PACIFICORP DEMAND-SIDE RESOURCE POTENTIAL ASSESSMENT FOR

Transcription

1 PACIFICORP DEMAND-SIDE RESOURCE POTENTIAL ASSESSMENT FOR VOLUME 2: CLASS 2 DSM ANALYSIS Prepared for: PacifiCorp February 14, 2017 Updated March 14, 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: K. Kolnowski Project Team: D. Costenaro K. Walter S. Yoshida ii

3 CONTENTS 1 I NTRODUCTION DSM Resource Classes Interactions Between Resources Report Organization Abbreviations and Acronyms A NALYSIS APPROACH Overview of Analysis Steps Definition of LoadMAP Model Market Characterization Segmentation for Modeling Purposes Market Profiles Baseline Projection Energy Efficiency Measure Analysis Calculating Class 2 Energy-Efficiency Measure Interactive Effects Achievable Measure Ramp Rates Market Ramp Rates Levelized Cost of Measures D ATA D EVELOPMENT Data Sources PacifiCorp Data Applied Energy Group Data Other Secondary Data and Reports Application of Data to the Analysis Data Application for Market Characterization Data Application for Market Profiles Data Application for Baseline projection Energy Efficiency Measure Data Application Emerging Technologies Data Application for Levelized Cost Calculations C LASS 2 DSM POTENTIAL R ESULTS Summary of Overall Energy Savings Residential Sector Commercial Sector Industrial Sector Irrigation Sector Street Lighting Sector C OMPARISON WITH P REVIOUS DSM P OTENTIAL ASSESSMENT Key Differences Results by Sector

4 Residential Sector Commercial Sector Industrial Sector iv

5 LIST OF FIGURES Figure 1-1 Characteristics of DSM Resource Classes Figure 2-1 Approach for EE Measure Assessment Figure 4-1 Cumulative Class 2 Achievable by Sector Figure 4-2 Residential Cumulative Achievable by Segment in Figure 4-3 Residential Cumulative Achievable by End Use in Figure 4-4 Commercial Cumulative Achievable by Segment in Figure 4-5 Commercial Cumulative Achievable by End Use in Figure 4-6 Industrial Cumulative Achievable by Segment in Figure 4-7 Industrial Cumulative Achievable by End Use in Figure 4-8 Street Lighting Cumulative Achievable by Segment in

6

7 LIST OF TABLES Table 1-1 Explanation of Abbreviations and Acronyms Table 2-1 Overview of Segmentation Scheme for Class 2 s Modeling Table 2-2 Number of Class 2 Measures Evaluated Table 2-3 Economic Components of Levelized Cost by State Table 3-1 Data Applied for the Market Profiles Table 3-2 Data Needs for the Baseline projection and s Estimation in LoadMAP Table 3-3 Residential Electric Equipment Standards Table 3-4 Commercial Electric Equipment Standards Table 3-5 Data Applied for the Market Profiles Table 3-6 Data Needs for the Measure Characteristics in LoadMAP Table 3-7 Line Loss Factors Table 4-1 Cumulative Class 2 DSM by Sector in Table 4-2 Cumulative Class 2 DSM by State in Table 4-3 Cumulative Class 2 DSM Achievable by Resource Type in Table 4-4 Residential Cumulative Class 2 DSM by State in Table 4-5 Residential Cumulative Class 2 DSM by End Use in Table 4-6 Commercial Cumulative Class 2 DSM by State in Table 4-7 Commercial Cumulative Class 2 DSM by End Use in Table 4-8 Industrial Cumulative Class 2 DSM by State in Table 4-9 Industrial Cumulative Class 2 DSM by End Use in Table 4-10 Irrigation Cumulative Class 2 DSM by State in Table 4-11 Street Lighting Cumulative Class 2 DSM by State in Table 4-12 Street Lighting Cumulative Class 2 DSM by End Use in Table 5-1 Comparison of Class 2 DSM with Previous Assessments Table 5-2 Residential Comparison of Class 2 DSM with Previous Assessment Table 5-3 Commercial Comparison of Class 2 DSM with Previous Assessment Table 5-4 Industrial Comparison of Class 2 DSM with Previous Assessment

