Topic:"Energy Efficiency Budgets" in M11108

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.

one 23 included in the annual HDD calculations. Looking at a rolling 10-year average of annual 24 minimum temperatures shows that the minimum is increasing at a rate of around 0.13 DATE: April 28, 2023 Page 22 of 98 REDACTED (CONFIDENTIAL...

AI summary The 2023 Load Forecast Report discusses trends in Heating Degree Days (HDD) and minimum temperatures, noting an increase in annual minimum temperatures at a rate of 0.13 degrees per year. The report references climate change scenarios and research on warming patterns in New England to support these assumptions.

of 98 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Load Forecast Report REDACTED 1 As the results above show, the differences are minimal. As no significant impact was noted, 2 this load-weighted approach was not incorporated into the...

AI summary The 2023 Load Forecast Report discusses the use of economic data, including median income and employment income, in forecasting load demand. The report notes that median income was evaluated as an alternative to household income, though data is limited to 2021. Economic statistics are sourced from the Conference Board of Canada’s 20-year forecast.

020 and 114 in 2021 according the 2021 DSM Programs Evaluation Report 2 filed by EfficiencyOne 13) despite an available rebate of $400. 3 4 Figure 25: Water Heater Forecast 5 Overall % Overall Intensity Year Saturation (kWh/household) 2023...

AI summary The text discusses the 2021 DSM Programs Evaluation Report, rebate availability, and electric vehicle (EV) incentives in Nova Scotia. It includes forecasts for water heater saturation and EV adoption, based on federal and provincial targets, with data up to 2035.

a flat profile in real terms. Figure 37 15 shows price forecasts by class. 16 17 Figure 37: Historical and projected real electricity prices (real dollars per kWh) 18 19 20 Settlement Agreement – NS Power General Rate Application – M10431...

AI summary The text discusses the impact of electricity prices on class sales using a price elasticity model estimated at -0.15, provided by Itron. This model is used to forecast load based on historical data and projected price changes.

fferent components for 13 2020, 2021 and 2022 actuals vs forecast and weather normalized totals, and the 2023 14 forecast. 15 16 Figure 39: Comparison of Forecast to Actuals 17 Year 2020 2021 2022 2023 Forecast Sales 4540 4718 4715 4830 We...

AI summary The document presents a comparison of forecasted and actual electricity sales for the years 2020 to 2023, highlighting the impact of weather variance, non-weather variance, and the ongoing influence of the COVID-19 pandemic on residential load forecasts, including assumptions about continued hybrid work models.

m and must continue 23 to plan for serving these customers in the long term, the full amount of the municipal 24 electric utilities’ peak demand is included in the Load Forecast. 25 DATE: April 28, 2023 Page 72 of 98 REDACTED (CONFIDENTIAL...

AI summary The document discusses the 2023 Load Forecast Report, highlighting system losses and unbilled sales, with system losses averaging 6.4% of NSR over the past five years and projected to remain between 6.0% and 7.0% over the 10-year forecast period. It also defines Net System Requirement (NSR) as the energy required to supply residential, commercial, and industrial sales, plus system losses, excluding certain factors like industrial self-generation and exports.

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.

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.

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.

he dependent variable (in this case, sales). To help eliminate this autocorrelation, a moving average, MA, of period 1, MA(1) was added, which estimates the autocorrelation with its the predecessor. Variable Coefficient StdErr T-Stat P-Val...

AI summary The text discusses the use of a moving average (MA(1)) to address autocorrelation in a statistical model, with variables related to heating, cooling, and energy savings. It includes regression results showing coefficients, standard errors, t-statistics, and p-values for various factors influencing energy sales.

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.

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.

les in the residential class at the end of 2022, continued use of a COVID variable helps model the change in sales from pre to post COVID until “new normal” is established in data set. • Previous model relied on economic variables to accou...

AI summary The document discusses the impact of the COVID-19 pandemic on residential and commercial electricity sales, the use of a COVID variable in modeling sales changes, and the forecasted increase in electric vehicle (EV) load due to federal ZEV sales targets. The 2023 forecast shows higher EV adoption and increased energy and peak demand compared to the 2022 forecast.

age 18 of 21 Forecast Comparison – Peak The peak forecast is similar to the 2022 forecast, and slightly higher by 2032 due to higher EV penetration and increased new customer count. 18 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2023 Loa...

