E-1-1Application
13 passages
11 Other Considerations 12 13 In addition to the above factors, EfficiencyOne considered DSM supply sector capacity 14 and NS Power's 2018 Load Forecast in determining the appropriate level of energy 15 savings. 16
AI summary EfficiencyOne evaluated DSM supply sector capacity and NS Power's 2018 Load Forecast to determine appropriate energy savings levels. This consideration is part of broader analyses in the regulatory proceeding.
1 4.4.2 2018 Load Forecast 2 3 NS Power's 2018 load forecast provided an outlook on the energy and peak demand requirements of in-province customers for the 2019 through 2028 period. [30](#page-28-1) 4 The load 5 forecast forms the basis f...
AI summary NS Power's 2018 load forecast outlines energy and peak demand needs for 2019–2028, relying on IRP Base-Level DSM rather than the optimal Mid-Level DSM. EfficiencyOne's Preferred Plan identifies the minimal required energy savings (130.5 GWh annually) as insufficient compared to Mid-Level DSM. The forecast underpins NS Power's fuel supply and investment planning.
1 8. ALTERNATE SCENARIO 2 3 EfficiencyOne has been directed by the Board to provide one or more alternate 4 scenarios of DSM budgets for the Board to consider. NS Power has been directed to 5 provide rate impact analysis on those scenarios...
AI summary EfficiencyOne is required to provide alternate DSM budget scenarios for the Board's consideration, while NS Power must analyze their rate impacts. The alternate scenario aims to offer a lower-cost plan.
4 Modelling 5 6 The "Model" is a DSM portfolio design tool used to inform EfficiencyOne's DSM 7 Resource Plans. EfficiencyOne engaged Navigant Consulting to provide its ProCESS 8 short-term DSM planning tool for these purposes. 9 10 Naviga...
AI summary EfficiencyOne transitioned from the ELRAM model to Navigant's ProCESS tool for short-term DSM planning. ProCESS offers improved modeling structure, portfolio optimization, and faster output generation compared to ELRAM. The model uses input data like line loss factors, customer rates, and measure technical details to optimize DSM portfolios for the 2020-2022 Preferred Plan.
EXECUTIVE SUMMARY EfficiencyOne's 2020-2022 Demand-Side Management (DSM) Resource Plan Rate and Bill Impact Analysis provides a broad trend-based picture of the rate and bill impacts of proposed DSM activities to be carried out during the...
AI summary EfficiencyOne's 2020-2022 DSM Resource Plan analysis compares rate and bill impacts of proposed DSM activities under Preferred and Alternate scenarios, modeling effects until 2035. It evaluates DSM vs. no-DSM scenarios, excluding utility-specific factors, to assess long-term average impacts on rates and bills.
1 3. METHODOLOGY AND ASSUMPTIONS 2 This section describes the overall modelling approach and key assumptions. 3
AI summary Section 3 outlines the methodology and key assumptions used in the analysis, focusing on modeling approaches for regulatory proceedings. It sets the foundation for evaluating demand-side management, efficiency programs, and cost tests within Nova Scotia's energy regulatory framework.
12 3.2 SCENARIOS 13 The models each compare two scenarios: a DSM scenario and a no-DSM scenario. 14 The DSM scenario includes the estimated administrative costs and resulting energy 15 and system-peak demand reductions of DSM programs that...
AI summary The analysis compares DSM and no-DSM scenarios (2020-2022), evaluating administrative costs, energy reductions, and system-peak demand. Rate and bill impacts are presented as differences between scenarios to isolate DSM effects. Results are detailed in Sections 4 (Preferred Plan) and 5 (Preferred vs. Alternate Plans).
7 3.9 CALCULATING PARTICIPATION IMPACTS 8 For illustrative purposes, participation graphs provided in Attachments 1 and 2 9 include historical participation in 2011-2019 DSM programs. These participation 10 figures are identical to those u...
AI summary The section references historical participation data in DSM programs from 2011-2019, as presented in Attachments 1 and 2, which are identical to those used in EfficiencyOne's 2018 RBIA.
the number of products sold or installed, and also de-duplicates these accounts across programs and across years to produce estimates of the number of unique accounts that are expected to participate. Annual participation rates for 2011-20...
