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HomeMy WebLinkAboutStaff Report 7718 City of Palo Alto (ID # 7718) City Council Staff Report Report Type: Consent Calendar Meeting Date: 3/6/2017 City of Palo Alto Page 1 Summary Title: Updated Ten-year Electric Energy Efficiency Goals Title: Approval of an Update to the City's Ten-Year Electric Energy Efficiency Goals (2018 to 2027) From: City Manager Lead Department: Utilities Recommended Motion (Utilities Advisory Commission Recommendation) I move to approve the proposed annual and cumulative Electric Energy Efficiency Goals for the period 2018 to 2027 as shown in the following table. Recommendation Utilities Advisory Commission and Staff recommend that Council approve the proposed annual and cumulative Electric Efficiency Goals for the period 2018 to 2027 as shown in the following table. Summary Table: Annual Electric Energy Efficiency Goals (% of total City customer usage) Electric (%) Electric MWh 2018 0.75% 7,300 2019 0.75% 7,300 2020 0.80% 7,800 2021 0.80% 7,800 2022 0.85% 8,300 2023 0.85% 8,300 2024 0.90% 8,600 2025 0.90% 8,600 2026 0.95% 9,100 2027 0.95% 9,200 Cumulative1 10-year EE Goal 5.7% 54,900 1 Cumulative EE savings are not equal to the sum of the annual incremental goals due to the differences in how long the electricity savings persist for different measures and different types of EE savings. For example, new hardware City of Palo Alto Page 2 Executive Summary Palo Alto has long recognized cost-effective energy efficiency (EE) as the highest priority energy resource, given that EE typically displaces relatively expensive electricity generation, lowers energy bills for customers, and contributes to economic development and job creation. As required by state legislation, the City adopted its first set of 10-year energy efficiency goals in April 2007, and updated these goals in 2010 and 2012. For the electric EE savings targets the City is required to establish under state law (AB 2227), EE savings that can be counted towards these goals are restricted to those savings directly attributable to utility programs which are funded by a mandated public benefits charge (2.85% of electric retail revenue). EE upgrades that customers undertake without participating in utility programs as well as EE savings achieved through federal and state appliance and building standards currently cannot be counted towards the City’s EE goals. Therefore the savings reported here and targeted by these goals represent a narrow subset of the actual energy efficiency upgrades taking place in Palo Alto. Over the past five years, building and appliance efficiency standards have become increasingly stringent. As federal and state efficiency standards increase, the energy savings attributable to utility programs decline. For this current EE goals update, staff proposes annual electric EE savings targets of 0.75% in 2018, increasing to 0.95% in 2027, with a cumulative 10-year EE savings of 5.7% of the City’s projected electric load. These are aggressive targets for Palo Alto, and are roughly 30% more ambitious than the previous goals adopted in 2012. Staff will explore various program strategies as well as innovative EE technologies to achieve these goals over the next decade. Committee Review and Recommendation The UAC considered staff’s recommendation at its February 1, 2017 meeting. After discussion, the UAC voted 5-0, with two commissioners absent, to accept the staff recommendation and recommended that Council adopt the proposed electric 10-year EE goals. Staff described the history and context for these EE goals, including previous updates of the electric 10-year EE goals in 2007, 2010, and 2012. Staff also described some of the modeling that went into developing the proposed goals and a few of the new low-cost EE programs under development. Utilities Advisory Commissioners inquired why CPAU would push for goals greater than the minimum acceptable goals in the face of negative load growth. Staff explained that a) the California Energy Commission is considering setting goals for mid-size POUs such as CPAU, and b) that energy efficiency can make room for additional loads (such as electric vehicle charging) on existing transmission and distribution infrastructure, and c) doing so with cost- effective measures was beneficial to the community. Commissioners also inquired how increased electric vehicle charging might affect CPAU’s ability to meet these EE goals in the future. Staff explained that the adopted percentage goals are translated into absolute energy upgrades contribute savings over their expected lifetimes, perhaps 15 years, whereas electricity savings from changing thermostat set-points are assumed to contribute savings over a much shorter period of time. City of Palo Alto Page 3 savings based on the current forecast, and therefore not affected by future load growth. Council also asked about the “Willingness and Awareness values” in the model related to customer behavior in Palo