E-12027-2031 DSM Plan Application
30 passages
2.2.2 2025 APPLICATION FOR APPROVAL OF NEW BCA TEST DECISION The following directives from the NSEB's 2025 Decision on E1's application for approval of a new BCA test are relevant to this Application: [13](#page-21-0) - (a) To use the Prog...
AI summary The NSEB outlines directives for E1's 2025 application to approve a new BCA test, requiring use of the PAC test with NS Power's WACC as the discount rate, strategic electrification programs to reduce GHG emissions and costs, inclusion of Eastward Energy in the DSM Advisory Group, and specific reporting requirements for DSM Plans.
Deferred Matters, Consensus Agreement, Appendix 1: Standardized Filing Framework, July 22, 2016. identified in NS Power's 2022 IRP Evergreen established an objective DSM target which is considered to provide the greatest benefits to Nova S...
AI summary The document discusses NS Power's 2022 IRP Evergreen setting DSM energy savings targets (683.1 GWh, 123.9 MW demand savings) for 2027–2031. E1 supports these targets as stakeholder-aligned and cost-effective, but adjusted scenarios to address DSMAG concerns about short-term affordability. E1's preferred plan prioritizes affordability while maintaining energy efficiency as a lower-cost option than supply-side alternatives.
ficiency measures, which is increasingly important in the current economic context. Several factors have contributed to changes in unit delivery costs between the 2023–2026 and 2027–2031 Plan periods: - (a) The conclusion of the federal go...
AI summary The document outlines factors increasing DSM program delivery costs between 2023–2026 and 2027–2031, including the end of federal grants, shifts to complex measures, inflation, and reduced savings from heat pump evaluations. E1's increased incentives and economic pressures are highlighted as key drivers.
4 9. ALTERNATE SCENARIO
AI summary The document introduces an 'Alternate Scenario' section within a Nova Scotia regulatory proceeding, though no specific content or analysis is provided in the given text. Key acronyms and entities related to energy regulation and utility management are referenced.
1 Table 3: 2023-2026 Expenditures by Rate Class 2023-2026 Plan as Approved ($ million) 2023-2026 Actual/Forecast Expenditures ($ million) Rate Class Spending as a Percentage of Total Spending - DSM Plan Rate Class Spending as a Percentage...
AI summary Table 3 compares planned and actual expenditures (2023-2026) across Nova Scotia rate classes, showing residential/charitable as the largest spending category (54.2% of DSM plan, 55.4% actual), while large general and small industrial classes show spending declines. Total expenditures remain nearly unchanged (236.8M planned vs. 235.1M actual).
3.3 MODELLING - The "modelling process" refers to the use of DSM portfolio design tools to assess the comparative costs, - savings, and cost-effectiveness of various DSM resource scenarios to determine the Preferred portfolio - design for...
AI summary The modelling process evaluates DSM resource scenarios using ProCESS™ and DRSIM™ tools to assess cost-effectiveness, energy impacts, and expenditures for the 2027–2031 DSM Resource Plan. Guidehouse supports E1 in developing the preferred portfolio design through these analyses.
8 3.3.1 MODELLING PROCESS - 9 The following sections provide a high-level overview of the 2027–2031 DSM Resource Plan modelling - process, followed by a description of each stage in the process.
AI summary The text outlines the high-level overview and stages of the 2027–2031 DSM Resource Plan modelling process, focusing on Demand Side Management strategies.
3.3.1.1 MODEL CONFIGURATION - At the outset of the modelling process, E1 and Guidehouse reviewed and confirmed the overall modelling - framework for the 2027–2031 DSM Resource Plan, and configured the following modelling tools - associated...
AI summary E1 and Guidehouse configured ProCESS™ and DRSim™ models for the 2027–2031 DSM Resource Plan, aligning with NSEB directives. Model updates ensured parameters, inputs, and methodologies met E1's planning requirements and regulatory standards.
3.3.1.2 MODEL INPUTS AND ASSUMPTIONS - Once the models were configured, E1 and Guidehouse compiled the key modelling inputs and - assumptions required for all subsequent modelling steps associated with the DSM Plan. These included - global...
AI summary E1 and Guidehouse compiled model inputs and assumptions for the DSM Plan, including global factors (avoided costs, discount rates) and program-specific data. Inputs were reviewed and adjusted for 2027–2031, with new measures informed by engineering assumptions and external data. Details are in Attachment 1.
