IDAHO POWER COMPANY ENERGY EFFICIENCY POTENTIAL STUDY

Transcription

1 IDAHO POWER COMPANY ENERGY EFFICIENCY POTENTIAL STUDY REPORT FINAL Prepared for: Idaho Power Company April, 2017 Applied Energy Group, Inc. 500 Ygnacio Valley Road, Suite 250 Walnut Creek, CA AppliedEnergyGroup.com

2 This work was performed by Applied Energy Group, Inc. 500 Ygnacio Valley Blvd., Suite 250 Walnut Creek, CA Project Director: I. Rohmund Project Manager: B. Kester Project Team: F. Nguyen S. Yoshida ii

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4 EXECUTIVE SUMMARY OBJECTIVES Idaho Power performs an Energy Efficiency (EE) potential study to assess the future potential for savings through its programs and to identify refinements that will enhance savings. As part of this well-established process, Idaho Power (IPC) contracted with Applied Energy Group (AEG) to conduct an energy efficiency (EE) potential assessment to quantify the amount, the timing, the cost and the cost-effectiveness of electric energy efficiency resources available within the Idaho Power service territory. Key objectives for the study include: Provide credible and transparent estimation of the technical, economic, and achievable energy efficiency potential by year over the next 21 years within the Idaho Power service territory Assess potential energy savings associated with each potential area by energy efficiency measure and sector Provide an executable dynamic model that will support the potential assessment and allow for testing of sensitivity of all model inputs and assumptions Review and update market profiles by sector, segment, and end use Develop a final report including summary data tables and graphs reporting incremental and cumulative potential by year from 2017 through 2036 This study provided enhanced analysis compared to the previous study completed in 2015: The base-year for the analysis was brought forward from 2013 to For the commercial sector, results from the 2015 Commercial Building Stock Assessment (CBSA), including hospital and university data, were incorporated into the sector characterization. In addition, more was done through mapping of customers to building type with some customers moving from the commercial sector to the industrial sector and vice versa. For the industrial sector, the market segmentation was refined to line up with the segmentation used by IPC s load forecasting department. Measure data has been updated based on the recently finalized Seventh Power Plan (Seventh Plan) completed by the Northwest Power and Conservation Council (Council). This study also incorporated changes to the list of energy conservation measures, as a result of research by the Regional Technical Forum (RTF). In particular, Light Emitting Diode (LED) lamps continue to drop in price and provide a significant opportunity for savings. All measures were updated using the most recent data available as of September Updated lighting assumptions for LED technologies from the updated Department of Energy (DOE) lighting report developed by Navigant Consulting 1. The study incorporates updated forecasting assumptions that line up with the most recent IPC load forecast. Measure-adoption rates were developed using the Seventh Plan s ramp rates as a starting point and adjusted to reflect IPC program results in recent years. The prior potential study utilized Sixth Power Plan ramp rates. 1 ii

5 In addition to analyzing annual energy savings, the study also estimated the opportunity for reduction of summer and winter peak demand. This involved a full characterization by sector, segment and end use of summer and winter peak demand in the base year. DEFINITIONS OF POTENTIAL In this study, the energy efficiency potential estimates represent gross savings developed into three types of potential: technical potential, economic potential, and achievable potential. Technical and economic potential are both theoretical limits to efficiency savings. Achievable potential embodies a set of assumptions about the decisions consumers make regarding the efficiency of the equipment they purchase, the maintenance activities they undertake, the controls they use for energy-consuming equipment, and the elements of building construction. These levels are described below. Technical Potential is defined as the theoretical upper limit of energy efficiency potential. It assumes that customers adopt all feasible measures regardless of their cost. At the time of existing equipment failure, customers replace their equipment with the most efficient option available. In new construction, customers and developers also choose the most efficient equipment options. Technical potential also assumes the adoption of every other available measure, where applicable. For example, it includes installation of high-efficiency windows in all new construction opportunities and air conditioner maintenance in all existing buildings with central and room air conditioning. These retrofit measures are phased in over a number of years to align with the stock turnover of related equipment units, rather than modeled as immediately available all at once. Economic Potential represents the adoption of all cost-effective energy efficiency measures. In this analysis, the cost-effectiveness is measured by the total resource cost (TRC) test, which compares lifetime energy and capacity benefits to the costs of delivering the measure through a utility program, with incentives not included, since they are a transfer payment. If the benefits outweigh the costs (that is, if the TRC ratio is equal to or greater than 1.0), a given measure is included in the economic potential. Customers are then assumed to purchase the most cost-effective option applicable to them at any decision juncture. Achievable Potential takes into account market maturity, customer preferences for energy-efficient technologies, and expected program participation. Achievable potential establishes a realistic target for the energy efficiency savings that a utility can hope to achieve through its programs. It is determined by applying a series of annual market adoption factors (or ramp rates ) to the economic potential for each energy efficiency measure. These factors represent the ramp rates at which technologies will penetrate the market. To develop these factors, the project team reviewed Idaho Power s past EE achievements and program history over the last five years, as well as the Northwest Power and Conservation Council (NWPCC) ramp rates used in the Seventh Power Plan. Details regarding the ramp rates appear in Appendix B. OVERVIEW OF ANALYSIS APPROACH AEG utilizes its custom market simulation tool, Load Management, Analysis, and Planning (LoadMAP ), to forecast energy usage and savings potential. Originally developed in 2007, it has been updated annually to accommodate the expanded scope of potential studies and the individual needs of our clients. The model can be used to develop a baseline forecast and alternative forecasts characterizing technical potential, economic potential, and achievable potential. LoadMAP is built in the Microsoft Excel spreadsheet framework so it is accessible and transparent with the following features: It embodies the basic principles of rigorous end-use models (such as EPRI's REEPS and COMMEND) but in a more simplified, accessible form. It includes stock-accounting algorithms that treat older, less efficient appliance/equipment stock separately from newer, more efficient equipment. Equipment is replaced according to the measure life defined by the user. The model isolates new construction from existing equipment and buildings. iii

6 It uses a simple logic for appliance and equipment decisions. Some models embody decision models based on efficiency choice algorithms or diffusion models. While these have some merit, the model parameters are difficult to estimate or observe and sometimes produce anomalous results that require calibration or even overriding. The model natively handles codes and standards changes and technology advances. The model can accommodate various levels of segmentation. At the highest level, users can perform analysis at the sector level or they may drill down into individual segments and uses. To perform the potential analysis, AEG used an approach following the major steps listed below. These analysis steps are described in more detail throughout the remainder of this chapter. 1. Perform a market characterization to describe sector-level electricity use for the residential, commercial, industrial, and irrigation sectors for the base year, This included using IPC data and other secondary data sources such as the Energy Information Administration (EIA). 2. Develop a baseline projection of energy consumption and peak demand by sector, segment, and end use for 2015 through Define and characterize several hundred EE measures to be applied to all sectors, segments, and end uses. 4. Estimate technical, economic, and achievable potential at the measure level in terms of energy and peak demand impacts from EE measures for Separately estimate potential for Idaho Power s special-contract customers. Figure ES-1 LoadMAP Analysis Framework The results from these steps are summarized below, with details provided in the body of the report. MARKET CHARACTERIZATION Idaho Power, established in 1916, is an investor-owned electric utility that serves more than 490,000 customers within a 24,000-square-mile area in southern Idaho and eastern Oregon. To meet its customers iv