Alto. Staff responded that the statewide Willingness and Awareness values will be updated by the state over the next two years. After discussion, the Utilities Advisory Commission voted unanimously (5-0, with 2 Commissioners absent) to recommend that the Council approve the proposed electric 10-year EE goals in this report as the updated electric EE goals for 2018 to 2027. The excerpted sense minutes from the Commission’s discussion is provided as Attachment D. Background Council adopted the City’s first 10-year electric EE goals in April 2007, which were to reduce the City’s electric usage cumulatively by 3.5% by FY 2017. These goals met the state legislative requirements established by AB 2021 (2006) requiring publicly owned electric utilities to adopt annual electric efficiency savings goals for the next 10-year period, with the first set of goals due by June 1, 2007 and every three years thereafter. These EE goals were used for the City of Palo Alto Utilities’ (CPAU’s) resource planning as well as for EE program budget planning. In May 2010 City Council updated the 10-year EE goals to reduce cumulative electric load by 7.2% between 2011 and 2020. The most recent set of 10-year EE goals was adopted by City Council in December 2012, with cumulative 10-year electric savings of 4.8% between 2014 and 2023. AB 2227 (2012) changed the triennial energy efficiency target-setting schedule to a quadrennial schedule, beginning March 15, 2013 and every fourth year thereafter. The next EE goals update is due to be submitted to the California Energy Commission by March 15, 2017. Figure 1 provides a summary of the annual electric EE goals and achievements since Fiscal Year (FY) 2008. The figure shows that actual CPAU EE achievements have exceed goals for most years. City of Palo Alto Page 4 Figure 1. Electric Efficiency Goals and Achievements for 2011-2016. 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 2008 2009 2010 2011 2012 2013 2014 2015 2016 Actual Savings 2012 Goals 2010 Goals 2007 Goals Percentages represent EE savings relative to load In 2015 California passed a landmark piece of energy legislation called Senate Bill 350 (SB-350) the “Clean Energy and Pollution Reduction Act of 2015”. SB 350 reinforces California’s position as a leader in clean energy and greenhouse gas reduction, and codifies Governor Brown’s ambitious “50/50/50” plan to procure 50% of electricity from renewable resources, reduce petroleum use by 50%, and double building efficiency in both electric and natural gas end uses by 2030. The statute lists a variety of programs to achieve the doubling of efficiency savings, including: 1) appliance and building standards; 2) utility programs that offer financial incentives, rebates, technical assistance and support to customers to increase EE; 3) programs that achieve EE savings through operational, behavioral and retrocommissioning activities; and 4) programs that save energy in final end uses through reducing distribution feeder voltage (i.e. conservation voltage reduction). In the spirit of SB 350’s goal to double building energy efficiency, staff proposes an ambitious set of 10-year EE goals for 2018 to 2027. It should be noted that since Palo Alto’s electric supply has been carbon-neutral since 2012 and that electric efficiency does not contribute additional greenhouse gas (GHG) reductions. City of Palo Alto Page 5 Discussion Overview of Palo Alto’s Past Energy Efficiency Activities CPAU has offered energy efficiency programs since the 1970s. Its Long-term Electric Acquisition Plan (LEAP), approved by City Council in March 2007 and last updated in 2012, affirmed cost- effective energy efficiency as the highest priority resource, with the goal of reducing average customer bills. The portfolio of EE programs has evolved over time. Originally the programs focused on rebates for customers administered by CPAU staff, but now include programs administered by third parties that provide EE audit and turnkey EE services to customers. Some of the notable programs in recent years include a turnkey lighting and refrigeration upgrade program for small businesses (Right Lights+), a comprehensive home efficiency audit and retrofit program that targets low income residences, a direct install program that implements sensors to power down beverage and snack vending machines at no charge to businesses, a new construction assistance program for commercial customers to increase building efficiency, a program that offers building commissioning services to large businesses, and a Home Energy Report program that provides individualized reports comparing residents’ home energy usage with their neighbors in similarly sized homes. Palo Alto was one of the first cities to pilot LED street light technology in 2009, in collaboration with the Pacific Northwest National Laboratory. Since 2014, the City has converted 85% of its streetlights to LED streetlights (the remaining are decorative streetlights.) CPAU also has an ongoing Program