AI summary The text outlines methods for estimating participation rates in energy efficiency programs from 2011-2022, using historical data, energy savings ratios, and product rebate metrics. EfficiencyOne tracked participation rates, while NS Power's accounts were projected based on program magnitude and measure mix changes.
dels and roles, with a heavy emphasis on distributed resources— 8 particularly energy efficiency, distributed generation, advanced metering infrastructure (AMI), and the 9 integration of these assets. From 2014 to 2017, I led a three-year...
AI summary The individual led studies on solar energy integration in Vermont and Pennsylvania, focusing on technical, regulatory, and business model implications. They emphasized energy efficiency's role in Vermont's energy future and modeled solar market pathways under the U.S. Department of Energy's SunShot Initiative.
ncrease of most 6 other non-lighting technologies over the next 20 years. Only two technologies are not expected to increase during this time period: behavior and hot water savings.[28](#page-319-0) 7 Direct Testimony of David Hill, Ph.D....
AI summary The text discusses forecasts of energy savings from non-lighting technologies, noting increases over 20 years except for behavior and hot water. It references Efficiency Vermont's data and a 2018 ENERGY STAR study projecting 50% residential savings from midstream non-lighting programs by 2028. Direct testimony by David Hill on behalf of EfficiencyOne is cited.
14 States or Canada? 1 A: Yes. I can comment and give two further examples of national studies for the United States. I 2 would expect that results would be consistent for Canada, although in this assignment I did not have 3 time to resear...
AI summary The testimony discusses energy efficiency studies in the U.S. and Canada, highlighting VEIC's 2018 commissioning of Synapse Energy Economics to assess historical and future cost-effectiveness of efficiency programs. Synapse projected 5.5% to 14.9% annual savings by 2030 under different scenarios, supported by EPRI's 2017 study showing 17.5% potential savings by 2035. The analysis emphasizes the strategic importance of scaling efficiency efforts.
24. SHARING OF DATA AND INFORMATION - 24.1 EfficiencyOne shall work co-operatively with NSPI to provide NSPI with information and data from time to time in order to assist NSPI with planning and load forecasting as may be reasonably requir...
AI summary EfficiencyOne must share data with NSPI for planning and load forecasting, consistent with past practices. Disputes over data requests can be referred to UARB.
E-9NSPI Evidence
9 passages
Q. How was DSM program potential assessed in the context of the latest IRP and GUO proceedings? A. On behalf of EfficiencyOne, Navigant conducted an analysis of DSM, i.e ., EE programs from 2015 to 2040 to support NS Power's IRP and GUO ef...
AI summary DSM program potential was assessed by Navigant for NS Power's IRP and GUO proceedings, analyzing EE programs from 2015–2040. Four scenarios (Low, Base, Mid, High) were developed using incentive levels and data from 2012 studies, surveys, and on-site collections. The 2014 report informed DSM strategies to meet provincial goals.
Q. Mr. Levitan, is EfficiencyOne's Preferred Plan inconsistent with the current experience in the DSM market in both Canada and the U.S.? A. Yes. I believe EfficiencyOne's Preferred Plan is too aggressive when compared to other program adm...
AI summary EfficiencyOne's Preferred Plan is deemed inconsistent with current DSM market trends in Canada and the U.S., as it proposes significant spending increases while other regions reduce DSM spending. The plan's aggressiveness is highlighted by comparisons to British Columbia, Newfoundland and Labrador, and Saskatchewan, which are decreasing their DSM investments.
Q. Did this iterative model development process continue until the final Preferred Plan and Alternate Plan were formulated? A. As I understand from EfficiencyOne's response to the information request, the use of the optimization tool ended...
AI summary The iterative model development process using an optimization tool ceased before finalizing the Preferred and Alternate Plans. EfficiencyOne noted that adding constraints made the model overconstrained and risked false precision. Non-model information and team judgment were used in the vetting stage to finalize plans.
21 Q. Mr. Levitan, what is your assessment of the vetting process? - 22 A. In my view, the vetting process is another way to allow for skewing the modelling results 23 in the desired direction, which may not be necessarily in the best inte...
AI summary Mr. Levitan criticizes the vetting process for potentially biasing modeling results and recommends early stakeholder involvement in setting constraints for ProCESS to ensure transparency and reduce subjectivity. EfficiencyOne's iterative model refinements were not adequately documented.
Did EfficiencyOne consider more DSM scenarios other than Preferred Plan and Q. 10 11 Alternate scenario? A. Yes. According to EfficiencyOne, in response to stakeholder requests, EfficiencyOne 12 jointly with Navigant produced 3 additional...