7 electricity demands, Idaho Power maintains a diverse generation portfolio which includes 17 hydroelectric projects. The company also actively seeks cost-effective ways to encourage wise use of electricity by providing energy efficiency programs for all customers. Total electricity use for the residential, commercial, industrial and irrigation sectors for Idaho Power in 2015 was 13,324 GWh. Special-contract customers are excluded from this total because their potential was estimated individually, rather than through the LoadMAP analysis. Special contracts account for 842 GWh. As shown in Figure ES-2, the residential sector accounts for more than one-third (37%) of annual energy use, followed by commercial with 27%. The remaining use is split roughly evenly between the industrial and irrigation sectors. Electric Use by Sector, 2015 Irrigation, 15% Industrial, 21% Residential, 37% Commercial, 27% Figure ES-2 Sector-Level Electricity Use in Base Year 2015 In terms of summer peak demand, the total system peak in 2015 was 3,402 MW. The residential sector contributes the most to the summer peak with 38%. This is due to the saturation of air conditioning equipment. The winter peak in 2015 was 2,241 MW, with the residential sector contributing almost half of the impact at peak. Table ES-1 Idaho Power Sector Control Totals, 2015 Sector Annual Electricity Use (GWh) % of Annual Use Summer Peak Demand (MW) % of Summer Peak Winter Peak Demand (MW) % of Winter Peak Residential 4,939 37% 1,297 38% 1,081 48% Commercial 3,515 26% % % Industrial 2,824 21% % % Irrigation 2,046 15% % 177 8% Total 13, % 3, % 2, % Figure ES-3 shows the distribution of annual electricity use by end use for all customers. Two main electricity end uses appliances and space heating account for 41% of total electricity use. Appliances include refrigerators, freezers, stoves, clothes washers, clothes dryers, dishwashers, and microwaves. The remainder of the energy falls into the water heating, lighting, cooling, electronics, and the miscellaneous category which is comprised of furnace fans, pool pumps, and other plug loads (all other usage not covered elsewhere such as hair dryers, power tools, coffee makers, etc.). Figure ES-4 shows the intensity by end use for each residential segment. v

8 Residential Electricity Use by End Use, 2015 Miscellaneous 8% Electronics 12% Cooling 12% Appliances 22% Heating 19% Exterior Lighting 2% Interior Lighting 12% Water Heating 13% Figure ES-3 Residential Electricity Use and Summer Peak Demand by End Use, 2015 Electric Intensity by End Use and Segment, 2015 Single Family Multifamily Manufactured Home Average Cooling Heating Water Heating Interior Lighting Exterior Lighting Appliances Electronics Miscellaneous - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 kwh per HH Figure ES-4 Residential Intensity by End Use and Segment (Annual kwh/hh, 2015) Figure ES-5 shows the distribution of annual electricity consumption and summer peak demand by end use across all commercial buildings. Electric usage is dominated by lighting and cooling, which comprise 40% of annual electricity usage. Summer peak demand is dominated by cooling. Figure ES-6 presents the electricity usage in GWh by end use and segment. Small offices, retail, and miscellaneous buildings use the most electricity in IPC s service territory. Cooling and lighting are the major uses across all segments. Although present in all facilities, office equipment is concentrated more in the small and large offices. vi

9 Food Preparation 4% Refrigeration 8% Exterior Lighting 10% Commercial Electricity Use by End Use, 2015 Interior Lighting 23% Office Equipment 7% Miscellaneous 11% Cooling 16% Heating 10% Commercial Peak Demand by End Use, 2015 Food Preparation 3% Refrigerati on 5% Exterior Lighting 1% Office Equipment 4% Interior Lighting 18% Miscellaneous 9% Cooling 55% Water Heating 2% Ventilation 9% Water Heating 1% Ventilation 4% Figure ES-5 Commercial Sector Electricity Consumption and Summer Peak Demand by End Use, 2015 Electric Intensity by End Use and Segment, 2015 Small Office Large Office Restaurant Retail Grocery College School Hospital Lodging Warehouse Miscellaneous kwh per Employee Miscellaneous Office Equipment Food Preparation Refrigeration Exterior Lighting Interior Lighting Water Heating Ventilation Heating Cooling Figure ES-6 Commercial Electricity Usage by End Use Segment (GWh, 2015) Figure ES-7 shows the distribution of annual electricity consumption and summer peak demand by end use for all industrial customers, not including the special contracts. Motors are the largest overall end use for the industrial sector, accounting for 48% of energy use. Note that this end use includes a wide range of industrial equipment, such as air compressors and refrigeration compressors, pumps, conveyor motors, and fans. The process end use accounts for 27% of annual energy use, which includes heating, cooling, refrigeration, and electro-chemical processes. Lighting is the next highest, followed by space heating, miscellaneous, cooling and ventilation. vii

10 Industrial Electricity Use by End Use, 2015 Industrial Peak Demand by End Use, 2015 Miscellaneous 5% Miscellaneous 4% Process 27% Motors 48% Cooling 2% Heating 7% Ventilation 2% Interior Lighting 6% Exterior Lighting 3% Process 23% Motors 43% Cooling 23% Ventilation 1% Interior Lighting 6% Figure ES-7 Industrial Sector Electricity Consumption by End Use (2015), All Industries The total electricity used in 2015 by Idaho Power s irrigation customers was 2,046 GWh, while summer peak demand was 888 MW and winter peak demand was 177 MW. Idaho Power billing data were used to develop estimates of energy intensity (annual kwh/service point). For the irrigation sector, all of the energy use is for the motors end use. BASELINE PROJECTION Prior to developing estimates of energy-efficiency potential, a baseline end-use projection was developed to quantify what the consumption would likely be in the future in the absence of any efficiency programs. The savings from past programs are embedded in the forecast, but the baseline projection assumes that those past programs cease to exist in the future. Possible savings from future programs are captured by the potential estimates. The baseline projection incorporates assumptions about: Customer population and economic growth Appliance/equipment standards and building codes already mandated Forecasts of future electricity prices and other drivers of consumption Trends in fuel shares and appliance saturations and assumptions about miscellaneous electricity growth Table ES-2 and Figure ES-8 provide a summary of the baseline projection by sector and for Idaho Power as a whole. Electricity use across all sectors is expected to increase by 36% between the base year 2015 and 2036, for an average annual growth rate of 1.5%. The industrial sector has the highest growth, with a 50% increase (1.9% annual growth rate) over the projection horizon. The residential sector has the second highest growth rate at 49% between 2015 and The commercial sector shows moderate growth of 27% over the projection period, or an average annual growth of 1.1%. Growth is particularly slow around 2020, due to the phase in of the next EISA lighting standards and other new equipment standards. The irrigation sector continues to remain flat with less than 1% growth over the forecast period. viii

11 Table ES-2 Baseline Projection Summary (GWh) Sector % Change ('15-'36) Residential 4,939 5,287 5,685 6,093 6,669 7, % Commercial 3,515 3,635 3,756 3,943 4,196 4, % Industrial 2,824 3,092 3,362 3,634 3,917 4, % Irrigation 2,046 1,938 1,969 1,999 2,032 2, % Total 13,324 13,953 14,772 15,669 16,814 18, % Electric Baseline Usage GWh 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Irrigation Industrial Commercial Residential Figure ES-8 Baseline Projection Summary (GWh) Figure ES-9 through Figure ES-12 present the baseline end-use projections for the residential, commercial, industrial, and irrigation sectors respectively. Residential Electricity Projection by End Use 8,000 7,000 Cooling 6,000 Heating 5,000 Water Heating GWh 4,000 3,000 2,000 1, Interior Lighting Exterior Lighting Appliances Electronics Miscellaneous Figure ES-9 Residential Baseline Projection by End Use (GWh) ix