for Emerging Technologies to evaluate, test and implement innovative emerging technologies that could help customers manage or reduce energy and water use. Besides utility programs, Palo Alto is also pursuing energy savings through its local green building code. In June 2015, City Council adopted an energy reach code within the Green Building Ordinance that requires 15% energy efficiency savings beyond California’s 2013 Title 24 building energy standards for single family, multifamily and non-residential new construction projects. This energy reach code was effective for the period from September 2015 to December 2016. Beginning January 2017 through December 2019, the energy reach code requires 10% energy efficiency savings beyond the state’s 2016 Title 24 building energy standards for single family, multifamily and non-residential new construction projects. As a reach code specific to only the City of Palo Alto, energy savings from this code are savings which may be counted towards these EE goals. From a supply resource planning perspective, CPAU has incorporated both historic EE savings as well as forecast EE savings (from Council-approved EE goals) when forecasting the aggregate customer loads for a 10-year planning period. Energy efficiency related savings impacted directly by utility programs over the past 10 years is estimated at 6.8% of 2016 loads, i.e. without such programs, Palo Alto’s electrical loads would have been 6.8% (60,300 MWh) higher in 2016. City of Palo Alto Page 6 Proposed Electric Efficiency Goals Staff proposes new annual electric EE targets at 0.75% of forecast electric load beginning in FY 2018, increasing 0.05% every two years, and eventually reaching 0.95% in FY 2026 and FY 2027. These proposed goals are approximately 30% more ambitious than the annual electric EE targets adopted in 2012 (see Figure 2). Figure 3 shows the actual historical EE savings and the proposed 2018 to 2027 EE goals. While the proposed EE goals may not initially appear aggressive relative to past goals and the historical savings, the proposed goals are quite ambitious considering that a) the computational model built by Navigant Consulting for Palo Alto2 suggests a market potential lower than the adopted 2012 goals for a business-as-usual approach, and b) the diminishing market potential due to more stringent codes and standards3. Staff believes these goals are ambitious but achievable given Palo Alto has consistently exceeded predicted EE market potential and that Staff has a number of new program strategies in preliminary stages. These proposed goals are also projected to be cost-effective based on both the model projections and past EE program costs. Lastly, they are also consistent with the principles expressed in Palo Alto’s Sustainability and Climate Action Plan (S/CAP). If adopted, these EE goals will be included in the Efficiency implementation plan currently being developed for the S/CAP. Savings from EE can be reported on a net basis, meaning they exclude energy impacts from free-riders (program participants who would have installed EE even without incentives), or on a gross basis, meaning they include impacts from program participants that are free-riders. The goals in Figure 4 are based on “net” EE savings rather than “gross” EE savings4. This means they do not include the energy savings that would have occurred in the absence of utility incentives, and therefore most accurately reflect the EE savings attributable to CPAU’s programs. CPAU also excludes savings attributable to building and appliance codes and standards. In order to allow comparison with other utilities which set goals on a gross basis, the proposed annual goals in Figure 2 are shown as proposed (on a net basis without including codes and standards), as well as on a gross basis. In addition, if energy savings from codes and standards were included, CPAU’s goals would be approximately 1.1% per year as illustrated in Figure 2. These distinctions are important, as California IOUs and a few large POUs actively participate in the development of these codes and standards and subsequently claim savings attributed to these codes and standards. For context, in 2015 nearly 45% of PG&E’s claimed savings came 2 Navigant Consulting was contracted by the Northern California Power Agency (NCPA) to build specific computational models for each of the NCPA member utilities. This model incorporates CPAU’s avoided costs, retail rates, and building stock data. Additional explanation of the Navigant Consulting EE potential model and Staff goal development is in Attachment A. 3 EE savings attributed to state mandated codes & standards are excluded from the EE potential for CPAU, and therefore also cannot count toward meeting its EE goals. 