E-32025 DSM Evaluation Reports
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2.2 Process and Market Evaluations Process and market evaluations were conducted using a range of activities such as program component documentation as well as secondary data reviews, jurisdictional scans, participant and non-participant s...
AI summary Process and market evaluations were conducted using program documentation, secondary data reviews, surveys, and interviews. Key tasks included evaluating Business Energy Rebates (BER) Application Rebates and Residential Demand Response (RDR) processes.
Components Bibliographic References Southern California Edison, Pool Pump Demand Response Potential, June 2008, p. 19. Northeast Energy Efficiency Partnership (NEEP), Mid-Atlantic Technical Reference Manual Version 10, May 2020, p. 195. Hy...
AI summary The document lists bibliographic references from various studies and reports on energy efficiency, demand response, and load forecasting, including works by Southern California Edison, Hydro-Québec, and Nova Scotia Power. These sources cover topics like heat pump systems, technical reference manuals, and energy use analysis.
Peak Demand Savings The differences between tracked peak demand savings and evaluation results were mainly due to the same adjustments made to the energy models described in the Electrical Energy Savings section above. The average adjustme...
AI summary Discrepancies between tracked peak demand savings and evaluation results stem from adjustments in energy models. The average adjustment ratio for gross peak demand savings was 0.937, with a 7.5% margin of error.
Smart Thermostat DLC, Battery Control, EV Telematic and Charger Control Pathway CLEAResult is responsible for the Smart Thermostat DLC, Battery Control, and EV Telematic and Charger Control pathway program delivery. CLEAResult reports that...
AI summary CLEAResult manages Nova Scotia's Smart Thermostat DLC and EV control programs, reporting high retention (97%) in Residential DR but noting call centre inefficiencies, device connectivity issues, and participant confusion. Privacy concerns and lack of real-time feedback during DR events are highlighted, with recommendations to adjust incentives and improve education for better engagement.
Participation Rate The participation rate captures all reasons enrolled devices did not participate. Indeed, participants can opt out of any event, not all EVs are connected to the grid during events, and connectivity issues can result in...
AI summary The participation rate reflects the proportion of enrolled devices that actually participate in demand response (DR) events. For Smart Thermostat DLC, opt-outs and connectivity issues do not affect the participation rate. However, for other pathways like Battery Control and EV Telematic, participation rates are low due to dispatching issues and lack of charging during events. Recommendations include using bidirectional chargers and conducting feasibility studies.
Battery Controls For battery controls, the Evaluator used device-level data to establish the amount of battery discharge during each event window, which counted as available DR capacity as long as no charging occurred during the event wind...
AI summary Battery controls were evaluated using device-level data to measure demand response (DR) capacity by analyzing discharge during event windows. Data cleaning removed duplicates, and hourly savings were calculated per device. The analysis also computed kW saved per kW of capacity to account for varying battery sizes, aiding future DR capacity estimates.
r devices once enrolled. Clearly define how Residential DR works, the importance of participating in events, and how to achieve ongoing participation incentives (participation in 50%+ of DR events). 2025 Res DR-Finding: Maximizing the numb...
AI summary Residential DR participation grew significantly in 2024/25. Key strategies include maximizing enrolled devices per household (40% of participants have unused eligible devices) and improving communication to reduce opt-out rates. Smart thermostat enrollment and EPI pathways were highlighted to optimize DR capacity and avoid backup heating system activation.
6.1 BNI DR Description In 2023, E1 officially launched the BNI DR program component now branded as Smart Synergy. Since the fall of 2020, E1 had implemented several pilot initiatives focused on reducing demand during the Nova Scotia peak p...
AI summary In 2023, EfficiencyOne launched the BNI DR program, branded as Smart Synergy, following pilot initiatives since 2020. The C&I Aggregator pathway, managed by Parsons Inc., allows load reduction through remote control or participant action during DR events, targeting systems like heating, cooling, and lighting.