8

9 1 INTRODUCTION In 2015, PacifiCorp commissioned Applied Energy Group, with subcontractor The Brattle Group, to conduct this Demand-Side Resource Assessment. This study provides estimates of the potential for electric demand-side management (DSM) resources in PacifiCorp s six-state service territory, 1 including supply curves, for the 20-year planning horizon of to inform the development of PacifiCorp s 2017 Integrated Resource Plan (IRP) and satisfy state-specific requirements associated with forecasting and DSM resource acquisition. Since 1989, PacifiCorp has developed biennial Integrated Resource Plans (IRPs) to identify an optimal mix of resources that balance considerations of cost, risk, uncertainty, supply reliability/deliverability, and long-run public policy goals. The optimization process accounts for capital, energy, and ongoing operation costs as well as the risk profiles of various resource alternatives, including: traditional generation and market purchases, renewable generation, and DSM resources such as energy efficiency, and capacity-focused resources i.e. demand response and direct load control. Since the 2008 IRP, DSM resources have competed directly against supply-side options, allowing the IRP model to selectively choose the right mix of resources to meet the needs of PacifiCorp s customers while minimizing cost and risk. Thus, this study does not assess cost-effectiveness of demand-side resources. This study primarily seeks to develop reliable estimates of the magnitude, timing, and costs of DSM resources likely available to PacifiCorp over the 20-year planning horizon mentioned above. The study focuses on resources assumed achievable during the planning horizon, recognizing known market dynamics that may hinder resource acquisition. Study results will be incorporated into PacifiCorp s 2017 IRP and subsequent DSM planning and program development efforts. This study serves as an update of similar studies completed in 2007, 2011, 2013, and DSM RESOURCE CLASSES For resource planning purposes, PacifiCorp classifies DSM resources into four categories, differentiated by two primary characteristics: reliability and customer choice (see Figure 1-1). These resources are captured through programmatic efforts promoting efficient electricity use through various intervention strategies, aimed at changing: energy use peak levels (load curtailment), timing (price response and load shifting), intensity (energy efficiency), or behaviors (education and information). From a system-planning perspective, Class 1 and Class 2 DSM resources (particularly Class 1 direct load control programs) are considered the most reliable, as once a customer elects to participate in a Class 1 DSM program, the resource is under the utility s control and can be dispatched as needed. Similarly, when a customer invests in a home or business efficiency improvement, the savings are locked in as a result of the installation and will occur during normal operation of the equipment. In contrast, savings resulting from energy education and awareness actions included in Class 4 DSM, tend to be the least reliable, as savings will vary due to greater customer control and the need for customers to take specific and consistent actions to lower their usage during peak periods. 1 Class 2 analysis for Oregon is excluded from this report because it is assessed statewide by the Energy Trust of Oregon. 2 The previous potential studies can be found at: Applied Energy Group 1-1

10 Introduction Figure 1-1 Characteristics of DSM Resource Classes PacifiCorp commissioned this DSM resource potential assessment to inform the Company s biennial IRP planning process, to satisfy other state-specific DSM planning requirements, and to assist PacifiCorp in revising designs of existing DSM programs and in developing new programs. The study s scope encompasses multi-sector assessments of long-term potential for DSM resources in PacifiCorp s Pacific Power (California, Oregon, and Washington) and Rocky Mountain Power (Idaho, Utah, and Wyoming) service territories. This study excludes an assessment of Oregon s Class 2 DSM potential, as this potential has been captured in assessment work conducted by the Energy Trust of Oregon, which provides Oregon energy-efficiency potential to PacifiCorp for resource planning purposes. This study does not include assessments of Class 4 DSM resources. Unless otherwise noted, all results presented in this report represent savings at generation; that is, savings at the customer meter have been grossed up to account for line losses. INTERACTIONS BETWEEN RESOURCES This assessment includes multiple resources, actions, and interventions that would interact with each other if implemented in parallel. As explained in more detail later in this report, we take specific actions to account for these interactions to avoid double-counting the available potential. The interactive effects that we have analyzed occur within the major analysis sections; meaning that the interactions of energy efficiency resources are considered across all Class 2 DSM resources. Likewise, the analysis of capacity-focused Class 1 and 3 DSM resources explicitly considers interactions. It should be noted, however, that this study does not attempt to quantify potential interactions between energy-focused and capacity-focused resources due to uncertainties regarding resources likely to be found economic and pursued. Applied Energy Group 1-2

11 Introduction REPORT ORGANIZATION This report is presented in five volumes as outlined below. This document is Volume 2, Class 2 DSM Analysis. Volume 1, Executive Summary Volume 2, Class 2 DSM Analysis Volume 3, Class 1 and 3 DSM Analysis Volume 4, Class 2 DSM Analysis APPENDIX Volume 5, Class 1 and 3 DSM Analysis APPENDIX Applied Energy Group 1-3