AI summary The peak forecast remains similar to the 2022 forecast but is expected to increase slightly by 2032 due to higher EV penetration and more new customers. The 2022 forecast showed variances compared to actuals, including impacts from weather, residential usage, and wind generation.

41.9 42.4 42.8 41.9 39.0 37.1 38.4 40.2 40.3 40.1 41.3 42.2 39.3 36.2 34.8 34.6 31.7 31.8 32.6 32.6 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...

AI summary The text provides data on energy use and GHG emissions in Nova Scotia, including heating and cooling degree-day indices and a table showing secondary energy use and GHG emissions by end-use from 2000 to 2019. The data excludes electricity production-related emissions and includes a note on the source of the information.

0.59 0.61 0.62 0.56 0.52 0.48 0.47 0.54 0.57 0.58 0.56 0.60 0.50 0.49 0.47 0.49 0.42 0.42 0.43 0.43 Total Space Heating GHG Emissions Excluding Electricity (Mt of CO2e) 1.4 1.5 1.5 1.4 1.3 1.1 1.2 1.4 1.5 1.5 1.5 1.7 1.3 1.2 1.2 1.2 1.0 1....

AI summary The text presents numerical data on space heating greenhouse gas (GHG) emissions and GHG intensity over time. It details emissions from various energy sources, including heating oil and wood, with corresponding emission levels and intensity measurements in tonnes per terajoule (TJ).

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 Shares (%) Electricity 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 99.3 99.4 99.6 99.6 99.5 99.5 99.4 99.4 99.4 99.2 99.2 Natural Gas 0.0 0.0 0.0 0.0...

AI summary The document presents statistical data on electricity and natural gas usage, household activity, and energy intensity over time. It outlines trends in energy consumption and the number of households across different years, providing insights into energy use patterns.

40.6 41.1 41.4 40.5 37.5 35.6 36.8 38.6 38.7 38.5 39.7 40.4 37.6 34.4 32.7 32.6 29.9 30.0 30.8 30.9 Heating Degree-Day Index 0.94 0.94 0.97 0.99 1.02 0.94 0.83 0.81 0.99 1.02 0.89 0.95 0.83 0.87 0.98 1.04 0.96 1.04 0.96 1.00 Cooling Degree...

AI summary The document presents data on energy use and GHG emissions for the residential sector in Nova Scotia from 2000 to 2019, including heating and cooling degree-day indices and secondary energy use by end-use. It excludes emissions related to electricity production and includes data on coal and propane under 'Other'.

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Shares (%) Space Heating 51.2 50.7 50.9 50.6 51.1 49.5 48.3 50.0 53.6 54.7 52.2 53.8 50.3 51.2 52.9 55.1 51.9 53.3 51.8 52.3 Water Heating 27.9 28.3 27.9 27.4...

AI summary The document presents data on energy usage distribution across various categories such as space heating, water heating, and lighting, along with statistics on total floor space and the number of households over a period of time.

33.5 33.8 32.1 33.9 36.9 35.6 35.4 32.9 30.4 25.8 27.1 30.9 29.2 28.1 26.8 27.9 25.2 23.3 20.1 20.8 Heating Degree-Day Index 0.95 0.95 0.99 0.98 1.02 0.94 0.85 0.92 0.97 0.99 0.87 0.93 0.80 0.86 0.97 1.03 0.94 1.03 0.95 0.99 Cooling Degree...

AI summary The text provides statistical data on heating and cooling degree-day indexes, along with GHG emissions data and energy use information, primarily for the commercial/institutional sector in Atlantic Canada, with a focus on Nova Scotia. It references a 2023 Load Forecast Report and mentions data exclusions related to electricity production and street lighting.

ity (GJ/m ) 1.18 1.18 1.15 1.19 1.38 1.20 1.06 1.20 1.11 0.98 0.89 1.09 1.06 0.97 1.07 1.08 1.04 0.99 0.96 0.98 Total GHG Emissions for Wholesale Trade Excluding Electricity (Mt of CO2e) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1...

AI summary The text provides data on energy efficiency and greenhouse gas emissions related to wholesale trade, excluding electricity. It lists metrics such as energy intensity and total GHG emissions by energy source, indicating minimal emissions from most sources except light fuel oil and kerosene.

ty (GJ/m2) 1.18 1.18 1.15 1.19 1.38 1.20 1.06 1.20 1.11 0.98 0.89 1.09 1.06 0.97 1.07 1.08 1.04 0.99 0.96 0.98 Total GHG Emissions for Wholesale Trade Excluding Electricity (Mt of CO2e) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0...