Table 33: Evaluated 2025 BNI DR Available DR Capacity Stratum 1 Meters Stratum 2 Meters Total Number of Participants 20 138 158 Unadjusted Available DR Capacity – at the Meter (MW) 6.115 2.972 9.087 Adjustment Ratio 65% 55% 62% Available D...
AI summary Table 33 evaluates the 2025 BNI DR available DR capacity, showing a decrease of 46% in available DR capacity from returning participants compared to 2024. New participants contributed 1.736 MW, while the new available DR capacity was -2.093 MW.
APPENDIX VI Residential DR Smart Thermostat DLC Regression Coefficients
AI summary Appendix VI presents regression coefficients analyzing the impact of Residential Demand Response (DR) Smart Thermostat Direct Load Control (DLC) programs. The data evaluates DLC's effectiveness in managing residential energy demand through statistical modeling, relevant to program evaluation and load management strategies.
General Guidelines Following are general guidelines that serve as the de facto assumptions for any DR M&V - › High 6 of 10 baseline - › Additive adjustment - › Adjustment lookback window spans two hours - › Adjustment lookback window start...
AI summary The guidelines outline baseline assumptions for Demand Response (DR) Measurement and Verification (M&V), including a 60% baseline threshold, symmetric adjustments capped at ±20%, a two-hour lookback window starting three hours pre-event, exclusion of holidays/weekends, and additive adjustment methodology.
Additive Adjustment Additive adjustments involve a fixed kW adjustment across all event time intervals. It's suitable for situations where there's a known, constant change in the load that is not proportional to the baseline, like the addi...
AI summary Additive adjustments apply a fixed kW change across all event intervals for constant, non-proportional load shifts (e.g., equipment addition/removal). Examples include industrial processes active during events but not baselines, or shutdowns during slow days that typically operate on baseline days.
Scalar Adjustment Scalar adjustment are a percentage multiplier applied across all event time intervals. It's appropriate when the baseline needs to be adjusted proportionally due to predictable changes that affect the entire load profile,...
AI summary Scalar adjustment involves applying a percentage multiplier across all event time intervals to proportionally adjust baselines for predictable load profile changes, such as variations in operational hours or outdoor temperature impacts on HVAC load.
Business Rules 2) Only use a scalar adjustment if the curtailed load is known to be sensitive to changes in temperature.
AI summary The rule restricts the use of scalar adjustments in load management to cases where curtailed load is confirmed to be sensitive to temperature variations, emphasizing the need for precise conditions in energy efficiency and demand response strategies.
Set Timing Align the timing of the lookback window with the DR event's expected impact on consumption.
AI summary The text emphasizes aligning the lookback window's timing with the anticipated impact of Demand Response (DR) events on consumption, ensuring accurate evaluation of program effectiveness and consumer behavior.
Based on Historical Data The cap can be based on a percentage of historical adjustments observed during similar DR events or established through statistical analysis.
AI summary The cap can be determined using historical data from similar demand response (DR) events or through statistical analysis, providing two methodological approaches for establishing limits.
Summary [Table](#page-100-0) 129 presents a summary of the values used to calculate electric thermal storage savings. The detailed methodology follows. 177 Nova Scotia Power. 2019 Load Forecast Report - Redacted , April 30, 2019, p. 28.
AI summary Table 129 summarizes the values used to calculate electric thermal storage savings, with a detailed methodology provided. A reference is made to Nova Scotia Power's 2019 Load Forecast Report.
Unitary Peak Demand Savings For electric thermal storage, an eight-hour charging period is assumed based on the residential off-peak period in Nova Scotia Power's time of day rate. It is also assumed that the stored heat is provided equall...
AI summary The calculation of unitary peak demand savings for electric thermal storage assumes an 8-hour charging period based on Nova Scotia Power's off-peak rate, with stored heat distributed evenly over 16 hours. Savings are derived using an 8/16 ratio, factoring in manufacturer-specified storage capacity.
