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Automated Demand Response Pilot 2005/2004 Load Impact Results and Recommendations Final Report © 2005 Rocky Mountain Institute (RMI) Research & Consulting.

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Presentation on theme: "Automated Demand Response Pilot 2005/2004 Load Impact Results and Recommendations Final Report © 2005 Rocky Mountain Institute (RMI) Research & Consulting."— Presentation transcript:

1 Automated Demand Response Pilot 2005/2004 Load Impact Results and Recommendations Final Report © 2005 Rocky Mountain Institute (RMI) Research & Consulting Snowmass, CO (303) CEC April 18, 2006: Sacramento Working Group 3

2 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 2 Discussion Outline Project Background Load Impact results –Summer 2004 results –Summer 2005 results –Comparison of summer 2005 with summer 2004 results Recommendations for Future Program Design –Targeting high performance customers Physical characteristics Behavioral characteristics –Program Implementation recommendations

3 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 3 Project Background ALJ ruling October 29, 2004 to extend ADRS pilot through 2005 Research objectives: –Analyze average load impact from 2004 to 2005 –Compare performance between the two years –Evaluate and compare customer satisfaction levels from 2004 and 2005 ADRS customers: –Single family, central a/c, climate zone 3 –ADRS technology –Dynamic rate (CPP-F) –Loads adjusted for selection bias

4 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 4 Project Background Load reduction measured against “control” homes (2004 and 2005) –Single family, central a/c, climate zone 3 –No technology –No rate Load reduction measured against “A07” homes (2004) –Single family, central a/c, climate zone 3 –No technology –Dynamic rate (CPP-F) –Loads adjusted for selection bias

5 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 5 Project Background Focus load impact performance on: –2 p.m. to 7 p.m. (5 hours) Event days - “Super Peak” period Non-event days - “peak” period –Weekdays and non-holidays, July 1 - September 30 –High consumption homes (> 24 kWh ADU) Results reported –kW load reduced and total final kWh energy consumed –Utility specific results and statewide weighted average

6 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 6 Load Impact Results

7 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Load Impact Results: ADRS technology worked! 2004 statewide high consumption load impact results

8 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Load Impact Results: ADRS technology worked! ADRS load impact results by utility, high consumption (control – ADRS) only, 2004

9 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Load Impact Results: ADRS technology worked again! ADRS high consumption load impact results 2005 Statewide Event (1.42) Statewide Non-event (0.73)

10 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Load Impact Results: Performance was best when ambient temperatures were hottest High consumption ADRS percent load reductions by temperature bin and by utility, July – September 2005

11 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 11 Comparison of average peak temperatures reveals that 2005 was the hotter summer Peak Temp (º F) Statewide Average High Consumption Temperature, Super Peak Weekdays Statewide Average High Consumption Temperature, Non-event Weekdays

12 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) load reductions were less than in 2004 on event days, it was because 2005 control loads were lower 2004 control 2005 control 2004 ADRS 2005 ADRS Statewide High Consumption Event Day Load Curves (Adjusted) KW

13 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 13 but on non-event days, it was because 2005 ADRS loads were higher 2004 control 2005 control 2004 ADRS 2005 ADRS Statewide High Consumption non-Event Day Load Curves (Adjusted) KW

14 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 14 Summary of load impact results and conclusions ADRS customers successfully reduced load on event and non- event weekdays ADRS load reductions were consistent across a range of hottest temperatures above 90 o F Reductions were greatest during first two hours of Super Peak and Peak period Technology appears to be an important driver in reducing load, especially Super Peak load in high consumption homes In 2004, load reduction was best in September. In 2005 performance was best in July Customers were shifting more aggressively in 2005 than in 2004 Where present, pool made a significant contribution to reductions

15 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 15 Future Program Recommendations

16 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 16 Recommendations: Maximizing performance, minimizing costs Program benefits proportional to kwh/home Program cost proportional to cost/home Recruit into program the biggest savers Targeted recruitment strategy based on: –Physical characteristics –Behavioral characteristics Implementation recommendations to maximize performance

17 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Physical characteristics Target homes greater than 32 kWh ADU instead of 24 kWh Event Days: Average daily usage vs. percentage of total Super Peak period reduction Average Daily Usage (kWh)

18 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Physical characteristics Target customers who live in the hottest places, and include climate zone 4 Woodland (n=8) Stockton (n=52) Los Angeles (n=31) San Diego (n=25) Santa Clarita (n=3) Valencia (n=17) Saugus (n=3) SDGE SCE PGE 2005 ADRS Customer Locations

19 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Physical characteristics Look beyond air conditioning: e.g. pool pumps, electric water heating (easily interruptible loads) Average high consumption ADRS pool pump load, 2005 Average Pool Load (KW)

20 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 20 SCEPG&ESDG&E Average 2005 Super Peak load reduction, KW Average air conditioner size (tons)** % of homes > 2,000 sq. ft.42%29%23% Household income > $100,000/yr 59%10%41% *Source for all data with exception of average a/c size from Utility Home energy Survey for ADRS pilot and Statewide Pricing Pilot programs **Source for a/c sizing data fro ADRS Installer Survey conducted April-May 2004 based on respondants. 1. Physical characteristics Look for newer, larger homes with higher incomes Summary Characteristics of ADRS Homes*

21 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Physical characteristics Target areas with high total avoided costs Highest avoided costs when: –Constrained supply –Constrained delivery (T&D) –Constrained environmental quality Avoided costs vary –In space –In time Valuation standardized by CPUC and CEC

22 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Behavioral characteristics Look for customers that are: Away from home during the day Receptive to automation Receptive to learning about new technology

23 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program implementation Call events when temperatures are highest Temp ( o F)

24 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program Implementation Shift timing of Super Peak events closer to actual system peak Shift start of Super Peak to 3 p.m. Shift end of Super Peak to 5:30 p.m. Use CPP-V rate instead of CPP-F Stagger load shed of participants (when possible)

25 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program Implementation Limit consecutive Super Peak days as much as possible Customers fatigue Discomfort In interviews, a number claimed they considered opting out Pilot mostly 3-day events tested, only one 2-day event tested (July 26-27, 2004) –Unclear if dissatisfaction lower if more 2-day vs. 3-day events –Consider limiting number of consecutive days and number of consecutive events in future

26 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program Implementation Always use technology with residential demand response especially for high consumption homes Automated technology provides… –Greater load reductions –More consistent performance throughout program and conditions (e.g. temperatures) –Greater customer control –Interval data available in real-time is valuable in a variety ways Key, then is selecting a technology system that is cost effective

27 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program Implementation Always use technology with residential demand response, especially for high consumption homes 2004 Statewide High Consumption Event Day Load Curves (Adjusted) control ADRS Average reduction KW

28 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) Program Implementation Always use technology with residential demand response, especially for high consumption homes Price effect control ADRS A07 Incremental technology reduction (Approx.) KW

29 ADRS Load Impact Results and Recommendations (18 April 2006)Rocky Mountain Institute (RMI) 29 Nevada Power ACLM Pilot Load Impact Results Average Load Shed (kW), by Customer Stratum, September 2004 Average Load Shed per Hour (kW) Peak Daily Temp (°F) Strata Wt Avg. Note: Excludes pre-cooling, tests, and events with offsets < 2º F; excludes overrides and load impacts from curtailment of pool pumps on select event days, both of which are addressed separately Source: GoodWatts reports server, Nevada Power Company Load Research Group, and RMI analysis


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