12 Introduction ABBREVIATIONS AND ACRONYMS Table 1-1 provides a list of abbreviations and acronyms used in this report, along with an explanation. Table 1-1 Explanation of Abbreviations and Acronyms Acronym Explanation amw Average Megawatt, obtained by dividing Megawatt-hours by 8760 C&I Commercial and Industrial CAC Central Air Conditioning Council Northwest Power and Conservation Council (NWPCC) DHW Domestic Hot Water DEER California s Database for Energy Efficient Resources DSM Demand-Side Management DLC Direct Load Control EE Energy Efficiency EIA Energy Information Administration EUL Effective Useful Life EUI Energy Usage Intensity FERC Federal Energy Regulatory Commission HVAC Heating Ventilation and Cooling IECC International Energy Conservation Code IOU Investor Owned Utility NEEA Northwest Energy Efficiency Alliance NPV Net Present Value O&M Operations and Maintenance PCT Programmable Communicating Thermostat RTF Regional Forum TRC Total Resource Cost UCT Utility Cost Test UEC Unit Energy Consumption UES Unit Energy Savings WH Water Heater Applied Energy Group 1-4

13 2 ANALYSIS APPROACH Class 2 DSM resources, or energy efficiency resources, are measures that reduce customers energy consumption relative to what it would have been without installing or enacting the measure. In this chapter we discuss the approach used to estimate the Class 2 DSM resource potential. This process is largely similar to the Class 2 DSM analysis in the 2015 Resource Assessment, with all assumptions updated using the most recent and applicable sources available. OVERVIEW OF ANALYSIS STEPS To perform the Class 2 DSM analysis, AEG used a bottom-up analysis approach following the major steps listed below. We describe these analysis steps 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, irrigation, and street lighting sectors for the base year, in five states within PacifiCorp s service territory: California, Washington, Idaho, Utah, and Wyoming. Oregon is not covered in this analysis because the Energy Trust of Oregon handles the planning and implementation of all energy efficiency within PacifiCorp s Oregon service territory. To perform the market characterization, we used results from primary market research conducted by PacifiCorp wherever possible, supplemented by other secondary data sources available from regional and national organizations such as the Northwest Energy Efficiency Alliance (NEEA) and the Energy Information Administration (EIA). 2. Develop a baseline projection of energy consumption by state, sector, segment, and end use for 2015 through 2036, building upon the base year characterization performed in step 1 above. 3. Define and characterize energy efficiency measures to be applied to all sectors, segments, and end uses. 4. Estimate the potential from the efficiency measures. While this analysis ultimately develops estimates of annual potential for each year in the 20-year planning horizon for use in PacifiCorp s IRP, results presented in this volume focus on cumulative impacts at the end of the planning horizon, Compare the results of the present study with those from PacifiCorp s previous (2015) DSM potential assessment 4 to identify important trends and changes. DEFINITION OF POTENTIAL To assess the various levels of resource potential available in the PacifiCorp service territory, we investigated the following cases: Class 2 DSM This case is defined as the theoretical upper limit of energy efficiency potential. It assumes that customers adopt all feasible measures regardless of their cost or customer preferences. At the time of existing equipment failure, customers replace their was selected as the base year for analysis, as it was the most recent calendar year with complete account data available at this step in the process. 4 All five volumes of the 2015 study are available on the PacifiCorp website, Applied Energy Group 2-1

14 Analysis Approach equipment with the most efficient option available relative to applicable standards. In new construction, customers and developers also choose the most efficient equipment option relative to applicable codes and standards. Non-equipment measures which may be realistically installed apart from equipment replacements are implemented according to ramp rates developed by the Northwest Power and Conservation Council ( The Council ) for its Seventh Power Plan, applied to 100% of the applicable market. This case is a theoretical construct, and is provided primarily for planning and informational purposes. Class 2 DSM Achievable - This case refines technical potential by applying customer participation rates that account for market barriers, customer awareness and attitudes, program maturity, and other factors that may affect market penetration of DSM measures. We used achievability assumptions from the Council s Seventh Plan as the customer adoption rates for this study. For the achievable technical case, ramp rates are applied to at most 85% of the applicable market, per Council methodology. This achievability factor represents potential which can reasonably be acquired by all mechanisms available, regardless of how conservation is achieved. Thus, the market applicability assumptions utilized in this study include savings outside of utility programs. 5 LOADMAP MODEL For the energy efficiency potential analysis, we used AEG s Load Management Analysis and Planning tool (LoadMAP TM ) version 5.0 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 Study and numerous utility-specific forecasting and potential studies since. Built in Microsoft Excel, the LoadMAP framework has the following key features. Embodies the basic principles of rigorous end-use models (such as EPRI s REEPS and COMMEND) but in a simplified and more 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. 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, rather than complex decision choice algorithms or diffusion assumptions which tend to be difficult to estimate or observe and sometimes produce anomalous results that require calibration or manual adjustment. 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). Consistent with the segmentation scheme and the market profiles we describe 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. 5 Council s 7 th Power Plan applicability assumptions reference an Achievable Savings report published August 1, Applied Energy Group 2-2