(3) Domestic Water Heater Load Control
AI summary The section discusses strategies for managing domestic water heater load control, likely focusing on demand-side management (DSM) initiatives, energy efficiency measures, and regulatory frameworks to optimize hot water usage. It may address technologies like heat pump water heaters (HPWH) and programs aimed at reducing peak demand through load-shifting or direct load control (DLC) mechanisms.
For smart thermostat load control, participants were not removed from the whole-home data analysis if they opted out of an event or had connectivity issues, meaning that the participation rate is already included in the unitary available D...
AI summary The analysis of smart thermostat load control participation indicates that participants who opted out or had connectivity issues were not excluded from the data analysis, meaning the participation rate is factored into the DR capacity values. The in-service rate corresponds to the portion of the season with enrolment, as shown in Table 134.
E-12E1 (NSEB) RIRs 1-66 - Redacted
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BUSINESS, NOT-FOR PROFIT AND INSTITUTIONAL SECTOR The commercial sector accounts for businesses, not-for-profits and institutional customers. In Nova Scotia, there are 37,679 accounts that are represented by the Small Business Advocate. Th...
AI summary The commercial sector in Nova Scotia includes businesses, not-for-profits, and institutional customers. It is divided into different rate classes, with the majority of accounts represented by the Small Business Advocate. The sector uses a variety of lighting technologies, with fluorescent lighting being dominant, and has significant opportunities for energy efficiency improvements, particularly in lighting controls and space cooling systems.
LOAD AND CONSUMPTION INFORMATION Peak Demand (2017 Forecast): 11,681 MW Consumption (2017 Forecast): 63,238 GWh o Residential: 19,761 GWh (31%) o Commercial: 17,815 GWh (28%) o Industrial: 19,016 GWh (30%)
AI summary The document presents 2017 forecasts for peak demand and electricity consumption in Nova Scotia, with peak demand at 11,681 MW and total consumption at 63,238 GWh. Residential, commercial, and industrial consumption account for 31%, 28%, and 30% respectively.
Load Serving Entities California is served by approximately 81 load serving entities (LSEs) 3 . These are broken down as: - Investor-Owned Utilities 6 - Electricity Service Providers 22 - Publicly Owned Utilities 46 - Rural Electricity Coo...
AI summary The document discusses load serving entities (LSEs) in California, categorizing them into investor-owned utilities, electricity service providers, publicly owned utilities, rural cooperatives, and community choice aggregators. It also lists the five largest utilities based on electricity consumption in 2014.
16 assess E1's fulfillment of its legislative mandate. 1 Request IR-43: 1 an ongoing reduction in peak demand (system peak), and its load impact is contingent on 2 when events are called by the utility. 3 4 This distinction is reflected in...
AI summary The document discusses E1's demand response (DR) program, highlighting the distinction between system peak and firm peak in load forecasting. It outlines how DR capacity is measured and the factors influencing actual peak demand reduction.
3.4.1.1 DHW DLC direct install There are three strategic objectives of the DHW DLC direct install pilot, centered around demand response capability, as follows. The first objective is to work closely with services, business development man...
AI summary The DHW DLC direct install pilot has three strategic objectives: evaluating EPI DHW DLC delivery for MURBs, designing solutions for central hot water control in MURBs and other segments, and testing flexible load use cases for DHW controllers. The pilot aims to achieve significant demand response capacity by targeting MURBs and other customer segments.
E-33NSPI (IG) RIR 1 to 15
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1 2 12 The IESO-NS 2026 10YSO improves the reserve margin, closer aligned with the 2025 reserve margin. The updated 2026 reserve margin is on average five percentage points higher than the initial 2026 reserve margin based on NS Power's 20...
AI summary The IESO-NS 2026 10YSO report improves the reserve margin compared to the 2025 version, with capacity levels increasing due to changes in accreditation and additions/retirements schedules. Demand response (DR) remains a key tool for managing load growth, and the maximum potential DR penetration value of 1.3% is for 2036, not 2031.