AI summary EfficiencyOne (E1) considered three additional DSM scenarios beyond the Preferred Plan and Q.10 11 Alternate scenario, including varying investment levels and demand reduction factors. However, E1 concedes these scenarios were not vetted and may not be deliverable.
Figure 24. Annual Investment and Peak Demand Reduction Differences between the Preferred Plan and the $27 million E1-Navigant A Scenario
AI summary The figure compares annual investment and peak demand reduction differences between the Preferred Plan and the $27 million E1-Navigant A Scenario in a Nova Scotia regulatory proceeding, highlighting financial and demand management contrasts.
Q. Mr. Levitan, what are your key findings and observations? - A. I have eight key findings and observations. - First, EfficiencyOne's Preferred Plan does not meet the Board's definition of affordability as the certain and significant near...
AI summary Mr. Levitan outlines eight key findings: EfficiencyOne's Preferred Plan lacks affordability, the Alternate scenario is suboptimal, lifetime energy savings are uncertain, less costly DSM plans are feasible, organic efficiency measures exist, jurisdictional analysis is flawed, ProCESS modeling is subjective, and inflated fuel costs skew cost-effectiveness. These critiques focus on DSM plan evaluation, cost-benefit analysis, and modeling methodologies.
INDUSTRY PRESENTATIONS & PUBLICATIONS Law Seminars International Conference; Transmission and Clean Energy in the Northeast "Offshore Energy Policy Issues, Where Are We Headed, How & When?," March 2019 "Renewable Initiatives in the Greater...
AI summary The document lists industry presentations and publications from 2014 to 2019, covering topics like renewable energy, infrastructure, natural gas, system reliability, and gas-electric coordination. Key entities include conferences, energy associations, and regulatory bodies such as PJM, IEEE, and NEPOOL.
RBIA Observations - The apportionment of DSM costs and benefits to rate classes in the current RBIA methodology uses a static allocator factor based on class shares in one historic year (2014 test year) throughout the RBIA period of 2011-2...
AI summary The current RBIA methodology's static allocation of DSM costs and benefits across rate classes, based on 2014 data, fails to account for dynamic changes in class usage, line losses, and long-term load forecasts. Additionally, the use of levelized fuel costs extending beyond the RBIA's 2011-2033 timeframe overstates early savings and understates later ones.
E-11E1(CA) RIR-1 to RIR-19
6 passages
NON-CONFIDENTIAL Request IR-05: - Could more savings than E1 is proposing from market-driven opportunities – such as new - construction and failed equipment replacement – be cost-effectively acquired in 2020-2022? - If so, how much more? R...
AI summary EfficiencyOne (E1) responds to the Consumer Advocate (CA) that 850 GWh of additional energy savings could be cost-effectively achieved through market-driven opportunities (e.g., new construction, failed equipment replacement) by 2022, citing the 2013 DSM Potential Study. However, E1 notes that direct comparisons with past studies are challenging due to changes in measure mix and market strategies, with the 2013 study’s 950 GWh estimate representing a theoretical maximum under ideal conditions.
11 Table 1: EfficiencyOne Spending by Year Nominal vs. 2019 Dollars Year Investment in Nominal Dollars ($ millions) Investment in 2019 Dollars ($ millions) 2015 Actual 32.0 34.7 2016 Actual 30.8 33.0 2017 Actual 30.3 32.0 2018 Actual 33.9...
AI summary Table 1 compares EfficiencyOne's annual spending from 2015 to 2022 in nominal dollars and 2019-adjusted dollars, showing increasing investment trends. The document also notes EfficiencyOne's responses to the Consumer Advocate, indicating regulatory scrutiny of their spending plans.
Overall Prevalence (OP) of Low Income Nova Scotians From the county-level low income census data, the provincial average percentage of low income residents was calculated to be 14%. This is the Overall Prevalence (OP).
AI summary The provincial average percentage of low income residents in Nova Scotia, calculated from county-level census data, is 14%. This figure is designated as the Overall Prevalence (OP) of Low Income Nova Scotians.
Energy, Demand, Expenditures, and Participants The calculations used in this document use the general term "savings". The same calculations are applied to energy savings, peak demand savings, program expenditures, and in most cases, partic...
AI summary The document explains that calculations use the term 'savings' to represent energy savings, peak demand savings, program expenditures, and participation numbers, with 'savings' substituted for these parameters in most cases.