15 Analysis Approach 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 the equipment that is currently being used and its associated energy consumption. This characterization begins with a segmentation of PacifiCorp s electricity footprint to quantify energy use by state, sector, segment, end-use application, and the current set of technologies used. SEGMENTATION FOR MODELING PURPOSES The market assessment first defined the market segments (building types, end uses, and other dimensions) that are relevant in the PacifiCorp service territory. The segmentation scheme for this project is presented in Table 2-1. Table 2-1 Overview of Segmentation Scheme for Class 2 s Modeling 6 Dimension Segmentation Variable Description 1 State California, Washington, Idaho, Utah, Wyoming 2 Sector Residential, commercial, industrial, irrigation, street lighting 3 Customer Category Residential: single family, multifamily, manufactured home Commercial: by building type Industrial: by industry type Irrigation: by pump horsepower size Street lighting: Customer-owned vs Companyowned 4 Vintage Existing and new construction 5 End uses Cooling, space heating, lighting, water heat, motors, etc. (as appropriate by sector) 6 7 Appliances/end uses and technologies Equipment efficiency levels for new purchases Technologies such as lamp type, air conditioning equipment, motors by application, etc. Baseline and higher-efficiency options as appropriate for each technology With the segmentation scheme defined, we then performed a market characterization of electricity sales in the base year to allocate sales to each customer segment. We used PacifiCorp billing data and customer saturation surveys to inform the bottom-up assembly of energy consumption among the various sectors and segments such that the total customer count and total energy consumption matched actual PacifiCorp system totals for 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 base-year 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 the number of households. In the commercial sector, it is the floor space, measured in 6 For complete listings of the segmentation categories, please see Market Characterization and Energy Market Profiles in appendix A in Volume 4 of this report. Applied Energy Group 2-3

16 Analysis Approach square feet. For the industrial sector, it is the number of employees. For irrigation, it is the number of service points. For street lighting, it is the number of fixtures. Saturations define the fraction of the market where various technologies are installed. (e.g., percent of homes with electric space heating). UEC (unit energy consumption) or EUI (energy-use intensity) describes the average energy consumed in 2014 by a specific technology within buildings that have the technology. UECs are expressed in kwh/household for the residential sector, and EUIs are expressed in kwh/square foot or kwh/employee for the commercial and industrial sectors, respectively. Intensity for the residential sector represents the average energy use for the technology across all homes in 2014 and is computed as the product of the saturation and the UEC. For the commercial and industrial sectors, intensity, computed as the product of the saturation and the EUI, represents the average use for the technology per square foot or per employees in The sum of all energy intensities in a specific market segment will yield the total consumption per market unit (e.g., total kwh per household). Usage is the total annual energy use by an end use technology within a given segment. It is the product of the market size and intensity, and is quantified in gigawatt-hours (GWh). As mentioned above, this usage is calibrated to actual sales in the base year. The market characterization results and the market profiles are presented in appendix A in Volume 4 to this report. BASELINE PROJECTION The next step was to develop the baseline projection of annual electricity use for 2015 through 2036 by state, sector, customer segment, end use and technology without new utility DSM programs to avoid double counting of the available potential. The end-use projection includes the impacts of building codes and equipment efficiency standards that were enacted as of January 2016, even if they would not go into effect until a future date. The study does not, however, attempt to project future changes to codes and standards beyond those which already have a known effective date. For a list of equipment efficiency standards included in residential and commercial baseline projections, see Table 3-3 and Table 3-4. 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 PacifiCorp customer growth forecasts Trends in equipment saturations Existing and approved changes to building codes and equipment standards Regarding customer purchase behaviors, the study held purchase trends constant at current levels, except where overridden by a forthcoming code or standard. Although it uses many of the same input assumptions and aligns very closely with PacifiCorp s official load forecast, the baseline projection for the potential model was developed as an independent projection to ensure that baseline assumptions were consistent with those used to assess energy efficiency measure savings and applicability. We present the baseline-projection results for the system as a whole and for each sector in appendix B in Volume 4 to this report. 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 determining measure-level savings and levelized costs as well as the subsequent build up to sector- and state-level savings and levelized costs. For all measures, AEG assembled information to reflect equipment performance, incremental costs, and equipment lifetimes. Figure 2-1 outlines the framework for measure analysis. Applied Energy Group 2-4

17 Analysis Approach Figure 2-1 Approach for EE Measure Assessment 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 preparing for integration with the greater potential modeling process. We compiled a robust list of energy efficiency measures for each customer sector, drawing upon PacifiCorp s program experience, the Council s Seventh Power Plan, the Regional Forum (RTF), the Energy Trust of Oregon, AEG s own measure databases and building simulation models, and other secondary sources. This universal list of EE measures covers all major types of end-use equipment, as well as devices and actions which reduce energy consumption when installed or implemented. 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. These measures are applied on a stock-turnover basis, and in general, are referred to as lost opportunity (LO) measures due to the fact that once a purchase decision is made, there will not be another opportunity to improve the efficiency of that equipment item until the lifetime expires again. Non-equipment measures save energy by reducing the need for delivered energy, but do not involve replacement or purchase of major end-use equipment on a stock-turnover schedule (such as a refrigerator or air conditioner). For this reason, these measures are generally termed Applied Energy Group 2-5