4 The 2016 EE Potential model assumes free-ridership at the measure level using a net-to-gross (NTG) ratio. The NTG ratios are based on California statewide evaluation studies and are documented in Database of Energy Efficiency Results (DEER). Generally, mature, low-cost technologies tend to have higher free-ridership. City of Palo Alto Page 7 from these codes and standards so one would expect California IOUs to have substantially higher EE goals than CPAU for claimed savings. Figure 2. Comparison of Proposed 2017 Electric EE goals and 2012 Electric EE Goals. Figure 3 shows the reported EE savings as well as the proposed annual electric EE goals expressed in MWh. The big jump in 2012’s reported savings was due to the completion of a significant EE project at a large commercial site, which is unlikely to be replicable. Figure 3. Historic EE Savings and Proposed Annual Electric EE Goals on an Energy Basis. City of Palo Alto Page 8 On a cumulative basis, the total EE savings from the proposed 2018 to 2027 targets represent 5.7% of the forecasted electric load in 2027, a 19% increase over the goals adopted in 2012. If the EE savings from codes and standards are included, the cumulative EE savings in 2027 is 8.5% of the forecasted electric load. The cumulative impact of the annual targets for this 10-year period is shown in Figure 4. It is important to note that some EE savings have a longer-lasting effect than others, as different EE measures have different useful lifetimes. Measure life for Light Emitting Diode (LED) bulbs can be up to 12 years, whereas behavioral savings last only last a few years. Due to the differences in EE savings persistence, the cumulative EE impact over the 10-year period is not equal to the sum of the annual EE goals for the 10 years. Figure 4. Proposed 2018-2027 Cumulative Electric EE Goals. Strategies for Achieving the Proposed EE Goals Achieving these ambitious EE goals will require the deployment of new innovative programs structures, increasing awareness of existing programs, and developing other program approaches to reach previously stranded sections of the energy efficiency market potential. One example of an innovative program structure that research suggests is highly effective is the idea of gamification5 for behavioral residential energy savings. The Energy Lottery pilot program currently being developed by staff could provide extremely cost-effective residential behavioral savings through gamification. 5 Gamification is the concept of applying of game-design elements and game principles to other areas in order to improve user engagement and other metrics. City of Palo Alto Page 9 Another example of a new pilot program currently underway is a training seminar for facilities managers called Building Operator Certification. This training could help tap into potential energy efficiency savings for large commercial and industrial customers. Also, as discussed in an earlier section, Palo Alto has adopted Green Building Ordinance that requires additional energy savings beyond the state’s building energy standards. Staff is currently investigating how to track and verify the energy savings attributed to the Green Building Ordinance. In addition, if the City chooses to implement an Advanced Metering Infrastructure (AMI) backbone of a smart-grid system staff will investigate a conservation voltage reduction program using the AMI infrastructure on its 68 primary feeders. Potential savings from this program are estimated to be up to 1% of the City’s annual electricity load, and could be realized by 2025 or 2026. All these plans are subject to Council consideration and approval. Staff is also investigating a distributed energy resources pilot, which could potentially contribute EE savings from smart thermostats and other emerging technologies. There are also potentially large EE savings from many City facilities, particularly if EE measures do not impact other operating constraints. This evolution of our EE portfolio is consistent with the general consensus among utilities that new approaches are needed to reach increasingly aggressive EE targets as traditional EE programs approach market saturation limitations. Projected Electric EE Program Costs Funding for EE programs comes from a mandated Public Benefit (PB)6 surcharge of 2.85% of the electric utility bill for all customers. Since all EE portfolios must be cost-effective, they can also be funded by supply resource funds. To meet the proposed EE goals, staff estimates that the annual EE budget will grow from about $3.4 million in FY 2018 to about $4.2 million in FY 2027. This projected EE program budget is anticipated to be roughly 85% of the annual PB collections. Figure 5 shows the actual electric EE program expenditures for FY 2008 through FY 2016 and the estimated annual EE budget between 2018 and 2027. 