AI summary The text presents data on energy efficiency metrics and greenhouse gas (GHG) emissions. It includes metrics such as energy use intensity (GJ/m2) and total GHG emissions for wholesale trade, excluding electricity, as well as emissions by end use categories like space heating and water heating.

ty (GJ/m ) 1.37 1.37 1.34 1.38 1.59 1.52 1.33 1.49 1.39 1.22 1.11 1.36 1.33 1.22 1.34 1.38 1.33 1.27 1.23 1.25 Total GHG Emissions for Retail Trade Excluding Electricity (Mt of CO2e) 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.2 0.2 0.3 0.3 0.2...

AI summary The text provides data on energy efficiency and greenhouse gas (GHG) emissions, including metrics like energy intensity (GJ/m) and total GHG emissions (Mt of CO2e) across different energy sources such as natural gas, light fuel oil, and kerosene. The data spans multiple years, showing fluctuations in emissions and energy use.

ace Cooling 0.5 0.7 0.6 0.8 0.7 0.9 0.6 0.6 0.7 0.5 0.6 0.5 1.0 0.8 0.8 1.0 1.0 1.0 1.3 1.0 Shares (%) Space Heating 50.2 49.2 50.6 47.6 54.5 51.8 51.3 54.8 51.1 46.4 40.8 49.7 49.3 48.5 49.3 50.5 49.4 49.0 43.0 45.4 Water Heating 5.6 5.3...

AI summary The text presents data on energy usage across various categories such as cooling, heating, and lighting, along with energy intensity and floor space metrics. It includes percentages of energy consumption by category and annual figures from 2003 to 2022.

y (GJ/m ) 1.05 1.04 1.02 1.05 1.23 1.07 0.94 1.07 0.99 0.88 0.79 0.98 0.94 0.86 0.95 0.98 0.94 0.91 0.87 0.90 Total GHG Emissions for Other Services Excluding Electricity (Mt of CO2e) 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0...

AI summary The text presents data on energy efficiency metrics and greenhouse gas (GHG) emissions related to other services excluding electricity. It includes metrics such as energy efficiency (GJ/m) and GHG emissions by energy source, all of which are reported as zero or minimal values.

ty (GJ/m2) 1.05 1.04 1.02 1.05 1.23 1.07 0.94 1.07 0.99 0.88 0.79 0.98 0.94 0.86 0.95 0.98 0.94 0.91 0.87 0.90 Total GHG Emissions for Other Services Excluding Electricity (Mt of CO2e) 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0....

AI summary The document presents data on energy efficiency metrics and greenhouse gas emissions related to services excluding electricity. It includes metrics such as energy use intensity and total GHG emissions by end use, with most values recorded as zero, except for a few instances where emissions are noted.

0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.1 Shares (%) Electricity 92.5 92.2 92.5 92.3 91.3 91.0 90.7 91.4 91.4 95.1 94.9 95.0 95.1 94.9 95.1 95.0 97.4 93.6 93.6 93.6 Natural Gas 0.0 0.0 0.0 0.0 0.0 0.0...

AI summary The text presents a table showing the percentage distribution of shares in different energy sources over time, with electricity accounting for the majority of shares, while other energy sources like natural gas and fuel oils show minimal or no presence.

1 2 (b) Water heaters must meet the following standard as of 2008: CAN/CSA-C191-04, 3 Performance of Electrical Storage Tank Water Heaters for Domestic Hot Water Service. 4 NS Power is not aware of any proposed standards to improve efficie...

AI summary The text discusses water heater efficiency standards, rebate programs, and projected adoption rates of heat pump water heaters. It highlights the current standard (CAN/CSA-C191-04), the lack of proposed efficiency improvements, and the impact of increased heat pump usage on water heating intensity. A table provides estimated intensity per electric water heating household from 2023 to 2027.

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.

ty, through a DERMS. Potential technical and economic benefits associated with a DERMS solution are expected to provide value and benefit to all customers, regardless of participation in DER programs. Included in the SGNS Compliance Filing...

AI summary NS Power is implementing a DERMS solution as part of the SGNS project, with testing underway since 2021. The report outlines modifications to testing plans, including the addition of data collection for C&I customer demand reduction under EV V2G chargers. A financial model is being developed based on collected data.

inter, would result in higher energy requirements in the winter. This will continue to be monitored for analysis in future reporting. Figure 7 – ChargePoint Daily Average Seasonal Energy Delivered Page 11 of 48 . . REDACTED (CONFIDENTIAL I...