12 GWh 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Commercial Electricity Forecast by End Use Cooling Heating Ventilation Water Heating Interior Lighting Exterior Lighting Food Preparation Refrigeration Office Equipment Miscellaneous Figure ES-10 Commercial Baseline Projection by End Use (GWh) Industrial Electricity Forecast by End Use GWh 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Cooling Heating Ventilation Interior Lighting Exterior Lighting Motors Process Miscellaneous Figure ES-11 Industrial Baseline Projection by End Use (GWh) x

13 Irrigation Electricity Projection Idaho Power Company Energy Efficiency Potential Study 2,500 2,000 GWh 1,500 1,000 Motors Figure ES-12 Irrigation Baseline Projection (GWh) ENERGY EFFICIENCY MEASURES The first step of the energy efficiency measure analysis was to identify the list of all relevant EE measures that should be considered for the potential assessment. Sources for selecting and characterizing measures included Idaho Power s programs, the Northwest Power and Conservation Council s Regional Technical Forum (RTF) deemed measure databases, and AEG s measure databases from previous studies and program work. The measures are categorized into two types according to the LoadMAP taxonomy: equipment measures and non-equipment measures: Equipment measures are efficient energy-consuming pieces of equipment that save energy by providing the same service with a lower energy requirement than a standard unit. An example is an ENERGY STAR refrigerator that replaces a standard efficiency refrigerator. For equipment measures, many efficiency levels may be available for a given technology, ranging from the baseline unit (often determined by code or standard) up to the most efficient product commercially available. For instance, in the case of central air conditioners, this list begins with the current federal standard, Seasonal Energy Efficiency Ratio (SEER) 13 unit, and spans a broad spectrum up to a maximum efficiency of a SEER 24 unit. Non-equipment measures save energy by reducing the need for delivered energy, but do not involve replacement or purchase of major end-use equipment (such as a refrigerator or air conditioner). An example would be a programmable thermostat that is pre-set to run heating and cooling systems only when people are home. Non-equipment measures can apply to more than one end use. For instance, addition of wall insulation will affect the energy use of both space heating and cooling. Non-equipment measures typically fall into one of the following categories: o o o o o o Building shell (windows, insulation, roofing material) Equipment controls (thermostat, energy management system) Equipment maintenance (cleaning filters, changing set points) Whole-building design (building orientation, passive solar lighting) Lighting retrofits (included as a non-equipment measure because retrofits are performed prior to the equipment s normal end of life) Displacement measures (ceiling fan to reduce use of central air conditioners) xi

14 o Idaho Power Company Energy Efficiency Potential Study Commissioning and retro commissioning (initial or ongoing monitoring of building energy systems to optimize energy use) Table ES-3 summarizes the number of equipment and non-equipment measures evaluated for each sector. Table ES-3 Number of Measures Evaluated Sector Total Measures Measure Permutations w/ 2 Vintages Measure Permutations w/ Segments Residential Commercial ,288 Industrial ,188 Irrigation Total Measures Evaluated ,068 SUMMARY OF ANNUAL ENERGY SAVINGS Table ES-4 and Figure ES-13 summarize the EE savings in terms of annual energy use for all measures for three levels of potential relative to the baseline projection. Figure ES-14 displays the EE projections. Technical potential reflects the adoption of all EE measures regardless of cost-effectiveness. Firstyear savings are 422 GWh, or 3% of the baseline projection. Cumulative technical savings in 2036 are 4,805 GWh, or 26.5% of the baseline. Economic potential reflects the savings when the most efficient cost-effective measures, using the total resource cost test, are taken by all customers. The first-year savings in 2017 are 259 GWh, or 1.9% of the baseline projection. By 2036, cumulative economic savings reach 2,825 GWh, or 15.6% of the baseline projection. Achievable potential represents savings that are possible when considering the availability, knowledge and acceptance of the measure. Achievable potential is 104 GWh savings in the first year, or 0.7% of the baseline and by 2036 cumulative achievable savings reach 2,226 GWh, or 12.3% of the baseline projection. This results in average annual savings of 0.6% of the baseline each year. Achievable potential reflects 79% of economic potential by the end of the forecast horizon. Table ES-4 Summuary of EE Potential (Annual Energy, GWh) Baseline projection (GWh) 13,953 14,772 15,669 16,814 18,118 Cumulative Savings (GWh) Achievable Potential ,209 1,702 2,226 Economic Potential 259 1,087 1,758 2,300 2,825 Technical Potential 422 1,812 3,032 3,977 4,805 Cumulative Savings as a % of Baseline Achievable Potential 0.7% 4.1% 7.7% 10.1% 12.3% Economic Potential 1.9% 7.4% 11.2% 13.7% 15.6% Technical Potential 3.0% 12.3% 19.3% 23.7% 26.5% xii

15 Overall Cumulative Savings (% of Baseline) Idaho Power Company Energy Efficiency Potential Study 30% 25% 20% 15% 10% 5% 0% Achievable Potential Economic Potential Technical Potential Figure ES-13 Summary of EE Potential as % of Baseline Projection (Annual Energy) GWh 20,000 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 - Overall Electric Potential Projections Baseline Forecast Achievable Potential Economic Potential Technical Potential Figure ES-14 Baseline Projection and EE Forecast Summary (Annual Energy, GWh) SUMMARY OF SUMMER PEAK DEMAND SAVINGS Table ES-5 and Figure ES-15 summarize the summer peak demand savings from all EE measures for three levels of potential relative to the baseline projection. Figure ES-16 displays the EE forecasts of summer peak demand. Technical potential for summer peak demand savings is 87 MW in 2017, or 2.5% of the baseline projection. This increases to 1,154 MW by 2036, or 24.7% of the summer peak demand baseline projection. Economic potential is estimated at 46 MW or a 1.3% reduction in the 2017 summer peak demand baseline projection. In 2036, savings are 639 MW or 13.7% of the summer peak baseline projection. Achievable potential is 20 MW by 2017, or 0.6% of the baseline projection. By 2036, cumulative savings reach 488 MW, or 10.4% of the baseline projection. xiii

16 Table ES-5 Summuary of EE Potential (Summer Peak, MW) Baseline projection (MW) 3,532 3,776 4,046 4,345 4,680 Cumulative Savings (MW) Achievable Potential Economic Potential Technical Potential ,154 Cumulative Savings as a % of Baseline Achievable Potential 0.6% 3.0% 5.8% 8.1% 10.4% Economic Potential 1.3% 5.7% 9.4% 11.7% 13.7% Technical Potential 2.5% 10.4% 17.1% 21.5% 24.7% 30% 25% 20% 15% 10% 5% Overall Cumulative Peak Savings (% of Baseline) 0% Achievable Potential Economic Potential Technical Potential Figure ES-15 Summary of EE Potential as % of Summer Peak Baseline Projection MW 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Overall Electric Potential Forecasts Baseline Forecast Achievable Potential Economic Potential Technical Potential Figure ES-16 Summer Peak Baseline Projection and EE Forecast Summary xiv