6 Public Benefits funds are required to be collected by legislative statute and can only be used on cost-effective energy efficiency, low income programs, renewable electricity, and research and development. City of Palo Alto Page 10 Figure 5. Actual and Projected Electric EE Program Costs. Retail Rate & Average Customer Bill Impact of the Proposed Electric EE Goals: EE programs impact retail rates in two ways. First, a lower electric load means that fixed costs (capital investments and fixed operating costs to run the electric utility) must be distributed over a lower electric sales volume, thereby increasing the average electric retail rate. Second, the use of funds to support EE programs increases the revenue requirements for the electric utility. Overall, these proposed goals are estimated to amount to a cumulative increase in the retail rate of approximately 5% by the year 2027. The majority of this retail rate increase is due to the cumulative load reduction from the proposed 10-year EE goals. Increased charging of electric vehicles, electrification of natural gas appliances, and other electric load growth could mitigate the retail rate impact of the EE programs. The total bill impact of the proposed goals is estimated to be neutral over the lifetime of the EE savings. Resource Impact As discussed above, staff estimates that the annual EE budget will grow from about $3.4 million in FY 2018 to about $4.2 million in FY 2027, and these goals will have other rate and bill impacts. Although this report contains preliminary estimates of the costs of achieving the proposed electric and gas EE goals, the detailed budget plan and staffing needs to meet the annual EE goals will be part of the annual City budgeting process. The annual budget will present the costs for both internally administered, as well as contractor supported, efficiency programs. Policy Implications Adoption of the proposed electric 10-year EE goals will replace the 2012 10-year electric EE City of Palo Alto Page 11 goals and will inform the EE program planning and load forecasting for the next four years. These goals will also be included in the LEAP, the Electric Utility Integrated Resource Plan, and the City’s Sustainability Implementation Plan. The proposed 2017 electric EE goals are consistent with the Utilities Strategic Plan and the City’s S/CAP. Environmental Review Council approval of the 2017 10-year electric EE goals does not require California Environmental Quality Act review, because the plan does not meet the definition of a project under Public Resources Code Section 21065 and CEQA Guidelines Section 15378(b)(5), as an administrative governmental activity which will not cause a direct or indirect physical change in the environment. Attachments:  Attachment A: Energy Efficiency Potential Modeling  Attachment B: Cost-Effectiveness Tests For Energy Efficiency Programs  Attachment C: Top 20 Electric Efficiency Measures in 2018  Attachment D: Excerpted Draft UAC Minutes of February 1, 2017 Page A-1 ATTACHMENT A: ENERGY EFFICIENCY POTENTIAL MODELING Energy Efficiency Potential Model Overview The first step in establishing EE goals is to model the potential for energy savings within the City. This step was completed using an EE potential model developed by Navigant Consulting. The 2016 EE potential model is similar to the one used by CPAU and other California publicly owned utilities (POUs) in 2012 as well as the model most recently used by the California Public Utilities Commission to determine the EE potential for investor-owned utilities (IOUs). The model estimates the technical, economic and market potential for energy efficiency measures for residential and non-residential customers, defined as follows: • Technical potential is the energy savings that would result from installation of the most energy efficient measures that are commercially available, regardless of cost- effectiveness. • Economic potential includes only savings from the installation of cost-effective EE measures. • Maximum Market potential is a subset of the economic potential which is scaled by customers’ awareness and willingness to adopt energy efficient equipment. • Market potential is the achievable portion of the maximum market potential calculated by the model, given: 1) the calibration of the model based on actual EE savings for a specific utility, and 2) the programs the utility chooses to include. The model is calibrated based on the achieved EE savings by end use, and uses a 3-year average from 2013 to 2015 is used as the base year. The model also takes into account past EE program achievements as well as user-specified input such as projected avoided energy costs, retail rates of electricity, discount rate, building stock, and assumptions regarding appliance and equipment penetration. Efficiency measures included in the analysis cover over 170 current and emerging electric efficiency measures. For each year starting in 2015, the model steps through the calculation of the technical potential, then filters out the uneconomic measures to determine the economic potential, then estimates the maximum market potential based on customers’ awareness and willingness to adopt and, finally, computes the market potential by applying a diffusion curve function to the maximum market potential for the portfolio of EE programs. The calculated market potential forms the basis of the proposed EE goals for 2018 to 2027. Figure A-1 shows the model’s sequential narrowing from technical potential to market potential. Page A-2 Figure A-1. 