08...
AI summary The Smart Grid Nova Scotia Demonstration is 93% complete, with asset installations partially completed. Key activities include the launch of a community solar program with ~75% subscription, and the completion of solar array and BESS installations at three customer sites, along with successful UL9540 certifications and electrical inspections.
tivity #4: Tobique Microgrid Demonstration % Completion Status Comments Details of Activity Performed • Unrestricted Page 4 of 15 Document # PM-FM-011 Version 5 2022-08-10 . . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load...
AI summary The document outlines several activities related to energy projects in Nova Scotia, including the Tobique Microgrid Demonstration, North Branch Smart Energy Development, and Program Governance and Management. It also mentions the Energy Services Platform and Microgrid Control Platform software development, as well as the Shediac Smart Energy Community Demonstration. These activities are in various stages of completion.
i-Annual Report Attachment 6d Page 6 of 15 Activity #2 Shediac Smart Energy Community Demonstration % Completion Status Comments Details of Activity Performed • • Activity # 3 Smart Grid Nova Scotia Demonstration % Completion Status Commen...
AI summary The document outlines several demonstration activities related to energy initiatives in Nova Scotia, including the Shediac Smart Energy Community, Smart Grid Nova Scotia, Tobique Microgrid, and North Branch Smart Energy Development. These projects involve university collaborations, community engagement, and advisory support from Efficiency One.
Not a formal work package under this program, however, collaborations noted below: Unrestricted Page 5 of 15 Document # PM-FM-011 Version 5 2022-08-10 . . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synap...
AI summary The text outlines various activities related to energy projects in Nova Scotia, including DER monitoring by Dalhousie University, a student's involvement in modeling DER program potential, and community engagement for a solar garden site. It also mentions the Tobique Microgrid Demonstration and North Branch Smart Energy Development, though details are sparse. A cost report for Siemens is included, focusing on foreign costs and deviations from the Contribution Agreement.
nstallation is underway. • NS Power has scheduled the installation of the Fermata 15kW bi-directional charger for October 2022 and anticipates this project will be completed by the end of October. ev.energy • ev.energy program launched 14...
AI summary NS Power is installing a Fermata 15kW bi-directional charger, with completion expected by late October 2022. The ev.energy program was launched in June 2022, onboarding new and transitioning existing Tesla drivers. Challenges include delays in bi-directional charger deliveries due to certification requirements, leading to the cancellation of one purchase order.
mers pay a fee for the capacity and are credited for the energy (in $) on their bill, as well as receive a monthly subscription report. This launched on November 29, 2021. 3) Between Jan 2021 and Dec 2021, has your business or institution...
AI summary The text discusses a capacity and energy credit program launched on November 29, 2021, where participants pay a fee for capacity and are credited for energy on their bill, along with receiving a monthly subscription report. It also asks if any new or significantly improved processes were developed between January 2021 and December 2021 as a result of the project.
Residential BES - kW/unit per year Peak impact Assumed negligible impact Scenario (Demand Response) DER Estimated Incremental Peak Impact (Optimistic Case) Source Influenced by NSPI Residential BES (5.00) kW/unit per year incremental peak...
AI summary The text provides information on residential BES (Battery Energy Storage) and its peak impact, as well as a demand response scenario influenced by NSPI (Nova Scotia Power Inc.). It includes a forecast of residential customer count for 2033 and notes that the data is based on estimates as of April 12, 2021, with uncertainties noted.
AL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-11 Attachment 1 Page 2 of 3 Residential Customer Uptake Scenario 50% 25% 10% 5% Year 2033 2033 2033 2033 DER Peak Impact (kW) Residential BES - - - - DER Incremental Peak Impact...
AI summary The document discusses the impact of distributed energy resources (DER) and demand response (DR) programs on residential peak load in Nova Scotia, under different uptake scenarios (50%, 25%, 10%, and 5%) for the year 2033. It includes figures for battery peak impact with and without optimal DR control.
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.
1 Request IR-16: 2 3 Demand Side Management (Section 4.6, pp 54-56). 4 5 (a) Please provide the source data for the DSM values used in this forecast. 6 (b) Please provide the DSM values used in the latest IRP. 7 (c) Please provide the DSM...
AI summary The request seeks source data for DSM values used in the forecast, including the latest IRP and E1 potential study. The response indicates that DSM values for 2023-2025 are based on EOne’s Settlement Plan and values beyond 2025 are based on the EOne Potential Study. A table is referenced with energy and demand values for various years.
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.
DSM DSM DSM DSM captured Adjustme Forecast Forecast Forecast captured by Adjustment by Residential nt for Residential Commercial Industrial Comm/Ind for Residential Res Non-Res Year end use Comm/In DSM savings DSM savings DSM savings end u...
AI summary The table outlines DSM savings forecasts for residential, commercial, and industrial sectors from 2023 to 2027, including captured energy, adjustments, and coefficients for residential and non-residential sectors.
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.
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.
DSM DSM Total Res Model GWh New Customers Solar EV RTR DSM Total included additional Sales 2023 4834 54 -39 7 0 -62 -36 -27 4830 2024 4853 106 -62 15 -14 -117 -67 -50 4847 2025 4839 155 -90 31 -14 -176 -101 -75 4846 2026 4873 201 -117 60 -...
AI summary The table provides a detailed breakdown of demand-side management (DSM) metrics including total residential load, new customers, solar, EV, and other factors from 2023 to 2033. The data includes sales and other DSM-related figures, with the 2023 Load Forecast Report referenced as a source.
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.
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.
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.
2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-26: 2 3 Other Industrial (Section 7.3). 4 5 (a) Please explain and quantify the specific reasons for th...
AI summary NSPI responded to Synapse Energy Economics' request regarding the 2023 Load Forecast Report. The response highlights a 19 GWh increase in new projects, offset by a 120 GWh decrease from an existing customer. The 24 survey responses represent 75% of the sector load, with an aggregate load change of -5.1%. Uncertainty in long-term forecasts is addressed through scaling factors.
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.
Res Modeled C&I Large Firm Inter. System Heat EV DR DSM Peak Elect. Cust. Peak Cust. Peak Peak (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW) (MW) 2022 2,011 2 3 -4 2 107 -14 2105 146 2,255 2023 2,028 3 6 -12 3 109 -26 2111 147 2,271 2024 2,...
AI summary The table presents modeled peak demand and related factors for various years, including heat, electric vehicle (EV), demand response (DR), and demand-side management (DSM) contributions. The text explains the concept of 'unexplained' components in forecasting models, which account for uncertainty and differences between predictions and actual outcomes.
..........101 Page ii ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 5 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 20...
AI summary The document presents the 2023 Load Forecast Report, which includes an attachment from Synapse IR-30, focusing on Nova Scotia Energy Efficiency and Demand Response Potential Study for the period 2021-2045.
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.
he calculation only to those measures that have passed the benefit-cost test chosen for measure screening, in this case the Total Resource Cost (TRC) test or the Program Administrator Cost (PAC) test. Market potential (also referred to as...
AI summary The text discusses the calculation of market potential for demand-side management (DSM) measures, considering factors like equipment turnover, incentive levels, and consumer adoption. It differentiates between gross and net potential savings and references appendices for detailed analysis.
http://en.wikipedia.org/wiki/System_dynamics for a high-level overview. ©2019 Navigant Consulting, Ltd. Page 1 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 10 of 355 Nova Scotia En...
AI summary Navigant conducted a bottom-up analysis to estimate demand response (DR) potential and costs in Nova Scotia, using data from EfficiencyOne and secondary sources. The analysis involved five steps and provided input data for Navigant’s Demand-Response Simulator (DRSim™) model to calculate total DR potential across the province.
sis, as documented in this report. The DR potential analysis efforts provide input data to Navigant’s Demand- Response Simulator (DRSim™) model, which calculates total DR potential across Nova Scotia. The foundation for the DR potential as...
AI summary The document discusses the methodology for assessing demand response (DR) potential in Nova Scotia, including the use of the Demand-Response Simulator (DRSim™) model and the development of bottom-up peak demand projections based on energy efficiency scenarios. It outlines the DR options considered in the study.
representative of commonly deployed and emerging DR programs in the industry and are listed in Figure ES-2. Figure ES-2. Summary of Demand Response Options Considered in Study
AI summary The text mentions a figure that summarizes demand response options considered in a study, highlighting commonly deployed and emerging DR programs in the industry.
Eligible Customer DR Option Brief Description End Use Classes Electric Furnace 3 Residential Heat Pump 4 Control of electric loads by a thermostat Direct Load Control (DLC) Small Commercial and/or load control switch. HVAC 5 Small Industri...
AI summary The text outlines different demand response (DR) options available to eligible customers, including Direct Load Control (DLC) and BNI Curtailment, specifying the types of customers and end-use classes applicable to each program.
party aggregators. Total Facility Use of batteries for load shifting and BTM Battery Control All classes Batteries dispatching to the grid. Charging modulation to reduce EV EV Charging Control EV EV demand during peak periods A rate schedu...
AI summary The document discusses demand response (DR) programs and their technical and market potential, focusing on methods like battery control, EV charging control, critical peak pricing, and behavioral demand response. Navigant calculated technical potential by multiplying eligible load by unit impact, noting that overlaps in participation are not considered.
at is that, by definition, technical potential calculation does not consider participation overlaps. Therefore, the technical potential estimates for each DR option should be considered independently. Navigant assessed cost-effectiveness o...
AI summary The document discusses the technical and economic potential of energy efficiency (EE) and demand response (DR) in Nova Scotia, noting that technical potential calculations do not account for participation overlaps. Navigant assessed DR options, calculating achievable potential by multiplying participation assumptions with technical estimates and considering customer opt-out during events.
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.
Load (%) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 10 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 19 of 355 Nova Scotia Energy Efficiency and Demand Response...
AI summary The document discusses the market winter peak demand savings potential from energy efficiency (EE) and demand response (DR) in Nova Scotia, estimating scenarios ranging from 15% to 24% over a 25-year period, as analyzed by Navigant Consulting.
t Report Synapse IR-30 Attachment 1 Page 20 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Figure ES-11 presents the level of investment in nominal dollars for each of the four achievable scenarios o...
AI summary The document presents investment levels for energy efficiency and demand response scenarios over a 25-year period, showing varying investment ranges from $18 million to $190 million annually. It also evaluates the cost-effectiveness of demand response options, with most being cost-effective except for BTM Battery Control and EV Charging control.
ing control are cost-effective for this case. Figure ES-12. DR Achievable Base Case, Benefit-Cost Assessment by Option (TRC and PAC Test Benefit-Cost Ratios) DR Option Benefits Costs TRC Test PAC Test (NPV 2020 (NPV 2020 Benefit-Cost Ratio...
AI summary The document presents a benefit-cost analysis of various demand response (DR) options, showing that some are cost-effective while others are not. The analysis indicates that cost-effective DR potential is expected to increase to about 80 MW by 2027 and then plateau at around 70 MW from 2035 onward. DLC and CPP are the most significant contributors to this potential.
emand in 2027 and increases slightly during the following years. DLC constitutes almost half of the total potential followed by CPP at 42%. BNI Curtailment share is significantly smaller at around 8%. Figure ES-13. DR Achievable Potential...
AI summary The document discusses demand response (DR) investment levels, highlighting that DLC program investment increases steadily until 2024, then drops until 2026 before spiking again in 2031 due to the 10-year program life cycle. Costs are influenced by technology enablement, program development, and customer participation in DR programs.
m costs are re-incurred again. The two main cost components for DLC are the control equipment cost (thermostats and switches), and customer incentives for DR program participation. • CPP program investment is relatively high during its ini...
AI summary The text discusses the cost components associated with Demand Load Control (DLC) and the Cost-Effective DR (Demand Response) Options, including control equipment, customer incentives, and program development. It also notes the investment timeline for the CPP program and BNI curtailment, highlighting the costs tied to aggregator payments and delivery costs.
hment 1 Page 23 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 E. 2.2.2 Scenario Analysis For the low and high cases, Navigant adjusted assumed participation levels, incentive amounts, marketing spen...
AI summary The document discusses scenario analysis for demand response (DR) and energy efficiency programs in Nova Scotia from 2021 to 2045. It outlines how different participation levels and assumptions affect DR achievable potential, program costs, and cost-effectiveness under low, base, and high scenarios.