The 1.3 15 percent maximum potential DR penetration value is for 2036, not 2031. 1 M12861, NS Power 2026 Load Forecast Report, May 15, 2026, page 9, Figure 68. Request IR-7: Reference: E-22, Page 9. Preamble: Brattle references a 2026 Otte...
AI summary The text refers to a 2026 load forecast report by NS Power, and includes a request for clarification on demand response (DR) potential in Nova Scotia, referencing a study by Otter Tail Power and the relevance of its findings to Nova Scotia's system. It raises questions about the applicability of DR benchmarks, contractual obligations of large customers, and residential DR potential.
ailed program design, and final DSM plan for regulatory submission. The same study notes that it also assessed electric DR market potential for Manitoba Hydro, using common inputs and assumptions from the DSM market potential study. The st...
AI summary The document discusses various demand-side management (DSM) and energy efficiency studies from different regions, including Manitoba Hydro, Prince Edward Island (PEI), and Puget Sound Energy (PSE). These studies assess the potential for energy savings and demand response (DR) programs, with a focus on winter peak load reductions and long-term planning periods.
so it is still nascent relative to the incremental levels assumed in the ELCC study. Even with increasing participation, residential DR will have an ELCC of >90 percent until it hits 10 MW. At levels above that, it can still offer meaningf...
AI summary The text discusses the effectiveness of residential demand response (DR) programs, noting their high ELCC until reaching 10 MW. It also addresses managed EV charging programs, confirming that E1's 2027-2031 Preferred Plan does not include such programs, though savings from managed EV charging can be captured within a DR program framework.
102579Letter NSPI re: requests that its third-party experts, Sanem Sergici and/or Sai Shetty of The Brattle Group, participate virtually
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through 2040, and quantified the peak, energy and EV charging infrastructure implications of these EV forecasts. - For Pepco DC, conducted analysis to forecast how the utility's load would increase if aggressive decarbonization goals are m...
AI summary The text discusses electrification-related analyses conducted for Pepco DC and SRP, focusing on load growth from EV adoption and the role of energy efficiency and load flexibility in managing this growth. Sanem Sergici from Brattle is mentioned as an expert involved in the analysis.
- Developed a blueprint for integrating energy efficiency program impacts into the load forecasts for a Canadian Utility. This effort involved estimating the future impact of energy efficiency programs to be included in the load forecasts...
AI summary The text discusses various load forecasting projects and evaluations, including energy efficiency integration, pumping load modeling, evaluation of forecasting models for Florida Power and Light, PJM peak demand forecasting, analysis of electric sales decline in New York, and review of Tennessee Valley Authority's forecasting models.
tarios-Full-Scale-Roll-Out-of-TOU-Rates.pdf - Comparative Generation Costs of Utility-Scale and Residential Scale PV in Xcel Energy Colorado's Service Area, with Bruce Tsuchida, Bob Mudge, Will Gorman, Peter Fox-Penner and Jens Schoene (En...
AI summary This section lists various whitepapers and reports prepared by Sanem Sergici and others, covering topics such as generation costs of solar energy, load factor adjustments, demand response products, and the impact of dynamic pricing on low-income customers. These reports were prepared for organizations including First Solar, The Sustainable FERC Project, and IEE.
- such as target pilot customer, target recruitment samples, and pilot evaluation plans. Brattle also assisted EPE with recruitment strategies for customer enrollment in the pilots. - Rate Impact Analysis for Nevada Energy. As part of Neva...
AI summary The text outlines various consulting projects involving rate impact analysis, cost allocation reviews, and load impact evaluations for multiple utilities. These projects include work for Nevada Energy, the University of Alaska, People's Electric Cooperative, and a Missouri electric utility, focusing on aspects such as demand-side management, rate design, and transmission cost allocation.