Calculation Low income replacement savings = (total replacement savings) Low income retirement savings = (total retirement savings) $\times$ (OP) $\times$ (10%) Low income total savings = (low income replacement savings) + (low income reti...
AI summary The document outlines formulas for calculating low-income savings, including replacement savings, retirement savings (using Overall Prevalence and a 10% factor), and total savings. Calculations are presented as mathematical expressions without explicit stakeholder positions or regulatory references.
Assumption EfficiencyOne Estimation of Incidental Low Income Impacts No low income participation.
AI summary EfficiencyOne's assumption in estimating incidental low-income impacts is that there is no low-income participation. This assumption is critical for the analysis of potential effects on low-income households.
E-17E1 (SBA) RIR-1 to RIR-49
16 passages
1.3 Energy Efficiency Economic Potential Results Appendix B presents the total Economic Potential results of the analysis, 2015 through 2040, across all sectors; Residential, Commercial and Industrial. The total (Gross at Generator) energy...
AI summary The analysis estimates total energy efficiency economic potential savings from 2015 to 2040 at 6,354 GWh (46% of forecast sales) and 1,334 MW (52% of peak winter demand). High economic potential is attributed to including nearly economically feasible measures in DSM portfolios and setting an economic screen of 0.75 to ensure cost-effectiveness.
1.4 Energy Efficiency Achievable Potential Results The total Achievable Potential savings (Net at Generator) from energy efficiency programs, 2015 through 2040, are presented in Appendix C. Four scenarios of Achievable Potential have been...
AI summary The document outlines four scenarios (Low, Base, Mid, High) for energy efficiency achievable potential savings from 2015 to 2040, categorized by Residential, Commercial, and Industrial sectors. Total savings are presented in Appendix C, with sector-specific breakdowns.
The four scenarios are: - Base Scenario: incentive in the calibration year is set to what was offered in that calibration year. - Low Scenario: incentive is set to ½ of the calibration year incentive. - Mid Scenario: incentive is set to 1....
AI summary The document outlines four scenarios for setting incentives: Base (calibration year level), Low (50% of calibration), Mid (1.5x calibration up to 100% incremental cost), and High (2x calibration up to 100% incremental cost). These scenarios likely relate to energy efficiency program design or regulatory analysis.
2.1 Overview of the Energy Efficiency Resource Assessment Model (EERAM) The Energy Efficiency Resource Assessment Model (EERAM) is an energy efficiency potential model designed to estimate technical, economic, and achievable energy efficie...
AI summary The Energy Efficiency Resource Assessment Model (EERAM) is a 20–25 year forecasting tool developed by Navigant to estimate energy efficiency potential across residential, commercial, and industrial sectors. It uses Excel for transparency and customization, employing a bottom-up approach with building stock data, technology costs, and adoption rate algorithms, including Bass diffusion curves for emerging technologies.
2.2 EERAM Model Features The EERAM model incorporates a number of innovative features, including: - Utilization and wherever possible, direct linkage to province‐wide or program administrator specific deemed, per unit measure energy saving...
AI summary The EERAM model features include linking to province-wide energy savings databases, using building characteristics data from surveys, incorporating decision-maker awareness variables, calibrating with historical program achievements, and enabling scenario creation based on incentive levels or alternative inputs.
2.8 Dual Baseline Measures Certain DSM measures are candidates for an early replacement program that utilize a dual baseline. A dual baseline measure uses a less efficient baseline in the first part of its measure life, resulting in higher...
AI summary Dual baseline measures in DSM programs use a less efficient initial baseline, leading to higher early savings that decline over time. Examples include early replacement of T-12 lighting with HPT8 fixtures. Program administrators prioritize rapid replacement to maximize initial savings, but models adjust cumulative savings and costs over the measure's lifespan, splitting avoided costs between early and later periods.
2.9 Transitioning to Market Transformation EERAM recognizes that a program administrator‐sponsored DSM program measure reaches a point where it can be considered part of a transformed market. This market transformation point is estimated w...
AI summary EERAM's model identifies a Market Transformation Point (MTP) where DSM programs shift from direct incentives to market-wide impact. Post-MTP, administrative and incentive costs are removed, but avoided costs are still claimed. Market penetration increases, and benefit/cost tests assume no further costs after transformation.
2.11 Behaviour Based Energy Savings Potential Savings potential from behaviour‐based initiatives was included in the EERAM model. For the purposes of this study, Navigant defines behaviour‐based initiatives as those providing information a...