18 Analysis Approach discretionary or non-lost opportunity measures. 7 An example is a programmable thermostat, which can be pre-set to run space heating and cooling systems only when people are home and which can be installed at any time. Non-equipment measures can apply to more than one end use. For instance, adding wall insulation will reduce the energy use of both space heating and cooling systems. Non-equipment measures typically fall into one of the following categories: o o o o o o o o Building shell (windows, insulation, roofing material) Equipment controls (thermostat, integrated lighting fixture controls) Equipment maintenance (cleaning filters, changing setpoints) Whole-building design (building orientation, passive solar lighting) Displacement measures (ceiling fan to reduce use of central air conditioners) Commissioning and retro-commissioning Residential behavioral programs Energy Management programs We developed a preliminary list of EE measures, which was distributed to the PacifiCorp project team for review. We started will all measures analyzed in the previous study, updated or excluded obsolete measures (e.g., programmable thermostat removed in favor of Wi-Fi connected thermostat). The list was finalized after incorporating comments and is presented in appendix H of Volume 4 to this report. Once we assembled the list of EE measures, the project team assessed their energy-saving characteristics. For each measure we also characterized incremental cost, effective useful life, and other performance factors. Table 2-2 summarizes the number of measures evaluated for each segment within each sector. The study considered 324 unique measures across sectors, which expand to over 30,000 permutations when assessed separately by state, vintage, and market segment. This is lower than the 50,088 permutations in the prior study. A reduction in measure count from 465 to 324 was driven mainly by the consolidation of measures per 7 th Power Plan updates. For example, previously discrete lighting controls measures have been bundled into new enhanced fixture controls measures. In addition, obsolete measures have also been removed or consolidated, such as the residential home energy management system and programmable thermostat measures being replaced by a single WiFi/ interactive thermostat option. Industrial motor and process measures were consolidated into a streamlined suite of optimization and controls measures, reflecting research performed by the United Nations Industrial Development Organization. Table 2-2 Number of Class 2 Measures Evaluated Sector Measure Count Measure Count w/ Permutations (States, Vintages, & Segments) Residential 83 2,490 = count * 5 * 2 * 3 Commercial ,260 = count * 5 * 2 * 14 Industrial 99 14,850 = count * 5 * 2 * 15 Irrigation = count * 5 * 2 * 1 Street Lighting = count * 5 * 2 * 2 Total Measures Evaluated ,040 =sum 7 An exception to this general definition is in the case of New Construction, where all measures, both equipment and non-equipment, are considered lost opportunity since there is a unique, one-time opportunity to install DSM measures at this time. Applied Energy Group 2-6

19 Analysis Approach CALCULATING CLASS 2 ENERGY-EFFICIENCY POTENTIAL The approach we used to calculate the energy efficiency potential adheres to the approaches and conventions outlined in the National Action Plan for Energy-Efficiency (NAPEE) Guide for Conducting Studies (2007) 8 and the Northwest Power and Conservation Council s Seventh Power Plan (2016). 9 These sources represent authoritative and comprehensive industry standard practices for estimating energy-efficiency potential. MEASURE INTERACTIVE EFFECTS When calculating potential, one cannot merely sum up savings from individual measure installations, as significant interactive effects can occur among measures. This analysis accounts for those interactions in the following ways: Interactions between equipment and non-equipment measures As equipment burns out, the potential analysis assumes it will be replaced with higher-efficiency equipment available in the marketplace, which reduces average consumption across all customers. The lower average consumption causes non-equipment measures to save less than they would have, had the average efficiency of equipment remained constant over time. The stock-turnover accounting applied in the model manifests this effect as annual trends in equipment energy consumption. For example, installing insulation in a home where the central heating system has been upgraded produces lower savings than installing insulation in a home with an older heating system. Interactions among non-equipment measures There are often multiple non-equipment measures that affect the same technology or end use. In this case, the savings (as a percentage of the relevant end use consumption) are stacked upon one another such that those with lower levelized cost are applied first. 10 TECHNICAL POTENTIAL As described in Chapter 1, two types of potentials were developed as part of this effort: technical potential and achievable technical potential. The calculation of technical potential is a straightforward algorithm, aggregating the full, energy-saving effects of all the individual Class 2 DSM measures included in the study at their maximum theoretical deployment levels, adjusting only for applicability. While theoretically, all discretionary resources could be acquired in the study s first year, this would skew the potential for equipment measures and provide an inaccurate picture of measure-level potential. Therefore, the study assumes the realization of these opportunities over the 20-year planning horizon according to the shape of corresponding Seventh Power Plan ramp rates, applied to 100% of applicable market units. By applying this assumption, natural equipment turnover rates, and other adjustments described above, the annual incremental and cumulative potential was estimated by state, sector, segment, construction vintage, end use, and measure. ACHIEVABLE TECHNICAL POTENTIAL To develop estimates for achievable technical potential, we constrain the technical potential by applying market adoption rates for each measure that estimate the percentage of customers that would be likely to select each measure, given consumer preferences (partially a function of incentive levels), retail energy rates, imperfect information, and real market barriers and conditions. These barriers tend to vary, depending on the customer sector, local energy market conditions, and other, hard-to-quantify factors. In addition to utility-sponsored programs, alternative acquisition methods, 8 National Action Plan for Energy Efficiency (2007). National Action Plan for Energy Efficiency Vision for 2025: Developing a Framework for Change. 9 Seventh Northwest Conservation and Electric Power Plan (2016) This is in contrast to equipment measures, which may require a mutually exclusive decision among multiple efficient options with energy savings relative to the baseline unit. In these cases the algorithm selects the option that is most energy efficient for the Case and the unit that is most efficient for less than $250/MWh levelized for the Achievable Case. For example, a SEER 13 central air conditioning baseline unit might be replaced with a SEER 24 variable refrigerant flow unit for and a SEER 16 unit for Achievable. Applied Energy Group 2-7

