1. December 18, 2004 ADRS Load Impact R OCKY M OUNTAIN I NSTITUTE

2. 1 Executive Summary – ADRS homes with technology consume less on-peak energy than comparable homes* on standard rates or the CPP-F; the technology benefit is even stronger on Super Peak days  On non-event weekdays from July through September, average ADRS homes with technology consumed less on-peak energy (between 2 p.m. and 7 p.m.) than comparable homes* on standard tiered-rates (A03 subset) or on the SPP CPP-F rates (A07 subset) –ADRS homes with technology used 3.7 kWh less on-peak electricity per home (34% lower) than comparable homes* on standard rates (A03 subset) –ADRS homes used less on peak than CPP-F homes (A07 subset) as well, 1.6 kWh lower on average (savings of 18%)  Over the twelve Super Peak days, technology-enabled ADRS homes consumed considerably less on-peak energy per home than their comparable control groups –ADRS homes consumed 7.4kWh (or 50%) less Super Peak energy per day than homes on standard rates (A03 subset) –With ADRS technology, participants consumed 2.5 kWh less super peak electricity per day (26% savings) than comparable homes in the SPP on CPP-F rates (A07 subset) Note: ADRS participants were enrolled on a first-come, first-served basis; results were not modified to address potential self-selection bias * Homes in the treatment and control groups are comparable in that they all lie in Climate Zone 3 and are single-family (detached) units with central air conditioning; further, raw load data for the A03 and A07 control groups have been weighted according to the distribution of the ADRS population with respect to utility and historical consumption strata

3. R OCKY M OUNTAIN I NSTITUTE 2 Executive Summary – Performance of technology-enabled ADRS homes improved relative to both control groups from July to September  ADRS technology enabled homes reduced load by ~50% consistently across the summer Super Peak events relative to homes without technology or rates (A03 subset)  Relative to CPP-F homes (A07 subset), ADRS homes’ performance improved throughout the summer. Load reduction during the Super Peak hours increased from 25% in July and August to 31% in September  This observed improvement in ADRS performance does not seem to be explained by weather differences or other variables other than occupant behavior  Technology enabled-ADRS homes’ reduction of Super Peak load decreased over the five- hour Super Peak period, but still out-performs the comparable subset of A07 homes on the CPP-F rate without technology. Performance again improved in September, when the load reduction was sustained better in the last 1-2 hours of the Super Peak events  Total daily energy consumption of ADRS houses was 5% lower than that of the comparable subset of A03 homes on non-event weekdays and 12% lower on Super Peak days. Compared to the comparable subset of A07 homes, ADRS homes’ total daily usage was 2% lower on both Super Peak and non-event weekdays

4. R OCKY M OUNTAIN I NSTITUTE 3 Executive Summary – more granularly, ADRS proved very useful to pool owners and to moderate/high-consumption homes; less so for homes with modest consumption  Where present, pool pumps make a significant contribution to reduction of peak load vs. A03* –Relative to a control group of pools (from a Nevada Power load management program), ADRS pools reduce on-peak / Super Peak consumption by 2.8 kWh per day –For the average ADRS home with a pool, this 2.8 kWh reduction is 48% of the 5.8 kWh total reduction on non-event weekdays and 29% of the 9.5 kWh expected on Super Peak days –As just 44 of the ~175 ADRS have pools, reductions from pool loads comprise roughly 20% of total peak load reduction and 10% of the reduction in Super Peak consumption  Breaking down the population by energy-consumption stratum, technology appears to be an important driver in reducing Super Peak load for high-consumption homes, while the price signal appears to be a stronger driver of reduction in low-consumption homes  Household level analysis reveals that the majority of ADRS homes (52%) actively experimented with the technology to control home energy use, while an additional 7% made minor adjustments. Furthermore, about 10% of the ADRS population are “Supersavers,” reducing load at 2 p.m. by more than 30% consistently across the summer months on a daily basis * Total reduction of on-peak/Super Peak load by homes with pools is calculated algebraically rather than by direct measurement

