Estimating Energy Savings for a Midstream Program Design Targeting Plug Load Steve Blanc, Engineering Services Brian Smith, Evaluation, Measurement and Verification September 17, 2014

Deck Overview Background Estimating Unit Energy Consumption Proposed Methodology for Estimating Program Savings Our Requests of the Cal TF Members

1. Background The Growing Challenge of Plug Loads Retail Plug-Load Portfolio (RPP) Program Concept The Long-Term Vision: RPP as A National Platform Current RPP Trial Overview

The Growing Challenge of Plug Loads Forecasted Change in Residential Electric Consumption, 2012 – 2040. Source: U.S. Energy Information Administration Annual Energy Outlook 2014

The Retail Plug-Load Portfolio Concept What is the Retail Plug-Load Portfolio (“RPP”)? RPP is a portfolio-based program design to address plug load and appliances with the ultimate goal of reducing unit energy consumption of products sold at retail. Short-term trial objective Motivate participating retailer to promote and sell more efficient models. Longer-term objective Motivate retailers to demand, stock, and promote the most efficient models available from their manufacturer partners. The Retail Plug-Load Portfolio Concept. The Retail Plug-load Portfolio (RPP) Program is a holistic, multi-product intervention that uses retailer engagement to increase the demand and supply of more energy efficient appliance and consumer electronic products. The long-term vision for the RPP Program, which incorporates both resource acquisition and market transformation approaches, is based on Pacific Gas and Electric Company (PG&E) and Sacramento Municipal Utilities District (SMUD) (and in the future, other investor-owned utilities and program administrators, such as NEEA) partnering with retailers to align business objectives and lower retailers’ product portfolio “energy profiles” in a variety of target product categories (the energy profile is defined as the sales-weighted energy use of products sold by a retailer within a defined period of time). The energy use of each model is estimated by matching models sold by the retailer to a database (like ENERGY STAR) on a model by model basis. The fundamental program theory of the RPP Program is that, with the right combination of incentives and engagement, retailers will lower their product category-specific energy profiles over time by selling more energy efficient models than they would have absent the program, thereby generating energy savings, and with sustained engagement, transforming the retail channel market in delivering energy efficient plug load products and appliances. The RPP Program focuses on capturing plug load savings at the category level, which would not be cost-effective if delivered through a traditional downstream program. Rather, such products must be promoted and delivered for consumer adoption through the retail channel, where consumers already visit on a regular basis. This approach allows retailers to do what they do best: sell program-qualified products using their own proven strategies. Retailers have been chosen as the point of intervention because of their ability to impact the entire value chain. Retailers work with manufacturers early in product development to determine feature sets and consumer demands and finish the value chain when they sell the product to the end customer. A retailer’s ability to drive these multiple points makes them one of the more effective market actors in the creation of sustainable market effects. In the short term, the Program is intended to motivate participating retailers to promote and sell more efficient models. Over time, other retailers, investor-owned utilities (IOUs), and administrators outside of PG&E’s service territory will collaborate in this effort to regularly demand, stock, and promote the most efficient models available. The resulting increase in regional and/or national demand for these models will eventually cause their manufacturing partners to permanently shift to production of these models, thus transforming the marketing and reversing the trend of increasing energy use due to plug loads and appliances. *The notes section contains a more detailed program description.

Long-Term Vision: A Nationwide ENERGY STAR® Retail Products Platform Vision: Transform the way energy efficient products and energy efficiency messages are delivered to residential customers using an omni-channel retail approach. Concept: Leading program sponsors and retailers negotiate a mutually-acceptable suite of ENERGY STAR products in exchange for incentives, then work together to increase the market share of higher efficiency products. Potential Advantages of a Nationwide, Platform Approach to Program Delivery Aggregation Collaboration Customization Transformation Evaluation Operating a single program at a large scale will reduce program administration costs Leading energy efficiency program administrators and retailers working together will provide national scale while retaining the ability for partners to address local market needs Proposed program design provides flexibility to retailers to tailor marketing tactics to meet their specific business needs The approach is the most likely to promote retailer and manufacturer behavior change towards EE A national scale program is most likely to produce measureable impact by influencing buying decisions that are often made by a central purchasing group