20 Analysis Approach such as improved codes and standards and market transformation, can be used to capture portions of these resources, and are included within the achievable technical potential, per 7 th Power Plan methodology. This proves particularly relevant in the context of long-term Class 2 DSM resource acquisition plans, where incentives might be necessary in earlier years to motivate acceptance and installations. As acceptance increases, so would demand for energy-efficient products and services, likely leading to lower costs, and thereby obviating the need for incentives and (ultimately) preparing for transitions to codes and standards. These market adoption rates are based on ramp rates from the Council s Seventh Power Plan. As discussed below, two types of ramp rates have been incorporated for all measures and market regions. Estimated achievable technical potential principally serves as a planning guideline. Acquiring such DSM resource levels depends on actual market acceptance of various technologies and measures, which partly depend on removing barriers (not all of which a utility can control). Additionally, achievable technical potential does not account for cost-effectiveness, which is assessed within PacifiCorp s IRP modeling. MEASURE RAMP RATES The study applied measure ramp rates to determine the annual availability of the identified potential for lost opportunity and discretionary resources, interpreting and applying these rates differently for each class (as described below). Measure ramp rates generally matched those used in the Council s Seventh Power Plan, although the study incorporated additional considerations for Class 2 DSM measure acquisition: The second year of the Seventh Power Plan ramp rates (2017) was aligned with this study s first year (2017). Since the Seventh Power Plan begins in 2016, it was appropriate to begin lost opportunity measures on the same calendar year to reflect the maturity of PacifiCorp s existing programs. For discretionary measures (utilizing the Seventh Plan s retrofit ramp rates), we began in year 1 since these measures may be installed at any time. For measures not included in the Seventh Power Plan, the study assigned a ramp rate considered appropriate for that technology (i.e., the same ramp rate as a similar measure in the Seventh Power Plan). Lost Opportunity Resources Lost opportunity energy efficiency measures correspond to equipment measures, which follow a natural equipment turnover cycle, as well as non-equipment measures in new construction instances that are fundamentally different and typically easier to implement during the construction process as opposed to after construction has been completed. In the Seventh Power Plan, lighting fixture control measures are also modeled as lost opportunity measures, assumed that these advanced controls must be installed alongside new linear LED panels. In addition to natural timing constraints imposed by equipment turnover and new construction rates, the AEG team applied measure ramp rates to reflect other resource acquisition limitations over the study horizon, such as market availability. These measure ramp rates had a maximum value of 85%, reflecting the Council s assumption that, on average, up to 85% of technical potential could be achieved by the end of a 20-year planning horizon. Measures on the Seventh Power Plan s emerging technology ramp rate are constrained to 65% of technical potential. To calculate annual achievable technical potential for each lost opportunity measure, the study multiplied the number of units turning over or available in any given year by the adoption factor provided by the ramp rate, consistent with the Council s methodology. Because of the interactions between equipment turnover and new construction, the lost opportunities of measure availability until the next life cycle, and the time frame limits at 20 years, the Council methodology for these measures produces potential less than 85% of technical potential. Discretionary Resources Discretionary resources differ from lost opportunity resources due to their acquisition availability at any point within the study horizon. From a theoretical perspective, all achievable technical potential Applied Energy Group 2-8