5. R OCKY M OUNTAIN I NSTITUTE 4 Executive Summary – ADRS load reductions relative to both control groups are statistically significant for the high consumption homes  As an indication of the statistical quality of the results, the coefficient of variation allows us to compare relative variation between populations  The coefficient of variation (CV), which allows for comparison of the relative variation of values between populations, is defined as the standard deviation (SD) of a sample divided by the sample’s mean value  A CV value less than ~0.5 implies that the statistic is significant within a ~95% confidence interval  A CV value greater than ~1 implies that the statistic is not significant (less than ~70% confidence) Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Coefficient of Variation in Load or Reduction (2pm-7pm) Population Non-Event Weekday Super Peak Day ADRS consumption A03 consumption A07 consumption Low Stratum 1.18 1.00 1.26 High Stratum 1.05 0.74 Low Stratum 1.16 1.18 0.83 1.26 High Stratum 0.99 0.87 0.93 A03-ADRS reduction A07-ADRS reduction 0.82 1.84 0.37 0.54 0.58 1.85 0.18 0.29 A03-A07 reduction 0.56 0.63 1.24 0.25

6. R OCKY M OUNTAIN I NSTITUTE 5 High CVs for load-reduction in low-consumption ADRS homes relative to A07 homes confirms our hypothesis that those results are not statistically significant, due to small and diverse samples  Variation in total consumption of ADRS and control groups is high for both high- and (especially) low-consumption homes.  The CV declines when we look at the difference in consumption between ADRS and each of the control groups, particularly for Super Peak days; this suggests that the variations among the ADRS homes loads and the control groups are not independent, but are correlated ( i.e., relatively high or low values tend to occur at similar times in each population  The CV values for load reductions of high-consumption ADRS homes relative to both control groups are substantially less than 0.5, suggesting that results are statistically significant at a 95% confidence level  Results of ADRS load reductions relative to control groups for low-consumption homes are mixed –The CV for low-consumption ADRS load reductions relative to the A03 control group and between low-consumption A03 and A07 homes are 0.6-0.8, suggesting that the results are statistically significant with ~80-90% confidence –The CV for low-consumption ADRS load reductions relative to the low consumption A07 control group confirms our hypothesis that these results are not statistically significant –Similarly, the low-consumption A07 control group load reductions relative to the low- consumption A03 control group on Super Peak days are not statistically significant –The small size of the low-consumption home populations seems to limit statistical quality

7. R OCKY M OUNTAIN I NSTITUTE 6 Executive Summary – An initial comparison with CRA’s results for the SPP of comparable homes indicate that ADRS savings relative to A07 homes may be too low  It appears that the price response of our pilot’s A07 control population (subset of the SPP A07 population) is greater than the performance observed in the 2003 statewide pricing pilot. –The ADRS study finds a 32% savings for its subset group of A07 homes that are single-family with central air, relative to comparable A03 control sample in the pilot. Charles Rivers Associates (CRA),using a different methodology shows in their Summer 2003 final report (August 9, 2004), Table 5-9 for zone 3, that single family homes saved 14.27%, and with central AC saved 13.45%. –Whether the A07 population is representative of residential customers statewide is still an open issue –The price response performance of the A07 population continues to be studied in detail in the statewide pricing pilot with, a larger sample population –Collaboration with CRA to investigate into the nature of these differing results has been proposed for 2005.  For ADRS homes, pretreatment data was not adequately available to investigate consumption behavior prior to participating in the pilot.

8. R OCKY M OUNTAIN I NSTITUTE 7  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analytical Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

9. R OCKY M OUNTAIN I NSTITUTE 8 Project Background  The Automated Demand Response System (ADRS) program is an additional and parallel pilot alongside the Statewide Pricing Pilot (SPP)  ADRS focuses on the further impact of energy management technology on residential customers in addition to the time-differentiated tariffs experienced under the SPP’s critical peak pricing  Rocky Mountain Institute (RMI) was tasked to conduct an independent analysis and evaluation of the ADRS pilot with respect to additional load impact and economic efficacy  Demand response evaluation of ADRS must answer two questions: –What is the range of demand response/load drop observed? –Is the range and average demand drop larger or smaller than that observed in the larger statewide pilot (SPP), given comparable rates and weather conditions?