17 SUMMARY OF ENERGY EFFICIENCY BY SECTOR Table ES-6, Figure ES-17, and Figure ES-18 summarize the range of electric achievable potential summer peak savings by sector. The industrial sector contributes the most savings throughout the forecast, followed by the commercial sector. Table ES-6 Achievable EE Potential by Sector (Annual Use and Summer Peak) Cumulative Annual Energy Savings (GWh) Residential Commercial Industrial Irrigation Total ,209 1,702 2,226 Cumulative Summer Peak Demand Savings (MW) Residential Commercial Industrial Irrigation Total ,500 Cumulative Achievable Savings by Sector (GWh) 2,000 1,500 1, Residential Commercial Industrial Irrigation Figure ES-17 Achievable EE Potential by Sector (Annual Energy, GWh) xv

18 Cumulative Achievable Savings by Sector (MW) Idaho Power Company Energy Efficiency Potential Study Residential Commercial Industrial Irrigation Figure ES-18 Achievable EE Potential by Sector (Summer Peak Demand (MW) SPECIAL CONTRACT CUSTOMERS The special contract customers were not analyzed within LoadMAP, but instead, potential was assessed separately. Consideration for the analysis of achievable savings from special contract customers included EE measures and actions already implemented, general business plans, and planned future efficiency measures. Based on this analysis, potential savings from special contract customers is estimated at approximately 8,220 MWh annually. Table ES-7 Achievable EE Potential Total, Including Special Contracts (Annual Use) Cumulative Annual Energy Savings (GWh) Residential, Commercial, Industrial, Irrigation ,209 1,702 2,226 Special Contracts Grand Total ,291 1,825 2,390 RESIDENTIAL SECTOR POTENTIAL SAVINGS Table ES-8 shows the estimates of potential energy savings for select years for all three levels of potential. Achievable potential savings in 2021 are 127 GWh, which is 2.2% of the baseline. In the previous study, savings five years out were higher at 4.4% of the baseline projection. The biggest driver of the difference is the change in assumptions for interior lighting, which, based on the DOE lighting Report includes smaller steps in efficacy of LED lamps in 2020 than assumed in the previous study. xvi

19 Table ES-8 Residential EE Potential (Annual Energy, GWh) Baseline projection (GWh) 5,287 5,685 6,093 6,669 7,350 Cumulative Net Savings (GWh) Achievable Potential Economic Potential Technical Potential Cumulative Net Savings as a % of Baseline Achievable Potential 0.4% 2.2% 3.9% 6.0% 7.9% Economic Potential 0.5% 2.7% 4.6% 6.5% 8.2% Technical Potential 2.3% 7.2% 8.2% 10.0% 11.3% Table ES-9 focuses on the residential cumulative achievable potential in Lighting, primarily the conversion of both interior and exterior lamps to LED lamps, represents 88,649 MWh or 70% of savings. Cumulative savings from water heating measures contribute 14,908 MWh of savings in These measures include faucet aerators, low-flow showerheads, heat pump (COP 1.8) water heaters, temperature setbacks, and pipe insulation. Behavioral programs include the home energy reports that are sent to customers. It was assumed that only single-family customers receive the home energy reports and that the savings are across all end uses. Cumulative savings from the measure in 2021 reach 2,642 MWh. xvii

20 Table ES-9 Residential Top Measures in 2021 (Annual Energy, MWh) Rank Residential Measure 2021 Cumulative Energy Savings (MWh) % of Total 1 Interior Lighting - General Service Screw-In (LED) 49, % 2 Interior Lighting - Exempted Screw-In (LED) 31, % 3 Exterior Lighting - Screw-in (LED) 8, % 4 Water Heater - Faucet Aerators 5, % 5 Water Heater - Low-Flow Showerheads 3, % 6 Interior Lighting - General Service CFLs 3, % 7 Water Heating - Water Heater (<= 55 Gal) (EF 1.8) 3, % 8 Room AC - Removal of Second Unit 2, % 9 Behavioral Programs 2, % 10 Water Heater - Temperature Setback 2, % 11 Insulation - Ceiling Installation 1, % 12 Ducting - Repair and Sealing 1, % 13 School Kit 1, % 14 Insulation - Wall Cavity Installation 1, % 15 Insulation - Ducting 1, % 16 Water Heater - Pipe Insulation 1, % 17 HVAC - Air-Source Heat Pump % 18 ENERGY STAR Home Design % 19 Insulation - Radiant Barrier % 20 Pool Pump - Timer % Total 122, % COMMERCIAL SECTOR POTENTIAL SAVINGS Table ES-10 shows the estimates of potential energy savings for select years for all three levels of potential estimated in LoadMAP. In 2017, the first year of the projection, achievable potential is 20 GWh, or 0.6% of the baseline projection. By 2021, savings are 140 GWh, or 3.7% of the baseline projection. This level of savings is only slightly lower than the previous study, primarily due to the changes in the lighting assumptions. Table ES-10 Commercial EE Potential (Annual Energy, GWh) Baseline projection (GWh) 3,635 3,756 3,943 4,196 4,472 Cumulative Net Savings (GWh) Achievable Potential Economic Potential Technical Potential ,164 1,369 Cumulative Net Savings as a % of Baseline Achievable Potential 0.6% 3.7% 8.4% 12.1% 15.7% Economic Potential 1.6% 7.1% 12.6% 16.4% 19.8% Technical Potential 3.4% 14.2% 22.9% 27.8% 30.6% xviii

21 Table ES-11 focuses on the commercial cumulative achievable potential in Idaho Power Company Energy Efficiency Potential Study Like the residential sector, converting lighting to more efficient LED lamps dominates the savings in Replacing high bay fixtures, exterior lighting, and interior linear and screw-in lamps with LED lighting, represents 53,092 MWh or 38% of cumulative savings in Retrocommissioning and commissioning measures account for 15,176 MWh of savings in 2021, or 11% of total savings. Overall, refrigeration measures such as night covers for open display cases, refrigerator evaporator fan controls and door gasket replacements account for 6% of the total commercial savings in 2021, but they make a significant portion of savings when looking at the grocery segment. Table ES-11 Commercial Top Measures in 2021 (Annual Energy, MWh) Rank Commercial Measure 2021 Cumulative Energy Savings (MWh) % of Total 1 Interior Lighting - High-Bay Fixtures (LED) 13, % 2 Exterior Lighting - Area Lighting (LED) 12, % 3 Interior Lighting - Linear Lighting (LED) 10, % 4 Interior Lighting - Screw-in (LED) 9, % 5 Interior Fluorescent - Delamp and Install Reflectors 9, % 6 Retrocommissioning 9, % 7 Commissioning 5, % 8 RTU - Advanced Controls 5, % 9 Office Equipment - Desktop Computer 5, % 10 Windows - High Efficiency Glazing 4, % 11 Exterior Lighting - Screw-in (LED) 4, % 12 Strategic Energy Management 3, % 13 Advanced New Construction Designs 3, % 14 Chiller - Chilled Water Variable-Flow System 3, % 15 Cooling - Water-Cooled Chiller 3, % 16 Grocery - Open Display Case - Night Covers 3, % 17 Destratification Fans (HVLS) 2, % 18 Refrigeration - Evaporator Fan Controls 2, % 19 Refrigeration - Door Gasket Replacement 2, % 20 Exterior Lighting - Linear Lighting (LED) 1, % Total 118, % INDUSTRIAL SECTOR POTENTIAL SAVINGS Table ES-12 presents potential savings estimates at the measure level for the industrial sector, from the perspective of annual energy savings. Achievable savings in the first year, 2017, are 50 GWh, or 1.6% of the baseline projection. In 2021, savings reach 269 GWh, or 8.0% of the baseline projection. This is slightly higher than the potential estimates for five years out in the previous study (7.5% of baseline). This is due to the continued success that Idaho Power has had with its industrial customers. This trend is expected to continue throughout the forecast period. xix