Current RPP Trial Overview Current trial Duration is November 1, 2013 - December 31, 2014 26 stores (24 PG&E, 2 SMUD) participating Key objectives are to assess operational readiness for broader rollout and to test multiple evaluation approaches Additional chain(s) may be added for 2015 DVD/Blue-Ray Players Home-Theaters-in-a-Box Air Cleaners Room ACs Refrigerators Freezers Overview: PG&E’s 2013-2014 RPP Program Trial is a proof of concept initiative with one retailer, Kmart, to test and validate program operations, evaluation methods, and incentive structure mechanisms. An understanding of these components is needed to support a scalable program design with multiple retailers and partners in 2015 and beyond. PG&E is partnering with SMUD on the trial, which includes 24 Kmart stores in PG&E’s service territory and two stores in SMUD’s territory for a total of 26 participating Kmart stores. Targeted Product Categories: PG&E and SMUD identified seven product types across appliance and consumer electronics categories (the 7th category, compact audio, is not shown in this slide because it turns out Kmart does not stock any program-qualified models and, thus, none have been incented as part of the Trial). Each product type has its respective efficiency tier and incentive level that is also shown. Kmart will be incentivized to promote and sell these targeted products. These products were selected because they represent an array of both appliance and plug load/consumer electronics, which there is available energy and sales data.   Points of Note. While most stocking and buying choices by the retailer have already been made due to the nature of the buying cycles and relatively short trial term, the trial design includes opportunities for Kmart to make different stocking/assortment choices to more efficient models, where possible. Therefore, while these changes are not an explicit focus for the trial, PG&E will be tracking indicators of changing stocking/assortment patterns thorough the data collection process. Retailer responsibilities include the following: Prior to trial launch, Kmart will submit at least 12 months of historical sales data on above product categories for participating stores. Prior to trial launch, Kmart will develop a marketing plan that outlines how it intends to promote and sell qualified products. Based on lessons learned, Kmart will be able to update the plan as needed. Kmart will comply with data collection requirements, including uploading monthly sales data for 26 participating stores within PG&E/SMUD’s territory, 16 non-participating within PG&E/SMUD’s service territories, as well as select stores outside of the service territories. Kmart will provide monthly written accounts of promotional activities that were executed as part of trial participation. 20 qualifying models across six product categories *The notes section contains a more detailed trial description.

2. Estimating Unit Energy Consumption (UEC) The Challenge with the Ex Ante Review Process Data Sources for Estimating UECs UECs in DEER vs. Model-Level UECs Estimated Using the RPP Methodology Example: Estimating UEC for a DVD Player

The Challenge with the CPUC Ex Ante Review Process for the RPP’s Suite of Measures Limited information about product energy use is available. DEER doesn’t track most of the products comprising the proposed products of the RPP program. Energy Star and DOE don’t provide values for key parameters for many products in the RPP product suite such as hours of use, IMC, and EUL. Many of the RPP product categories are evolving rapidly.

The Challenge with the CPUC Ex Ante Review Process for the RPP’s Suite of Measures Limited information about product energy use is available. DEER doesn’t track most of the products comprising the proposed products of the RPP program. Energy Star and DOE don’t provide values for key parameters for many products in the RPP product suite such as hours of use, IMC, and EUL. Many of the RPP product categories are evolving rapidly. Work paper dispositions often call for additional research to substantiate estimates of energy use and other key parameters. Requirements for additional research do not include assessments of cost effectiveness. For measures with low per-unit energy savings and/or rapid model turnover, balancing precision, cost, and timeliness is critical when calling for additional research.

Data Sources for Estimating UECs Retailer sales data. 22 months of historical (pre-trial) and ongoing program-period sales data that identifies volume of all unique models sold within each RPP product category, by date.