21 Analysis Approach for discretionary resources could be acquired in the study s first year, but from a practical perspective, this outcome is realistically impossible to achieve due to infrastructure and cost constraints as well as customer preferences and considerations. As a result, the study addresses achievable technical potential for discretionary resources by spacing the acquisition according to the ramp rates specified for a given measure, thus creating annual, incremental values. To assess achievable technical potential, we then apply the 85% market achievability limit defined by the Council. Consistent with lost opportunity, discretionary measures on the Seventh Power Plan s emerging technology ramp rate are constrained to 65% of technical potential. Tables of all measure ramp rates are available in appendix E in Volume 4 to this report, both with and without the market achievability limits applied. MARKET RAMP RATES The 2015 assessment applied market ramp rates on top of measure ramp rates to reflect state-specific considerations affecting acquisition rates, such as age of programs, small and rural markets, and current delivery infrastructure. A market ramp rate was applied to the industrial market sector in Wyoming. Recent program accomplishments point to this trend continuing into future years, therefore the current assessment applies the same Emerging market ramp rate from the 2015 assessment, presented in Table E-1 in Volume 4 of this report, to industrial measures in Wyoming. LEVELIZED COST OF MEASURES Using the cost data for measures developed in the characterization step above, we calculate the levelized cost of conserved energy (levelized cost) in order to create Class 2 DSM supply curves. Where possible, the study aligned its approach for calculating levelized costs for each measure with the Council s levelized-cost methodology, while recognizing differences in cost-effectiveness screening in each state within PacifiCorp s service territory. 11 Table 2-3 summarizes components of levelized cost in each PacifiCorp state assessed in this study. Table 2-3 Economic Components of Levelized Cost by State Parameter WA ID CA WY UT Initial capital cost Included Utility incentive Annual Incremental O&M Included Not included Secondary Fuel Impacts Included Not included Non-Energy Impacts Included Not included Administrative costs 20% of incremental cost Utah s levelized cost is assessed on a Utility Cost Test (UCT) basis, while the other states are evaluated on a Total Resource Cost (TRC) basis. To maintain consistency with the Council, RTF and accepted regulatory practices, secondary benefits, non-energy impacts, and incremental O&M have been included for Washington and Idaho. For Washington resources, the Council s 10% conservation credit will be applied during the IRP modeling process, and this credit has not been included in the levelized costs presented in this report. The approach to calculating a measure s levelized cost of conserved energy aligns with that of the Council s, considering the costs required to sustain savings over a 20-year study horizon, including reinstallation costs, for measures with useful lives less than 20 years. If a measure s useful life extends beyond the end of the 20-year study, the analysis incorporates an end effect, treating the measure s levelized cost over its useful life as an annual reinstallation cost for the remaining portion of the Failure to align costs used for IRP optimization with methods used to assess program cost-effectiveness could lead to an inability to deliver selected quantities in a cost-effective manner in a given jurisdiction. Applied Energy Group 2-9

22 Analysis Approach year period. 12 For example, if a particular measure life is 15 years, a reinstallation of the measure will occur after year 15, and years 16 through 20 will reflect an annual levelized cost of installing that measure, prorated for the 5 of its 15 years. In this way, all measures are considered on an equivalent, 20-year basis. For PacifiCorp s Utah service territory, the study adopted the utility s share of initial capital costs (i.e., an incentive amount) in the levelized cost calculation. The following assumptions regarding incentive amounts applied for Utah: Specific program measure (e.g., evaporative coolers and appliance recycling) incentives aligned with the current program design. Behavioral initiatives for residential customers included an incentive of 100%; indicating that the entire measure delivery is subsidized by the program. Behavioral initiatives for business customers, that is, energy management, included an incentive of 90% of the measure cost; indicating that most of the costs are subsidized by the program. Measures with zero or negative incremental cost used incentives based on existing PacifiCorp program offerings and typical industry levels. Company-owned street lighting incentives were set to 100% of incremental measure costs. Incentives for all other measures represented 70% of the incremental measure cost 13, based on a robust incentive level aimed at achieving 85% of the technical potential. An assumption of 20% of incremental costs was used to align with program history, previous potential assessments, industry benchmarks the assumption in the Seventh Power Plan. 12 This method applied both to measures with a useful life greater than 20 years and those with useful lives extending beyond the 20th year at the time of reinstallation. 13 Incremental measure costs vary by resource type (i.e., discretionary or retrofit), with incremental costs equaling full costs for discretionary resources, and for lost opportunities, the incremental cost is the difference between the standard-efficiency and higherefficiency alternatives. Applied Energy Group 2-10