AI summary The document discusses seasonal energy delivery data from ChargePoint and baseline energy consumption patterns from ev.energy, noting that data is not yet sufficient for analysis. It also references a figure showing average baseline EV consumption by hour of the day and day of the week, consistent with previous reports.

power (kW) by control of • Vehicle charging session data • Unmanaged energy consumption (kWh) $/kWh) be compared with system times when renewables output N/A (ev.energy) (ev.energy) renewable generation output platform algorithim Deferred...

AI summary The text discusses the analysis of vehicle charging session data and unmanaged energy consumption in relation to renewable generation output and system peaks. It references an algorithm used for scheduling and deferred transmission and distribution system upgrades.

and discharge power (kW) and energy (kWh) versus actuals (%)

AI summary The text discusses the comparison between projected and actual values for discharge power (kW) and energy (kWh), highlighting the percentage differences.

llars: • Residential Average Monthly Community Solar Bill Impact = +$9.35 / month (cost) • Commercial Average Monthly Community Solar Bill Impact = +$24.56 / month (cost) 2 1 F Estimated Cumulative Benef its to Participants and Non-Partici...

AI summary The text provides a financial analysis of a community solar program, showing the average monthly cost impact on residential and commercial participants, along with estimated cumulative benefits and costs for both subscribers and non-subscribers.

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.

COSTS - COÛTS TOTAUX 447,260 9,959,113 10,406,373 Please use the Current Claim total (1) and the Total to Date (2) to complete the Appendix A - Recipient's Claim Summary . Veuillez SVP utiliser le total de la Réclamation Actuelle (1) ainsi...

AI summary The document includes a table with cost data and instructions for completing a claim summary, a redacted section referencing a 2023 Load Forecast Report, and a progress report related to the Smart Grid Atlantic project under the Strategic Innovation Fund. The report is submitted by Siemens Canada Limited, New Brunswick Power Corporation, and Nova Scotia Power Incorporated.

2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 127 of 205 REDACTED CI C0010788 – Smart Grid Semi-Annual Report Attachment 8a Page 1 of 5 Smart Grid Program Per the quarterly claim due date indicated in Schedule C of the Contribut...

AI summary The document outlines the Smart Grid Program's quarterly claim for the Collaborative Grid Innovation for Atlantic Smart Energy project, detailing eligible expenditures such as salaries, overhead, professional services, travel, equipment, and other expenses for the 2022-23 quarter.

one) No change or impact on project ☒ (i.e. change within the budget and baseline schedule) Minor change – little impact on project (i.e. change impacts budget or schedule but is still within the scope as defined in the approved ☐ agreemen...

AI summary The project status report outlines progress on ESP development, including EVSE use cases and BMS time shifting in a test environment. Residential and commercial battery system integration is ongoing, with initial data collection in progress.

nd Products Note: Expenses above $100,000 for equipment, materials, or products (or contracting services related to these purchases) must be supported by a copy of the invoice. ELIGIBLE EXPENDITURES Name of Equipment or Product Purpose of...

AI summary The document outlines eligible and ineligible expenditures related to equipment and products, including costs for distributed battery installations, SIM cards for DER devices, and insulation materials. Total eligible costs amount to $56,479.58, with no ineligible costs reported.

tem Costs Incurred Total Ineligible Equipment and Products Costs $ - . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Page 139 of 205 REDACTED CI C0010788 – Smart Grid Semi-Annual R...

AI summary The text outlines eligible and ineligible expenses related to a Smart Grid project, including shipping costs for materials from M Power Energy Solutions. It specifies that expenses above $50,000 require invoice support and lists categories of eligible other expenses such as field supplies, printing services, data collection, and facility expenses.

including processing, analysis and management; 4) Facility expenses for seminars, conference room rentals, etc. (excluding hospitality); 5) License fees and permits; and 6) Field testing services. . REDACTED (CONFIDENTIAL INFORMATION REMOV...

AI summary The document includes a certification and project update from Nova Scotia Power, detailing project tasks completed and expected in the next quarter, alignment with the contribution agreement, and the absence of environmental permits required during the quarter ending September 30, 2022.

