E-22024 DSM Programs Evaluation Reports
17 passages
d Response Potential, June 2008. Southern California Edison, Pool Pump Demand Response Potential, June 2008, p. 13. Southern California Edison, Pool Pump Demand Response Potential, June 2008, p. 19. Northeast Energy Efficiency Partnership...
AI summary The text lists bibliographic references to studies and technical documents on energy efficiency, including demand response potential analyses, heat pump water heater validations, and technical reference manuals from organizations like Southern California Edison, NEEP, and Hydro-Québec, focusing on residential energy measures and program savings.
23.2.3 Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity demand peak period in Nova Scotia is defined as the cold...
AI summary Peak demand savings in Nova Scotia are defined as demand reductions during the coldest days (−15°C) between 5 p.m. and 7 p.m. in December to February. Calculation methods differ for measures modeled in HOT2000 and prescriptive approaches within Home Energy Assessments (HEA).
27.2.2 Peak Demand Savings Peak demand savings correspond to the demand savings that coincide in time with the peak demand period of the electricity system. The projected electricity peak demand period in Nova Scotia is between 5 p.m. and...
AI summary Peak demand savings in Nova Scotia are calculated using a 0.283 MW/GWh ratio from Navigant's 2016-2018 DSM Plan, except for heat pumps, which use updated 2024 unitary values (0.148 W/(Btu/hr)). The Evaluator validated Navigant's method, while MHEEP's savings are tracked via appliance replacements and updated in the 2024-2025 DSM MA.
AND/OR "NO" IN [A1](#page-101-0)[96.99.M](#page-101-2) (SMART THERMOSTAT) AND "YES" IN EITHER [A1](#page-101-0)[96.99.C](#page-101-3) (PIPE INSULATION), [A1](#page-101-0)[96.99.D](#page-101-4) (HOT WATER TANK WRAP), [A1](#page-101-0)[96.99...
AI summary The text outlines conditional eligibility criteria for energy efficiency measures, requiring a 'NO' response for smart thermostats and 'YES' for other domestic hot water (DHW) measures like pipe insulation or low-flow showerheads. It directs to specific sections for further evaluation.
4 Residential DR Key Findings and Recommendations As mentioned previously, the main objective of the 2024 Residential DR evaluation was as follows: › Calculate Residential DR results, namely new and total available DR capacities This secti...
AI summary The 2024 Residential DR evaluation found that targets were unmet, with 0.057 MW achieved versus 0.210 MW planned. Mysa thermostats dominated (83% of devices), but preheating errors reduced capacity. A regression model using whole-house data calculated DR capacity, and a 2025 Eco Shift Pilot data analysis is recommended to improve accuracy.
Participation in Events Based on the review of 30 projects, it was found that participants did not participate in events around 31% of the time. In conducting project reviews, participants with a lower available DR capacity were found to h...
AI summary Analysis of 30 projects revealed 31% non-participation in events, with lower DR capacity correlating to fewer participations. Non-participation included no demand reduction or events during non-operational hours. Extrapolated participation averaged 64 meters per event. Projects were categorized for morning/evening events, with 74 suited for mornings and 19 for evenings, influencing event calling strategies.
Project Reviews with Meter Data Analysis E1 staff sampled and reviewed a total of 30 projects to establish tracked available DR capacity. The sample was stratified so that the 20 projects generating the largest amount of tracked available...
AI summary E1 staff reviewed 30 stratified DR projects to assess tracked available DR capacity, validating adjustments using updated M&V methodology. The review aimed to evaluate DR capacity and confirm the appropriateness of new M&V rules for BNI DR projects, with results detailed in Subsection 3.2.1.
7.2.1 Project Reviews and Meter Data Analysis For the Aggregator pathway, available DR capacity is established by comparing the predicted loads (baseline) to the actual loads during DR events using meter data. Baselines for DR events are e...
AI summary The document outlines E1's methodology for calculating available DR capacity using meter data and adjustment factors, including a shift from scalar to additive adjustments. It describes project reviews of 30 projects, stratified sampling, and evaluation processes guided by the BNI DR Baseline Considerations document to verify compliance and assess new rules.
.or.us/efdocs/HAD/um1708had165015.pdf](https://edocs.puc.state.or.us/efdocs/HAD/um1708had165015.pdf) (last accessed 04-10-2024). Demand Response Final Appendix Report 2 1 CADMUS, prepared for CenterPoint Energy, 2023 Demand Response Impact...
AI summary The text lists references to demand response and energy efficiency program evaluations conducted by various organizations, including CADMUS, PG&E, Navigant, and EPRI. These reports assess the impact of initiatives like smart thermostats and load control, focusing on program effectiveness and outcomes.
Residential DR: Event Day Graphs of Expected Vs Actual Loads 0 5 10 15 20 25 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Load (kW) Hour of Day Event Hour Tag Expected Load (kW) Actual Load (kW) H...
AI summary The document presents event day graphs comparing expected versus actual residential demand response (DR) loads, highlighting discrepancies between projected and real-world energy consumption during specific events. Visual data illustrates load (kW) by hour of day, including HDD15 metrics, to evaluate DR program performance.
Test and Validate Test the lookback window against past DR events to validate its effectiveness.
AI summary The text instructs testing the lookback window against historical Demand Response (DR) events to assess its effectiveness in validating program performance or outcomes.
Business rules 3) If the lookback window is observing an abnormal condition such as a building opening or closing earlier than normal, consider whether this point in time is a valid point of comparison. Consider the example below. The faci...
AI summary Business Rule 3 addresses adjustments for demand response (DR) events when abnormal conditions, like temporary facility shutdowns, occur. If a facility resumes normal operations after an abnormal shutdown, the adjustment window should not apply a significant negative adjustment. The example illustrates a facility shutdown before a DR event, where the adjustment should be excluded as operations returned to normal.
Reflect Actual Conditions The cap should reflect the actual conditions and operational changes that could reasonably affect the DR event day's load.
AI summary The cap should reflect actual conditions and operational changes that could reasonably affect the DR event day's load.
Summary [Table](#page-5-0) 129 presents a summary of the values used to calculate electric thermal storage savings. The detailed methodology follows. 195 Nova Scotia Power. 2019 Load Forecast Report - Redacted , April 30, 2019, p. 28.
AI summary Table 129 summarizes 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, which discusses load forecasting.
(4) Domestic Water Heater Load Control
AI summary The section titled 'Domestic Water Heater Load Control' introduces a regulatory proceeding topic focused on managing energy demand from domestic water heaters, likely involving efficiency measures or load-shifting strategies.
$$\Delta kW = kW_{door} x BF x PCF$$
AI summary The formula provided calculates the change in kilowatts (ΔkW) based on the door kilowatts (kW_door), a base factor (BF), and the peak coincidence factor (PCF). This formula is used in energy efficiency calculations and may relate to load management or demand-side management practices.
$$\Delta kW = P_{heater} \, x \, PCF / \, 1,000$$
AI summary The text presents a mathematical formula that calculates the change in kilowatts (ΔkW) based on the power of a heater (P_heater), a power conversion factor (PCF), and a divisor of 1,000. The formula is likely used in the context of energy efficiency or load management calculations.