1 3.3.1.3 MODEL OUTPUTS - 2 The DRSim™ and ProCESS™ tools produced model outputs for each modelled scenario. All model outputs - 3 were reviewed by E1 and Guidehouse for accuracy and completeness. Outputs were further shared with - 4 the D...
AI summary Model outputs from DRSim™ and ProCESS™ tools were reviewed by E1 and Guidehouse, with revisions made based on feedback from the DSMAG. Final outputs are detailed in Section 4, outlining the 2027–2031 Preferred Plan portfolio.
11.1 EVALUATION FRAMEWORK - E1's independent evaluation consultant is engaged to develop an Evaluation Framework that defines the - policies, priorities, and methodologies used to conduct the DSM evaluation. It provides a common - understa...
AI summary E1's independent evaluation consultant is tasked with creating an Evaluation Framework for DSM programs. The framework outlines evaluation definitions, goals, metrics, deliverables, and roles, establishing principles and prioritization criteria for annual evaluation plans. It aims to standardize best practices in DSM evaluation.
load reduction will most often never reappear (although it would no longer be accredited to E1's resource acquisition activities, which is the context of this analysis). Attachment 4, NS Power, Methodology for determination of changes in N...
AI summary The document analyzes the rate impacts of DSM (Demand Side Management) from 2027–2031, showing that residential electricity rates are projected to rise 42% over 2027–2046 due to factors unrelated to DSM. Figure 5 compares rate trajectories with and without DSM for three customer classes, using NS Power's 2022 cost-of-service model with 2% annual inflation assumptions.
8 5.1 ACTIVE PARTICIPATION METHODOLOGY - 9 Previously, participant estimates were calculated using a 'cumulative' methodology. This did not account - for the measure life of savings, resulting in the potential for the number of cumulative...
AI summary The document discusses a shift from a cumulative to an annual/active participation methodology in the 2026 DSM Extension RBIA, addressing overestimation of participants and underestimation of savings by considering measure life and separating active from expired participation.
6. METHODOLOGY AND ASSUMPTIONS Attachment 5 describes the overall modelling and key assumptions that apply to the 2027–2031 DSM Plan and 2026 Historical RBIA.
AI summary Attachment 5 outlines the methodology and key assumptions for the 2027–2031 Demand Side Management (DSM) Plan and the 2026 Historical Rate and Bill Impact Analysis (RBIA).
2. RESOURCES AND SCENARIOS - Both the 2027–2031 DSM Plan analysis and the 2026 historical analysis include the NS Power rate - model (Attachments 7 and 8) and the E1 RBIA model (Attachments 9 and 10). The analyses - compare two scenarios:...
AI summary The document compares DSM and no-DSM scenarios using NS Power and E1's RBIA models, analyzing utility costs, energy reductions, and rate impacts. It outlines resource combinations (e.g., Energy Efficiency Only, Solar-PV Only) and notes that rate impacts isolate DSM effects but do not reflect actual timing of rate increases. Results are summarized in Appendix B, Attachment 1.
7 2.3 DEMAND RESPONSE INPUTS - 8 Demand response inputs for the 2027–2031 DSM Plan RBIA come from Guidehouse's DRSim™ - 9 model results. Rate class allocations for the BNI Curtailment program were calibrated for the - 2027–2031 DSM Plan RB...
AI summary Demand response inputs for the 2027–2031 DSM Plan RBIA are derived from Guidehouse's DRSim™ model, historical data (2011–2024), and the approved 2023–2025 DSM Plan. Modeling assumes no energy impacts and a one-year measure life for demand response programs.
3. LOAD FORECAST DATA - NS Power has provided historical and projected energy and demand sales; customer counts; and - line losses within each rate class for 2011–2055.
AI summary NS Power has submitted historical and projected energy and demand sales data, customer counts, and line losses by rate class from 2011 to 2055 as part of load forecast analysis for regulatory proceedings.
9 Annual Tracked Participation - For years where approved/proposed rather than historical participation is used (2025–2031), - annual tracked participation was first estimated at the program component level. For some - program components t...
AI summary Annual tracked participation for 2025–2031 was estimated using Guidehouse's ProCESS model and scaled RBIA data from 2024 with energy/unit factors. Participation figures were allocated to rate classes proportionally. E1 tracked 2011–2024 participation rates.