$ - $ - $ 226,202.00 $ - $ - $I$22 PROGRAM'S % of ELIGIBLE / % des coûts admissibles du programme 0% 0% 0% 0% 0% 0% 0% 0% PROGRAM'S % of TOTAL PROJECT COSTS/ % des coûts totaux du projet du programme 0% 0% 0% 0% 0% 304% 0% 0% SITUATIONAL H...

AI summary The text provides a financial summary of a program, showing zero percentages for both eligible and total project costs, with no funds available for release. The project name is listed as 'EVID-1017 Nova Scotia Vehicle Integration Pilot,' signed by Shawn Connell, Diretor Key Accounts & Customer Solutions.

SIGNED BY/ Signé par: Shawn Connell, Diretor Key Accounts & Customer Solutions SIGNATURE: 2022-23 By Eligible Exp. 1 of 1 10/13/2022 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2...

AI summary This document is a financial and status report for the Nova Scotia Vehicle Grid Integration Pilot under the Green Infrastructure – EVID program. It outlines project information, including the case file number, proponent, and reporting period. The project timing status is under review, and the report is submitted by Nova Scotia Power Inc. to Natural Resources Canada.

23. • The delay in availability and delivery of this bi-directional charger availability continues to remain a concern. In response, NS Power canceled one of its purchase orders for this device. pg. 2 . REDACTED (CONFIDENTIAL INFORMATION R...

AI summary The document discusses delays in the delivery of bi-directional chargers, leading to NS Power canceling a purchase order. It also mentions ongoing issues with ChargePoint charger network connectivity and updates to the ESP backend configuration to prevent incorrect customer notifications.

(choose one) On budget ☒ (i.e. forecasted expenditure follows the budget as planned) Forecasted expenditures by any nature of cost is greater than 20% above or below the approved budget, but no changes are required to NRCan funding, either...

AI summary NS Power reports that due to vendor delays, the planned spend for the Smart Grid program has been delayed, but full budget spend is anticipated within the 2022-23 fiscal year. The budget remains on track, with no amendments required to NRCan funding.

from Canadian suppliers suppliers over the baseline period is $0.00, is this correct? Table 2: Data Sheet Completion Validation Data Sheet Number of Entries APBR 2020-21 Requirements: Confirmation: You have indicated that project activitie...

AI summary The text includes a question about whether the cost of goods from Canadian suppliers over a baseline period is $0.00, and a table validation related to project locations and complex enterprise status.

PTIONAL) Title of Photo Photo Caption Detailed Description n/a n/a n/a Note – these images may be used on the NRCan webpage pg. 5 . REDACTED (CONFIDENTIAL INFORMATION REMOVED) REDACTED 2023 Load Forecast Report Synapse IR-9 Attachment 2 Pa...

AI summary The document discusses the Technology Readiness Level (TRL) of a project, noting that it has advanced to TRL 9, with V2G charging technology now commercially available. It also references the need to complete an Environmental Impacts Template as part of the project proposal.

2023 Load Forecast Report (NSUARB M11108) NSPI Responses to Synapse Energy Economics Information Requests NON-CONFIDENTIAL 1 Request IR-15: 2 3 Price Data (Section 4.5, pp 52-53) 4 5 (a) Please provide the data and calculations used to pro...

AI summary NSPI provided responses to Synapse Energy Economics' information requests regarding the 2023 Load Forecast Report, including the data and calculations for electricity prices and the use of a -0.15 price elasticity in SAE models. NSPI referenced historical studies on price elasticity ranging from -0.05 to -0.30 and noted that the -0.15 value is near the mid-range.

Year Month NonResSales2 EESavingsProfiled WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct Bad XMissing YMissing 2013 1 368,026.23 21,446.63 0.00 103,122.63 149,839.49 37,577.00 0.00 0.00 0.00 0.00 0.00 2013 2 373,804.64 21,111.38 0.00 1...

AI summary The text presents a table with data on non-residential sales, energy efficiency savings, weighted cooling, heating, and other factors for the years 2013 and 2014, along with the number of non-residential customers and various metrics such as missing data indicators.

Year Month NonResSales2 EESavingsProfiled WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct Bad XMissing YMissing 2019 4 319,864.16 51,305.00 0.00 91,701.92 139,620.89 38,828.00 0.00 0.00 0.00 0.00 0.00 2019 5 316,423.96 47,537.28 0.00 66...