AI summary The EERAM model includes behavior-based energy savings, defined by Navigant as initiatives using information and education rather than incentives. Savings are categorized into equipment-based (e.g., upgrading to efficient appliances) and usage-based (e.g., reducing energy use through behavioral changes). Equipment-based behavior is further divided into incentivized and non-incentivized categories, while usage-based savings are modeled independently. The model assumes a one-year measure life for behavioral reinforcement.
3.2 Base and Efficient Consumption, Effective Useful Life, and Incremental Measure Cost The overall approach for estimating base and efficient consumption (of electricity and other resources), effective useful life (EUL), and incremental m...
AI summary The document outlines criteria for selecting data sources to estimate base/efficient consumption, effective useful life (EUL), and incremental measure costs, prioritizing local relevance, recency, parameter alignment, and method transparency. Compromises were sometimes necessary when older local data conflicted with more recent but less relevant sources. Econoler and Ontario Power Authority documents were frequently cited for residential measures.
3.3 Loadshapes The potential study relied on annual end use loadshapes to develop results, allocating savings potential across the different sectors (residential, commercial, and industrial.) Loadshapes were derived by Navigant from a vari...
AI summary The study utilized annual end-use loadshapes derived from Navigant's energy modeling and secondary data sources like PG&E Climate Zone 2/16 to allocate savings potential across sectors. These loadshapes supported Nova Scotia's potential study and the Technical Reference Manual (TRM).
Loadshapes for TRMs Loadshapes used for TRMs are normalized to an annual maximum value for each end use, such as HVAC or lighting. Where the hourly fraction is 1.00, the hourly usage is at the annual maximum kW per whole building energy mo...
AI summary Loadshapes for TRMs are normalized to annual maximum values for end uses like HVAC, using eQuest modeling. Part-load operations (hourly fractions <1) enable estimation of energy savings during specific periods, such as winter peaks, via TRM analysis.
5. Business, Nonprofit and Institutional Baseline Study Methodology This section provides an overview of the data sources and methods used to develop the BNI baseline characterization.
AI summary This section outlines the data sources and methods used to develop the Business, Nonprofit and Institutional (BNI) baseline characterization as part of a regulatory proceeding in Nova Scotia.
7.3 BNI Lighting Figure 87 summarizes the location of the BNI lighting surveyed for ENSC (weighted by wattage). Sixty‐ one percent of all lighting types were indoor while 39 percent were outdoor. 61% 39% Indoor Outdoor Figure 87. BNI Light...
AI summary The document details that 61% of BNI lighting surveyed by ENSC is indoor, while 39% is outdoor, with data visualized in Figure 87. The analysis focuses on lighting distribution weighted by wattage, sourced from BNI on-site surveys.
NA VIGANT ENSC Commercial Baseline Sun1ey Site# Form 21 21 D QC Date Filed: May 13, 2019 ENSC Commercial Baseline Survey Site # Form 22
AI summary A commercial baseline survey form submitted by Efficiency Nova Scotia Corporation (ENSC) on May 13, 2019, under Nova Scotia Power's regulatory proceeding. The form collects data for energy efficiency programs, referencing site-specific information and potential demand-side management initiatives.
NON-CONFIDENTIAL b) For the proposed budget of $41.9 million of Preferred Plan for 2020, please provide EOne's best estimate of the DSM spending levels by rate class for direct program expenditures including cost for program delivery that...
AI summary The document requests EfficiencyOne's 2020 DSM spending estimates by rate class and administrative costs, referencing prior submissions to the SBA and Synapse. EfficiencyOne directs to previous responses for 2019 and 2020-2022 plans.
Request IR-33: How does E1 explain the differences in first year impacts across both E1 and NSP's plans? Response IR-33: - Table 1 below presents the first-year impacts from the model outputs provided to NS Power from - Navigant and Effici...
AI summary The regulatory proceeding seeks an explanation from EfficiencyOne (EOne) regarding discrepancies in first-year impact estimates between their Preferred Plan and Alternate Scenario and Nova Scotia Power's (NSP) plan, referencing model outputs in Table 1.