10. R OCKY M OUNTAIN I NSTITUTE 9 Overview of ADRS Experimental Design  The ADRS pilot installed full-scale system technology, capable of automatically controlling the electrical load of multiple appliances in a limited number of residential customers across the three participating CA IOUs (SCE, PG&E, SDG&E)  ADRS targeted 175 participants in the 3 major California IOU service territories: –75 SCE –75 PG&E –25 SDG&E  ADRS participants were recruited only from climate zone 3 of the four climate zones defined by the SPP and were required to have central air conditioning  Participants were placed on the SPP’s critical peak pricing-fixed (CPP-F) tariff –Time of use tariff with rates differentiated by time of day  Off peak (weekdays, excluding 2 p.m. – 7 p.m.; all hours weekends and holidays)  On peak (weekdays 2 p.m. - 7 p.m.)  Super Peak (select weekdays 2 p.m. – 7 p.m.) –Maximum of 12 super peak days during the summer season; 15 total annually –Maximum of three consecutive super peak days

11. R OCKY M OUNTAIN I NSTITUTE 10 Overview of ADRS Technology  Invensys’ GoodWatts technology was selected for the ADRS pilot  The ADRS control technology includes: –Two-way communicating interval whole house meter –Wireless internet gateway and cable modem –Smart thermostat(s) –Load control and monitoring device (LCM) to manage select loads (e.g., pool pump) –Web-enabled user interface and data management software  At all times, ADRS displays the current price of electricity, both on the thermostat and on the Web  Via the Internet, pilot participants can –View real time interval demand and trends in historical consumption –Set climate control and pool runtime preferences –Program desired response to increase in electricity price  Change in thermostat temperature set point  Reschedule operation of LCM controlled appliance (e.g., pool pump)  Once programmed, technology automatically changes operations in response to electricity prices

12. R OCKY M OUNTAIN I NSTITUTE 11 Pilot Design  Pilot customers were recruited from owner-occupied, single-family homes from climate zone 3 in geographies served by appropriate cable providers and in zip codes identified by the participating utilities  Otherwise, pilot homes were recruited at random regardless of historical consumption, although homes were screened for eligibility with respect to presence of central air conditioning, within prescribed zip codes  Pilot homes were screened for availability of other loads (i.e., swimming pool pumps and spas), but not disqualified from participation in their absence  Pilot homes were segmented into two strata by historical consumption according to the methodology established for the SPP –Modest consumers, those with summer average daily usage below 24 kWh, comprised the low stratum –All other homes, those with Summer ADU above 24 kWh, fell into the high stratum

13. R OCKY M OUNTAIN I NSTITUTE 12 Recruitment of pilot participants  Eligible customers were mailed an announcement describing the pilot and benefits of participation –Technology and user tools for greater control of energy in the home –Potential to achieve bill savings by managing consumption  Package indicated that customers would be paid incentives totaling $100 –$25 for enrollment and completion of the home energy survey –$75 payable at the end of the pilot for continuous participation and completion of mid- and end-of-pilot customer satisfaction surveys –Incentive payment parallels structure of offer to SPP participants  Enrollment packages were then mailed to the customers; the packages included enrollment application and informed prospective participants of three avenues by which to enroll: –Mail in enrollment application –Phone call –ADRS website  Reminder postcards were sent out noting deadline for enrollment  Third-party call center (Cypress) was contracted to handle inbound enrollment calls and for outbound calls as needed to fulfill enrollment goals by target deadline

14. R OCKY M OUNTAIN I NSTITUTE 13 The analytical approach accounts for changes in ADRS enrollment  Installation completed for 175 homes by mid-June (76 SCE, 75 PG&E, and 24 SDG&E)  With opt outs, total enrollment declined to 164 active participants by October  ADRS analysis is executed on a per home basis  Data from homes that ultimately opted out is included in the analysis for the period during which they both were subject to the CPP-F rate and had use of GoodWatts Total Program Participation, June–September, 2004 72 70 22 PG&E SDG&E SCE

15. R OCKY M OUNTAIN I NSTITUTE 14 Design and selection of control group  The SPP collects interval meter data on many customers for purposes of program evaluation. Populations selected for the SPP were intended to be representative of the statewide residential population  One SPP population, known as A03, is comprised of homes that: –Are on standard, tiered rates –Do not possess ADRS technology, and –Are unaware of their role as a control group for the SPP or ADRS  A second SPP population, known as A07, is comprised of homes that: –Voluntarily enrolled to test the CPP-F experimental rate –Were not provided any additional technology by their utility  The two SPP populations were filtered to only single-family homes in climate zone 3 with central air, SPP populations both used as control groups against the ADRS population  The subset of single-family, A03 homes with central air in climate zone 3 is used to assess the total ADRS impact of technology and CPP-F rate  The subset of single-family, A07 homes with central air in climate zone 3 is used to assess the incremental impact of ADRS technology over and above SPP rate impacts