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.
apse IR-30 Attachment 1 Page 24 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 1. INTRODUCTION This section provides an overview of the potential study, including background and study goals, a discus...
AI summary This section introduces the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045, outlining its goals, methodology, and collaboration with EfficiencyOne and stakeholders through the Demand Side Management Advisory Group (DSMAG). The study uses validated modeling tools and incorporates stakeholder feedback to ensure accuracy and relevance to current market conditions.
bal input assumptions and measure characterizations. We also carefully considered, and as appropriate, were responsive to stakeholders’ input, incorporating their feedback into the analysis approach. 1.1 Context and Study Goals Navigant wa...
AI summary Navigant was retained by EfficiencyOne to estimate the potential for electric energy efficiency and demand response in Nova Scotia from 2021 to 2045. The study involves analyzing current energy use patterns, characterizing potential efficiency measures, and estimating achievable energy savings. The findings will support integrated resource planning and program design.
Residential and Business, Nonprofit and Institutional (BNI) Climate Single Weather Zone Time Horizon 2021- 2045 (25 years) 1.2 Stakeholder Engagement EfficiencyOne engaged stakeholders beginning in the fall of 2018 to encourage input into...
AI summary EfficiencyOne engaged stakeholders starting in the fall of 2018 for the 2019 Electricity Demand Side Management Potential Study. The process included webinars, technical conferences, and multiple review periods to incorporate feedback and improve the study's accuracy and relevance to Nova Scotia.
brium market share, behavioural measures, investment and incentive strategy, re-participation, and model calibration. Section 7 – discusses the Reference Forecast Approach and scenario configuration. Section 8 – presents the Energy Efficie...
AI summary The document outlines various sections of a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045. Sections cover market share, forecasting approaches, energy efficiency measures, demand response methodologies, and results of cost-effectiveness analyses.
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.
site visits (see Appendix B) • ENS program evaluation reports • NS Power end use intensity-based forecasts (Load Forecast Models) • Previous Nova Scotia DSM potential studies Where Nova Scotia-specific information was not available, Naviga...
AI summary Navigant used various data sources, including NS Power's load forecast models and previous DSM studies, to estimate energy consumption. They segmented customer sectors based on consumption, demand, and end-use allocations, working with EfficiencyOne and the DSM Advisory Group. The DSMSim™ model was used to represent efficiency measures at the segment level, incorporating fuel choices and equipment efficiency.
within the model for each segment, as required and as permitted by data availability. ©2019 Navigant Consulting, Ltd. Page 20 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 29 of 355...
AI summary The document discusses the segmentation of residential and BNI customers for energy efficiency and demand response studies, highlighting different customer segments based on building type and income level.
756 Total 480,971 Source: Navigant analysis based on Nova Scotia and StatsCan data ©2019 Navigant Consulting, Ltd. Page 21 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 30 of 355 No...
AI summary The document discusses the BNI sector in Nova Scotia, divided into four segments: Small Commercial, Large Commercial, Institutional, and Industrial. The segmentation is based on 2018 NS Power Data and EfficiencyOne’s Rate and Bill Impact Analysis reports, reflecting blended averages based on rate-code / size bins.
pact Analysis (customer counts) reports, and Institutional reflect blended averages based on rate-code / size bins. Industrial Source: Navigant Navigant selected the BNI segments with the goal that the building types within those segments...
AI summary Navigant selected BNI segments based on similar building characteristics to ensure consistency in modeling. They used household counts for residential and floorspace for BNI, applying Nova Scotia Power’s 2018 End Use Intensity Model to forecast floorspace. Residential households were estimated from account forecast data, with one household per account assumed on average.
specifying the particular type of equipment used to satisfy that need. ©2019 Navigant Consulting, Ltd. Page 22 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 31 of 355 Nova Scotia En...
AI summary The document outlines the end uses by sector in the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It emphasizes the categorization of energy use for quality control and forecasting purposes, with specific examples provided for residential and other sectors.
t Report Synapse IR-30 Attachment 1 Page 32 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 2.5 Electricity Consumption EfficiencyOne provided Navigant with information on actual sales and customer nu...
AI summary The document discusses electricity consumption in Nova Scotia in 2019, segmented into residential and BNI sectors. Data from the 2018 Emera MD&A report and the 2018 Nova Scotia Power load forecast were used to estimate consumption levels before demand-side management (DSM) was applied.
11,159 Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 24 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 33 of 355 Nova Scotia Energy Efficiency and Demand Response Po...
AI summary Navigant and EfficiencyOne characterized 183 energy efficiency measures across Nova Scotia’s residential and BNI sectors, prioritizing those with high impact, data availability, inclusion in EfficiencyOne’s DSM Plan, and cost-effectiveness. The measures included are currently available in the market and economically viable.
qualitatively define each characterized measure: • Replacement Type: Replacing the baseline technology with the efficient technology can occur in three variations:
AI summary The text discusses the replacement type as a characterized measure, which involves replacing baseline technology with efficient technology in three variations. The specific variations are not detailed in the provided text.
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.
pse IR-30 Attachment 1 Page 34 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045
AI summary This document is part of a study on Nova Scotia Energy Efficiency and Demand Response Potential for the period 2021-2045, focusing on analyzing opportunities for energy efficiency and demand response initiatives.
2. Sector, and End Use Mapping: The team mapped each measure to the appropriate end uses, customer segments and sectors. Where Nova Scotia-specific information was not available, Navigant utilized secondary data, including internal Navigan...
AI summary The text outlines the methodology used to map energy efficiency measures to customer segments, sectors, and end uses, utilizing both primary and secondary data sources. It also describes parameters such as annual energy consumption, peak demand, fuel type applicability, measure lifetime, and incremental costs for energy-efficient technologies.
fficient technology, using the following variables: o Base Costs: The cost of the base equipment, including both material and labor costs. This is zero for retrofit measures. o EE Costs: The cost of the energy-efficient equipment, includin...
AI summary The text outlines key variables used in the analysis of efficient technology, including base and EE costs, technology densities, saturation levels, applicability, and competition groups. These metrics help assess the feasibility and impact of replacing baseline technologies with energy-efficient alternatives.
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.
ngs by Sector (GWh, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 29 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 38 of 355 Nova Scotia Energy...
AI summary The document presents technical savings potential for winter peak demand by sector in Nova Scotia, showing that the BNI sector ranges between 660 to 730 MW annually, while the residential sector ranges from 1,460 to 1,570 MW. The savings potential as a percentage of consumption for both sectors ranges between 70% to 81% annually, due in part to fuel-switching measures.
avings by End Use (GWh, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 33 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 42 of 355 Nova Scotia Ene...
AI summary The document presents data on energy efficiency and demand response potential in Nova Scotia, highlighting residential and BNI lighting as major contributors to winter peak demand savings. HVAC systems are identified as having the largest impact on demand reduction, accounting for over 75% of the potential savings through energy efficiency.
generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 36 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 45 of 355 Nova Scotia Energy Efficiency and Demand Respons...
AI summary The document presents the top forty energy efficiency measures ranked by their winter peak demand technical savings potential for residential and BNI sectors in 2021. HVAC-related measures like Wi-Fi Thermostats, Wood Furnaces, and Air Source heat pumps are highlighted as major contributors to demand reduction.
port Synapse IR-30 Attachment 1 Page 48 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 5.2 Energy Efficiency Economic Potential Results by Sector Figure 5-1 shows economic electricity savings potenti...
AI summary The document presents economic electricity savings potential and winter peak demand savings by sector for Nova Scotia's energy efficiency and demand response initiatives from 2021 to 2045. Residential and BNI sectors show similar growth in economic savings potential, and high avoided costs from the 2014 Integrated Resource Plan influence the screening of high-saving measures.
generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 42 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 51 of 355 Nova Scotia Energy Efficiency and Demand Respons...
AI summary The document discusses the economic winter peak demand potential in residential and BNI sectors using a PAC cost test screen of 1.0, showing that economic potential is close to that using a TRC screen. This suggests that cost effectiveness screening was not a major limiting factor for economic potential.
generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 43 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 52 of 355 Nova Scotia Energy Efficiency and Demand Respons...
AI summary The document discusses the economic electricity savings potential in Nova Scotia, comparing BNI and Residential sectors. It highlights similarities in economic savings opportunities despite differences in industrial processes and emphasizes the importance of HVAC equipment and lighting in BNI for energy efficiency.
Report Synapse IR-30 Attachment 1 Page 53 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Figure 5-6 shows the economic winter peak demand potential as a percentage of consumption. Very similar trends...
AI summary The document discusses the economic winter peak demand potential as a percentage of consumption across various sectors in Nova Scotia, highlighting trends in energy efficiency and demand response. It notes that the BNI sector's economic potential closely follows technical potential due to fuel switching in HVAC, while residential sector differences are attributed to line loss factors.
ort Synapse IR-30 Attachment 1 Page 54 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 5.3 Energy Efficiency Economic Potential Results by End Use Figure 5-7 shows the economic electricity potential,...
AI summary The document presents economic potential results for energy efficiency and demand response in Nova Scotia from 2021 to 2045. HVAC and lighting are highlighted as key areas with significant savings potential, particularly in residential and BNI sectors. Wi-Fi enabled plugs also show potential due to their current low penetration.
W, gross at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 51 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 60 of 355 Nova Scotia Energy Efficiency and De...
AI summary The text discusses market potential for energy efficiency and demand response in Nova Scotia, focusing on factors such as equipment turnover, simulated incentives, consumer adoption, and marketing effectiveness. It outlines two approaches to calculating DSM resource acquisition: equilibrium market share and dynamic approaches.
e adoption of DSM measures can be broken down into calculation of the “equilibrium” market share and calculation of the dynamic approach to equilibrium market share, as discussed in more detail below. Market potential differs from program...
AI summary The text discusses the methodology for calculating market potential in energy efficiency, distinguishing it from program potential. It emphasizes the use of Total Resource Cost (TRC) as a cost-effectiveness measure with a threshold of 0.7, aligned with Nova Scotia regulatory practices. The approach focuses on portfolio-level or sector-level analysis rather than program-specific details.
gy presented here focuses primarily on portfolio-level or sector-level approaches. ©2019 Navigant Consulting, Ltd. Page 52 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 61 of 355 No...
AI summary The text discusses the methodology used in assessing energy efficiency market potential, emphasizing the use of the Total Resource Cost (TRC) as a primary screening tool, adjusting diffusion parameters based on industry data, and incorporating administrative costs at both measure and portfolio levels.
with kWh saved. Market potential estimates are developed using net savings based on historical Net-to-Gross (NTG) program NTG values. Assume 85% of measures re-participate as an efficient measure at the end of Re-participation their measur...
AI summary The text discusses the calculation of equilibrium market share for demand-side management (DSM) measures, based on the payback time of efficient technologies relative to baseline technologies. It assumes that consumers make economically rational decisions when choosing technologies, and that measures with more favorable payback times after incentives will achieve higher market shares.
s certainly have limitations, they are nonetheless directionally reasonable and simple enough to permit estimation of market share for the hundreds of technologies appearing in most potential studies. To inform this study, the team used eq...
AI summary The study uses equilibrium 'payback acceptance' curves developed by Navigant based on surveys of residential and BNI customers to estimate how different customer segments accept energy efficiency investments with varying payback periods.
y efficiency investment is different for residential and BNI customers. 10 The model uses this information to simulate how customers in each sector will accept measures with differing payback periods. Since the payback time of a technology...
AI summary The document discusses how efficiency investment differs between residential and BNI customers, and how the model simulates technology adoption based on payback periods. It explains that equilibrium market share is recalculated annually due to changing technology and energy costs, and outlines two approaches for calculating equilibrium market share. Behavioral measures are modeled differently due to their low cost and reliance on marketing efforts.
s not allocated back down to measures or sectors for cost effectiveness or net benefits calculations, but was included in total portfolio spending, cost effectiveness, and net benefits calculations. 10 These payback curves represent custom...
AI summary The text discusses the allocation of costs in energy efficiency programs, noting that costs are not allocated back down to specific measures or sectors for cost effectiveness or net benefits calculations, but are included in total portfolio spending. It also mentions payback curves and challenges in estimating indirect costs related to energy information feedback.
ogram participants that revert to the baseline after the effective useful life of the measure, this savings is removed from the cumulative potential to reflect the in-situ condition at each time-step. Behaviour measures, such as home energ...