E-23NSPI (IG) RIR-1 to RIR-10 - Redacted
9 passages
NON-CONFIDENTIAL 1 Request IR-5: 2 3 Please file the 2018 10-Year System Outlook or provide a link to it for use in this matter. 4 5 Response IR-5: 6 7 Please refer to Attachment 1. Date Filed: May 13, 2019 NSPI (IG) IR-5 Page 1 of 1
AI summary The document includes a request (IR-5) for the 2018 10-Year System Outlook and a response directing the reader to Attachment 1. Filed by NSPI on May 13, 2019, the exchange pertains to regulatory proceedings involving NSPI and the NSUARB.
15 3.2 Changes in Capacity 16 17 [Figure](#page-20-2) 4 provides the firm Supply and DSM capacity changes in accordance with the 18 assumption set developed for the 2017-2019 Base Cost of Fuel forecast and the 2018 19 Load Forecast. 6 5 En...
AI summary Section 3.2 discusses changes in firm supply and demand-side management (DSM) capacity based on 2017-2019 fuel cost forecasts and 2018 load forecasts. It references assumptions about wind projects (ERIS and NRIS) contributing 17% firm capacity, as detailed in Section 8.3.1.
11 3.3.2 Projections of Unit Utilization 12 13 NS Power prepares a 10 year forecast of projected unit utilization parameters annually in 14 in this report using the Plexos modeling tool and the current assumptions regarding fuel 15 and mar...
AI summary NS Power annually prepares a 10-year unit utilization forecast using the Plexos model, considering factors like fuel prices, load forecasts, and system constraints. Adjustments are made yearly as assumptions change, affecting utilization strategies and forecasts.
2020-2022 DSM IG IR-05 Attachment 1 Page 17 of 158 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 1 Figure 7 below provides the current forecasted unit utilization of NS Power's steam fleet. 2 The Company notes that the outcome of the carbon...
AI summary NS Power discusses the potential impact of carbon policy changes, particularly a Cap and Trade program, on the utilization forecast of its steam fleet. The company states that as policy outcomes clarify, the forecast model will be updated and results shared in future 10-Year System Outlook reports.
1 Figure 10: Forecasted Annual Investment (in 2018$) by Unit 3 Note: Figure does not include escalation as it is used for asset planning.
AI summary Figure 10 presents forecasted annual investment by unit in 2018 dollars, excluding escalation for asset planning purposes. The figure is part of a regulatory proceeding related to Nova Scotia's energy sector, though no specific claims or arguments are directly stated in the provided text.
6.2 Environmental Regulatory Requirements 3 4 5 6 The Nova Scotia Greenhouse Gas Emissions Regulations 22 specify emission caps for 2010 - 2030, as outlined in & lt;sup>19 Port Hawkesbury Paper LP (PHP) is approved to operate under the Loa...
AI summary The Nova Scotia Greenhouse Gas Emissions Regulations set emission caps from 2010-2030. Compliance forecasts include/exclude Port Hawkesbury Paper LP's load. Hydro generation reduction in 2020 is due to maintenance. NSR and Losses data from the 2018 NS Power forecast (M08670).
2020-2022 DSM IG IR-05 Attachment 1 Page 42 of 158 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 1 In previous 10 Year System Outlook reports, NS Power provided calculations for the 2 capacity value of wind using both the LOLE and Cumulative...
AI summary NS Power is transitioning from Cumulative Frequency to LOLE methodology for assessing wind generation capacity value, citing LOLE's industry standard status and robustness. The LOLE approach considers system-wide wind penetration impacts but has year-to-year variability. The International Energy Agency (IEA) Wind Task 25 Final Report is referenced for ELCC calculation requirements.
2020-2022 DSM IG IR-05 Attachment 1 Page 48 of 158 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 1 [Figure 23](#page-55-0) is a graphical representation of the assessment completed in [Figure 22](#page-54-0) above. 2 It provides a breakdown...
AI summary Figure 23 illustrates the forecasted system demand and planning reserve margin for 2020-2022, detailing how system capacity will meet demand. It builds on the analysis from Figure 22, focusing on firm capacity and peak demand relationships.
Appendix G Stability Results 2021LL Cases 2018 10 Year System Outlook Report Appendix B Page 83 of 84 2020-2022 DSM IG IR-05 Attachment 1 Page 157 of 158 REDACTED (CONFIDENTIAL INFORMATION REMOVED) 2017 NRIS Wind Study
AI summary Appendix G discusses stability results from the 2021LL cases, referencing the 2018 10 Year System Outlook Report and the 2017 NRIS Wind Study. Confidential information has been redacted, and the document highlights studies related to Nova Scotia's power system planning and renewable energy integration.