16. R OCKY M OUNTAIN I NSTITUTE 15 Design and selection of control group Characteristics of ADRS and Control Group Populations and Distribution of Homes, as of September 2004 A03 A07 Rate Standard tiered-block pricing CPP-F Technology Not Provided Price Response Monthly billing Manual shift & save ADRS CPP-F GoodWatts Automated shift & Save Pools Penetration 23.1% 23.7% 25.6% Participants PGE SDGE SCE PGE SDGE SCE Low Stratum 2 2 3 3 14 10 1 1 16 High Stratum 12 3 3 22 21 5 5 38 Total 14 6 6 36 31 6 6 54 PGE SDGE SCE 22 15 4 4 49 7 7 65 71 22 69

17. R OCKY M OUNTAIN I NSTITUTE 16  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analytical Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

18. R OCKY M OUNTAIN I NSTITUTE 17 Sources of Load Data  Control Groups: –Revenue-grade utility meters measure time of use consumption in 15-minute intervals –Data collected from meters on monthly basis and aggregated and distributed to RMI ~ six to eight weeks following close of each month  SCE and SDG&E transferred data directly to RMI  PG&E meter data for ADRS homes were posted on a secure website for direct download  ADRS participants –GoodWatts meters report demand and consumption data for all utilities in near real-time –Although data from Invensys meters proved commensurate with utility revenue-grade interval meters in pilot testing, utilities chose to rely upon utility meters and manual collection of data for ADRS participants –Data was aggregated and reported to RMI with control group data –In an effort to speed availability of data for load impact analysis, Invensys data was used for SCE service territory for month of September in response to administrative issues in scheduling an accelerated final read of revenue meter –GoodWatts load control monitors (LCM) provide 15-minute interval load data for pool pumps

19. R OCKY M OUNTAIN I NSTITUTE 18 Sources of Additional Data  Address/zip code information were collected for treatment group homes and extracted from SPP database for A07 control group homes  Hourly outdoor temperature data at the zip code level were provided by Invensys via weather data subscription service  Data on home characteristics were collected to help gain greater insight into impact findings –Installation survey collected air conditioning and pool pump nameplate data –SPP Customer Characteristics Survey gathered information on appliance saturation, house size, and demographics for both treatment and control group homes

20. R OCKY M OUNTAIN I NSTITUTE 19  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analytical Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

21. R OCKY M OUNTAIN I NSTITUTE 20 Methodology for Analysis of Energy Impacts  Daily 15-minute interval load data used to construct average load profiles for homes in the pilot and each of the two control groups  Comparison of load curves is the primary means of analysis –The differences in mean load profiles of the A03 control group versus ADRS participants reflect the overall impact of ADRS enabling technology in conjunction with time-varying rate –The differences in mean load profiles of the A07 control group versus ADRS participants reflect the incremental impact attributed to the ADRS enabling technology  Differences were studied on both Super Peak days and non-event weekdays  Weekends and Holidays are excluded from the analysis –Weekends and holidays are charged only off-peak rates within the CPP-F experimental rate structure –Occupancy patterns on these days are distinctly different from weekdays; there is typically higher and more constant occupancy, resulting in higher loads relative to weekdays

22. R OCKY M OUNTAIN I NSTITUTE 21 Methodology for Analysis of Energy Impacts, continued  Results are reported in terms of 5-hour averages (duration of 2 p.m. – 7 p.m. on peak and Super Peak periods) and hour-by-hour reductions  Results are reported state-wide and sample average is weighted according to distribution of participants by utility for each customer stratum  Greater granularity is shown as well, with results broken out by consumption strata  Trends in impact across the summer months are reported  Load reduction is also analyzed in the context of peak daily temperature for both super peak pricing days and typical non-event days to test two competing hypotheses: –Controllable load increases with outside temperature since air conditioning demand also increases –Controllable load decreases with outside temperature as homeowner willingness to contribute decreases and rate of overrides increases

23. R OCKY M OUNTAIN I NSTITUTE 22 Methodology for Analysis of Energy Impacts, continued  A03 and A07 load data were weighted according to the distribution of the ADRS population (by utility and consumption strata) so as to permit direct comparison among populations which vary by geography, weather, and baseline consumption –For each month, A03 and A07 load data were recorded per utility and strata (high/low) –The data were then multiplied by a constant, reflecting ADRS population distribution per utility and strata (i.e., 32% high-stratum ADRS homes in SCE territory)  Customers that opted out of A07 were included in the analysis for the period during which they were subject to the CPP-F rate and excluded at the point of rate expiration; the A07 opt outs contributed to less than a one percent increase in average A07 monthly load and therefore the impact on results was negligible  A03 and A07 results were not adjusted based on pool penetration to match the ADRS population –Control group pool loads have not been measured separately –Difference in pool ownership between A03 and A07 homes vs. ADRS homes yielded less than one percent decrease in total average control group load