AI summary The text discusses how energy efficiency savings from measures are calculated, noting that savings from measures reverting to baseline after their useful life are removed. Behaviour measures, such as home energy reports, are an exception, with incentives for re-adoption added to program spending. The section also introduces the topic of model calibration for energy efficiency forecasts.
Report Synapse IR-30 Attachment 1 Page 64 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 the calibration process. It is important to note that although the team calibrated to historical results for t...
AI summary The report discusses the calibration process of a model used to estimate energy efficiency and demand-side management potential in Nova Scotia. It highlights that while the model was calibrated using historical data from specific measures, the total market potential may differ from past program achievements due to the inclusion or exclusion of certain measures.
orically not been included in programs, but may exclude certain historical measures as well. This discrepancy can cause calibrated base year potential to be lower or higher than historic program data. To obtain close agreement with Efficie...
AI summary The text discusses the calibration process used to align forecasted savings with historical data, involving adjustments to incentive levels and diffusion parameters. It also describes a backcasting exercise using the DSMSimTM model to simulate adoption of energy efficiency measures based on historical data.
EfficiencyOne’s Historical Navigant Simulated Achievements Backcast Source: Navigant Analysis ©2019 Navigant Consulting, Ltd. Page 58 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 6...
AI summary The document compares historical energy efficiency achievements with simulated backcast results for BNI Linear Replacement Lamps in Nova Scotia. It notes that the historical savings align well with the simulated trajectory, suggesting that these measures are past the inflection point and will experience slower growth due to market saturation.
EfficiencyOne’s Historical Navigant Simulated Source: Navigant Analysis Achievements Backcast ©2019 Navigant Consulting, Ltd. Page 59 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 6...
AI summary The document outlines different scenarios for energy efficiency and demand-side management, including the Max Achievable Scenario with 0-year paybacks and 200% marketing effects, and the Mid Scenario with half the targeted payback times of the reference scenario. These scenarios were developed by Navigant for the Nova Scotia Energy Efficiency and Demand Response Potential Study.
odel at varying levels of aggregation, using the TRC benefit-cost test as a screen set to 0.7. At-the-meter, net savings results are shown by sector, end use category, and by highest-impact measures. 8.1 Comparison of Energy Efficiency Sav...
AI summary The text discusses energy efficiency market potential across different scenarios, showing cumulative electricity savings by 2045. It outlines how measures are reparticipated after reaching the end of their useful life and provides forecasted savings for low, base, and maximum scenarios.
2040 $171.85 $195.66 $329.25 -$29.78 2045 $144.20 $163.29 $309.15 -$35.90 Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 80 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1...
AI summary The document presents a 25-year investment forecast for energy efficiency and demand response programs in Nova Scotia, showing annual investment levels ranging from $40 million to $65 million. Administrative costs are estimated at approximately 50% of total spending and remain constant across scenarios.
t Report Synapse IR-30 Attachment 1 Page 90 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 8.7 Non-Programmatic Savings For this study, Navigant defines non-programmatic savings as reductions in savi...
AI summary The report discusses non-programmatic savings, defined as reductions in savings potential due to codes and standards and freeridership. It highlights that non-programmatic savings contribute 11% of total savings in 2021 and peak at 17% in 2025. The study also includes hourly loadshape disaggregation for base case achievable potential savings, developed at the request of the DSMAG stakeholder group.
Response Potential Study for 2021-2045 Figure 9-1. EE Residential 2045 Cumulative Achievable Potential Sensitivity (GWh, net at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 84 . REDACTED (CONFIDENTIAL INFORMATI...
AI summary The document discusses the sensitivity analysis of energy efficiency (EE) residential and BNI (Business and Non-Industrial) achievable potential up to 2045. It highlights how changes in marketing, incentives, awareness, and other factors influence the cumulative potential, particularly in areas with steep payback curves and significant diminishing returns.
there are not large measures that are near a tipping point. Figure 9-2. EE BNI 2045 Cumulative Achievable Potential Sensitivity (GWh, net at generator) Source: Navigant analysis ©2019 Navigant Consulting, Ltd. Page 85 . REDACTED (CONFIDENT...
AI summary The document discusses the methodology used by Navigant to estimate demand response potential and costs for Nova Scotia, utilizing a bottom-up analysis and primary data from Nova Scotia Power. The customized DRSimTM model is highlighted as a key tool in this process.
, which uses this data as inputs, for this study. The following subsections detail Navigant’s DR potential and cost estimation methodology, as summarized in Figure 10-1. The methodology is as follows: 1. Characterize market for DR potentia...
AI summary This text outlines the methodology used by Navigant for assessing demand response (DR) potential and cost estimation. It details steps such as market characterization, baseline projections, defining DR options, and developing assumptions for participation and costs.
er classes. Step 4: Develop Key Assumptions for •Develop assumptions for participation, unit load reduction, and itemized Potential and Costs cost for each DR option. Step 5: Estimate Potential and Costs, •Present potential estimates, annu...
AI summary The document outlines a six-step process for assessing demand response (DR) potential, including developing assumptions, estimating potential and costs, and conducting scenario analysis. It emphasizes market characterization, segmentation, and the use of data from NS Power’s rate schedules and energy efficiency studies.
end-use to use as inputs into the model. Navigant based the segmentation on the examination of NS Power’s rate schedules and the customer segments established in the energy efficiency potential study. Figure 10-2 presents the different lev...
AI summary The text discusses the segmentation of customer data for demand response (DR) potential assessment, based on NS Power’s rate schedules and customer segments from an energy efficiency study. Dun & Bradstreet data is also used to break down BNI customers by business type.
s. Figure 10-2. Market Segmentation for DR Potential Assessment Level Description
AI summary The text presents a figure titled 'Market Segmentation for DR Potential Assessment,' which outlines different levels and their corresponding descriptions related to demand response (DR) potential assessment.
• Residential Level 1: By Sector • Commercial and Industrial (BNI) • Electric Vehicles (EV) • Residential • BNI » Small Commercial » Large Commercial Level 2: By Customer Class 13 » Small Industrial » Large Industrial » Interruptible Rider...
AI summary The text outlines a hierarchical classification of customer classes and sectors for energy programs, including residential, commercial and industrial (BNI), and electric vehicles, with further breakdowns by building type and customer class.
Services » Market Rate • Electric Vehicles Source: Navigant Level 1: Sector Navigant segmented customers into residential and BNI sectors. Additionally, Electric vehicles (EVs) are considered in aggregate across all customer classes since...
AI summary Navigant segmented customers into residential and BNI sectors for the load forecast, considering electric vehicles across all classes. BNI customers were further divided into five categories based on demand values, with DR program offers varying by size. Residential customers and EV owners were not segmented further due to low demand variation.
nd values across residential customers is low. Electric vehicle owners also were not segmented further. Mapping between NS Power rate classes to those used in the DR study are provided in Figure 10-3. Figure 10-3. Mapping Between Nova Scot...
AI summary The document discusses the segmentation of residential and commercial customers for a demand response (DR) study, mapping Nova Scotia Power rate classes to DR customer classes and building types. This mapping helps in analyzing customer segments and their impact on demand response potential.
ers into segments or building types by mapping the DnB business types to the building types considered in the analysis. Mapping between NS Power building and business types is provided in Figure 10-4. Figure 10-4. Mapping Between Nova Scot...
AI summary The document discusses the process of segmenting customers into building types based on DnB business classifications and the development of baseline projections for demand response accounts and winter peak demand by customer class and segment over the analysis period.
building type • Winter peak demand projections o By customer class, building type and end use 10.2.1 Customer Count Projections The steps to generate customer count projections include: • Separate out Interruptible Rider customers using BN...
AI summary The text outlines the process for generating customer count projections by separating interruptible rider customers, excluding certain account types, and disaggregating residential and municipal customers based on NS Power and NSP data. It also mentions peak period definitions and peak demand projections by customer class and building type.
ponse Potential Study for 2021-2045 Figure 10-6. Customer Count Forecast by Building Type Source: Navigant 10.2.2 Peak Period Definition and Peak Demand Projections A key element of market characterization for the DR potential study is to...
AI summary The document outlines the methodology for developing peak demand projections for the DR potential study, including defining the peak period, calculating coincident peak demand factors, obtaining end use shares, and calibrating results to align with demand distribution. It also includes steps for forecasting energy sales after DSM and developing separate peak demand projections for EVs.
Develop Separate Peak Demand •Use EV adoption forecast and estimated peak demand from charging to develop peak Projections for EVs demand projections Source: Navigant The first step in this approach was to define the peak period. Based on...
AI summary This text discusses the methodology used to develop peak demand projections for electric vehicles (EVs) in Nova Scotia. Navigant identified the peak period as 5-8 pm during December, January, and February. They used data such as 8760 system data, retail sales forecasts, and load forecasts from NSP to estimate coincident peak demand by customer class and building type. EV-specific projections were based on per-vehicle impacts and vehicle adoption forecasts from a simulation tool.
fficiency Potential Study were considered in the baseline winter peak projections: (1) low, (2) base, and (3) mid. These correspond to the scenarios low, base, and high, respectively, in the DR study. Figure 10-8 shows the baseline peak pr...
AI summary The document discusses winter baseline peak demand projections under different energy efficiency scenarios, noting that higher energy efficiency savings lead to lower peak demand. It clarifies that the peak demand definition differs from NS Power's forecast and excludes certain customer segments from the DR forecast.
ers since these segments tend to be ineligible for DR programs. ©2019 Navigant Consulting, Ltd. Page 91 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 100 of 355 Nova Scotia Energy E...
AI summary The document provides baseline peak demand forecasts by customer class and building type for Nova Scotia, with residential and small commercial segments dominating. It also introduces a section on battery adoption projections.
Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 10.2.3 Battery Adoption Projections Due to a lack of information on battery adoption projections in Nova Scotia, Navigant developed high- level battery adoptio...
AI summary The document discusses battery adoption projections in Nova Scotia, using assumptions from Navigant Research and industry expertise. It estimates battery size, costs, and savings based on NS Power tariffs, and includes a non-economic adoption adder for residential customers. Projections follow a Bass-diffusion curve with a 10-year ramp rate.
0.2 Grand Total 7.9 85.2 91.2 Source: Navigant analysis 10.2.4 Electric Vehicle Projections The forecasts produced for this report are derived from Navigant’s Vehicle Adoption Simulation Tool (VASTTM). VASTTM integrates a provincial-level...
AI summary The document discusses electric vehicle (EV) projections using Navigant's Vehicle Adoption Simulation Tool (VASTTM), which models PEV adoption based on factors like cost, range, and customer behavior. Due to limited data on battery projections in Nova Scotia, Navigant used a simplified payback analysis approach for battery adoption.
re 10-13 presents the forecasted load impacts from PEVs. Figure 10-13. Demand Impacts from PEVs Source: Navigant analysis, NSPI 2019 Load Forecast 21 The decision to use EV forecasts using Navigant’s VAST model was based on discussions wit...
AI summary The document discusses the characterization of demand response (DR) options to curtail winter peak demand, including load curtailment, load shifting to behind-the-meter batteries, EV charging control, critical peak pricing, and behavioral demand response. These DR options are based on industry-standard programs.
r (BTM) batteries, control of EV charging, critical peak pricing (CPP), and behavioural demand response (BDR). Figure 10-14. Summary of DR Options Considered in Study Eligible Customer DR Option Brief Description End Use Classes
AI summary The text discusses demand response (DR) options, including batteries, control of EV charging, critical peak pricing (CPP), and behavioural demand response (BDR), with a focus on eligible customer classes and end use.
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.
s dispatching to the grid. Charging modulation to reduce EV EV Charging Control EV EV demand during peak periods A rate schedule with significantly higher Critical Peak Pricing peak prices to discourage consumption All classes Total Facili...
AI summary The text outlines methods to manage electric vehicle (EV) demand during peak periods, including EV charging control and critical peak pricing (CPP), as well as behavioral demand response (BDR) strategies to encourage peak shaving. It also references a study on energy efficiency and demand response potential in Nova Scotia from 2021 to 2045.
ia Energy Efficiency and Demand Response Potential Study for 2021-2045 Figure 10-15. Summary of DR Sub-Options DR Option DR Sub-Option End Use DLC-Thermostat-Electric Furnace Electric Furnace DLC-Thermostat-Heat Pump Heat Pump DCL Direct L...
