E-12024 DSM Annual Progress Report
9 passages
4. FORECAST FOR 2023-2025 DSM PLAN PERIOD E1's three-year Plan forecast provides additional insight on the DSM Plan implementation after the first two years. It includes E1's actual savings results and expenditures from 2023 and 2024, and...
AI summary E1's 2023-2025 DSM Plan forecasts $173 million in investment, aiming to meet 90% compliance on three performance targets (energy, demand savings, and affordable housing). However, challenges in the Demand Response program, including low participant engagement and paused initiatives, are expected to cause a shortfall in the 17.9 MW capacity target. E1 highlights ongoing learning and adaptation of the program as key to future improvements.
Demand Response Residential Highlights - The Residential Demand Response program component achieved 0.1 MW of available capacity for the 2023/2024 season, through the Eco Shift pilot. Two eligible devices (smart thermostats, EV telematics...
AI summary The Residential Demand Response program achieved 0.1 MW of available capacity in 2023/2024, below the 0.2 MW mid-course adjustment. Key factors include preheating implementation issues and E1 finalizing its capacity methodology. The pilot involved 353 participants with 1,257 smart thermostats and 50 EV devices, with 10 demand response events triggered by NS Power.
DEMAND RESPONSE (2024) - o Not all registered devices participated in each event as registration was ongoing throughout the season, participants could opt out of any event, and device connectivity issues meant that not all enrolled partici...
AI summary The 2023/2024 Demand Response season faced challenges with device participation due to ongoing registration, opt-outs, and connectivity issues. Post-season efforts focused on recruiting for the 2024/2025 season, including expanding eligible devices, installing smart thermostats, and resuming domestic hot water controller installations after a 2023 pause due to quality concerns. 803 controllers were installed in 2024.
5.5.2 Performance Indicator The NSUARB also established a Performance Indicator of incidental cumulative annual energy savings of 23.6 GWh applicable to low-income and underserved communities from non-targeted programs. [24](#page-42-1) In...
AI summary The NSUARB set a performance indicator for 23.6 GWh annual energy savings from non-targeted programs targeting low-income and underserved communities. E1 updated its methodology in 2023 and met its 2024 mid-course target, achieving 26.4 GWh (112% of the three-year goal). Results reflect revised low-income estimation assumptions, with 2024 non-targeted program savings at 16.1 GWh.
ENABLING STRATEGIES 2024 ACTIVITY HIGHLIGHTS - six DSMAG sessions on developing an optimal cost-effectiveness test (benefit cost analysis) for Nova Scotia, and a revised report by Energy Futures Group was distributed to the DSMAG on the re...
AI summary Activities in 2024 focused on DSMAG sessions related to cost-effectiveness testing, distribution of reports on E1's 2026-2030 DSM Plan, stakeholder engagement, and modelling assumptions. Sessions included reviews of annual progress reports, evaluation reports, and savings verification, alongside advance materials distribution.
Other regulatory activities included: - ongoing development of the 2026-2030 DSM Plan (in February 2025 the provincial government introduced legislation to extend E1's current DSM Plan by one year to include 2026. Assuming the legislation...
AI summary Regulatory activities include development of the 2026-2030 DSM Plan, pending legislation to extend E1's DSM Plan, 2024 DSM evaluation, NS Power project participation, and information requests on load forecasts. Key entities involve E1, provincial government, and NS Power.
Rate Class Expenditures - E1 reports on planned and actual DSM expenditures by rate class to aid in cost recovery - allocations.[32](#page-51-0) - In an effort to provide insight into how E1 tracks and calculates its rate class results, th...
AI summary E1 reports on planned and actual Demand Side Management (DSM) expenditures by rate class to support cost recovery allocations. The document outlines E1's methodology for tracking rate class results, historical investment data, 2024 results, and 2025 forecasts.
1.1 Rate class allocation for 2023-2025 DSM Plan - Rate class investment allocations for the 2023-2025 DSM Plan were the sum of rate class - allocations calculated by program component. For each program component, the spending by - rate cl...
AI summary E1 allocates DSM Plan funds by rate class using historical spending data, with exceptions for Custom. The methodology ties to NS Power's DCR Rider applications and references a 2015 NSUARB order and the Public Utilities Act. E1 commits to refining estimates for future DSM Plans.
1 4. 2025 FORECAST AND 2023-2025 DSM PLAN RESULTS & FORECAST BY RATE CLASS 2 The 2025 forecast rate class allocation has been developed as described in Sectio[n](#page-52-0) 1.2, using 2022-2024 rate class spending data. 4 [Table 9 p](#pag...
AI summary The document outlines the 2025 forecast for rate class allocation using 2022-2024 spending data. Table 9 details 2023-2025 DSM plan expenditures, actuals, and variances, incorporating 2023-2024 results and 2025 forecasts. It emphasizes the Plan outlook based on historical and projected data.
E-22024 DSM Programs Evaluation Reports
4 passages
Metering Data Analysis Methodology After reviewing existing literature to identify the most appropriate baseline methodology for the 2024 evaluation, the Evaluator decided to rely on whole-house consumption data to establish a regression m...
AI summary The Evaluator used whole-house consumption data and regression models with time-of-week and outdoor temperature variables to assess smart thermostat program impacts. 168 hourly regression models were created, excluding inconsistent data, resulting in analysis of 199 households. This approach accounts for interactive heating effects and uses a large dataset with cold-temperature data.
Select Duration Choose a duration that is long enough to provide a representative sample of usage but not so long that it includes outdated values.
AI summary The text advises selecting a duration for data sampling that balances representativeness with the risk of including outdated values, ensuring accurate and relevant analysis.
Weather Data To determine whether each hour of the year is below the heating balance temperature or above the cooling balance temperature, a typical meteorological year based on a 15-year average (2008 to 2023) was built.
AI summary A typical meteorological year (TMY) was constructed using a 15-year average (2008–2023) to analyze hourly weather data, determining whether each hour falls below heating or above cooling balance temperatures for energy efficiency planning.
Final Report 341 Current A lgorithm Adjuste d Algorithm Cross-Influence (CI): IF(F2 OR F6=Agree; 1) + IF(F3 OR F7=Agree; 1) + IF(F4 OR F8=Agree; 1) Cross-Influence (CI): IF(F2 =Agree; 1) + IF (F7=Agree; 1) + IF(F4 OR F8=Agree; 1) Revi Revi...
AI summary The text presents a table comparing a current algorithm and an adjusted algorithm for calculating cross-influence (CI) and revised free-ridership (FR) in a regulatory proceeding. The algorithms involve conditional logic based on agreement responses in specific fields (F2, F3, F4, F7, F8). The revised algorithm adjusts the FR calculation based on the CI value.