24. R OCKY M OUNTAIN I NSTITUTE 23  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analytical Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

25. R OCKY M OUNTAIN I NSTITUTE 24 On non-event weekdays, technology enabled ADRS homes consumed less on-peak energy than homes on standard tiered rates (A03) or the SPP CPP-F rate (A07) Average Non-Event Weekday Load Profile July through September - All Homes Electric Load per Home (kWh/hr) Time of Day  On Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: Non-event weekdays exclude weekends, holidays and Super Peak event days Summer period is defined as July through September - June data was excluded because its results differed significantly due to unfamiliarity with technology, lower average temperatures, and lack of event days. Difference in On-Peak Usage 2.1 kWh3.7 kWh5-hr Total 0.43 kWh/hr0.74 kWh/hrAverage A03-A07A03-ADRS 19%34% % Reduction 1.6 kWh 0.31 kWh/hr A07-ADRS 18% A03 ADRS A07

26. R OCKY M OUNTAIN I NSTITUTE 25 ADRS technology enabled homes further reduced their load in comparison to standard tiered rate or SPP CPP-F customers during Super Peak hours on the 12 event days Average Event Day Load Profile July through September - All Homes Electric Load per Home (kWh/hr) Time of Day  Super Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: Load data for the A03 and A07 control groups has been weighted according to the distribution of the ADRS population with respect to utility and historical consumption strata. Difference in Super Peak Usage 4.8 kWh7.4 kWh5-hr Total 0.96 kWh/hr1.47 kWh/hrAverage A03-A07A03-ADRS 32%50% % Reduction 2.5 kWh 0.51 kWh/hr A07-ADRS 26% A03 ADRS A07

27. R OCKY M OUNTAIN I NSTITUTE 26 Average Reduction in Super Peak Consumption, All Homes on Event Days Source: Utility Data, RMI analysis ADRS technology-enabled homes reduced load consistently across the summer events, though performance vs. CPP-F homes improved in September Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. ADRS % Reduction from A03 A07 % Reduction from A03 ADRS % Reduction from A07 A03-ADRS A03-A07 A07-ADRS Consumption (kWh)

28. R OCKY M OUNTAIN I NSTITUTE 27 Reduction of Super Peak load for all ADRS homes decreased over the five hour peak period, but continued to out-perform the A07 homes on the CPP-F rate without technology Hour of the Super Peak Period (2 p.m. - 7 p.m.) ADRS % Reduction from A07 ADRS % Reduction from A03 A07 % Reduction from A03 Average Percent Reduction in Super Peak Consumption, All Participants for all Summer Events % Reduction in Super Peak Consumption Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis

29. R OCKY M OUNTAIN I NSTITUTE 28 On a month-by-month basis, however, hourly reduction was more sustained in September, compared to the A07 homes % Reduction from Control Group Average Reduction in Super Peak Consumption, All Participants Aug A07-ADRS July A07-ADRS Sept A07-ADRS Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Hour of the Super Peak Period (2 p.m. - 7 p.m.)

30. R OCKY M OUNTAIN I NSTITUTE 29 As September events days were slightly warmer than the others, temperature does not explain September’s improvement in ADRS performance, suggesting improvements may be behavioral Statewide Average Peak Temperature, Non-event Weekdays Peak Temp (º F) Statewide Average Peak Temperature, Super Peak Weekdays

31. R OCKY M OUNTAIN I NSTITUTE 30 Reduction in Super Peak Consumption (kWh/hr) High Temp ( o F) Average Reduction In Super Peak Consumption Relative to Homes on Standard, Tiered Rate, All ADRS Homes Event Date ADRS reduction in Super Peak consumption varied from 1.1 to 1.9 kWh/hr relative to homes on standard tiered rates (A03) Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population.