AI summary The document outlines various demand response (DR) sub-options, including direct load control and BNI curtailment, across different end uses such as electric furnaces, heat pumps, HVAC, lighting, and water heating. These options are part of a broader energy efficiency and demand response potential study for the period 2021-2045.
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.
considered for all customer classes to shift facility load during peak demand periods. EV control includes reduction in EV load through charging interruptions during the peak demand period. CPP applies to all customer classes and impact ra...
AI summary The text discusses demand response mechanisms, including EV control and Critical Peak Pricing (CPP), which aim to shift load during peak demand periods. It also describes Automated Demand Response (Auto-DR) as a platform for automatic load reduction in response to signals from a Demand Response Automation Server (DRAS).
IR-30 Attachment 1 Page 105 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 small commercial and small industrial customers, and Auto-DR for the remaining customer classes). Industry experience sugges...
AI summary The document discusses demand response (DR) programs, including Critical Peak Pricing (CPP) and Behavioural Demand Response (BDR), and their assumptions for potential and cost-effectiveness. It highlights the need for smart meters and opt-in participation, with the start of CPP in 2022 following smart meter deployment.
ided as Appendix C to the report, presents detailed documentation of the basis for these assumptions. Figure 10-16. Key Variables for DR Potential and Cost Estimates Key Variables Description • Percentage of eligible customers enrolled in...
AI summary The text discusses key variables influencing DR (Demand Response) potential and cost estimates, including participation rates, unit impacts, and various costs. It outlines factors such as customer enrollment, participation ramp, kW reduction, and fixed and variable costs for program development and implementation.
customer incentives, O&M, etc. Global Parameters Program Lifetime, Discount Rate, Inflation Rate, Line Losses, Avoided Costs 27 Source: Navigant 26 The DR analysis assumed “default with opt-out” type of offer under the High Scenario and th...
AI summary The document discusses the calculation of demand response (DR) program potentials, including technical and market potentials, based on assumptions such as 'default with opt-out' and 'opt-in' offer types. It references avoided costs from the 2014 Integrated Resource Plan and Transmission and Distribution (T&D) costs provided by NS Power in 2018.
Response Potential Study for 2021-2045 Navigant calculated both technical and market potential associated with implementing DR programs for this study. Technical potential refers to the theoretical maximum potential under 100% participatio...
AI summary This document outlines the calculation of technical and achievable potential for Demand Response (DR) programs from 2021 to 2045. Technical potential is defined as the theoretical maximum under 100% participation, while achievable potential considers participation assumptions and customer opt-out during DR events.
below) by the technical potential estimates. Achievable potential also accounts for customer opt-out during DR events. The achievable technical potential calculation is summarized through Equation 3. Equation 3. DR Achievable Potential 𝑀𝑀𝑀...
AI summary The text discusses the calculation of demand response (DR) achievable potential, which considers both technical potential and customer opt-out during DR events. It also outlines the development of annual and levelized costs for DR programs, including various cost components such as program development, equipment, and customer incentives.
rticipant marketing and recruitment costs, annual program administration costs, O&M costs, and customer incentives. 10.4.1 Demand Response Base Case Assumptions 10.4.1.1 Participation and Hierarchy Participation assumptions are based on re...
AI summary The text discusses assumptions related to demand response (DR) participation, including the use of industry-standard S-shaped ramp curves over a 5-year period, and references participation assumptions by customer class and DR option. It also mentions the use of secondary sources such as FERC's DR program survey and detailed documentation in an Excel spreadsheet.
IR-30 Attachment 1 Page 107 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 For achievable potential estimates, Navigant accounted for participation overlaps among different DR options offered to the...
AI summary The document discusses the participation hierarchy for demand response (DR) programs in Nova Scotia, emphasizing the need to avoid double-counting potential by prioritizing more reliable and dispatchable resources. The hierarchy places BTM battery control at the top, followed by DLC, curtailment, and behavioral DR.
Customers without dispatchable batteries and 4 Behavioural DR not enrolled in DLC or CPP Source: Navigant 10.4.1.2 Unit Impact Assumptions The unit impacts specify the amount of load that could be reduced during a DR event once customers a...
AI summary The text discusses unit impact assumptions for demand response (DR) programs, specifying how load reductions are estimated based on customer participation. Residential DLC impacts for space heating are defined in kW per device, while other impacts are defined as a percentage of enrolled load. The model inputs data from Appendix C are used for these assumptions, with examples given for BNI Curtailment and different control types.
alues are tied to the end uses and the type of control. For example, the load reductions associated with Manual HVAC control and Auto-DR HVAC control are different and are specified accordingly. 31 29 This assumption applies to the Base an...
AI summary The text discusses load reduction strategies, including Manual HVAC control and Auto-DR HVAC control, and highlights differences in their effectiveness. It also addresses the potential for extended DR events and the challenges of managing load reductions across multiple batches, including the impact of snapback effects.
Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 10.4.1.3 Program Costs and Related Assumptions for Cost-Effectiveness Navigant developed detailed itemized cost assumptions for each DR option to assess annual...
AI summary The document outlines the detailed cost assumptions for demand response (DR) programs, categorizing them into one-time fixed, one-time variable, annual fixed, and annual variable costs. These assumptions are used to calculate levelized costs and assess the cost-effectiveness of DR options using the TRC test.
load reduction ($/kW reduction), depending on the program type. It also includes additional O&M costs that may be associated with servicing technology installed at customer premises. Other than the itemized program costs, the key variables...
AI summary The text discusses cost-effectiveness calculations for demand response (DR) programs, including variables such as discount rates, line loss values, and avoided capacity costs. It outlines benefits and costs associated with DR options, such as wholesale and distribution avoided costs, program development, and participant costs.
Technology Enablement Cost O&M Cost Participant Cost 32 The enabling technology costs represents the incremental costs associated with controls and communications for making the device DR-enabled. These costs are not expected to decline me...
AI summary The text discusses enabling technology costs for demand response (DR) devices, which are modeled as static due to their incremental nature. It also references a 2019 NS Power WACC/AFUDC value and a 2014 Cost of Service Study. A cost-effectiveness assessment is mentioned, focusing on DR options with benefit-to-cost ratios of 1.0 or greater, and describes three scenarios for potential estimates.
enarios and Related Assumptions Navigant developed achievable potential estimates under three scenarios – base, high, and low. These scenarios represent variations in the following input assumptions: • Baseline peak demand projections: The...
AI summary Navigant estimated demand response (DR) potential under three scenarios (base, high, low), influenced by baseline peak demand projections and heat pump saturation values. The low scenario assumes lower energy efficiency savings and lower heat pump adoption, while the high scenario assumes higher adoption. The base scenario lies between the two extremes.
under the low scenario for DR 36). These variations in market adoption from the energy efficiency potential analysis were fed into the saturation assumptions to calculate DR potential. • Programmatic assumptions: In addition to these two i...
AI summary The document discusses variations in demand response (DR) program participation based on different scenarios, including differences in incentives, marketing expenditures, and enrollment levels. It notes that residential and small BNI customer participation is more sensitive to these factors than larger BNI customers.
fficiency and Demand Response Potential Study for 2021-2045 Figure 10-19. Summary of Changes in Programmatic Assumptions Across Scenarios % change in % change in % change in marketing Scenario Applicable Customer Class incentives over part...
AI summary The document presents a summary of changes in programmatic assumptions across different scenarios for energy efficiency and demand response potential from 2021 to 2045, showing variations in incentives, marketing costs, and participation rates for different customer classes.
-50% No change -15% Industrial, and Interruptible Source: Navigant analysis For CPP specifically, Navigant assumed that the CPP rate is offered as “default with opt-out” under the high scenario, while both base case and low scenario assume...
AI summary The analysis discusses the assumptions made regarding the Critical Peak Pricing (CPP) rate under different scenarios. Under the high scenario, CPP is offered as a default with opt-out, placing it at the top of the participation hierarchy, while lower scenarios assume an opt-in model. The high scenario also assumes lower unit impacts for CPP due to the default offer structure, supported by research from the Brattle Group. Low-income residential customers are excluded from CPP in all scenarios due to concerns about affordability.
ponse-market- research.pdf Page 102 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 111 of 355 Nova Scotia Energy Efficiency and Demand Response Potent...
AI summary This section of the 2023 Load Forecast Report discusses the demand response (DR) potential and cost-effectiveness results from a study conducted by Navigant. The analysis includes base case results, cost-effectiveness screening, and scenario analysis comparing potential and cost results.
t-effectiveness results change across scenarios and present potential and cost result comparisons across the scenarios. Accordingly, this chapter presents the analysis results in the following order: 1. Base Case potential results for all...
AI summary This section outlines the analysis of demand response (DR) options, presenting results in multiple scenarios, including base case potential and cost-effectiveness results, as well as scenario analysis comparisons. Technical potential is defined as the upper limit assuming full customer participation, while economic potential is not considered due to differences in DR and energy efficiency (EE) analysis approaches.
ness can only be considered at the program level. Page 103 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 112 of 355 Nova Scotia Energy Efficiency and...
AI summary The document discusses the calculation of technical potential for implementing Demand Response (DR) programs in Nova Scotia, emphasizing that it represents the theoretical maximum under 100% participation and does not account for participation overlaps.
total technical potential. Therefore, the technical potential estimates for each DR sub-option should be considered independently. The technical potential calculation is summarized through Equation 4. Equation 4. DR Technical Potential 𝑇𝑇𝑇...
AI summary The document discusses the technical potential of Demand Response (DR) sub-options, using Equation 4 to calculate it. Technical potential results are shown in Figure 11-1, with savings trends varying based on customer class and DR option. The analysis is sourced from Navigant.
vigant analysis Page 104 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 113 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study f...
AI summary The document discusses the achievable demand response (DR) potential in Nova Scotia, estimating that DR potential will increase to 93 MW by 2027 and stabilize at 92 MW by 2045. This potential represents over 6% of Nova Scotia Power’s peak demand in 2027 and over 7% by 2045, as energy efficiency measures reduce overall peak demand.
enerator) Source: Navigant analysis Page 105 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 114 of 355 Nova Scotia Energy Efficiency and Demand Respon...
AI summary This section of the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 presents base case cost-effectiveness results for various Demand Response (DR) options using the Total Resource Cost (TRC) test. It also includes results based on the Program Administrator Cost (PAC) test.
e Total Resource Cost (TRC) test. Navigant also calculated the cost-effectiveness results based on the Program Administrator Cost (PAC) test. 11.3.1 Benefit-Cost Assessment by Demand Response Option Figure 11-4 shows the TRC benefits, cost...
AI summary The document presents a benefit-cost assessment of various demand response (DR) options using the Total Resource Cost (TRC) and Program Administrator Cost (PAC) tests. It shows that most DR options are cost-effective, except for Behavioral DR, BTM Battery Control, and EV Charging control. The TRC and PAC tests yield different benefit-cost ratios due to differences in how incentives are treated.
0.73 EV Charging Control 3.05 11.08 0.28 0.26 Source: Navigant analysis 11.3.2 Demand Response Levelized Costs & Supply Curves Figure 11-5 shows the levelized costs and the corresponding 2045 achievable potential for all DR options. The le...
AI summary The document discusses the levelized costs and achievable potential for various demand response (DR) options in Nova Scotia through 2045. Critical Peak Pricing (CPP) is identified as the least costly option, while EV charging control is significantly more expensive due to high technology enablement costs.
gy Efficiency and Demand Response Potential Study for 2021-2045 Figure 11-5. DR Base Achievable Scenario Levelized Costs vs. 2045 Potential (MW at generator) Cumulative Achievable Cost-Effective Levelized Costs Cost-Effective DR Option Pot...
AI summary This section discusses the achievable potential results for cost-effective demand response (DR) options, including various DR strategies such as Critical Peak Pricing (CPP), Demand Load Control (DLC), and Behavioural DR, along with their corresponding levelized costs and potential contributions by 2045.
s under the base case scenario. 11.4.1 Achievable Potential by DR Option for Cost-Effective DR Options Figure 11-6 shows the MW breakdown of the DR achievable potential by cost-effective DR option. Figure 11-6. DR Achievable Potential by D...