AI summary The text presents a table containing data related to non-residential sales, energy efficiency savings, weighted cooling, heating, and other factors, along with customer counts and missing data indicators for various months from 2019 to 2020.

Year Month NonResSales2 EESavingsProfiled WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct Bad XMissing YMissing 2031 10 66,533.17 13,818.61 25,150.68 130,394.92 39,609.00 0.00 0.00 0.00 0.00 1.00 2031 11 72,729.97 288.19 58,921.91 131,4...

AI summary The text presents a table with data spanning multiple years and months, including non-residential sales, energy efficiency savings, weighted cooling, heating, and other factors, along with customer counts and various metrics. The data appears to be related to energy consumption and efficiency in Nova Scotia.

Year Month Pred ESavingsProfile WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct X-Missing 2013 1 360,568.521 -8,506.258 0.000 88,524.072 133,211.053 147,339.654 0.000 0.000 0.000 2013 2 365,306.248 -8,373.288 0.000 99,916.035 126,392.47...

AI summary The text presents a table with various data points spanning multiple months and years, including energy savings profiles, weighted cooling and heating values, non-residential customers, and other related metrics. The data appears to be used for analysis or reporting purposes, likely within a regulatory or utility context.

Year Month Pred ESavingsProfile WtXCool WtXHeat WtXOther NonResCusts 18-Feb 22-Oct X-Missing 2023 7 289,564.13 -21,498.48 21,712.83 13,250.22 120,792.42 155,307.139 0.000 0.000 0.000 2023 8 302,810.86 -22,284.62 44,196.43 2,305.85 123,286....

AI summary The text presents a table with data spanning from February 2023 to August 2024, including metrics such as energy savings, weighted cooling, heating, and other factors, along with the number of non-residential customers. The data shows fluctuations in various metrics over time.

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.

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.

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.

for the main measure characterization variables. 3.3.1 Energy and Demand Savings Navigant took three general bottom-up approaches to analyzing residential and BNI measure energy and demand savings: 1. Program Evaluation Data: For most meas...

AI summary Navigant used multiple methods to analyze energy and demand savings, including program evaluation data, TRM algorithms, and engineering analysis. They also relied on EfficiencyOne and TRM data for incremental cost analysis and considered building stock and densities.

st data. Navigant conducted secondary research and used a variety of other publicly-available cost data sources indicated in the accompanying measure input detail. 3.3.3 Building Stock and Densities Navigant relied heavily on the 2019 Nova...

AI summary Navigant used the 2019 Nova Scotia Baseline Study and other secondary sources to estimate equipment densities and saturations for energy efficiency measures. Adjustments related to future codes and standards were also estimated based on Canada’s Forward Regulatory Plan 2019-2021.

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.

ve to economic potential, and a given amount of time to reach that equilibrium state. Equilibrium saturation levels were derived from Navigant’s review of mature behavioral programs in North America. 6.4 Energy Efficiency Investment Strate...

AI summary EfficiencyOne's energy efficiency investment strategy assumes no explicit budget constraints, with spending determined by per-unit-of-savings incentives and administrative costs. Administrative spending is divided into fixed and variable components, with fixed costs applied at the portfolio level rather than individual measures or sectors.

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.

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.

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.

Economic potential is a subset of technical potential and uses the same assumptions regarding immediate replacement as in technical potential, however, only includes those measures that have passed the cost-benefit (B/C) tests chosen for m...

AI summary The text discusses economic potential as a subset of technical potential, focusing on measures that pass cost-benefit tests, specifically the Total Resource Cost (TRC) test with a B/C ratio of 1.0 or higher. It explains how DSMSimTM calculates cost tests and includes administrative costs in economic potential calculations when aggregating measures.

This section presents the approach to calculating achievable potential, which is fundamentally more complex than the calculation of technical or economic potential. The potential study will estimate the annual and cumulative achievable pot...

AI summary This section outlines the approach to calculating achievable energy and peak demand savings potential through energy efficiency (EE), involving up to five scenarios with varying incentives. The process includes simulating market adoption of energy-efficient measures and determining equilibrium market share, considering stakeholder feedback and sector-level analysis.

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.

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.

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.

ject has NO impact on the QUALITY of lighting, heating, and cooling in your business, but changes the amount of energy consumed. An example might be installing or implementing a(n) [ITEM SEEN IN Q21]. Would you generally pursue an energy e...