4 7.2.2 UNTRACKED PARTICIPATION - 5 E1 operates two program components that offer rebates at the point-of-sale: residential Instant - 6 Savings and the Instant Rebates portion of Business Energy Rebates (BER-IR). These program - 7 componen...
AI summary E1's Instant Savings and BER-IR programs use transaction records and research to estimate participation due to lack of direct data collection. The methodology changed in 2027, abandoning the prior assumption that all large commercial/industrial customers participated annually, due to declining participation from lighting phase-outs in Instant Rebates.
Annual Participation - For 2025–2031, annual untracked participants were estimated using the same methodology as - tracked annual participants. For historical untracked participants (2011–2024) E1 utilizes survey - data to estimate the num...
AI summary The text outlines E1's methodology for estimating annual untracked participants from 2025–2031 using the same approach as tracked participants, while historical data (2011–2024) relies on survey data segmented by rate class.
Active Participation - For historical years (2011–2024), to calculate the number of active participants, E1 starts with - the annual participation and for each subsequent year adds the number of new participants - (calculated based on surv...
AI summary E1 calculates active participants for historical years (2011–2024) by adding new participants and subtracting expiring ones based on savings lifespan. For future years, a re-participation factor derived from 2019–2023 data is applied, with participant numbers degrading post-2032 at the same rate as cumulative energy savings.
7.2.3 RESIDENTIAL BEHAVIOUR PARTICIPATION - The Residential Behaviour program component applies the rate class weighted-average measure - life to estimate active participants; this is consistent with other tracked programs. For program- -...
AI summary The Residential Behaviour program uses a rate-class weighted-average measure life to estimate participants, ensuring accurate tracking without overestimation. A cross-participation factor prevents double-counting across tracked/untracked participation. Residential Behaviour is excluded from the 2027–2031 DSM Plan RBIA, focusing on post-delivery year participation decay aligned with energy savings.
7.4 DEMAND RESPONSE PARTICIPATION - For 2023–2024 years, demand response participation was based on historical results. For 2025– - 2031 years, demand response participation inputs by rate class come from Guidehouse's DRSim™ - model. - 1 T...
AI summary For 2023–2024, demand response participation is based on historical results. For 2025–2031, it uses Guidehouse's DRSim™ model. Participation counts annual active participants, assuming a one-year measure life.
while potentially more intuitive, would require a single assumption of avoided costs to 7 be used and would not allow for the analysis of scenarios of 75%, 100%, and 125% of estimated 8 avoided costs. A new "Avoided" savings calculation ha...
AI summary NS Power updated its rate model with a new 'Avoided' savings calculation and 'Master Output' tab to analyze DSM scenarios (75%, 100%, 125% avoided costs) and time periods. The 'Total-Savings (Avoided)' tab provides informational summaries, while the 'Total-Savings (Added)' tab is used for modeling. The E1 RBIA model now references the 'Master Output' tab for scenario analysis.
Cost of Service Studies COSS provides the most insight into class cost causation as based on changes in its energy and demand usage. It shows in a transparent way how rate class usage of demand and energy services within each functional ar...
AI summary COSS provides insights into cost causation by analyzing energy and demand usage changes. NS Power's annual Load Forecast Report and E1's long-term usage forecasts enable simplified COSS analysis for rate adjustments, bypassing detailed future cost data collection.
Comments The applied process is a simplification of a more elaborate cost allocation process where some FAM costs, such as fuel costs, are allocated to rate classes based on their shares in monthly energy requirements; some other FAM costs...
AI summary The document outlines a simplified cost allocation process for FAM (Fuel Adjustment Mechanism) and non-FAM costs, distinguishing between energy and demand-related allocations. It details methods like load factors, DSM integration, and inflation adjustments for 2023–2035, using data from COSS and prorating tables to distribute costs across rate classes.
17 1.4 ALTERNATE SCENARIO – RATE CLASS ALLOCATIONS 18 Planned rate class expenditures for the Alternate Scenario are provided i[n Table 9,](#page-332-2) below, by year and by 19 Plan period. 20
AI summary The Alternate Scenario outlines planned rate class expenditures by year and plan period, referencing Table 9 for detailed allocation data. This section focuses on financial planning and resource distribution under the proposed scenario.
ngs (reported by program and rate class); - iii. Annual lifetime energy savings (reported by program and rate class); - iv. Cumulative lifetime energy savings (reported by program and rate class); Annual incremental system-peak demand savi...