E-24NSPI (NSUARB) RIR-1 to RIR-24 - Redacted
5 passages
EMERGING POTENTIAL AND OPPORTUNITIES FOR THE SOLAR INDUSTRY TO ENGAGE IN NEW SERVICE AREAS 3 The solar industry is well positioned to meet future market demand for emerging technologies; namely battery storage and electric vehicles chargin...
AI summary The solar industry in Nova Scotia is poised to expand into battery storage and EV charging infrastructure, though current residential storage adoption is limited (15–35% by 2035). Alternative rate structures could boost demand and create 10–30 FTEs by 2030. EV home charger installations may generate 20–70 FTEs, offering new revenue streams and lead generation for solar businesses, based on NSPI's EV adoption forecasts.
The study used the following approach: - Use of Dunsky's Solar Adoption Model (SAM) to forecast the market potential for residential solar in Nova Scotia. - o Compile data on key market characteristics and inputs, including building stock,...
AI summary The study employs Dunsky's Solar Adoption Model (SAM) to forecast residential solar potential in Nova Scotia, using historical data calibration and scenario analysis. It also assesses job creation through supply chain studies and stakeholder engagement, with detailed methodology in Appendices A and B.
2020-2022 DSM NSUARB IR-11 Attachment 1 Page 16 of 40 REDACTED (CONFIDENTIAL INFORMATION REMOVED) Figure 4: Range of Projections for Residential Solar Deployment in Nova Scotia
AI summary The document includes Figure 4, which presents projections for residential solar deployment in Nova Scotia. Efficiency Nova Scotia (ENS) is highlighted as the entity providing these projections, with the Nova Scotia Utility and Regulatory Board (NSUARB) overseeing the proceeding. The figure illustrates a range of potential outcomes for solar adoption, reflecting key considerations in demand-side management (DSM) initiatives.
APPENDIX A: METHODOLOGY
AI summary The document is an appendix outlining methodology from a Nova Scotia regulatory proceeding. It references various acronyms and entities involved in energy regulation and management but does not provide detailed content beyond the heading.
DUNSKY'S SOLAR ADOPTION MODEL (SAM) Dunsky's Solar Adoption Model (SAM) was built in-house to address a growing need by our clients to understand the potential for solar deployment in their jurisdictions. The model is based on a methodolog...
AI summary Dunsky's Solar Adoption Model (SAM) uses NREL methodology and local data to forecast distributed solar PV demand across regions. It considers customer economics, technology barriers, and historical adoption data for calibration, supporting policy and market scenario analysis in jurisdictions like California, New York, and Ontario.
79334Letter from EOne enclosing VRF Program Review Report
7 passages
2. Geographical Coverage – Areas Where Natural Gas is Available Natural gas service is available to a building where: 1. A service line can be installed to the building from a natural gas main located in front of the property or from a nea...
AI summary Natural gas service availability criteria include installable service lines or economic feasibility determined by the Nova Scotia Utility And Review Board. EfficiencyOne coordinates with Heritage Gas to assess gas availability for MURBs, with Heritage Gas providing a 15-day response to developers.
tall cost, it is more expensive to operate than a central gas fired boiler system. Since the landlord is often responsible for providing heat, building owners tend to prefer the lower operating costs. Cooling is increasing in importance Tr...
AI summary The document discusses HVAC system preferences in Nova Scotia's residential market, noting that central gas systems are cheaper to operate than VRF systems. Cooling is gaining importance in higher-end markets, with developers prioritizing capital costs over long-term expenses. Ductless mini-split heat pumps are common in lower-tier markets, while high-end developments favor VRF or CGC Bulldog systems to preserve balcony space and reduce noise.
2 JURISDICTIONAL SCAN This chapter describes the methodology used for the jurisdictional scan of similar programs and the insights they provided. The objective of this part of the study is primarily to answer the following question: How is...
AI summary This chapter outlines the methodology for a jurisdictional scan of similar programs, aiming to determine how analogous issues are addressed in other relevant jurisdictions through comparative analysis and insights gathered from such studies.
2.1 – Methodology The jurisdictional scan focused on local energy efficiency programs and not on the local market context or recent market transformation. The following criteria were initially set by Dunsky, E1 and Heritage Gas to identify...