22 Table ES-12 Industrial EE Potential (Annual Energy, GWh) Baseline projection (GWh) 3,092 3,362 3,634 3,917 4,231 Cumulative Net Savings (GWh) Achievable Potential Economic Potential Technical Potential Cumulative Net Savings as a % of Baseline Achievable Potential 1.6% 8.0% 13.9% 15.1% 15.8% Economic Potential 2.1% 9.8% 16.7% 18.0% 18.7% Technical Potential 2.8% 12.5% 20.4% 21.8% 22.7% Table ES-13 focuses on the industrial cumulative achievable potential in The top measure in the industrial sector is variable speed drives on fan systems. Pumping system equipment upgrades and system optimization provide about 18% of cumulative savings in In general, industrial sector opportunities are unique to the specific customer, more so than the commercial or residential sectors. xx

23 Table ES-13 Industrial Top Measures in 2021 (Annual Energy, MWh) Rank Industrial Measure 2021 Cumulative Energy Savings (MWh) % of Total 1 Fan System - Variable Speed Drive 29, % 2 Pumping System - Equipment Upgrade 28, % 3 Pumping System - System Optimization 19, % 4 Fan System - Flow Optimization 19, % 5 Pumping System - Variable Speed Drive 18, % 6 Fan System - Equipment Upgrade 17, % 7 Compressed Air - Equipment Upgrade 16, % 8 Dairy - Milk Precoolers 15, % 9 Compressed Air - Leak Management Program 12, % 10 Refrigeration - System Optimization 9, % 11 Interior Lighting - High-Bay Fixtures 8, % 12 Destratification Fans (HVLS) 8, % 13 Motors - Synchronous Belts 7, % 14 Retrocommissioning 6, % 15 Refrigeration - Floating Head Pressure 6, % 16 Exterior Lighting - Area Lighting 5, % 17 Interior Lighting - Embedded Fixture Controls 5, % 18 Compressed Air - System Controls 5, % 19 Switch from Belt Drive to Direct Drive 5, % 20 Transformer - High Efficiency 4, % Total 250, % IRRIGATION SECTOR POTENTIAL SAVINGS Table ES-14 shows the estimates of potential energy savings for select years at the measure level for the irrigation sector from the perspective of annual energy savings. In 2017, the first year of the projection, achievable potential is 14 GWh, or 0.7% of the baseline projection. By 2021, cumulative savings are 68 GWh, or 3.4% of the baseline projection. This level of savings is lower than the previous study, which had 4.5% savings from the baseline five years out. This is primarily due to the changes in the measure assumptions for scientific irrigation practices, which no longer passes the economic screen. xxi

24 Table ES-14 Irrigation EE Potential (Annual Energy, GWh) Baseline projection (GWh) 1,938 1,969 1,999 2,032 2,065 Cumulative Net Savings (GWh) Achievable Potential Economic Potential Technical Potential Cumulative Net Savings as a % of Baseline Achievable Potential 0.7% 3.4% 6.8% 10.0% 13.2% Economic Potential 0.8% 4.0% 7.9% 11.7% 15.3% Technical Potential 0.9% 4.6% 9.1% 13.4% 17.4% Table ES-15 focuses on the irrigation cumulative achievable potential in The top measure in the irrigation sector is pump equipment upgrades which accounts for almost 36% of total savings in Low-energy spray applications could potentially contribute over 12 GWh of cumulative savings by Variable frequency drives provide another 13.5% of cumulative savings in These top three measures contribute two-thirds of the opportunities in the irrigation sector. Table ES-15 Irrigation Top Measures in 2021 (Annual Energy, MWh) Rank Irrigation Measure 2021 Cumulative Energy Savings (MWh) % of Total 1 Pump Equipment Upgrade 24, % 2 Center Pivot - Low Energy Spray Application 12, % 3 Motors - Variable Frequency Drive 9, % 4 Center Pivot/Linear - New Sprinkler Package 7, % 5 Low Pressure Regulators 6, % 6 Wheel/Hand - Drain Replacement 1, % 7 Wheel/Hand - Gasket Replacement 1, % 8 Green Motor Rewind 1, % 9 Wheel/Hand - Nozzle Replacement 1, % 10 Wheel/Hand - Pipe Maintenance % 11 Wheel Line - Leveler Maintenance % 12 Center Pivot/Linear - New Goosenecks % 13 Wheel/Hand - Flow Control Nozzle Replacement % 14 Center Pivot/Linear - Boot Gasket Replacement % Total 67, % xxii

25 REPORT ORGANIZATION The details on how the potential estimates were developed and the results by sector are included in more detail through the rest of this report. The body of the report is organized as follows: 1. Introduction 2. Analysis Approach and Data Development 3. Market Characterization and Market Profiles 4. Baseline Projection 5. Energy Efficiency Potential xxiii

26 CONTENTS 1 I NTRODUCTION Abbreviations and Acronyms A NALYSIS A PPROACH AND D ATA D EVELOPMENT Overview of Analysis Approach LoadMAP Model Definitions of Potential Market Characterization Baseline Projection Energy Efficiency Measure Analysis Energy Efficiency Potential Data Development Data Sources Data Application M ARKET C HARACTERIZATION AND M ARKET P ROFILES Energy Use Summary Residential Sector Commercial Sector Industrial Sector Irrigation Sector B ASELINE P ROJECTION Residential Sector Annual Use Residential Summer Peak Projection Commercial Sector Baseline Projection Annual Use Commercial Summer Peak Demand Projection Industrial Sector Baseline Projection Annual Use Industrial Summer Peak Demand Projection Irrigation Sector Baseline Projection Annual Use Irrigation Summer Peak Demand Projection Summary of Baseline Projections Across Sectors Annual Use Summer Peak Demand Projection E NERGY E FFICIENCY P OTENTIAL Overall Summary of Energy Efficiency Potential Summary of Annual Energy Savings Summary of Summer Peak Demand Savings Summary of Energy Efficiency by Sector Residential EE Potential Commercial EE Potential Industrial EE Potential xxiv