Data Sources for Estimating UECs Retailer sales data. 22 months of historical (pre-trial) and ongoing program-period sales data that identifies volume of all unique models sold within each RPP product category, by date. Data sources for estimating model-specific unit energy consumption (UEC). UEC estimates created by multiplying measured power in each operating mode by the corresponding hours of annual usage for that mode. Sources include: DEER EPA ENERGY STAR Qualified Product Lists (QPL) DOE Energy Guide label CEC Appliance Efficiency Program Metered data User surveys

UECs in DEER vs. Model-Level UECs Using RPP Methodology This slide shows the correlation of model-level UECs that we derived for refrigerators and freezers to the appropriate DEER UEC values for those size classes. Our estimated values do not represent huge deviations from DEER.

Example: Estimating UEC for a DVD Player Some product categories (predominantly consumer electronics), have no singular source (e.g., DEER; ENERGY STAR; DOE) for UEC values. In these cases, we estimate UECs based on multiple sources. Type Model # On Mode Power Consumption (Watts) Idle Mode Power Consumption (Watts) Standby Mode Power Consumption (Watts) Off Mode Power Consumption (Watts) On Mode-Hours of Use Idle Mode-Hours of Use Standby Mode-Hours of Use Off Mode-Hours of Use On Mode Energy Consumption (kWh/yr) Idle Mode Energy Consumption (kWh/yr) Standby Mode Energy Consumption (kWh/yr) Off Mode Energy Consumption (kWh/yr) UEC (kWh/yr) DVD Player DVD1041 10.2 7.9 1.3 0.0 548 1095 1520 5598 5.6 8.7 2.0 16.2 2013 EPA Consumer Electronics Savings Calculator LBNL 2013* DVDs are a product category where we had to develop proxy UEC estimates for every model because, even though there is an Energy Star designation, Energy Star does not provide UEC values. Instead, we do get power consumption estimates in Wattage in different operating modes from the Energy Star calculator. We then plug in hours of use that we sourced from an recent LBNL study. Same methodology was employed for Blu-Ray DVDs, home theatres in a box and sound bars. A different methodology was used for room air conditioners (based on EER values and DEER hours of use) and refrigerators and freezers (based on Energy Star UEC). * Lawrence Berkeley National Laboratory. 2013. Final Field data collection of miscellaneous electrical loads in Northern California: Initial results. Authored by Greenblatt, J.; S. Pratt, H. Willem, E. Claybaugh, L. Desroches, B. Beraki, M. Nagaraju, S. Price and S. Young.

3. Proposed Methodology for Estimating Program Savings

Using Sales-Weighted UEC to Estimate Program Effects Two years of historic retailer sales data used to project sales-weighted unit energy consumption (“SWUEC”) baseline moving forward (“forecasted SWUEC”) for each product category without program intervention. Future sales used to create “recorded SWUEC.” Program effects (“net effects”) is the difference between the two lines.

4. Our Requests to the Cal TF Materials Provided to Cal TF Questions on the Work Paper Abstract Questions on the Methodology Doc Questions on the UECs

Materials Provided A Work Paper Abstract for the RPP Program. Calculation Methodology that describes how UECs were calculated. An Excel-based spreadsheet with UEC estimates for six product categories created by this methodology.

Work Paper Abstract Questions Is the work paper abstract clear and complete? (If not, please provide any clarifying questions you may have.) What would you recommend as source(s) and/or a methodology for estimating the effective useful life (EUL) for the product categories for which EUL values do not exist in DEER? What would you recommend as source(s) and/or a methodology for estimating the incremental measure cost (IMC) for the product categories for which IMC values do not exist in DEER? Net-to-gross ratios (NTGRs) are not provided in DEER for comparable market transformation programs. A key challenge is to input NTGRs for each product category into the E3 calculator that best represent a market transformation strategy. Do you have any recommendations? (Optional)

Methodology Doc Questions Is the Calculation Methodology doc clear and complete? (If not, please provide any clarifying questions you may have.) What is your opinion of the methodologies proposed in the Calculation Methodology doc to estimate UECs? Given that some product categories more challenging than others, what recommendations do you have for alternative approaches? Is our approach for using model-specific UECs instead of DEER UECs acceptable/defensible? Do you have suggestions to modify or improve the proposed calculation methodologies for a larger roll-out that would involve more products, more product categories, and more participating vendors?

UEC Questions (Optional) Are the resulting UEC estimates plausible? (If not, please provide any suggestions or observations you may have.)