AI summary The document discusses the achievable potential of demand response (DR) options under a base case scenario, showing that the potential increases to about 81 MW by 2026 and then declines to 72 MW by 2035. DLC accounts for nearly half of the potential, followed by CPP and BNI Curtailment.
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.
total 2045 potential, respectively, or 13 MW and 5 MW. Page 109 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 118 of 355 Nova Scotia Energy Efficienc...
AI summary The document discusses the achievable demand response (DR) potential by customer class, highlighting that residential customers account for 86% of the total 2045 potential, associated with DLC and CPP. BNI curtailment programs contribute a smaller share, with Auto-DR HVAC control at 2% and industrial BNI at 1%.
hievable potential by customer class, and reveals: • Potential from residential customers makes up 62 MW or 86% of the total 2045 potential and is associated with DLC and CPP. • Potential from interruptible rider, small commercial, and lar...
AI summary The text discusses achievable demand response (DR) potential by customer class, highlighting residential customers as the largest contributors, followed by interruptible rider, small commercial, and large industrial customers. Large commercial and small industrial customers contribute smaller amounts. The analysis is based on a 2021-2045 study.
Options (MW at generator) Source: Navigant analysis 11.4.4 Achievable Potential by Building Type for Cost-Effective Demand Response Options This section presents the breakdown of cost-effective potential by building type or customer segmen...
AI summary This section discusses the achievable potential for cost-effective demand response (DR) by building type, highlighting that residential customers on market rates account for the majority of the potential in 2045. It also outlines the annual program investment for cost-effective DR options in the base case.
ential Study for 2021-2045 11.5 Demand Response Investment Levels Figure 11-11 summarizes the annual program investment for cost-effective DR options in the base case. Results indicate the following: • The program investment for DLC increa...
AI summary The document outlines the investment trends for various demand response (DR) programs from 2021 to 2045. Key points include steady DLC investment increasing until 2024, followed by a drop and subsequent spikes due to program lifecycle costs. CPP investment is high initially but stabilizes after 2028. BNI curtailment costs are tied to aggregator payments, while behavioral program investments remain low and consistent after 2026.
consistent once the program is fully ramped by 2026. Figure 11-11. DR Annual Program Costs by DR Option for Cost-Effective DR Options ($) Source: Navigant Analysis Page 112 ©2019 Navigant Consulting, Ltd. . REDACTED (CONFIDENTIAL INFORMATI...
AI summary The document discusses demand response (DR) scenario analysis results, including adjustments to participation levels, incentive amounts, marketing spending, and equipment saturation. These adjustments impact DR achievable potential and peak demand forecasts, which are tied to different demand reduction scenarios from an energy efficiency potential study.
lso varied by case as these were tied to the different demand reduction scenario impacts from the energy efficiency potential study. 11.6.1 Comparison of Cost Results Across Demand Response Scenarios Figure 11-12 shows the cost-effectivene...
AI summary The text compares the cost-effectiveness of various demand response (DR) options across three scenarios. Under the high scenario, DLC and BNI Curtailment are not cost-effective due to fixed costs and the opt-out nature of CPP, which captures more customers. Only CPP and BDR are cost-effective in the high scenario, while the base and low scenarios show similar cost-effective DR options.
Behavioural DR 5.52 5.78 0.96 Base BTM Battery Control 19.17 22.91 0.84 High CPP 177.98 36.08 4.93 High Behavioural DR 8.13 8.17 1.00 High DLC 39.46 43.56 0.91 High BNI Curtailment 9.47 10.21 0.93 High BTM Battery Control 5.53 9.54 0.58 Hi...
AI summary The document presents data on the performance of various demand response (DR) programs, including behavioural DR, BTM battery control, CPP, DLC, BNI curtailment, and EV charging control, under different scenarios (high and low) with associated cost metrics.
10.72 0.28 Source: Navigant Analysis ©2018 Navigant Consulting, Inc. Page 113 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 122 of 355 Nova Scotia Energy Efficiency and Demand Respo...
AI summary The document compares annual program costs across three scenarios (base, high, and low) for demand response (DR) in Nova Scotia from 2021 to 2045. The high scenario has lower costs due to the exclusion of less cost-effective measures like DLC and BNI Curtailment, while the low scenario has the lowest costs due to lower participation and incentives.
illion in 2022 to $12.0 million in 2045 for the high scenario • $2.4 million in 2021 to $12.1 million in 2045 for the low scenario 11.6.2 Comparison of Potential Results Across Scenarios Figure 11-14 and Figure 11-15 show a comparison of t...
AI summary The document compares achievable demand response (DR) potential across different scenarios, showing that the high scenario has 18% more potential than the base scenario, while the low scenario has 24% less. These potentials are expressed as percentages of NSP’s peak demand in 2045.
ercent of Peak Demand) Source: Navigant Analysis ©2018 Navigant Consulting, Inc. Page 115 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 124 of 355 Nova Scotia Energy Efficiency and...
AI summary The document discusses demand response (DR) achievable potential in Nova Scotia for the years 2021-2045, presenting data for both high and low scenarios. It highlights that default Critical Peak Pricing (CPP) in the high scenario accounts for over 95% of total potential, with similar trends observed in the low scenario, albeit with lower contributions from all DR options.
napse IR-30 Attachment 1 Page 125 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 11.7 Demand Response Snapback Effects The previous discussions on potential estimates only included demand reductions...
AI summary The document discusses demand response snapback effects, particularly in DLC programs, where customers may increase thermostat settings after a DR event to compensate for lost heat, potentially offsetting demand reductions during evening peak hours.
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.
In situations where customers might use gas and other supplemental fuels for heating, snapback effect could be mitigated by some of the other fuel types being used for heating during the event period. For water heating load control, some p...
AI summary The text discusses the snapback effect in demand response (DR) programs, particularly in water heating load control. It explains that standby losses in water heaters can cause a significant increase in electricity use shortly after a DR event, though the effect is short-lived and the overall impact is less than the event period itself.
response programs and initiatives. While much energy efficiency (and demand response) potential remains, there are unique challenges in Nova Scotia in realizing this potential over the next 25 years.
AI summary The text highlights the remaining potential for energy efficiency and demand response programs in Nova Scotia, while noting unique challenges in realizing this potential over the next 25 years.
cal and economic potential are attributed to HVAC fuel switching measures that completely remove the end-use load from a home. Although still a significant portion of potential, achievable results indicate that efficient electrification te...
AI summary The text discusses the potential of HVAC fuel switching and efficient electrification technologies like heat pumps for reducing energy use, but notes market barriers to adoption. It also highlights the dominance of Critical Peak Pricing (CPP) and Direct Load Control (DLC) in achieving demand response savings over a 25-year period, with the remaining savings coming from BNI curtailment.
DLC sub-options. The remaining savings are estimated from BNI curtailment. ©2018 Navigant Consulting, Inc. Page 118 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 127 of 355 Nova Sco...
AI summary The document discusses demand response (DR) options in Nova Scotia that can provide significant demand savings over a 25-year period. It emphasizes the need for collaboration between NS Power and EfficiencyOne to realize these savings. The text also outlines a modelling plan for an energy efficiency and demand response study.
between Navigant and EfficiencyOne, and to inform stakeholders regarding the methodology, activities, deliverables, and timelines related to the EE and DR studies being conducted. i . Date Filed: August 14, 2019 Page 2 of 40 REDACTED (CONF...
AI summary Navigant, on behalf of EfficiencyOne, will use two Analytica-based models to assess energy efficiency (EE) and demand response (DR) potential in Nova Scotia. The models will be presented in Excel for review by EfficiencyOne, regulatory intervenors, and the Nova Scotia Utility and Review Board (UARB).
ded in Excel, to facilitate inspection by EfficiencyOne, regulatory intervenors and the Nova Scotia Utility and Review Board (UARB). 1.1 Energy Efficiency Potential Navigant’s Demand-Side Management Simulator (DSMSimTM) model, a transparen...
AI summary The document outlines the use of Navigant’s Demand-Side Management Simulator (DSMSimTM) model for energy efficiency potential analysis. The model will be customized for this study and used to estimate energy and peak demand savings under multiple scenarios, with stakeholder input. Five scenarios, including a maximum achievable one, will be considered due to budget and timeline constraints.
l (CASPM) or the subsequent 2007 revision 2 to the CASPM; or the 2017 National Standard Practice Manual 3 by the National Efficiency Screening Project • Allows analyst to define custom cost test definitions • Rigorous treatment of early-re...
AI summary The text outlines various standards and practices for evaluating energy efficiency and demand response programs, referencing the 2007 revision of the California Standard Practice Manual (CASPM), the 2017 National Standard Practice Manual, and methods for defining custom cost tests and evaluating early-retirement measures.
ecast Report Synapse IR-30 Attachment 1 Page 131 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A • Ability to handle avoided costs, retail rates, and load shape profiles at multiple levels...
AI summary The text outlines key features of a modeling tool used for energy efficiency and demand response studies. It highlights capabilities such as handling avoided costs, load shape profiles, and evaluating cost-effectiveness at various intervals. The model also supports recurring incentives, administrative costs, and switching between net and gross savings.
s added compared to the recent measure set developed. 2 . Date Filed: August 14, 2019 Page 4 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 132 of 355 Nova Scotia Energy Efficien...
AI summary The document outlines the use of Navigant’s DRSim™ and DSMSimTM models to analyze demand response and energy efficiency potential in Nova Scotia. It mentions the inclusion of programs such as direct load control, peak time rebates, and behind-the-meter batteries in the analysis.
nt, Navigant will develop a set of energy sales forecasts for electric consumption and electric peak demand, disaggregated by sector (e.g., residential and BNI), and end use. The reference forecasts will span the 25-year study period, from...
AI summary Navigant will develop energy sales forecasts for electric consumption and peak demand, disaggregated by sector and end use, to serve as a reference for calculating DSM savings potential over a 25-year period from 2021 to 2045.
SM potential will be disaggregated by sector, end use, and building-type, and if possible, by region. Sector, end use, building type, and region are described in the following table. Figure 3. Potential Study Customer Segments Category Sub...
AI summary The text outlines how SM potential will be categorized by sector, end use, building type, and region, with subcategories provided for each. This segmentation is intended to better understand and analyze energy use patterns across different customer segments.
f this effort, Navigant will collaborate with E1 on the segmentation. 6 . Date Filed: August 14, 2019 Page 8 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 136 of 355 Nova Scotia...
AI summary The document outlines the process of base year calibration analysis, focusing on establishing specific end uses for each customer sector. It also discusses the development of a reference case forecast, which serves as a benchmark for calculating potential savings from energy efficiency and demand response initiatives.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 137 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary The document is a page from the 2023 Load Forecast Report, specifically Attachment 1 of Synapse IR-30, which includes the Nova Scotia Energy Efficiency and Demand Response Potential Study for the period 2021-2045. This appendix provides supporting information for the study.
trends to be applied to each customer segment. EUI trends are intended to reflect natural changes in electricity consumption as a result of two factors: (1) natural conservation and (2) natural growth. • Natural conservation is a well-esta...
AI summary The text discusses the concept of natural conservation and natural growth in electricity consumption, particularly within the context of DSM programs. It outlines how EUI trends reflect changes in consumption due to these factors and highlights the importance of defining natural conservation, including the impact of future building codes and appliance standards on conservation potential.
ergy and capacity costs • Consumer price forecast • Retail rates • Line loss factors 2.1.2 Step 3: Define, Characterize and Screen Efficiency Measures The next step in the potential estimation process is to define and characterize energy e...
AI summary This section discusses the process of defining and characterizing energy efficiency measures (EEMs) as part of a study on Nova Scotia's energy efficiency and demand response potential from 2021 to 2045. The focus is on actions that increase efficiency or reduce demand through equipment, control strategies, or behavior changes.
chment 1 Page 139 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A 2.1.2.1 Define Efficiency Measures Navigant’s process for defining potential EEMs includes developing a comprehensive list...
AI summary Navigant's process for defining and characterizing energy efficiency measures (EEMs) includes compiling a comprehensive list from residential, BNI, and peak load reduction categories. They will use existing ENS DSM programs, the ENS TRM, and emerging technologies, while leveraging prior analyses to expedite the process. A final list of recommended measures will be presented for review.
rs for review and discussion, and highlight any which were not included in the 2018 ENS Potential Study Update or the 2020-2022 ENS DSM Plan. 2.1.2.2 Characterize Efficiency Measures After Navigant and E1 have reached agreement on a final...