AI summary The document outlines performance reporting requirements for energy efficiency, demand response, solar PV, and low-income programs. Metrics include energy savings, demand reductions, solar generation, and cost test results. Emphasis is placed on reporting for equity, census data, and program-specific outcomes like Affordable Homes and Mi'kmaw initiatives.
4.2.3 Integrated Resource Plan - Integrated resource planning establishes directional information for DSM planning. The Preferred - Resource Plan identified in the IRP will inform the development of a preferred DSM Resource Plan - by E1, i...
AI summary The Integrated Resource Plan (IRP) provides directional guidance for Demand Side Management (DSM) planning. The Preferred Resource Plan in the IRP will inform E1's development of a preferred DSM Resource Plan, including analysis of alternate DSM scenarios as per the Framework.
4.8.2 Quarterly Reports - E1 will file quarterly reports with the Board for quarters one through three of each year. Reporting - requirements were established under the 2013–2015 DSM Plan Settlement Agreement and - continue to evolve: [9](...
AI summary E1 is required to submit quarterly reports to the Nova Scotia Utility and Review Board, detailing program performance, variances, forecasts, and equity outcomes under the 2013–2015 DSM Plan Settlement Agreement. Reports must include mid-course adjustments, variance explanations, year-end forecasts, rate-class expenditures, and Enabling Strategies updates.
E-22025 DSM Annual Progress Report
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tment of $236.8 million. - This Annual Progress Report (APR) provides: - a summary of the activities and milestones achieved in the prior year, including status of the annual performance indicators; - management's discussion of any materia...
AI summary The Annual Progress Report (APR) outlines prior-year activities, performance indicators, management discussions on discrepancies (25%+ variance), program expenditures, energy savings, system-peak demand reductions, capacity availability, four-year forecasts, and E1's rate class results.
Program, page 118, March 13, 2026. evaluation parameters. A comprehensive impact evaluation is a full impact evaluation involving review of baselines, methodologies, values, and net-to-gross-ratios.
AI summary The text outlines evaluation parameters for a 2025 initiative, emphasizing a comprehensive impact evaluation that reviews baselines, methodologies, values, and net-to-gross-ratios as key components of the assessment process.
2025/2026 season (December 1, 2025 to February 28, 2026) preparations - Eco Shift device installation ramped up throughout 2025, with 13,687 demand response eligible devices (smart thermostats and domestic hot water direct load controllers...
AI summary Eco Shift program installed 13,687 demand response devices in 2025, with 18,234 total since August 2024. E1 ensured device readiness, ran marketing campaigns, and expects residential demand response capacity to double in 2025/2026. Efforts focus on optimizing thermostat strategies and connectivity.
4.5.2 Performance Indicator The NSEB also approved a Performance Indicator of incidental cumulative annual energy savings of 30.2 GWh applicable to low-income and underserved communities from non-targeted programs. [19](#page-42-1) 2025 en...
AI summary The NSEB approved a 30.2 GWh cumulative energy savings target for low-income and underserved communities via non-targeted programs. By 2025, cumulative savings reached 29.9 GWh (99% of the target), but 2025's 3.5 GWh fell short of the annual target due to E1's updated methodology, which reduced assumptions about low-income and equity impacts.
topics including heat loss/heat gain calculations, duct testing and sizing, electrical load calculations and panel sizing, and heat pump sizing and selection. Date Filed: March 31, 2026 Page 41 of 47
AI summary The document outlines technical topics related to energy efficiency, including heat loss/heat gain calculations, duct testing, electrical load calculations, panel sizing, and heat pump selection. No specific claims, entities, or references are explicitly mentioned in the provided text.
1. RATE CLASS SPENDING ALLOCATION METHODOLOGY - As part of efforts to enhance rate class spending reporting, E1 introduced a new rate class - allocation methodology for quarterly and annual (where applicable) forecasts in 2025. This - meth...
AI summary E1 introduced a new rate class spending allocation methodology in 2025 for quarterly and annual forecasts, used to calculate 2026 DSM Plan allocations. This marks a shift from the allocation approach used in the 2023-2025 DSM Plan.
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 The 2023-2025 DSM Plan's rate class allocations are determined by applying 2020 spending percentages to total program costs, except for one component where 2017-2020 data was used due to high variations.