23 3 DATA DEVELOPMENT This section details the data sources used for the Class 2 DSM analysis, followed by a discussion of how these sources were applied. In general, data were adapted to local conditions. For example, local data sources were used for measure data and local weather was used for building simulations. DATA SOURCES The data sources are organized into the following categories: PacifiCorp data AEG s databases and analysis tools Other secondary data and reports PACIFICORP DATA Our highest priority data sources for this study were those specific to PacifiCorp s system and customers, including: Paci icorp customer data: PacifiCorp provided customer-level billing data for all states and sectors including segment identifiers to parse out the various housing types and business types. Market research data: Data collected by PacifiCorp customers through recent saturation survey efforts. Load forecasts: PacifiCorp provided state- and sector-level forecasts of energy consumption and customer counts. Economic information: PacifiCorp provided a system wide discount rate and line loss factors by state and sector to calculate levelized costs and energy efficiency potential at the generator. Paci icorp program data: PacifiCorp provided information about past and current energy efficiency programs, including program descriptions, achievements to date, and evaluation reports. APPLIED ENERGY GROUP DATA AEG maintains several databases and modeling tools that we use for forecasting and potential studies. AEG Energy Market Pro iles: For more than 10 years, AEG staff have maintained profiles of end-use consumption for the residential, commercial, and industrial sectors. These profiles include market size, fuel shares, unit energy consumption estimates and annual energy use by fuel (electricity and natural gas) by customer segment and end use for 10 regions in the U.S. The Energy Information Administration surveys (RECS, CBECS and MECS) as well as state-level statistics and local customer research provide the foundation for these regional profiles. Building Energy Simulation Tool (BEST). AEG s BEST is a derivative of the DOE 2.2 building simulation model, used to estimate base-year UECs and EUIs, as well as measure savings for the HVAC-related measures. AEG s Database of Energy Ef iciency Measures (DEEM): AEG maintains an extensive database of measure data for our studies. Our database draws upon reliable sources including the Council s Seventh Power Plan and RTF workbooks, the California Database for Energy Efficient Resources (DEER), the EIA Technology Forecast Updates Residential and Commercial Building Technologies Reference Case, RS Means cost data, and Grainger Catalog Cost data. Applied Energy Group 3-1

24 Data Development Recent studies. AEG has conducted numerous studies of EE potential in the last five years. We checked our input assumptions and analysis results against the results from these other studies, which include studies in nearby jurisdictions for Avista Energy, Idaho Power, Tacoma Power, Seattle City Light and Cowlitz PUD. In addition, we used the information about impacts of building codes and appliance standards from our recent reports for the Edison Electric Institute 14. OTHER SECONDARY DATA AND REPORTS Finally, a variety of secondary data sources and reports were used for this study. The main sources are identified below. Council Seventh Power Plan Conservation Supply Curve Workbooks, To develop its Power Plan, the Council created workbooks with detailed information about measures, available at Regional Forum (RTF) Unit Energy Savings Measure Workbooks: The RTF maintains workbooks that characterize selected measures and provide data on unit energy savings (UES), measure cost, measure life, and non-energy benefits. These workbooks provide Pacific Northwestspecific measure assumptions, drawing upon primary research, energy modeling (using the RTF s Simple Energy Enthalpy Model (SEEM), regional third party research, and well-vetted national data. Workbooks are available at RTF Standard Protocols: The RTF also maintains standard workbooks containing useful information for characterizing more complex measures for which UES values have not been developed, such as commercial sector lighting. Residential Building Stock Assessment: NEEA s 2011 Residential Building Stock Assessment (RBSA) provides results of a survey of thousands of homes in the Pacific Northwest: Commercial Building Stock Assessment: NEEA s 2014 Commercial Building Stock Assessment (CBSA) provides data on regional commercial buildings. Industrial Facilities Site Assessment: NEEA s 2014 Industrial Facilities Site Assessment (IFSA) provides data on regional industrial customers by major classification types. Bonneville Power Administration (BPA) Reference Deemed Measure List, version 2.5, which was the most recent available when the study was performed. Other relevant regional sources: These include reports from the Consortium for Energy Efficiency (CEE), the Environmental Protection Agency (EPA), and the American Council for an Energy- Efficient Economy (ACEEE). Annual Energy Outlook. The Annual Energy Outlook (AEO), conducted each year by the U.S. Energy Information Administration (EIA), presents yearly projections and analysis of energy topics. For this study, we used data from the 2015 AEO. American Community Survey: The US Census American Community Survey is an ongoing survey that provides data every year on household characteristics. Data for PacifiCorp were available for this study AEG staff who performed the PacifiCorp study have prepared three white papers on the topic of factors that affect U.S. electricity consumption, including appliance standards and building codes. Links to all three white papers are: Applied Energy Group 3-2