32. R OCKY M OUNTAIN I NSTITUTE 31 % Reduction in Super Peak Consumption High Temp ( o F) Average Reduction In Super Peak Consumption Relative to Homes on Standard, Tiered Rate, All ADRS Homes Event Date ADRS technology-enabled homes consistently reduced Super Peak load by ~50% vs. homes on standard tiered rates (A03) Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis

33. R OCKY M OUNTAIN I NSTITUTE 32 Reduction in Super Peak Consumption (kWh/hr) High Temp ( o F) Average Reduction In Super Peak Consumption Relative to Homes on CPP-F Rate, All ADRS Homes vs. A07 Event Date Super Peak consumption relative to homes on the CPP-F rate without technology (A07) was consistently lower by ~0.5 kWh/hr on average, improving somewhat in September Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population.

34. R OCKY M OUNTAIN I NSTITUTE 33 % Reduction in Super Peak Consumption High Temp ( o F) Average Reduction In Super Peak Consumption Relative to Homes on CPP-F Rate, All ADRS Homes vs. A07 Event Date On a percentage basis, however, the load reduction during Super Peak hours of ADRS homes relative to homes on the CPP-F rate without technology (A07) varied more, averaging 26% lower demand Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis

35. R OCKY M OUNTAIN I NSTITUTE 34 Load reducing behavior varied across several consecutive Super Peak events, but was strongest in September compared to A07 ADRS Homes vs. Standard Tiered Rates (A03) ADRS Homes vs. Homes on CPP-F Rates without Technology (A07) % Reduction from Control Group High Temp ( o F) Event Date Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis

36. R OCKY M OUNTAIN I NSTITUTE 35 ADRS homes used technology to lower average daily energy consumption overall, compared to homes without technology, both on the CPP-F rate (A07) and without dynamic rates (A03) Average Daily Consumption, All Homes on non-Event Weekdays Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Consumption (kWh) Average Daily Consumption, All Homes on Event Weekdays A03 A07 ADRS A03 A07 ADRS

37. R OCKY M OUNTAIN I NSTITUTE 36 It appears that ADRS homes are conserving energy in addition to shifting some load to off-peak hours; however, most of the conservation effect occurs during the peak hours Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Consumption (kWh) Average Consumption, July-September Non-Event Weekdays Average Consumption, July-September Super Peak Event days A03 A07 ADRS

38. R OCKY M OUNTAIN I NSTITUTE 37 Additional analysis can isolate the portion of load impact attributable to control of pool pumps  Other than penetration rates, there is little information available on the contribution of pool pump loads to the A03 and A07 aggregate load profiles  Energy consumption by ADRS pools can, however, be compared against consumption of pools from a demand response program being conducted by Nevada Power –Since there is no financial incentive for pool owners to shift load away from peak in Nevada Power’s ACLM program, operation of pools in Las Vegas is presumed to provide an appropriate load shape for comparison purposes –Reported load is average of all pools and reflects load diversity in scheduling –The aggregate load profile from Nevada is scaled down to reflect the smaller operational load of pools participating in ADRS (from 1.8 kW in Nevada to 1.6 kW among ADRS participants)

39. R OCKY M OUNTAIN I NSTITUTE 38 ADRS homes reduce pool load during peak hours on all weekdays, regardless of whether or not a Super Peak event is called Average Pool Load (kWh/hr) Time of Day Nevada Power (n = 78) ADRS (n = 44) Source: Invensys GoodWatts Reports Server Average Pool Pump Load (July-September, 2004) ADRS Average Non-Event Weekday Nevada Power Average Weekday ADRS Average Super Peak Day  On Peak 

40. R OCKY M OUNTAIN I NSTITUTE 39 Where present, shifting of pool loads to off peak is a significant contributor to reduction of on-peak consumption  The average Nevada pool consumes 2.8 kWh between 2 p.m. & 7 p.m. (on a scale adjusted basis)  By scheduling pools to operate outside of the 2 p.m. to 7 p.m. period, ADRS homes effectively reduce on-peak or Super Peak consumption by 2.8 kWh each day –2.8 kWh is roughly 48% of the 5.8 kWh total on-peak reduction for a house with a pool* –With the further reduction of other loads on Super Peak days, 2.8 kWh constitutes 29% of the home’s 9.5 kWh total super peak reduction*  Since only one out of approximately each four ADRS homes has a pool, pools in aggregate comprise about 20% of peak load reduction and 10% of Super Peak load reduction Average Reduction of On-Peak / Super Peak Load ADRS Segment Non-Event Weekday Super Peak Day No Pool (131) With Pool (44) Pool -- 2.8 kWh Weighted Avg. (175) 0.7 kWh Other* 3.0 kWh Total 3.0 kWh 5.8 kWh Pool -- 2.8 kWh 3.0 kWh 3.7 kWh 0.7 kWh Other* 6.7 kWh Total 6.7 kWh 9.5 kWh 6.7 kWh 7.4 kWh 0.7 kWh / 3.7 kWh = 20%0.7 kWh / 7.4 kWh = 10% * Reduction of other loads calculated algebraically from total average load reduction and average pool load reduction rather than direct measurement