2016 Navigant EE Potential Model Steps for Modeling CPAU Market Potential. Limitations of the EE Potential Model The 2016 EE Potential model has some intrinsic limitations. One source of uncertainty is the values for “willingness and awareness” used within the model, which attempt to approximate customer awareness of energy efficiency measures and their willingness to install the measures. The projected market potential is extremely sensitive to these values, as the maximum market potential is essentially the product of the economic potential and the willingness and awareness values. The 2016 EE potential model applies generic values adopted from the IOUs’ EE potential model. Given the unique demographics of Palo Alto, the “willingness and awareness” numbers for Palo Alto will likely be different from the IOUs’. Cost projections from the model should also be treated as estimates, as the model’s measure costs are based on Navigant’s measure database, rather than actual CPAU measure costs. Also, the model does not readily accommodate analysis using different avoided costs. More broadly, this model cannot predict future disruptive technologies, or calculate savings from programs with completely new and different structures. One such example is the energy savings attributed to Palo Alto’s Green Building Code. Model Results For Palo Alto, this 2016 EE potential model estimates an annual incremental market potential of 0.6% of the forecasted load for 2018-2027 if Palo relies solely on a business as usual approach. This 10% decline in market potential from the 2012 EE goals is due to both the model limitations as well the effects of market saturation for mature EE programs like Palo Alto’s. However, given Palo Alto’s reputation as a leader in energy efficiency and the statewide vision for doubling energy efficiency, Staff has used the model to estimate the types of programs needed and funds required to achieve ambitious but still cost-effective EE savings goals. Page A-3 The 2016 EE Potential model also breaks down future market potential by end use. Figure A-2 shows that the majority of the 2018 energy savings are expected from the non-residential sector (74%) while only 26% of the savings are from the residential sector. Of the residential savings, over 80% is projected to come from behavioral programs, such as the Home Energy Reports. Savings from lighting make up another 27% of the total energy savings in 2018. However, it is important to note that with increasing lighting efficiency standards, lighting savings are predicted to only account for 12% of the incremental market potential by 2027. Non-residential comprehensive savings (14% of the incremental market share in 2018) are primarily energy savings from retrocommissioning activities such as resetting temperature and schedule of the building HVAC control system, recalibrating sensors and variable frequency drives. A list of the top twenty electric efficiency measures in 2018 is provided in Appendix B. Figure A-2. Composition of Electric EE Market Potential in 2018. Page B-1 ATTACHMENT B: COST-EFFECTIVENESS TESTS FOR ENERGY EFFICIENCY PROGRAMS The primary aim of cost-effective energy efficiency programs is to reduce utility cost and hence customer bills while improving the environment. Cost-effectiveness can be measured in many ways. The four perspectives most commonly used in efficiency program cost-effectiveness testing are: 1. Participant: An energy efficiency measure that provides net savings to a customer is cost-effective for them as a “participant.” If a customer’s initial investment, after accounting for utility rebates and tax incentives, can be recouped with lower operating cost over the life of the measure, the measure is considered cost-effective from a participant’s perspective. 2. Utility: A measure that lowers overall cost for the utility is cost-effective for the utility (also referred to as “Program Administrator”). For CPAU, this could also be considered the “all ratepayers test” or “average utility bill test,” as it reflects the change in the utility bill to the average customer. To be cost-effective from the utility perspective, the cost of the program (administrative and rebate costs) must be less than the savings from not purchasing the energy supply. 3. Total Resource: If the combination of the utility and all customers together save money, it is cost-effective from a “Total Resource Cost (TRC)” or societal viewpoint. 