Role of the Customer in Energy Efficiency and Conservation. Lisa Wood Montana s Energy Future Helena, Montana

Transcription

1 Role of the Customer in Energy Efficiency and Conservation Lisa Wood Montana s Energy Future Helena, Montana January 7-8, 2011

2 Today s breakfast talking points What s achievable with energy efficiency and demand response? EE potential estimates DR potential estimates What about low income customers? Engaging the customer what s new? Are we on cusp of the 2 nd electric revolution? 2

3 Ratepayer-Funded EE ($Billion, nominal) U.S. electric efficiency budgets growing rapidly ( ) Electric Efficiency Budget, and 2020 LBNL Forecast 12.4** * *LBNL MEDIUM Forecast **LBNL HIGH Forecast 3

4 Utilities play major role in ratepayer-funded electric efficiency budgets in U.S. Total Electric Efficiency U.S. Budgets Utility Non-Utility Utility Share of Total Percent Increase 2007 $2,722,788,884 $2,413,639,443 $309,149,441 89% 2008 $3,165,329,920 $2,704,072,429 $461,257,491 85% 16% 2009 $4,370,445,097 $3,796,110,308 $574,334,789 87% 38% 2010 $5,433,087,642 $4,789,681,107 $643,406,535 88% 24% Source: IEE Brief. Summary of ratepayer-funded electric efficiency impacts, expenditures, and budgets. January

5 TWh U.S. electric efficiency savings projected to exceed 100 TWh in U.S. Electric Efficiency Impacts ( & 2010 Forecast) * * IEE Projection Source: IEE Brief. Summary of ratepayer-funded electric efficiency impacts, expenditures, and budgets. January

6 Energy efficiency potential programs plus codes & standards 6

7 Energy Efficiency Savings TWh Energy efficiency savings growing rapidly but significant potential for much more savings TWh represented about 2% of total usage in CEE (Actual Achieved) 85.3 IEE (Codes and Standards Aggressive) EPRI (Maximum Achievable) McKinsey

8 How much EE can we expect by 2020? Energy efficiency potential forecasts cover wide range exact number doesn t really matter because there is so much left to do! EPRI predicts 372 TWh (maximum achievable potential) by 2020 (Jan. 2009) McKinsey: predicts 1,080 TWh saved by 2020 (July 2009) In 2008, electric efficiency programs saved 85.3 billion kwh (CEE) Enough to power 7.4 million homes for one year 58 million metric tons of CO 2 avoided In 2009, electric efficiency programs save 92.6 billion kwh (CEE) Enough to power 8 million homes for one year 66 million tons of CO 2 avoided Plus the potential savings due to codes and standards is huge and very cost effective! 8

9 A few words on demand response 9

10 Utility scale smart meter deployments, plans, and proposals about 65 million meters will be deployed (50% of US households over next 5-7 years). Will this drive retail DR? Deployment for >50% of end-users Deployment for <50% of end-users *This map represents smart meter deployments, planned deployments, and proposals by investorowned utilities and large public power utilities. IEE: September 2010 update 10

11 GW Saved GW Saved Potential peak demand reduction due to demand response wide range of estimates. Expanded BAU and MAP realistic in my view! FERC (June 2009): Peak Demand Savings due to Demand Response EPRI (January 2009): Summer Peak Demand Savings due to Demand Response BAU Expanded Achievable Full RAP MAP Technical BAU Potential Participation Potential (4%) (9%) (14%) (20%) (4.6%) (6.8%) (16.9%) Baseline Forecast (NERC): 950 GW by Baseline Forecast: 964 GW by 2020 (951 GW by 2019) 11

12 Portfolio of DR sources for peak demand savings Commercial Industrial Residential Price-Response DLC-Water Heating DLC-Central AC Price-Response Interruptible Demand DLC-Process Price-Response Interruptible Demand DLC-Other Direct Control-Lighting DLC-Cooling ,000 10,000 15,000 20,000 25,000 Cumulative Summer Peak Demand Savings (MW) Source: EPRI Report # January

13 % Reduction in Peak Load We know customers respond to prices; response even greater with technology 60% TOU TOU w/ Tech PTR PTR CPP CPP w/ w/ Tech Tech RTP RTP w/ Tech 50% 40% 30% 20% 10% 0% Pricing Pilot Source: Ahmad Faruqui, Brattle. 13

14 Historical vs. projected U.S. summer peak load reduction (GW) due to energy efficiency and demand response (EIA and EPRI Report # ) GW Total U.S. Summer Peak Load Reduction due to EE and DR Sources: EIA Form 861, EPRI, "Assessment of Achievable Potential from EE and DR Programs in the U.S." (2009) GW GW GW Actual Realistically Achievable Potential (EPRI, 2009) Source: EPRI Report # January