27 Irrigation EE Potential xxv

28 LIST OF FIGURES Figure 2-1 LoadMAP Analysis Framework Figure 2-2 Approach for Energy-Efficiency Measure Assessment Figure 3-1 Sector-Level Electricity Use in Base Year Figure 3-2 Residential Electricity Use and Summer Peak Demand by End Use, Figure 3-3 Residential Intensity by End Use and Segment (Annual kwh/hh, 2015) Figure 3-4 Commercial Sector Electricity Consumption and Summer Peak Demand by End Use, Figure 3-5 Commercial Electricity Usage by End Use Segment (GWh, 2015) Figure 3-6 Industrial Sector Electricity Consumption by End Use (2015), All Industries Figure 4-1 Residential Baseline Projection by End Use (GWh) Figure 4-2 Residential Baseline Projection by End Use Annual Use per Household Figure 4-3 Residential Summer Peak Baseline Projection by End Use (MW) Figure 4-4 Commercial Baseline Projection by End Use (GWh) Figure 4-5 Commercial Summer Peak Baseline Projection by End Use (MW) Figure 4-6 Industrial Baseline Projection by End Use (GWh) Figure 4-7 Industrial Summer Peak Baseline Projection by End Use (MW) Figure 4-8 Irrigation Baseline Projection (GWh) Figure 4-9 Irrigation Summer Peak Baseline Projection (MW) Figure 4-10 Baseline Projection Summary (GWh) Figure 4-11 Summer Peak Baseline Projection Summary (MW) Figure 5-1 Summary of EE Potential as % of Baseline Projection (Annual Energy) Figure 5-2 Baseline Projection and EE Forecast Summary (Annual Energy, GWh) Figure 5-3 Summary of EE Potential as % of Summer Peak Baseline Projection Figure 5-4 Summer Peak Baseline Projection and EE Forecast Summary Figure 5-5 Achievable EE Potential by Sector (Annual Energy, GWh) Figure 5-6 Achievable EE Potential by Sector (Summer Peak Demand (MW) Figure 5-7 Residential EE Savings as a % of the Baseline Projection (Annual Energy) Figure 5-8 Residential EE Savings as a % of the Summer Peak Baseline Projection Figure 5-9 Residential Achievable Savings Forecast (Annual Energy, GWh) Figure 5-10 Residential Achievable Savings Forecast (Summer Peak, MW) Figure 5-11 Commercial EE Savings as a % of the Baseline Projection (Annual Energy) Figure 5-12 Commercial EE Savings as a % of the Summer Peak Baseline Projection Figure 5-13 Commercial Achievable Savings Forecast (Annual Energy, GWh) Figure 5-14 Commercial Achievable Savings Forecast (Summer Peak, MW) Figure 5-15 Industrial EE Savings as a % of the Baseline Projection (Annual Energy) Figure 5-16 Industrial EE Savings as a % of the Summer Peak Baseline Projection Figure 5-17 Industrial Achievable Savings Forecast (Annual Energy, GWh) Figure 5-18 Industrial Achievable Savings Forecast (Summer Peak, MW) Figure 5-19 Irrigation EE Savings as a % of the Baseline Projection (Annual Energy) Figure 5-20 Irrigation EE Savings as a % of the Summer Peak Baseline Projection xxvi

29 LIST OF TABLES Table 1-1 Explanation of Abbreviations and Acronyms Table 2-1 Overview of Idaho Power Analysis Segmentation Scheme Table 2-2 Example Equipment Measures for Central AC Single Family Home Table 2-3 Example Non-Equipment Measures Single Family Home, Existing Table 2-4 Number of Measures Evaluated Table 2-5 Data Applied for the Market Profiles Table 2-6 Data Needs for the Baseline Projection and Potentials Estimation in LoadMAP Table 2-7 Residential Electric Equipment Standards Table 2-8 Commercial Electric Equipment Standards Table 2-9 Industrial Electric Equipment Standards Table 2-10 Data Needs for the Measure Characteristics in LoadMAP Table 3-1 Idaho Power Sector Control Totals, Table 3-2 Residential Sector Control Totals, Table 3-3 Average Market Profile for the Residential Sector, Table 3-4 Commercial Sector Control Totals, Table 3-5 Average Electric Market Profile for the Commercial Sector, Table 3-6 Industrial Sector Control Totals, Table 3-7 Average Electric Market Profile for the Industrial Sector, Table 3-8 Average Electric Market Profile for the Irrigation Sector, Table 4-1 Residential Baseline Projection by End Use (GWh) Table 4-2 Residential Baseline Projection by End Use and Technology (GWh) Table 4-3 Residential Summer Peak Baseline Projection by End Use (MW) Table 4-4 Commercial Baseline Projection by End Use (GWh) Table 4-5 Commercial Baseline Projection by End Use and Technology (GWh) Table 4-6 Commercial Summer Peak Baseline Projection by End Use (MW) Table 4-7 Industrial Baseline Projection by End Use (GWh) Table 4-8 Industrial Summer Peak Baseline Projection by End Use (MW) Table 4-9 Irrigation Baseline Projection (GWh) Table 4-10 Irrigation Summer Peak Baseline Projection (MW) Table 4-11 Baseline Projection Summary (GWh) Table 4-12 Summer Peak Baseline Projection Summary (MW) Table 5-1 Summuary of EE Potential (Annual Energy, GWh) Table 5-2 Summuary of EE Potential (Summer Peak, MW) Table 5-3 Achievable EE Potential by Sector (Annual Use and Summer Peak) Table 5-4 Residential EE Potential (Annual Energy, GWh) Table 5-5 Residential EE Potential (Summer Peak Demand, MW) Table 5-6 Residential Top Measures in 2021 (Annual Energy, MWh) Table 5-7 Residential Top Measures in 2021 (Summer Peak Demand, MW) Table 5-8 Commercial EE Potential (Annual Energy, GWh) Table 5-9 Commercial EE Potential (Summer Peak Demand, MW) Applied Energy Group xxvii

30 Table 5-10 Commercial Top Measures in 2021 (Annual Energy, MWh) Table 5-11 Commercial Top Measures in 2021 (Summer Peak Demand, kw) Table 5-12 Industrial EE Potential (Annual Energy, GWh) Table 5-13 Industrial EE Potential (Summer Peak Demand, MW) Table 5-14 Industrial Top Measures in 2021 (Annual Energy, GWh) Table 5-15 Industrial Top Measures in 2021 (Summer Peak Demand, MW) Table 5-16 Irrigation EE Potential (Annual Energy, GWh) Table 5-17 Irrigation EE Potential (Summer Peak Demand, MW) Table 5-18 Irrigation Top Measures in 2021 (Annual Energy, MWh) Table 5-19 Irrigation Top Measures in 2021 (Summer Peak Demand, MW) Applied Energy Group xxviii

31 1 INTRODUCTION Idaho Power (IPC) prepares an Annual Demand Side Management (DSM) report that describes its programs and achievements. Periodically, Idaho Power performs an energy efficiency (EE) potential study to assess the future potential for savings through its programs and to identify refinements that will enhance savings. As part of this well-established process, Idaho Power contracted with Applied Energy Group (AEG) to update the energy efficiency potential assessment completed in 2015, to quantify the amount, the timing, and the cost of electric energy efficiency resources available within the Idaho Power service territory. Key objectives for the study include: Provide credible and transparent estimation of the technical, economic, and achievable energy efficiency potential by year over the next 20 years, within the Idaho Power service territory Provide an executable dynamic model that will support the potential assessment and allow for testing of sensitivity of all model inputs and assumptions Develop a final report including summary data tables and graphs reporting incremental and cumulative potential by year from 2016 through 2035 This study provided enhanced analysis compared to the previous study completed in 2015: The base-year for the analysis was brought forward from 2013 to For the commercial sector, results from the 2015 Commercial Building Stock Assessment (CBSA), including hospital and university data, were incorporated into the sector characterization. In addition, a more through mapping of customers to building type was done which caused some customers to move from the commercial sector to the industrial sector and vice versa. For the industrial sector, market segmentation was refined to line up with the segmentation used by IPC s load forecasting department. Measure data has been updated based on the recently finalized Seventh Power Plan (Seventh Plan) completed by the Northwest Power and Conservation Council (Council). This study also incorporated changes to the list of energy conservation measures, as a result of research by the Regional Technical Forum (RTF). In particular, LED lamps continue to drop in price and provide a significant opportunity for savings. All measures were updated using the most recent data available as of September Updated lighting assumptions for LED technologies from the updated DOE lighting report developed by Navigant Consulting 2. The study incorporates updated forecasting assumptions that line up with the most recent IPC load forecast. Measure-adoption rates were developed using the Seventh Plan s ramp rates as a starting point and adjusted to reflect IPC program results in recent years. The prior potential study utilized Sixth Power Plan ramp rates instead. 2 Applied Energy Group 1-1