AI summary The document outlines the process for reviewing and characterizing energy efficiency measures, including identifying energy and demand savings, associated costs, and avoided costs from fuel-switching measures. The process involves combining market characteristics with measure-specific data to ensure accurate cost-effectiveness testing.
ics (energy/demand reduction, water, costs, market maturity, etc.). 10 . Date Filed: August 14, 2019 Page 12 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 140 of 355 Nova Scotia...
AI summary The document outlines the process for leveraging prior measure characterization analyses and updating them as necessary for the energy efficiency and demand response potential study. It emphasizes estimating energy savings, costs, and applicability for each measure, with defined units and supporting documentation.
le potential. Figure 9 illustrates the key inputs and the layers of the potential modelling approach. Figure 9. Approach to Market Potential Analysis The analysis for technical and economic potential is modeled on the measure level only. T...
AI summary The text describes a method for analyzing market potential in energy efficiency and demand response, focusing on technical and economic potential at the measure level, and considering the cost of incentives and program delivery.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 142 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary This document is part of a 2023 Load Forecast Report and includes an appendix from a study on Nova Scotia's energy efficiency and demand response potential for the period 2021-2045.
ated building controls are typically characterised as a percentage of customer 13 . Date Filed: August 14, 2019 Page 15 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 143 of 355...
AI summary The document discusses energy efficiency and demand response potential in Nova Scotia for the period 2021-2045, referencing a load forecast report and an appendix from a study.
ynapse IR-30 Attachment 1 Page 143 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary This document is part of a study on the potential for energy efficiency and demand response in Nova Scotia from 2021 to 2045. It is an appendix to a larger report and contains detailed information relevant to the analysis of energy efficiency and demand response initiatives.
homes, customer-segment consumption/sales, etc.). 14 . Date Filed: August 14, 2019 Page 16 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 144 of 355 Nova Scotia Energy Efficiency...
AI summary The document discusses the technical suitability and total technical potential (TTP) for energy efficiency and demand response in Nova Scotia from 2020 to 2029, based on the Annual Incremental Technical Potential (AITP) for each year.
homes, customer-segment consumption/sales, etc.). 15 . Date Filed: August 14, 2019 Page 17 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 145 of 355 Nova Scotia Energy Efficiency...
AI summary The document references a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for the period 2021-2045, indicating analysis related to energy consumption and demand response strategies in Nova Scotia.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 145 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary The document is a page from the 2023 Load Forecast Report by Synapse, specifically Attachment 1 of the Nova Scotia Energy Efficiency and Demand Response Potential Study covering the period 2021-2045. This appendix likely contains detailed data or analysis related to energy efficiency and demand response potential in Nova Scotia.
across measures (e.g., at the end use, customer segment, sector, service territory or total level). If a competition group is composed of more than one measure that passes the TRC test, then 17 . Date Filed: August 14, 2019 Page 19 of 40 R...
AI summary The text discusses the evaluation of energy efficiency and demand response potential in Nova Scotia for the period 2021-2045, referencing a 2023 Load Forecast Report and an appendix from a study. It mentions the use of TRC (Total Resource Cost) tests for competition groups composed of multiple measures.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 147 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A the economic measure that provides the greatest savings potential is included i...
AI summary The document outlines the process for calculating economic potential using the DSMSimTM model, which screens DSM measures based on a TRC threshold of 1.0. It emphasizes avoiding double-counting and ensures accurate representation of economic potential by using technical potential results as input.
Figure 11. Navigant’s Economic Potential Model Data Flow 18 . Date Filed: August 14, 2019 Page 20 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 148 of 355 Nova Scotia Energy Eff...
AI summary The document discusses the development of achievable potential in the context of energy efficiency and demand response initiatives in Nova Scotia, focusing on modeling and forecasting for the period 2021-2045.
ment 1 Page 148 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A 2.1.3.3 Develop Achievable Potential
AI summary This section discusses the development of achievable potential in the context of energy efficiency and demand response initiatives in Nova Scotia, focusing on strategies to realize the identified energy efficiency and demand response potential for the period 2021-2045.
costs, and measures with higher upfront costs but lower annual energy costs. 19 . Date Filed: August 14, 2019 Page 21 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 149 of 355 No...
AI summary The text discusses the cost considerations of energy efficiency and demand response measures, highlighting the trade-off between upfront costs and long-term energy savings. It references a payback acceptance curve from a 2015 study by Navigant.
Appendix A Figure 12. Example – Payback Acceptance Curves Source: Navigant, 2015 Since the payback time of a technology can change over time; as technology costs and/or energy costs change over time, the equilibrium market share can also c...
AI summary The text discusses the concept of equilibrium market share and its dynamic nature, influenced by changing technology and energy costs. It outlines two approaches for calculating the approach to equilibrium market share: one for retrofit and new technologies using an enhanced Bass diffusion model, and another for ROB measures.
diffusion model 10, 11 to simulate the S-shaped approach to equilibrium that is commonly observed for technology adoption. Figure 13 provides a stock/flow diagram illustrating the 9 Each of these approaches can be better understood by visi...
AI summary The text discusses the use of a diffusion model to simulate the S-shaped approach to equilibrium in technology adoption, referencing academic sources and a simulation tool. It is part of a load forecast report and energy efficiency study for Nova Scotia covering the period 2021-2045.
eport Synapse IR-30 Attachment 1 Page 150 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A causal influences underlying the Bass model. In this model, market potential adopters flow to adopt...
AI summary The text discusses the Bass model of product adoption, emphasizing the role of external and internal influences on adoption rates. It references payback acceptance curves and highlights the significance of word-of-mouth and marketing effectiveness in diffusion models, citing studies and parameter values from Mahajan et al. (2000).
keting Effectiveness parameter was assumed to be 0.04, representing a somewhat aggressive value that exceeds the most likely value of 0.021 (75th percentile value is 0.055) per Mahajan 2000. 21 . Date Filed: August 14, 2019 Page 23 of 40 R...
AI summary The document discusses the adoption dynamics of ROB technologies, noting that their adoption is more complex due to the need to simulate the turnover of long-lived technology stocks. The DSMSimTM model is used to track technology stocks and calculate retirements and additions based on technology lifetimes, ensuring accurate estimation of market potential.
echnologies are replaced each year, which affects how quickly technologies can 22 . Date Filed: August 14, 2019 Page 24 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 152 of 355...
AI summary The document discusses a model used to predict the replacement of technologies over time, incorporating the diffusion of technology familiarity. It references the DSMSimTM model and highlights the challenges of calibrating predictive models without future data.
management models, therefore, must rely on other techniques to provide both the developer and the recipient of model results with a level of comfort that simulated results are reasonable. 23 . Date Filed: August 14, 2019 Page 25 of 40 REDA...
AI summary The text discusses the importance of management models in providing confidence in simulated results, referencing a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045. It highlights the need for techniques that ensure the reasonableness of model outcomes for both developers and recipients.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 153 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary The document refers to a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for the period 2021-2045. It includes an appendix, likely containing detailed data or analysis related to energy efficiency and demand response initiatives in Nova Scotia.
For this project, Navigant proposes to take a number of steps to ensure that the initial, base year used (2018 or 2017) forecast model results are reasonable and consider historic adoption, including: 1. Comparing forecast values, by secto...
AI summary Navigant proposes several steps to ensure the accuracy of forecast models for demand-side management programs, including comparing forecast values with historic savings, identifying discrepancies, calculating costs, and comparing spending splits between incentives and administrative costs.
4. Calculating the split (percentage) in spending between incentives and variable administrative costs predicted by the model to historic values. 5. Calculating total spending by sector and end use and comparing the resulting values to his...
AI summary The text discusses methods for calculating spending splits between incentives and administrative costs, total spending by sector and end use, and portfolio-level costs. It also mentions the use of Navigant’s DSMSimTM model to set incentive levels for achievable potential scenarios.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 154 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary The document is a page from the 2023 Load Forecast Report and an appendix of the Nova Scotia Energy Efficiency and Demand Response Potential Study covering the period 2021-2045. It is part of a larger analysis related to energy efficiency and demand response in Nova Scotia.
Navigant will work with E1 to determine the incentive method desired for this study, as each has its advantages and disadvantages. Based on E1’s guidance, as a starting point, we will employ one of the following incentive strategies for th...
AI summary The text discusses two incentive strategies for energy efficiency programs: the Levelized Cost Threshold Approach and the Targeted Payback Approach. The former sets incentives to achieve a specified spending threshold, while the latter targets a specific payback period for each measure. Each approach has different implications for cost, savings, and portfolio composition.
ason is that the lower cost measures (such as lighting), which already tend to have short payback times, receive lower incentives relative to incremental cost than in the levelized cost threshold approach, thereby permitting greater spendi...
AI summary The text discusses different incentive approaches for energy efficiency measures, such as the percent of incremental cost approach and the targeted payback approach, and explains their impacts on portfolio comprehensiveness and total savings. It also outlines the process for developing and running an achievable potential model to estimate energy savings and demand reduction over time.
incentive level, and any relevant market barriers. Develop and Run the Achievable Potential Model The overall achievable potential modelling framework is illustrated in Figure 16. We will draw on the results of the economic potential analy...
AI summary The document outlines the process for developing and running the Achievable Potential Model, which uses economic potential analysis results to estimate achievable energy efficiency and demand response potential. The model incorporates specified avoided costs and is illustrated in Figure 16.
Figure 16. Navigant’s Achievable Potential Model Data Flow 26 . Date Filed: August 14, 2019 Page 28 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 156 of 355 Nova Scotia Energy E...
AI summary The text discusses the use of sensitivity analysis in energy savings potential modeling, emphasizing its importance for policy decisions and program design. Navigant plans to analyze factors such as discount rates, with a maximum of five scenarios due to budget and timeline constraints. DSMSimTM can analyze twelve influential factors simultaneously, providing insights into variable relationships.
the relationships between key variables and potential. DSMSimTM can easily and quickly run different levels of sensitivity analyses to compare results with each case of results developed. Figure 17. Tornado Chart Showing Model Sensitivitie...
AI summary The text discusses the use of DSMSimTM for sensitivity analysis and outlines Navigant's approach to estimating demand response (DR) potential, including a data flow diagram for the DR study. The document is part of a larger report on energy efficiency and demand response potential in Nova Scotia.
Efficiency and Demand Response Potential Study for 2021-2045 Appendix A Figure 18. Demand Response Potential Study Data Flow Navigant will develop DR potential and cost estimates using a bottom-up analysis. For the analysis, Navigant will...
AI summary This section outlines Navigant's methodology for estimating demand response (DR) potential and costs using a bottom-up analysis. The process includes characterizing the DR market, defining DR options, developing participation levels and cost assumptions, and presenting potential estimates and cost-effectiveness results for various scenarios over a 25-year period.
and cost- effectiveness results for each of the scenarios. 28 . Date Filed: August 14, 2019 Page 30 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 158 of 355 Nova Scotia Energy E...
AI summary The document outlines the steps in a demand response (DR) potential assessment, beginning with market characterization. The segmentation approach is based on Nova Scotia Power’s rate schedules and was agreed upon through discussions between E1 and Navigant, differing from the energy efficiency assessment method.
l assessment, where customers are not differentiated by rate class. 29 . Date Filed: August 14, 2019 Page 31 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 159 of 355 Nova Scotia...
AI summary The document outlines a market segmentation approach for demand response (DR) potential assessment, dividing customers into residential and business, non-profit, and institutional sectors without differentiating by rate class.
7. BNI: HVAC, electric water heating, lighting, industrial (for each segment) processes, electric vehicles, batteries Level 1: Sector Navigant will segment customers between the residential and business, non-profit and institutional (BNI)...
AI summary The document outlines the segmentation of customers for demand response (DR) and energy efficiency (EE) analysis, including residential, business, non-profit, institutional (BNI), and industrial sectors. It details how different customer segments are grouped and analyzed, with special attention to low-income and First Nation customers.
hich is the latest year for which full customer count and load data is available, 2018 or 2017. The selection of the base year will be consistent with that used in the EE potential. The baseline projection for DR potential assessment entai...