15 Percent of Summer Peak Demand EPRI: EE and DR programs together can reduce 8% (RAP) to 15% (MAP) U.S. summer peak demand in % 18% 19.5% 16% 14% 12% 15.3% 14.0% Split 7% from DR and 7% from EE 10% 8% 6% 8.2% 4% 2% 0% 4.9% 2.2% Maximum Achievable Potential Realistic Achievable Potential 2010 Source: EPRI Report # January

16 What about impact on low income customers? 16

17 Low income customers and dynamic pricing Two viewpoints Low income customers have flatter load shapes than average residential customers so would immediately benefit from dynamic pricing. Low income customers use less energy and therefore have limited ability to shift load from peak to off peak hours so would be harmed from dynamic pricing. Empirical evidence from five studies shows the following: Many low income customers can benefit from dynamic prices even without shifting load Low income customers do shift their energy usage in response to price signals Source: IEE Whitepaper, The Impact of Dynamic Pricing on Low Income Customers, September

18 Percent of Customers in Sample Low income customers benefit from smart prices even without shifting load 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Percent of Sample with Immediate Bill Decreases on CPP Rates (Even with No Load Shifting i.e., No Demand Response) 51% Residential 65% Low Income 61% 79% CPP Rate Design #1 CPP Rate Design #2 Source: IEE Whitepaper, The Impact of Dynamic Pricing on Low Income Customers, September

19 Low income customers do respond to smart prices the same as or less than other customers Program Results BGE 2008: Known Low Income vs. Known Average Customer Low Income Peak Reduction Average Peak Reduction Varies depending on rate type; low income customers respond similarly to average customer Low Income vs. Average 100% CL&P's PWEP Program: Known Low Income vs. Known Average Customer Varies depending on rate type; low income customers respond similarly to average customer 100% CL&P's PWEP Program (PTP high): Hardship vs. Average 13% 20% 67% Pepco DC (price only): Low Income vs. Average Residential 1 11% 13% 85% PG&E SmartRate 2008: CARE vs. Average 11% 17% 66% PG&E SmartRate 2009: CARE vs. Average 8% 15% 50% California SPP: Low Income vs. Average 11% 13% 84% California SPP: CARE vs. Average 3% 13% 22% Note: For the PepcoDC pilot, the average residential response excludes low income customers that qualify for the RAD program 19

20 How do dynamic prices affect low income customers: Conclusions based on 5 studies plus a load research sample Dynamic prices are not harmful to low income customers. In fact, just the opposite is true 65-79% are instant winners even without load shifting due to flatter-than-average load shapes. All five studies cited found that low income customers do respond to dynamic prices; evidence on the magnitude of their responsiveness is mixed. The vast majority of low income customers are likely to benefit from dynamic pricing. Restricting access to dynamic rates may, in fact, be harmful to a large percentage of low income customers. Source: IEE Whitepaper, The Impact of Dynamic Pricing on Low Income Customers, September

21 Engaging the customer 21

22 What s new in the EE space? Focus on the customer 22

23 Educating and engaging the customer 23

24 Utilities are partnering with vendors and using home energy reporting, goal setting, and rewards as motivators to save Utilities send out monthly Energy Reports to motivate customer action. Make comparisons to neighbors Flagship Product Home Energy Report Results are measured and accepted as an efficiency resource (average cost is $0.03 per kwh saved). Efficiency Portal Customers save energy (2-3%). Could save a lot more if coupled with technology. Utility Partners: Large-Scale Customer Engagement 24

25 Example: Commonwealth Edison launched major AMI and rate pilot with customer centric design (2010) Customer education innovations Web energy management tools including comparisons; educational modules; monthly updates In home displays, programmable communicating thermostats Community support Customer energy management assistance Via bill comparisons, web tools, call center, monthly educational meetings About 131,000 customers 8,500 smart rates responders 120,000 smart energy managers 25

26 The customer s energy management options Demand response - smart rates Many pilots but few smart (dynamic) rate opt out deployments In US, state regulators moving toward peak time rebate (PTR). Demand response direct load control Decades of experience Move to measurable and verifiable DLC DLC 2.0 Information induced conservation Info alone or information with technology PHEVs Distributed generation 26

27 Big question: How will we motivate customers to be smart energy managers? We know customers benefit from smart rates on smart meters But, for those customers with smart meters but no smart rates (i.e., the vast majority right now) or those without smart meters, empowering customers to be smart energy managers is the key Consumers are ready to be smart energy managers. Need to educate and engage the customer Technology can make a big difference 27

28 Smart meter platform will take EE and DR to new levels 2 nd electric revolution! It s all about giving customers the tools and the know-how to be smarter energy consumers. Educate, educate, educate! HAN communication SmartMeter communication 28

29 For more information, contact: Lisa Wood Executive Director Institute for Electric Efficiency 701 Pennsylvania Ave., N.W. Washington, D.C