AI summary The text discusses the impact of energy efficiency projects on a business, asking whether a project with a $7,500 cost after rebates and $6,500 annual savings would be pursued. It also includes a table with data on business premises, heating types, and responses to the energy efficiency question.

Other Regression Variables Year AContrib2Sales.AvgEESavings AContrib2Sales.May15 AContrib2Sales.Apr15 AContrib2Sales.Aug AContrib2Sales.Jan AContrib2Sales.Sep AContrib2Sales.Oct AContrib2Sales.Feb18AContrib2Sales.Oct22 2013 471.61 0.00 0.0...

AI summary The text presents a table of regression variables related to energy efficiency savings and sales contributions over various years, indicating data points for analysis in a regulatory proceeding.

Regression Sales Results Out of Monthly Model (kWh / HH) Year AContrib2Sales.Mar22 AContrib2Sales.ARMA AContrib2Sales.Covid_bin AContrib2Sales.AnnualAvgUse 2013 0.00 28.77 0.00 9,738.05 2014 0.00 1.76 0.00 9,598.06 2015 0.00 -40.77 0.00 9,...

AI summary The document presents regression sales results and monthly model data, including annual average usage and contributions from various factors such as the annual average use and a COVID-19 binary variable, spanning from 2013 to 2029.

0.00 0.00 307.60 9,985.61 2028 0.00 0.00 307.60 10,089.95 2029 0.00 0.00 307.60 10,105.96 2030 0.00 0.00 307.60 10,154.71 2031 0.00 0.00 307.60 10,204.20 2032 0.00 0.00 307.60 10,294.33 2033 0.00 0.00 307.60 10,301.11 REDACTED (CONFIDENTIA...

AI summary The text presents a table with financial data and load forecast report information, including years, indices for heating, cooling, and others, as well as various factors like energy efficiency savings and contributions to sales. The document also includes a reference to the 2023 Load Forecast Report, Synapse IR-35, Attachment 1, Page 5 of 8.

Year AvgEESavingsNew 15 May New 15 Apr New Aug New Jan New Sep New Oct New Feb18 New Oct22 NewMar22 New ARMA new Covid New Total (kWh) Regression Coefficients MStructR MStructR MStructR MSales.A MBin.Jan MBin.Aug MBin.Sep MBin.Oct MBin.Apr...

AI summary The text presents a table with data on average energy efficiency savings and regression coefficients for various months and years. It includes numerical values related to energy savings, coefficients, and standard errors, indicating an analysis of energy efficiency trends and statistical modeling.

MBin.Apr1 82.277 24.871 3.308 0.13% MBin.May 87.249 24.604 3.546 0.06% MBin.Feb1 60.064 22.045 2.725 0.75% MBin.Covi 70.957 16.079 4.413 0.00% MBin.Oct2 145.757 23.017 6.333 0.00% MBin.Mar2 61.873 21.517 2.876 0.49% MA(1) 0.49 0.096 5.108...

AI summary The text includes load forecast data from the 2023 Load Forecast Report, with various monthly figures and percentages. However, the content is partially redacted, indicating that some confidential information has been removed.

their existing non-electric heating as backup, through the 2022 IRP Evergreen 28 process, as well as a scenario analysis for ETS to moderate peak load. It will 29 monitor growth in heating load for commercial customers and work with 30 EOn...

AI summary The text discusses the use of heat pumps with a COP of 2.3 at -15°C in the 2022 IRP Evergreen process, as well as DSM allocation between commercial and industrial classes. NS Power is working with EOne on DR implementation and peak load mitigation strategies.

1 NS Power’s reply submission did not agree with a few of the intervenors’ 2 requests. NS Power does not consider the line loss determination model as a 3 tool for load planning and said a report on this topic is not required. NS Power 4 c...

AI summary NS Power disagrees with some intervenors' requests, including the need for a line loss determination model report and an ELCC factor for the LIIR. However, it agrees to consider an ELCC adjustment for EV load shapes. The response includes incorporating agreed changes into the report, such as analyzing multi-hour temperature and windspeed impacts on peak load forecasting and incorporating water heating load control with a peak sensitivity analysis.

1 • Given the continued population growth in Nova Scotia and 2 ongoing housing shortage in the province, re-evaluate the use of 3 housing completions for the near-term; 4 5 • Given the current inflationary environment, evaluate the use of...

AI summary The text outlines directives related to housing completions, income metrics, and household characteristics for energy demand analysis. It requests clarification on how these directives are incorporated into a report and references a response and table for further details.