AI summary The document discusses the methodology for projecting winter peak demand by customer segment and end use, utilizing a bottom-up approach calibrated with Nova Scotia Power’s projections and historical load data. Data sources include retail sales, baseline projections, and load shapes.
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.
ofiles-for-all-tmy3-locations-in-the-united-states 31 . Date Filed: August 14, 2019 Page 33 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 161 of 355 Nova Scotia Energy Efficienc...
AI summary The text provides a figure from a 2023 Load Forecast Report, which outlines a proposed list of demand response (DR) options by customer segment, including eligible customer classes and targeted end uses.
Customer Options Classes End Uses Direct Load Control (DLC) Residential, Control of electric loads by a Small space heating, electric • Thermostat thermostat and/or load General water heating. control switch. • Load Control Demand Switch
AI summary The text outlines customer options for direct load control (DLC), including thermostat and load control switch mechanisms, targeting residential and small general use classes for space heating and electric water heating.
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.
electric vehicles for peak All Electric vehicles. • Auto-DR demand reduction. enabled Use of BTM batteries for Behind the Meter load shifting and/or (BTM) Battery All BTM batteries. curtailment during peak Storage demand periods. 15 This i...
AI summary The text discusses the potential of electric vehicles and behind-the-meter (BTM) batteries for demand response (DR) and load management, particularly during peak demand periods. It references a study on Nova Scotia's energy efficiency and demand response potential for 2021-2045.
rt Synapse IR-30 Attachment 1 Page 162 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A Offer time varying rates (e.g., critical peak pricing), or discounts on electricity use reduction duri...
AI summary The document outlines a step in developing programmatic assumptions for energy efficiency and demand response initiatives, focusing on dynamic pricing strategies such as time-varying rates and peak time rebates.
Rebate). Consider an opt-in type of offer. 2.2.4 Step 4: Develop Programmatic Assumptions
AI summary The text outlines a step in the process of developing programmatic assumptions for a rebate program, suggesting an opt-in type of offer as a consideration.
The purpose of this task is to develop a set of assumptions that will ultimately drive the activity of modelling DR potential. We refer to this process as developing the DR program design parameters. The two key parameters that are needed...
AI summary This task involves developing assumptions to model demand response (DR) potential, focusing on participation rates, unit impact, and other parameters. It also includes evaluating market characteristics, previous studies, and industry best practices to formulate a representative DR portfolio.
program development process. We review information presented in well- established secondary sources, such as the FERC National DR Program Survey database 17, and publicly filed program evaluation reports and market assessment studies from...
AI summary The document discusses the program development process, referencing secondary sources like the FERC National DR Program Survey and publicly filed program evaluations to assess participation and impact assumptions in demand response (DR) programs. It also mentions the use of peak demand projections and unit impacts at the end use level to develop potential estimates.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 163 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix A
AI summary The document is a page from the 2023 Load Forecast Report, specifically Appendix A of the Nova Scotia Energy Efficiency and Demand Response Potential Study covering the period 2021-2045.
• Fourth, we draw on the internal stakeholders to provide valuable insights and perspectives on the results. In the program development process, we will solicit input to help guide and shape the set of programs to be considered in the DR p...
AI summary The text discusses the development of demand response (DR) programs, including administrative costs, incentive structures, and the use of enabling equipment. It outlines the process for calculating program costs, levelized costs, and the collaboration with E1 and Navigant for cost analysis and assumptions.
members for vetting these assumptions. Along with program costs and annual budgets, we also calculate levelized costs for DR programs. We routinely use levelized costs and potential savings results to develop supply curves, in which saving...
AI summary The document discusses the development of demand response (DR) potential estimates through modeling efforts, including the use of DRSim™ to simulate DR technology roll-out, costs, and interactions. Levelized costs and potential savings are used to develop supply curves, and key model inputs and outputs are outlined in Figure 22.
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.
of kW reduction per participant or in savings by DR program and by market terms of percentage of enrolled load) segment for Nova Scotia. • Customer attrition, event participation • Annual program costs and levelized assumptions costs by pr...
AI summary The text discusses the analysis of demand response (DR) potential in Nova Scotia, emphasizing the need for achievable potential estimates. It highlights the importance of considering factors like customer participation, cost components, and incentive levels to determine realistic DR outcomes.
ry of achievable potential where maximum incentives are offered to customers and marketing and outreach efforts are taken to the highest level to ensure high levels of participation. Realistic achievable potential represents likely custome...
AI summary The document discusses achievable energy efficiency and demand response potential, emphasizing the importance of maximum incentives, marketing efforts, and real-world constraints such as budgets and regulatory policies. It mentions using potential savings and cost data to create supply curves.
Appendix B Nova Scotia Residential Sector and BNI Sector Baseline Study
AI summary This document presents a baseline study focused on the residential sector and BNI sector in Nova Scotia. It provides an analysis of energy usage and demand patterns, serving as a foundation for future energy planning and regulatory decisions.
-3 – Residential Sector Online Survey Results ..................................... 37 Appendix B-4 – BNI Sector Online Survey Results .................................................. 38 Page iii ©2019 Navigant Consulting, Ltd. . Date Fi...
AI summary The document presents baseline study results for the Nova Scotia residential sector and BNI sector, derived from online survey data, as part of a larger energy efficiency and demand response potential study spanning 2021-2045.
Appendix B Nova Scotia Residential Sector and BNI Sector Baseline Study
AI summary This document introduces Appendix B, which contains a baseline study focusing on the residential sector and the BNI sector in Nova Scotia. It provides foundational data and analysis relevant to energy efficiency and demand-side management initiatives.
.........................................................................................30 Figure 39. Residential Water Conservation Profile ....................................................................................30 Figure 40....
AI summary This document outlines the purpose and methodology of a baseline study for the Nova Scotia residential sector and BNI sector, focusing on energy efficiency and demand response potential. It includes various profiles and figures that contribute to the 2023 Load Forecast Report.
045 Appendix B Nova Scotia Residential Sector and BNI Sector Baseline Study 1. PURPOSE AND METHODOLOGY 1.1 Background EfficiencyOne has indicated that Nova Scotia Power Inc. (NSPI) will be updating its Integrated Resource Plan (IRP) in 201...
AI summary This document outlines the purpose and methodology of a 25-year demand-side management (DSM) potential analysis conducted by Navigant for EfficiencyOne. The study aims to update baseline information on energy-consuming equipment and building stock in Nova Scotia, and to develop payback acceptance curves based on customer willingness to install energy efficiency equipment.
esidential Sector Section 3 – Summary of Findings – BNI Sector Section 4 – Online Survey Findings Appendix B-1 – Residential Sector Online Survey Instrument Page 2 ©2019 Navigant Consulting, Ltd. . Date Filed: August 14, 2019 Page 6 of 42...
AI summary The document contains sections and appendices related to a residential sector and BNI sector baseline study, including online survey instruments and results. It is part of a larger energy efficiency and demand response potential study for Nova Scotia from 2021-2045.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 175 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 2. SUMMARY OF FINDINGS – RESIDENTIAL SECTOR This section presents detailed find...
AI summary The document provides a summary of findings from a residential sector survey conducted in Nova Scotia, highlighting regional distribution of survey responses, with the majority coming from the Halifax region. It is part of a broader study assessing energy efficiency and demand response potential from 2021 to 2045.
Figure 20. Residential Sector – Household Income Type ©2019 Navigant Consulting, Ltd. Page 18 . Date Filed: August 14, 2019 Page 22 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page...
AI summary The document provides a summary of survey responses from the BNI sector in Nova Scotia, highlighting the regional distribution of responses, with the majority coming from the Halifax region. This data is part of a broader study on energy efficiency and demand response potential from 2021 to 2045.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 191 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 3.2 BNI – Primary Business Location and Type As shown in Figure 22, the majorit...
AI summary The document discusses the BNI (Business Newcomers Initiative) survey results, highlighting that most respondents own their primary business location, operate in spaces under 10,000 square feet, and pay for their own electricity. Common industries include healthcare, retail, construction, and education.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 192 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 3.3 BNI – Heating Source Types Figure 24 presents a combination of both primary...
AI summary The document discusses heating source types and power bars in the BNI sector. The majority of businesses use electric heating systems, followed by oil and heat pumps. Most power bars are not smart power bars.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 195 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 3.6 BNI – Hot Water Heaters As shown in Figure 28, most businesses (83%) have o...
AI summary The text discusses the distribution of hot water heater types and building systems among BNI businesses in Nova Scotia, highlighting the prevalence of various technologies and their potential impact on energy efficiency and demand response initiatives.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 196 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 3.8 BNI – HVAC Figure 30 shows on average, BNI respondents have multiple types...
AI summary The text discusses findings from the 2023 Load Forecast Report and the Nova Scotia Energy Efficiency and Demand Response Potential Study, focusing on the BNI sector. It highlights the prevalence of electrically commutated motors in HVAC units and the use of LED lighting technologies among BNI respondents.
Figure 31. BNI Sector – LED Lighting ©2019 Navigant Consulting, Ltd. Page 25 . Date Filed: August 14, 2019 Page 29 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 197 of 355 Nova...
AI summary The document discusses findings from the BNI Sector regarding LED lighting and lighting controls. It highlights the average number of occupancy sensors and the lack of automatic light fixtures. Additionally, it presents data on cost savings, showing that a majority of BNI respondents would pursue a $7,500 project with a $5,000 annual savings or a 1.5-year payback period.
Figure 33. BNI Sector – Cost Savings (Lower Cost Project) ©2019 Navigant Consulting, Ltd. Page 26 . Date Filed: August 14, 2019 Page 30 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1...
AI summary The text discusses cost savings for the BNI sector based on survey responses, indicating that businesses would pursue projects with annual savings of $65,000 or more, or a 1.5-year payback period. It also mentions online survey findings for residential and BNI sectors in a study on energy efficiency and demand response potential.
2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 202 of 355 Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045 Appendix B 4.1.5 Residential Appliances Figure 40 summarizes the findings for all refriger...
AI summary The text discusses residential appliance ownership in Nova Scotia, focusing on refrigerators and freezers. It highlights that most homes have one full-sized refrigerator, with varying ages, and that a significant number of homes have stand-alone freezers.
that turns off automatically when the room is empty. ©2019 Navigant Consulting, Ltd. Page 31 . Date Filed: August 14, 2019 Page 35 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page...
AI summary This section of the 2023 Load Forecast Report discusses findings from an online survey on business, nonprofit, and institutional sector buildings, focusing on BNI building characteristics and general building information as presented in Figure 41.
systems have been commissioned. ©2019 Navigant Consulting, Ltd. Page 33 . Date Filed: August 14, 2019 Page 37 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 205 of 355 Nova Scoti...
AI summary This section discusses findings related to controlling energy usage in BNI facilities as part of an energy efficiency and demand response potential study for Nova Scotia from 2021 to 2045.
equipment, or appliances had an ECM installed. ©2019 Navigant Consulting, Ltd. Page 34 . Date Filed: August 14, 2019 Page 38 of 42 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 206 of...
AI summary The document provides appendices containing online survey instruments used to gather data on energy-related characteristics of residential and non-residential buildings in Nova Scotia as part of an energy efficiency and demand response potential study.
e, that is, you can set schedules to 8 control the temperature? 28aa. [POSE ONLY IF 1 OR MORE IN Q 28B] Of this/these [INSERT NUMBER FROM Q.28B] programmable thermostat(s), how many are RECORD Don’t know NUMBER a) smart thermostats you ca...
AI summary The text includes survey questions about programmable thermostats and home energy efficiency, focusing on customer participation and technology usage. It is part of a larger study on energy efficiency and demand response potential in Nova Scotia.
How many are linear fluorescents (i.e., tube lights) 8 INSERT IMAGE © Narrative Research, 2019 9 . Date Filed: August 14, 2019 Page 9 of 15 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 P...
AI summary The document includes survey questions from the 2019 E1 Residential Survey, asking respondents about the presence of specific lighting and motion-sensor devices in their homes, such as manual light dimmers and indoor/outdoor motion sensors that control lights.
ervers on a single rack, but we want to know the number of individual servers. RECORD NUMBER _ 98 Don’t know/Not sure © Narrative Research, 2019 2 . Date Filed: August 14, 2019 Page 2 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023...
AI summary The text contains survey questions related to server usage and hot water heaters in a commercial setting, with responses indicating uncertainty or lack of knowledge. It is part of a larger energy efficiency and demand response study for Nova Scotia.