32 In addition to analyzing annual energy savings, the study also estimated the opportunity for reduction of summer and winter peak demand. This involved a full characterization by sector, segment and end use of summer and winter peak demand in the base year. ABBREVIATIONS AND ACRONYMS Throughout the report, several abbreviations and acronyms are used. Table 1-1 shows the abbreviation or acronym, along with an explanation. Table 1-1 Explanation of Abbreviations and Acronyms Acronym ACS AEO B/C Ratio BEST C&I CAC CFL CHP C&I DSM EE EIA EUL EUI HH HVAC kwh LED LoadMAP MW O&M RTU TRC UEC WH Explanation American Community Survey Annual Energy Outlook forecast developed by EIA Benefit to Cost Ratio AEG s Building Energy Simulation Tool Commercial and Industrial Central Air Conditioning Compact fluorescent lamp Combined heat and power Commercial and Industrial Demand Side Management Energy Efficiency Energy Information Administration Estimated Useful Life Energy Usage Intensity Household Heating Ventilation and Air Conditioning Kilowatt hour Light emitting diode lamp AEG s Load Management Analysis and Planning TM tool Megawatt Operations and Maintenance Roof top unit Total Resource Cost test Unit Energy Consumption Water heater Applied Energy Group 1-2

33 2 ANALYSIS APPROACH AND DATA DEVELOPMENT This section describes the analysis approach taken for the study and the data sources used to develop the potential estimates. OVERVIEW OF ANALYSIS APPROACH To perform the potential analysis, AEG used the following steps listed below. These analysis steps are defined in more detail throughout the remainder of this chapter. 1. Perform a market characterization to describe sector-level electricity use for the residential, commercial, industrial, and irrigation sectors for the base year, This included using IPC data and other secondary data sources such as the Energy Information Administration (EIA). 2. Develop a baseline projection of energy consumption and peak demand by sector, segment, and end use for 2015 through Define and characterize several hundred EE measures to be applied to all sectors, segments, and end uses. 4. Estimate technical, economic, and achievable potential at the measure level in terms of energy and peak demand impacts from EE measures for Separately estimate potential for Idaho Power s special-contract customers. LOADMAP MODEL For the measure-level EE potential analysis, AEG used its Load Management Analysis and Planning tool (LoadMAP TM ) version 4.0 to develop both the baseline projection and the estimates of potential. AEG developed LoadMAP in 2007 and has enhanced it over time, using it for the EPRI National Potential Study and numerous utility-specific forecasting and potential studies since. Built in Excel, the LoadMAP framework (see Figure 2-2) is both accessible and transparent and has the following key features. Embodies the basic principles of rigorous end-use models (such as EPRI s REEPS and COMMEND) but in a more simplified, accessible form. Includes stock-accounting algorithms that treat older, less efficient appliance/equipment stock separately from newer, more efficient equipment. Equipment is replaced according to the measure life and appliance vintage distributions defined by the user. Balances the competing needs of simplicity and robustness by incorporating important modeling details related to equipment saturations, efficiencies, vintage, and the like, where market data are available, and treats end uses separately to account for varying importance and availability of data resources. Isolates new construction from existing equipment and buildings and treats purchase decisions for new construction and existing buildings separately. Uses a simple logic for appliance and equipment decisions. Other models available for this purpose embody complex decision choice algorithms or diffusion assumptions, and the model parameters tend to be difficult to estimate or observe and sometimes produce anomalous results that require calibration or even overriding. The LoadMAP approach allows the user to drive the appliance and equipment choices year by year directly in the model. This flexible approach Applied Energy Group 2-1

34 allows users to import the results from diffusion models or to input individual assumptions. The framework also facilitates sensitivity analysis. Includes appliance and equipment models customized by end use. For example, the logic for lighting is distinct from refrigerators and freezers. Can accommodate various levels of segmentation. Analysis can be performed at the sector level (e.g., total residential) or for customized segments within sectors (e.g., housing type or income level). Incorporates energy-efficiency measures, demand-response options, combined heat and power (CHP) and distributed generation options and fuel switching. Consistent with the segmentation scheme and the market profiles described below, the LoadMAP model provides forecasts of baseline energy use by sector, segment, end use, and technology for existing and new buildings. It also provides forecasts of total energy use and energy-efficiency savings associated with the various types of potential. 3 Figure 2-1 LoadMAP Analysis Framework DEFINITIONS OF POTENTIAL In this study, the energy efficiency potential estimates represent gross savings developed into three types of potential: technical potential, economic potential, and achievable potential. Technical and economic potential are both theoretical limits to efficiency savings. Achievable potential embodies a set of assumptions about the decisions consumers make regarding the efficiency of the equipment they purchase, the maintenance activities they undertake, the controls they use for energy-consuming equipment, and the elements of building construction. These levels are described below. Technical Potential is defined as the theoretical upper limit of energy efficiency potential. It assumes that customers adopt all feasible measures regardless of their cost. At the time of existing 3 The model computes energy and peak demand forecasts for each type of potential for each end use as an intermediate calculation. Annual energy and peak demand savings are calculated as the difference between the value in the baseline projection and the value in the potential forecast (e.g., the technical potential forecast). Applied Energy Group 2-2

35 equipment failure, customers replace their equipment with the most efficient option available. In new construction, customers and developers also choose the most efficient equipment option. Technical potential also assumes the adoption of every other available measure, where applicable. For example, it includes installation of high-efficiency windows in all new construction opportunities and air conditioner maintenance in all existing buildings with central and room air conditioning. These retrofit measures are phased in over a number of years to align with the stock turnover of related equipment units, rather than modeled as immediately available all at once. Economic Potential represents the adoption of all cost-effective energy efficiency measures. In this analysis, the cost-effectiveness is measured by the total resource cost (TRC) test, which compares lifetime energy and capacity benefits to the costs of delivering the measure through a utility program, with incentives not included, since they are a transfer payment. If the benefits outweigh the costs (that is, if the TRC ratio is equal to or greater than 1.0), a given measure is included in the economic potential. Customers are then assumed to purchase the most costeffective option applicable to them at any decision juncture. Achievable Potential takes into account market maturity, customer preferences for energyefficient technologies, and expected program participation. Achievable potential establishes a realistic target for the energy efficiency savings that a utility can hope to achieve through its programs. It is determined by applying a series of annual market adoption factors ( ramp rates ) to the economic potential for each energy efficiency measure. These factors represent the ramp rates at which technologies will penetrate the market. To develop these factors, the project team reviewed Idaho Power s past EE achievements and program history over the last five years, as well as the Northwest Power and Conservation Council (NWPCC) ramp rates used in the Seventh Power Plan. Details regarding the ramp rates appear in Appendix B. MARKET CHARACTERIZATION The first step in the analysis approach is market characterization. In order to estimate the savings potential from energy-efficient measures, it is necessary to understand how much energy is used today and what equipment is currently being used. This characterization begins with a segmentation of Idaho Power s electricity footprint to quantify energy use by sector, segment, end-use application, and the current set of technologies used. Information from Idaho Power is primarily relied upon although secondary sources are used as necessary. Segmentation for Modeling Purposes The market assessment first defined the market segments (building types, end uses, and other dimensions) that are relevant in the Idaho Power service territory. The segmentation scheme for this project is presented in Table 2-1. Applied Energy Group 2-3

