Estimating Large Customer Demand Response Market Potential: A Scoping Study

1. Estimating Large Customer Demand Response Market Potential:A Scoping Study Charles Goldman E. O. Lawrence Berkeley National Laboratory CAGoldman@lbl.gov Co-authors: N. Hopper, R. Bharvirkar B. Neenan and P. Cappers (Utilipoint) DRCC Webinar March 16, 2007

2. Overview • Review of market potential concepts and methods • Proposed methodology for estimating DR market potential of large customers for certain types of DR programs • Data inputs: participation rates and elasticity values • Market potential simulation results • Recommendations

3. Introduction • Why estimate DR market potential? • To determine how much DR is available? from which market segments and DR options? • To develop the demand-side assessment section in a resource plan • To assist with planning or screening of potential demand response programs • To determine if DR goals are feasible • Goals of this study: • Review methods and present conceptual framework for estimating DR market potential for large customers • Compile and publish available data inputs—disaggregated participation rates and elasticity values • Identify gaps in data and methodologies

4. Technical Potential Less Meaningful for DR than Market Potential • Analytic framework used in EE potential studies: • Technical potential—complete penetration of all energy efficiency measures that were technically feasible • Economic potential—the subset of the technical potential that is cost-effective to implement • Market potential—”achievable” potential taking into account customer cost-effectiveness criteria, awareness, assumed levels of pgm incentives & activity • Conceptual and practical issues in applying framework to DR: • EE assumes constant service/amenity while DR depends on customer willingness and behaviors to curtail/shift load and accept reduced service/amenity level • Policymakers are interested in market—or achievable—potential directly

5. Approaches Used to Estimate DR Market Potential • Customer surveys— ask customers about expected actions if offered hypothetical DR options; derive participation rates and expected load curtailments • Benchmarking—use participation rates and load reductions observed among customers in other jurisdictions • Engineering approach—bottom-up engineering techniques (similar to EE market potential studies); apply assumed participation and response rates to data on local customers, loads or equipment stock • Elasticity approach—estimate price elasticities from customers exposed to DR programs or dynamic pricing tariffs and apply them (after determining expected participation levels) to estimate load impacts

6. What Makes DR Different from EE? • The nature of participation: • For DR, participation involves two steps: • enrolling in a program or tariff • providing load reductions during specific events (e.g., system emergencies or periods of high prices) • For EE, “participation” is a one-time decision to invest in energy-efficiency measures or equipment. • The drivers of benefits: • DR benefits hinge on customer behavior in response to hourly prices, financial incentives, and/or system emergencies • EE cost savings largely depend on technical characteristics and performance of installed equipment or measures • The time horizon and valuation of benefits: • From a customer perspective, DR benefit streams may be highly variable, and depend on short-term price fluctuations or emergency curtailment incentives • EE investments provide a multi-year stream of cost savings that the customer can value at expected retail energy rates

7. Five Steps to Estimating DR Market Potential • 1) Establish study scope—identify target population and types of DR options considered • 2) Customer segmentation—identify customer market segments • 3) Estimate net program penetration rates—use available data to estimate customer enrollment in voluntary programs and exposure to default pricing programs • 4) Estimate price response—develop elasticity estimates for various DR options, customer market segments, and factors found to influence price response • 5) Estimate load impacts —use info from steps 2 to 4 to estimate the amount of DR that can be expected from the target customer population at utility at reference price (or incentive level).