AI summary The jurisdictional scan focused on energy efficiency programs, identifying seven jurisdictions after broadening initial criteria. Three program types (prescriptive, new construction custom, and GHG-reduction-focused) were analyzed for VRF heat pump incentives. The limited program data suggests findings reflect common practices, not best practices.
3.1 – Methodology This section describes the methodology used to compare the five heating system's costs, including energy, maintenance and capital costs.
AI summary This section outlines the methodology for comparing five heating systems based on energy, maintenance, and capital costs. The analysis focuses on evaluating total costs across different system types to inform regulatory decisions in Nova Scotia.
3.1.2.3 – Standard Electric System The standard electric system is based on the current modeling specifications from NECB and ASHRAE 90.1, which is an air-source mini-split heat pumps with electric resistance auxiliary heating. Heat pump e...
AI summary The standard electric system uses NECB and ASHRAE 90.1 models, featuring air-source mini-split heat pumps with electric resistance heating. Efficiency standards from NECB-2015 apply to PTHP. The model uses professional judgment rather than all NECB specs, with HRVs for ventilation and gas-fired systems for hot water.
3.1.3.1 – Operating Costs The operating costs consist of energy and maintenance costs. Energy Costs: Monthly results from the building energy modeling are coupled with energy rates in order to get monthly energy costs for the five systems....
AI summary Operating costs include energy costs based on Heritage Gas and E1 rates, with specific pricing for natural gas and electricity. Maintenance costs are estimated using EIA data, a Pacific Northwest National Laboratory study on VRF heat pumps, and professional judgment.
80915EfficiencyOne Performance Alignment Study
6 passages
hese years are contained within a single Plan (the 2016-2018 Plan) and are thus the outcome of a single planning process. In 2016-2018 the NSUARB directed April 21, 2020 EfficiencyOne to reduce its planned costs by 14%, 15% and 18%, respec...
AI summary The document outlines a variance analysis conducted to identify factors that led to a historic overestimation of costs in 2015 and 2016-2018. The analysis focused on EfficiencyOne's planned costs and actual outcomes, revealing that overestimations were influenced by assumptions about future program components, participation rates, and market conditions.
DSM Resource Plan Development EfficiencyOne relies on external consultants to support the DSM planning process. Historically, this has included consultants such as: - Dunsky Energy Consulting to provide insight on energy efficiency perform...
AI summary EfficiencyOne uses external consultants (e.g., Navigant, VEIC) for DSM planning, relying on models to generate three-year resource plans. Input tables include incentive costs and energy savings, while outputs are adjusted iteratively. Plans are filed with NSUARB, allowing stakeholder input and public hearings before final approval.
Use of Modelling in 2016-2018 and 2020-2022 We identified that modelling was completed to inform the costs and energy savings of the 2016-2018 and 2020-2022 Plans. For the 2016-2018 Plan, we saw the input table that was used. For the 2020-
AI summary The document discusses the use of modelling to inform the costs and energy savings of the 2016-2018 and 2020-2022 Plans. Input tables were used for the 2016-2018 Plan, while the 2020-2022 Plan is referenced with an image.
measure level is a lengthy and costly process. Alternatively, EfficiencyOne identified it relied on historical information from 2013 on a program component level adjusting for future expectations for April 21, 2020 each of these programs a...
AI summary EfficiencyOne used historical data from 2013 adjusted for future expectations to estimate program costs, without conducting variance analysis on 2019 estimates. This approach may increase the risk of overestimation due to reliance on higher-level estimates rather than detailed measure-level data.
of EfficiencyOne to identify why and how the plan assumptions and the plan implementation experience was different. For both cost projections and energy saving estimates, we performed the following: - Assessed the assumptions used in estim...
AI summary The analysis evaluated cost projections and energy saving estimates by assessing assumptions, identifying variances, and conducting jurisdictional interviews. EfficiencyOne collaborated with KPMG to interview other jurisdictions using a standard guide, with participation voluntary and findings incorporated into the report without standalone deliverables.
2020-2022 Enabling Strategies – approach by EfficiencyOne Similar to the 2016-2018 and 2019 DSM Resource Plans, EfficiencyOne identified that the Enabling Strategies investment for the 2020-2022 DSM Resource Plan was informed by historical...
AI summary EfficiencyOne's 2020-2022 Enabling Strategies investment relied on historical spending rather than modeling. Increased investment linked to new initiatives like pilot programs, technology research, and demand response. Higher 2021-2022 costs are projected due to preparations for the 2023-2025 DSM Resource Plan.