36 Table 2-1 Overview of Idaho Power Analysis Segmentation Scheme Dimension Segmentation Variable Description 1 Sector Residential, commercial, industrial, irrigation Residential: single family, multi-family, manufactured home 2 Segment Commercial: small office, large office, restaurant, retail, grocery, college, school, hospital, lodging, warehouse, and miscellaneous Industrial: Food manufacturing, agriculture, general manufacturing, water and wastewater, electronics, and other industrial Irrigation: as a whole 3 Vintage Existing and new construction 4 End uses Cooling, heating, lighting, water heating, motors, etc. (as appropriate by sector) 5 6 Appliances/end uses and technologies Equipment efficiency levels for new purchases Technologies such as lamp type, air conditioning equipment, motors, etc. Baseline and higher-efficiency options as appropriate for each technology With the segmentation scheme defined, a high-level market characterization of electricity sales in the base year is performed to allocate sales to each customer segment. Idaho Power data and secondary sources were used to allocate energy use and customers to the various sectors and segments such that the total customer count, energy consumption, and peak demand matched the Idaho Power system totals from 2015 billing data. This information provided control totals at a sector level for calibrating the LoadMAP model to known data for the base-year. Market Profiles The next step was to develop market profiles for each sector, customer segment, end use, and technology. A market profile includes the following elements: Market Size is a representation of the number of customers in the segment. For the residential sector, it is number of households. In the commercial sector, it is floor space measured in square feet. For the industrial sector, it is number of employees and for the irrigation sector, it is number of service points. Saturations define the fraction of the market size with the various technologies. (e.g., homes with electric space heating). UEC (unit energy consumption) or EUI (energy-use index) describes the amount of energy consumed in 2015 by a specific technology in buildings that have the technology. For electricity, UECs are expressed in kwh/household for the residential sector, and EUIs are expressed in kwh/square foot, kwh/employee, or kwh/service point for the commercial, industrial and irrigation sectors, respectively. Annual Energy Intensity for the residential sector represents the average energy use for the technology across all homes in It is computed as the product of the saturation and the UEC and is defined as kwh/household for electricity. For the commercial, industrial, and irrigation Applied Energy Group 2-4

37 sectors, intensity, computed as the product of the saturation and the EUI, represents the average use for the technology across all floor space, all employees, or all service points in Annual Usage is the annual energy use by an end-use technology in the segment. It is the product of the market size and intensity and is quantified in GWh. Peak Demand for each technology, summer peak and winter peak are calculated using peak fractions of annual energy use from AEG s EnergyShape library and Idaho Power system peak data. The market characterization results and the market profiles are presented in Chapter 3. BASELINE PROJECTION The next step was to develop the baseline projection of annual electricity use and summer peak demand for 2015 through 2036 by customer segment and end use without new utility programs. The end-use projection includes the relatively certain impacts of codes and standards that will unfold over the study timeframe. All such mandates that were defined as of January, 2016 are included in the baseline. The baseline projection is the foundation for the analysis of savings from future EE efforts as well as the metric against which potential savings are measured. Inputs to the baseline projection include: Current economic growth forecasts (i.e., customer growth, income growth) Electricity price forecasts Trends in fuel shares and equipment saturations Existing and approved changes to building codes and equipment standards Idaho Power s internally developed sector-level projections for electricity sales A baseline projection was developed for summer and winter peak by applying the peak fractions from the energy market profiles to the annual energy forecast in each year. The baseline-projection results are presented for the system as a whole and for each sector in Chapter 4. ENERGY EFFICIENCY MEASURE ANALYSIS This section describes the framework used to assess the savings, costs, and other attributes of energy efficiency measures. These characteristics form the basis for measure-level cost-effectiveness analyses, as well as for determining measure-level savings. For all measures, AEG assembled information to reflect equipment performance, incremental costs, and equipment lifetimes. This information, along with Idaho Power s avoided costs data, were used in the economic screen to determine economically feasible measures. Energy Efficiency Measures Figure 2-2 outlines the framework for energy-efficiency measure analysis. The framework for assessing savings, costs, and other attributes of energy efficiency measures involves identifying the list of energy efficiency measures to include in the analysis, determining their applicability to each market sector and segment, fully characterizing each measure, and performing cost-effectiveness screening. Potential measures include the replacement of a unit that has failed or is at the end of its useful life with an efficient unit, retrofit or early replacement of equipment, improvements to the building envelope, the application of controls to optimize energy use, and other actions resulting in improved energy efficiency. A robust list of energy efficiency measures were compiled for each customer sector, drawing upon Idaho Power s measure database, and the Regional Technical Forum s (RTF) deemed measures databases, as well as a variety of secondary sources, compiled from AEG s work across the country. This universal list of energy efficiency measures covers all major types of end-use equipment, as well as devices and actions to reduce energy consumption. If considered today, some of these measures Applied Energy Group 2-5

38 would not pass the economic screens initially, but may pass in future years as a result of lower projected equipment costs or higher avoided costs. Inputs Client review / feedback Process AEG universal measure list Client measure data library (TRMs, evaluation reports, etc) AEG measure data library Measure characterization Energy savings Measure descriptions Costs Building simulations Lifetime Saturation and applicability Avoided costs, discount rate, delivery losses Economic screen Figure 2-2 Approach for Energy-Efficiency Measure Assessment The selected measures are categorized into two types according to the LoadMAP taxonomy: equipment measures and non-equipment measures. Equipment measures are efficient energy-consuming pieces of equipment that save energy by providing the same service with a lower energy requirement than a standard unit. An example is an ENERGY STAR refrigerator that replaces a standard efficiency refrigerator. For equipment measures, many efficiency levels may be available for a given technology, ranging from the baseline unit (often determined by code or standard) up to the most efficient product commercially available. For instance, in the case of central air conditioners, this list begins with the current federal standard, SEER 13 unit, and spans a broad spectrum up to a maximum efficiency of a SEER 24 unit. Non-equipment measures save energy by reducing the need for delivered energy, but do not involve replacement or purchase of major end-use equipment (such as a refrigerator or air conditioner). An example would be a programmable thermostat that is pre-set to run heating and cooling systems only when people are home. Non-equipment measures can apply to more than one end use. For instance, addition of wall insulation will affect the energy use of both space heating and cooling. Non-equipment measures typically fall into one of the following categories: o o o Building shell (windows, insulation, roofing material) Equipment controls (thermostat, energy management system) Equipment maintenance (cleaning filters, changing set points) Applied Energy Group 2-6