4. Non-Participant: Even if the bill for the average customer shrinks significantly, retail rates could increase slightly, so that customers who do not reduce consumption could see a slight increase in rates and therefore bills. This effect is due to the portion of retail revenue that must be collected to pay for fixed costs. For this reason it is important to design diverse programs to be widely available in order to facilitate efficiency implementation in as broad a manner as possible. The Non-Participant perspective is also called the Rate Impact perspective. The Total Resource Cost reflects the financial perspective of the Palo Alto community as a whole. The Utility Cost, Participant and Rate Impact perspectives should also be considered to ensure lower average bills and sufficient incentives to achieve participation The costs and benefits that are used to calculate the benefit-cost ratios for each of these different perspectives are illustrated below: Page B-2 Table B-1: Cost-Effectiveness Perspectives and Associated Costs and Benefits Cost Effectiveness Test Costs Benefits Participant Cost Test (PCT) Does the participant save money? Measure Cost Incentive to customer Bill Savings Tax Savings Program Administrator Cost (PAC)- Average Bill Are utility revenue requirements lowered? Incentive to customer Program Delivery Cost Avoided Supply Costs Total Resource Cost Test (TRC) Sum of Participant + Non-participant Are total community expenditures lowered? Measure Cost Program Delivery Cost Avoided Supply Costs Tax Savings Rate Impact Measure (RIM) Also known as non-participant test Are utility rates lowered? Incentives to customer Lost Revenues (=Bill Savings) Program Delivery Cost Avoided Supply Costs The Electric EE potential analysis assumes the cost of renewable energy as the avoided supply cost. Page C-1 ATTACHMENT C: TOP 20 ELECTRIC EFFICIENCY MEASURES IN 2018 The following table lists the top twenty electric efficiency measures in 2018. This list does not included behavioral programs. The combined energy savings from these 20 measures represents around 50% of the total market potential. Rank Top Fifty Measures – 2018 2018 - Energy Savings (MWh) Energy % of Total 1 Com-Office - Retro-commissioning (a) 565 12.3% 2 Com-ALL - Pump and Fan Variable Frequency Drive Controls (VFDs) 262 5.7% 3 Com-Office - Thermostat Replacement 172 3.7% 4 Res-SF New - T24 15% Stretch Goal Compliant Home 114 2.5% 5 Com-Office - Comprehensive Rooftop Unit Quality Maintenance (AC Tune-up) 112 2.4% 6 Electronics - Efficient Lighting Equipment 106 2.3% 7 Com-Retail - LED fixture: 33W, 3500 lumens 91 2.0% 8 Com-Retail - LED downlight, screw-in lamp, 1-3W, interior Average 2 Watts (a) 91 2.0% 9 Com-Office - Retro-commissioning (b) 86 1.9% 10 Com-Retail - LED downlight, screw-in lamp, 1-3W, interior Average 2 Watts (b) 74 1.6% 11 Other Industrial - Efficient Lighting Equipment 73 1.6% 12 Com-Office - Centrifugal Chiller - Average kW/Ton = 0.56 67 1.5% 13 Com-Office - Demand Controlled Ventilation 67 1.4% 14 Com-Lodging - LED fixture: 33W, 3500 lumens 59 1.3% 15 Com-Office - Bi-Level Lighting Fixture – Stairwells, Hallways, and Garages 58 1.3% 16 Com-Retail - Electronically Commutated (EC) Motor w/Fan Cycling Controls for Cold Storage Evaporator Fans 58 1.3% 17 Com-Office - Reciprocating Chiller - Average kW/Ton = 0.84 56 1.2% 18 Com-Office - Electronically Commutated (EC) Motor w/Fan Cycling Controls for Cold Storage Evaporator Fans 55 1.2% 19 Com-Office - Screw Chiller - Average kW/Ton = 0.68 52 1.1% 20 Com-Retail - LED fixture: 33W, 3500 lumens 49 1.1% Top 20 Total 2,267 49.2% EXCERPTED DRAFT MINUTES OF THE FEBRUARY 1, 2017 UTILITIES ADVISORY COMMISSION ITEM 3. ACTION: Utilities Advisory Commission Recommendation That Council Approve the Update on the City of Palo Alto’s Ten-Year Electric Energy Efficiency Goals (2018 to 2027) Resource Planner Lena Perkins presented on the electric energy efficiency goals. She mentioned one important context for this goal-setting exercise was the adoption of SB 350 in 2015. The legislation required doubling of energy efficiency in the State of California. Utility energy efficiency is only one component of that doubling, and utilities with aggressive goals like the City had less room to increase their energy efficiency goals. A computational model was used to assess market potential. The 2015 model, as compared to the 2012 model, included high impact, low cost behavioral programs. These new types of programs included training of facility managers and green building code adoption. Most savings were expected from commercial and industrial customers as compared to residential customers. Most residential savings were likely to come from behavioral programs. She showed the proposed goals compared to the previous goals. The proposed goals were ambitious, a 20-30% increase over existing goals. There were a number of new programs staff planned to use to achieve the goals. However, they were even more aggressive considering the energy efficiency measures that could not be counted because they were incorporated into Building Codes and Standards. She showed historical savings, noting that 2012 savings were particularly high due to a large data center project that was unlikely to be replicated. She noted that the long term rate impact of the efficiency measures was roughly 5%, but that