8. Selecting a Measure of Price Elasticity • Most studies of large customer price response estimate substitution or arc elasticities • Substitution elasticity: • change in peak: off-peak usage in response to a 1% change in peak: off-peak prices • requires several observations per customer • Arc elasticity: • % change in usage / percent change in peak price • Very easy to estimate, but limited explanatory power

9. DR Options Evaluated

10. Data Sources 1 Pacific Gas & Electric (PG&E), Southern California Edison (SCE) and San Diego Gas & Electric (SDG&E)

11. Participation Rates: Selected Values • DR participation = • enrollment in voluntary programs/tariffs • Or remained on default RTP tariff • Participation rates collected for 5 market segments and 4 customer size groups • Some data were not available—used “expert judgment” (red-italicized values)

12. Average Elasticity Values elasticity of substitution arc elasticity

13. Putting it all together: Estimating Market Potential at utility in Northeast • Simulation: Demonstrate approach by estimating DR market potential at a Northeast utility • Utility service territory characteristics: • Relatively small, urban utility • Peak demand of large non-residential customers is ~1700 MW (40% of utility’s peak demand) • Mostly commercial/retail, gov’t/education and healthcare facilities • Fewer manufacturing facilities than most suburban or rural areas • Load impacts estimated assuming that RTP peak and DR “event” prices were $500/MWh • Estimated DR market potential under several scenarios to illustrate effects of various factors (e.g., high participation rates, customer response at high prices)

14. Large-customer DR Market Potential at a NE Utility: Base Case Results • Base case results indicate market potential of up to 3% of class-peak demand for each DR option individually • Largest customers (> 2 MW) provide bulk of load response • Implicit Implication: necessary to target smaller customers too

15. Impact of Program Participation Rates on DR Market Potential • With very aggressive marketing (i.e., 3x current participation rates), market potential for 3 DR options increases to ~3 to 7% of eligible large customers’ peak demand at this utility (or ~75-165 MW)

16. Sensitivity Analysis: Accounting for Response at High Prices • Base-case elasticity estimates based on response over a range of prices • “High-price” case refines elasticity estimates to reflect response at higher prices (>$450/MWh) • Default hourly pricing—higher substitution elasticities at higher prices • Price response event program—removing arc elasticity observations based on lower prices produces lower average estimates because customer response (% change in load) is similar but the price differential (the denominator) is greater  illustrates limitation of arc elasticities

17. Summary of Findings: • Simulation exercise provides “reasonable range” of DR market potential values of large non-residential customers for DR options • Note: load impact results are not additive • Other analysts can use elasticity values (and participation rates) as starting point for market assessments for these five DR options • Customer participation rates have largest impact on DR market potential estimates; but represent the largest data uncertainty • Need for more info on eligible target population • Drivers for participation • Important to refine and disaggregate elasticity estimates for different groups of customers

18. Recommendations:A Market Assessment Research Agenda • Need to develop broader information base on customer participation and price responsiveness for more robust DR Market Assessments • 1) Link Program Evaluation to Market Potential Studies: • Evaluations of DR programs should systematically collect data on: • customer characteristics, • hourly loads and prices, • drivers of customer participation and response, and • size and characteristics of the target/eligible population • 2) Program Participation: • Develop predictive methods for estimating participation rates in DR programs and dynamic pricing tariffs that incorporate customer characteristics and other factors that drive participation. • 3) Price Response: • Estimate price elasticity values for different market segments, accounting for the relative impact of driving factors. Where possible, estimate demand or substitution elasticities rather than arc elasticities.

19. Recommendations (cont): • 4) Assess the Impacts of Demand Response Enabling Technologies: • Document the impacts of specific DR enabling technologies on customer participation and load response (given limited evidence and mixed results from existing evaluations) • At a minimum, gather information on onsite generation, peak load controls, EMCS and EIS. • 5) Publicize Results: • Current situation: Data reporting and methods are not standardized; disaggregated results are often not transparent • Need to explore ways to pool customer-level data, while protecting customer confidentiality.

20. LBNL Reports on DR Market Potential • “Estimating Demand Response Market Potential among Large Commercial and Industrial Customers: A Scoping Study,” • Charles Goldman, Nicole Hopper, Ranjit Bharvirkar (LBNL), Bernie Neenan, and Peter Cappers (Utilipoint), LBNL-61498, January 2007. Reports available at: http://eetd.lbl.gov/ea/EMS