Q8 TOTAL MINUS Q9A TOTAL] hot water heaters, how many are heat pump water heaters? RECORD NUMBER: 98 Don’t know/Not sure © Narrative Research, 2019 3 . Date Filed: August 14, 2019 Page 3 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a survey question about the number of heat pump water heaters in a building, part of a larger study on energy efficiency and demand response potential in Nova Scotia. It also references a confidential report and a load forecast study.
al service lights (i.e. have a base sized like a normal light bulb _ _ 8 and screw into regular light sockets) © Narrative Research, 2019 6 . Date Filed: August 14, 2019 Page 6 of 16 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load F...
AI summary The text discusses a survey item from the 2019 E1 Commercial Survey, asking respondents to report the number of smart LED light bulbs connected to the internet among those previously reported in question 15. This relates to energy efficiency and demand response potential.
Number of Smart LED Don’t know Bulbs a) Linear lighting (i.e., Tube lamps) _ 8 b) Pole mounted area lights which is _ exterior lighting generally used to provide illumination to areas for 8 vehicle and pedestrian use, with light fixtures...
AI summary The text lists different types of lighting systems and asks for the number of Smart LED bulbs in each category, with a significant number of respondents indicating they do not know the answer.
highlight outdoor objects and features). f) General service lights (i.e. have a base sized like a normal light bulb _ 8 and screw into regular light sockets) g) Parking garage lights _ 8 17. Approximately, how many occupancy sensors does...
AI summary The text includes survey questions related to lighting fixtures and occupancy sensors in commercial buildings, focusing on energy efficiency measures such as daylighting controls and occupancy sensors. The questions are part of a 2019 survey on energy efficiency and demand response potential in Nova Scotia.
2 Head office or other department pays 3 No, landlord pays 98 Don’t know/Not sure 99 Other (Please specify: _) 26a. [POSE IF CODES 1 OR 2 IN Q.26] Please select the electric utility provider that provides electricity at your business’ Nova...
AI summary The text includes survey response options related to electricity provider selection and rate codes for businesses in Nova Scotia, along with a reference to a redacted 2019 commercial survey and a load forecast report attachment.
0 1.1 .8 .9 1.0 .9 .6 1.1 1.2 .5 .7 1.2 1.6 1.0 .9 1.3 .8 1.7 1.3 1.0 .7 Responses of 'Don't know' were excluded from calculation of the mean. TABLE 28aa: [IF Q28B '1' OR MORE] Of this/these [Q28B RESPONSE] programmable thermostat(s), how...
AI summary The text presents a table asking respondents about the number of programmable thermostats they own that can be controlled via a cell phone or the internet. Responses of 'Don't know' were excluded from the calculation of the mean.
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.
3.7 3.8 3.7 3.1 8.9 2.0 3.9 2.6 3.3 3.3 This question was randomly posed to approximately one in four respondents. 26 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 26 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary The text includes a table with numerical data and mentions a survey question posed to one in four respondents. It references a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045, as well as an appendix from a Synapse report. The entity 'NAVIGANT' is mentioned, likely as a consulting firm involved in the study.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 30 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 30 of 54 REDACTED (...
AI summary The document includes a table with numerical data and notes that it excludes respondents who answered 'Don't know' to any of Q35a-i. It also references a 2023 Load Forecast Report, an attachment from Synapse IR-30, and an Energy Efficiency and Demand Response Potential Study for 2021-2045, including Appendix B-3.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 32 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 32 of 54 REDACTED (...
AI summary This document includes a table with numerical data and a narrative research section from a 2019 filing. It references a 2023 Load Forecast Report and an Energy Efficiency and Demand Response Potential Study for 2021-2045, with a redacted section indicating confidential information has been removed.
192 243 248 111 162 220 129 334 336 157 206 289 145 233 260 123 370 96 84 62 80 This table excludes those who responded 'Don't know' to any of Q35a-i. 33 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 33 of 54 REDACTED (...
AI summary This document contains a table with numerical data and references to a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It also includes a redacted page from a legal or regulatory proceeding, filed on August 14, 2019.
4.0 4.2 8.3 2.4 4.6 3.8 5.2 4.3 3.6 4.1 This question was randomly posed to approximately one in nine respondents. 34 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 34 of 54 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 20...
AI summary This section includes a table with numerical data and mentions a survey question posed to approximately one in nine respondents. It also references a load forecast report and an energy efficiency and demand response potential study conducted 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. 44 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 44 of 54 REDACT...
AI summary The document includes a table with numerical data, a narrative research section, and a redacted portion of the 2023 Load Forecast Report and the Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045. It also references an attachment and appendix from the report.
12.4 5.8 8.0 29.3 3.9 13.3 33.1 11.7 14.7 11.7 Responses of greater than 225 were excluded from calculation of the mean. 2 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 2 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED...
AI summary The text includes statistical data, a narrative research section, and a redacted table from a load forecast report. It references a survey on smart power bars and mentions a study on energy efficiency and demand response potential in Nova Scotia.
6.8 5.3 1.4 5.7 13.7 2.9 6.5 13.1 5.0 7.6 7.4 Responses of greater than 100 were excluded from calculation of the mean. 4 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 4 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED)...
AI summary The text includes a table from a 2019 electricity usage survey focusing on business servers and their use of server virtualization and decommissioning. It also mentions a load forecast report and a study on energy efficiency and demand response potential in Nova Scotia.
1.7 1.0 .0 1.2 2.0 1.7 .9 2.3 1.6 1.8 3.3 This question was randomly posed to approximately one in seven respondents. 18 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 18 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED)...
AI summary The text includes a table from a 2019 electricity usage survey focusing on business respondents' familiarity with heat pump water heaters. It also references a load forecast report and a study on energy efficiency and demand response potential in Nova Scotia.
lling or implementing a(n) [ITEM SEEN IN Q20]. Would you generally pursue an energy efficiency project where the cost to you after utility rebates is $100,000 if the project saved $[80,000] per year? REGION BUSINESS PREMISES FT EMPLOYEES S...
AI summary The text presents survey data on energy efficiency project adoption, showing that 58% of respondents would pursue a project with a $100,000 cost after rebates if it saved $80,000 annually. The data is segmented by region, business premises type, employment size, square footage, and heating type.
162 99 19 44 97 63 47 44 38 43 57 32 53 96 41 27 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 27 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 322 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. It references a 2023 Load Forecast Report and an appendix from a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045.
ling or implementing a(n) [ITEM SEEN IN Q20]. Would you generally pursue an energy efficiency project where the cost to you after utility rebates is $100,000 if the project saved $[125,000] per year? REGION BUSINESS PREMISES FT EMPLOYEES S...
AI summary The text presents a question regarding the pursuit of an energy efficiency project with a cost of $100,000 after rebates and annual savings of $125,000. It also includes a table with data on regional and business premises characteristics, as well as a question about familiarity with networked/connected lighting systems.
149 89 17 43 96 48 42 45 35 36 54 33 54 94 41 44 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 44 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 339 of 355 No...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and an appendix from a Nova Scotia Energy Efficiency and Demand Response Potential Study. It references a 2019 Electricity Usage Survey for businesses and mentions rate codes from Nova Scotia Power bills.
149 88 17 44 90 55 46 46 34 38 55 34 57 103 44 48 Narrative Research . NAV002-1000 Date Filed: August 14, 2019 Page 48 of 51 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-30 Attachment 1 Page 343 of 355 N...
AI summary The text includes a redacted section from a 2023 Load Forecast Report and references a 2019 Electricity Usage Survey focused on business heating systems in Nova Scotia. It mentions a study on energy efficiency and demand response potential for 2021–2045.
e., a heat 8 8 10 7 10 6 16 4 0 11 12 4 4 8 15 pump system that has an indoor unit placed on a wall, that typically only heats a small area of the building) 6 8 0 4 8 3 3 8 0 0 12 4 7 6 11 49 Narrative Research . NAV002-1000 Date Filed: Au...
AI summary The text includes a table with numerical data and mentions a 2023 Load Forecast Report and a Nova Scotia Energy Efficiency and Demand Response Potential Study for 2021-2045, with a reference to an appendix and a redacted section.
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.
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.
Customer count and housing New housing usage NSPI and other Programs Before future DSM Year SAE model Historical Increment Cumlative New Cumulative Forecasted New New Structural Forecast RTR Electric End-Use Solar PV Total Total SAE + Regr...
AI summary The document presents data on customer count, housing usage, and energy consumption from 2013 to 2015, including metrics such as SAE model, historical customer counts, and electric end-use. It also includes information on programs and adjustments related to demand-side management.
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.
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.
NSPI and other Programs Before future DSM After DSM Year SAE model Forecasted Forecast All Small Solar PV EV (GWh) Total Total SAE + DSM Final Sales Regression Customer without General (GWh) Programs adjsutments (GWh) Sales (kWh Count HP a...
AI summary The text presents a table comparing energy usage metrics before and after the implementation of demand-side management (DSM) programs, with data spanning from 2013 to 2021. It includes metrics such as SAE model sales, forecasted customer counts, and total energy consumption in gigawatt-hours.
of 9 Year 22-Sep Covid ARMA XHeatNew XCoolNew XOtherNew Feb 18 New May 20 New Jun 20 New Oct 22 22-Sep Covid ARMA new Total (kWh)
AI summary The text presents a table with dates, energy-related terms, and values in kilowatt-hours, indicating data collection or reporting related to energy usage, possibly during the pandemic.
NSPI and other Programs Before future DSM After DSM Year SAE model Forecast RTR EV Solar PV Total Total SAE DSM Final Regression without (GWh) Programs + (GWh) Sales Sales (kWh / HP and (GWh) adjsutme HH) without nts DSM outside (GWh) mode...
AI summary The table presents electricity sales data for NSPI before and after the implementation of DSM programs from 2013 to 2020, showing the impact of these programs on sales figures over time.
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.
NTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-41 Attachment 1 Page 1 of 19
AI summary The document is the 2023 Load Forecast Report from Synapse, Attachment 1, Page 1 of 19. It contains information related to load forecasting, though the content is partially redacted.
2023 Load Forecast Report Synapse IR-43 Attachment 1 Page 1 of 1 Sensitivity: 2024 Peak Sensitivity: 2024 No DSM Total Sales Sensitivity: 2033 No DSM Total Sales Assumptions ContributionToVariance RankCorrelation Assumptions ContributionTo...
AI summary The text presents sensitivity analysis from the 2023 Load Forecast Report by Synapse, focusing on assumptions related to temperature, economics, and wind at peak, along with their contribution to variance and rank correlation for different forecast years.
† 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.
2023 Load Forecast Report Synapse IR-44 Attachment 1 Page 4 of 8 Electric Vehicle Adoption Stock Rollover EV adoption/stock estimate was developed in E3’s PATHWAYS stock rollover model ▪ PATHWAYS is an infrastructure-based model reflecti...
AI summary The document discusses the 2023 Load Forecast Report, focusing on electric vehicle (EV) adoption and stock rollover modeled by E3’s PATHWAYS model. It outlines assumptions for light-duty vehicle (LDV) and parcel truck/medium-duty vehicle (MDV) stock growth, including a target of 100% electric LDV sales by 2035 and 95% MDV sales by 2040. The report also mentions transportation load shaping processes.
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.
REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report Synapse IR-44 Attachment 1 Page 7 of 8 LDV Charging Profiles Charging profiles represent population-level charging scaled down to one vehicle In unmanaged charging,...
AI summary The document discusses LDV charging profiles, explaining the difference between unmanaged and managed charging. Unmanaged charging occurs immediately upon arrival, while managed charging shifts timing to reduce costs and flatten peak loads. E3's input assumes 70% of managed charging is coordinated by an aggregator. The second section introduces heating equipment stock rollover, though details are redacted.
1 Request IR-45: 2 3 General Forecast Report Improvements – The Board Decision of October 31, 2022 notes that 4 NS Power agreed to the following changes in the 2023 forecast report (p 4): 5 6 In its Reply, NS Power addressed the concerns r...
AI summary NS Power has agreed to improve its 2023 forecast report by incorporating multi-hour temperature and windspeed analysis, multi-station weather data, and electrification impacts. It will also refine DR estimates, evaluate heat pump impacts, and update EV adoption rates, among other changes.
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.
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.
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.