the loss of revenue could be offset by increases in load due to electric vehicle adoption or other load growth. Commissioner Johnston asked about the rate impact why we were setting such aggressive targets when we might be seeing negative load growth in the future. Perkins replied that the state was considering setting EE goals for mid-sized POUs. Perkins also stated that we were required to both collect funds to spend on EE and show that our EE programs delivered real, cost-effective savings. She noted that any EE savings in City facilities would allow the City to save money. Abendschein added that EE savings could make room on the existing distribution system for load growth such as increased EV charging. Abendschein also made the distinction between retail rates and bills, as bills are projected to remain nearly flat while the retail rates may go up. ATTACHMENT D ATTACHMENT D Commissioner Schwartz asked why we were setting such aggressive targets. Perkins stated that the State was considering setting utility specific EE targets, and that these goals might not be aggressive compared to those targets. Commissioner Schwartz asked about the cost effectiveness of the City’s portfolio. Perkins stated that staff was focused on creative, cost-effective programs. The portfolio as a whole is projected to be cost-effective. Commissioner Schwartz asked about the home energy reports, since many residents were unhappy with the comparison to their neighbors. Perkins replied that the Home Energy Reports had been discontinued two years ago, but that Staff was working on a Behavioral Program centered around “carrots” and gamification in the form of the potential residential Energy Lottery. Commissioner Schwartz asked about the Program for Emerging Technologies. Perkins responded that the Program for Emerging Technologies was housed within the Utilities Department, and offered to send more information about the program. Commissioner Forssell asked about the Navigant energy efficiency model discussed in Attachment A of the staff report. She referenced that the model was highly sensitive to the “Willingness and Awareness value” used. She asked what value was used for Palo Alto. Perkins said the values used were a bit stale but were in the process of being updated by the State. There is also a question of how people actually behave around conservation, whether there is a rebound effect or whether perhaps once people conserve in one area they view themselves as conservation type people. Commissioner Schwartz stated that she has seen people where they define themselves as “green” and they want to do more and more conservation and environmental programs. Commissioner Ballantine asked how EVs factored into these programs Perkins stated EVs were not factored in. The CEC had recommended that these not be included. Commissioner Ballantine asked how EVs were factored in to the behavioral programs. He noted that his EV had made him get a bad score on his Home Energy Report. ATTACHMENT D Perkins stated the savings were based on actual measures installed according to a reporting methodology. The City was only able to make savings claims based on rebates actually provided and assumptions about the savings from that specific measure. Commissioner Schwartz said this was a regular problem for measurement and verification, particularly for education and marketing programs. She disclosed she was starting a measurement and verification project with Navigant to do a project in Colorado, and asked the City Attorney to let her know if she needed to recuse herself. Commissioner Ballantine asked whether load increase from EVs would negatively affect the savings the City could claim. Perkins said that load increase from EVs or others would not affect those savings as the EE savings will be reported in absolute energy savings numbers to the CEC. Commissioner Schwartz recommended approval of the staff recommendation since any model is inherently limited. Perkins responded that a great deal of work and sensitivity analysis went into these recommended goals, but that any model was limited by the quality of the data and the structure of the model. Trumbull moved to approve the staff recommendation, Ballantine seconded staff recommendation. Passed 5-0 ACTION: Commissioner Trumbull moved, seconded by Commissioner Ballantine to recommend Council approve the proposed annual and cumulative Electric Energy Efficiency Goals for the period 2018 to 2027 as shown in the following table: Summary Table: Annual Electric Energy Efficiency Goals (% of total City customer usage) Electric (%) Electric MWh 2018 0.75% 7,300 2019 0.75% 7,300 2020 0.80% 7,800 2021 0.80% 7,800 2022 0.85% 8,300 2023 0.85% 8,300 2024 0.90% 8,600 2025 0.90% 8,600 ATTACHMENT D 2026 0.95% 9,100 2027 0.95% 9,200 Cumulative 10-year EE Goal 5.7% 54,900 The motion carried unanimously (5-0, with Commissioners Ballantine, Forssell, Johnston, Schwartz, and Trumbull voting yes and Chair Cook and Vice Chair Danaher absent).