41. R OCKY M OUNTAIN I NSTITUTE 40 Stratified results suggest that technology is a significant driver of behavior among moderate & high consumption homes; for lower- consumption homes, price signals appear to be the primary driver  High-consumption homes use the ADRS technology to further reduce load during Super Peak hours –On non-event weekdays, average load is reduced by 2.5 kWh vs. CPP-F rate homes without technology, compared to 1.6 kWh for the overall population –On Super Peak event days, average load is reduced by 3.8 kWh vs. CPP-F rate homes without technology, compared to 2.5 kWh for the overall sample  Compared to low-consumption ADRS homes, low-consumption CPP-F rate homes without technology (A07) have consistently lower loads at all hours of the day on both Super Peak and Non-Event Weekdays*  Low-consumption homes appear to more sensitive to price signals—the CPP-F rate structure alone changes their load profile significantly and technology appears to add little incremental benefit for this (albeit small) population sample  This suggests that high-consumption homes should be targeted for the ADRS technology—low-consumption homes may be less likely to be cost effective * Consistently lower loads suggests the potential for a systemic bias between treatment and control group homes in the low consumption stratum that is not accounted for in the pilot; otherwise we would expect low stratum A07 homes to have higher demand in the off-peak period to “catch up” for their reduction during peak hours

42. R OCKY M OUNTAIN I NSTITUTE 41 High-consumption, technology-enabled ADRS homes reduce load further than the overall population Average Non-Event Weekday Load Profile - High-Consumption Homes Electric Load per Home (kWh/hr) Time of Day  On Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. A03 ADRS A07 Difference in On-Peak Usage 1.8 kWh4.3 kWh5-hr Total 0.37 kWh/hr0.87 kWh/hrAverage A03-A07A03-ADRS 15%35% % Reduction 2.5 kWh 0.50 kWh/hr A07-ADRS 24%

43. R OCKY M OUNTAIN I NSTITUTE 42 Super Peak Event days also see greater load reductions for high- consumption ADRS homes vs. the overall population Average Super Peak Event Day Load Profile – High-Consumption Homes Electric Load per Home (kWh/hr) Time of Day  Super Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 4.7 kWh8.5 kWh5-hr Total 0.94 kWh/hr1.70 kWh/hrAverage A03-A07A03-ADRS 28%51% % Reduction 3.8 kWh 0.77 kWh/hr A07-ADRS 32% A03 ADRS A07

44. R OCKY M OUNTAIN I NSTITUTE 43 Low-consumption, CPP-F rate (A07) homes have lower load than technology-enabled ADRS homes on non-event weekdays, suggesting price signals drive their load reduction Average Non-Event Weekday Load Profile - Low-Consumption Homes Electric Load per Home (kWh/hr) Time of Day  On Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in On-Peak Usage 3.1 kWh1.9 kWh5-hr Total 0.61 kWh/hr0.38 kWh/hrAverage A03-A07A03-ADRS 44%28% % Reduction -1.2 kWh -0.2 kWh/hr A07-ADRS -30% A03 ADRS A07

45. R OCKY M OUNTAIN I NSTITUTE 44 Price signal is again suggested as the stronger driver of load reduction in low-consumption homes on Super Peak days—CPP-F rate (A07) homes demonstrate lower loads than ADRS homes Average Super Peak Event Day Load Profile - Low Consumption Homes Electric Load per Home (kWh/hr) Time of Day  Super Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 5.2 kWh4.0 kWh5-hr Total 1.05 kWh/hr0.81 kWh/hrAverage A03-A07A03-ADRS 55%43% % Reduction -1.2 kWh -0.2 kWh/hr A07-ADRS -27% A03 ADRS A07

46. R OCKY M OUNTAIN I NSTITUTE 45 Household level analysis reveals that the majority of ADRS homes (52%) actively experimented with the technology to control home energy use, with an additional 7% made minor adjustments  For each month (June-September), the instantaneous load drop at 2 p.m. for each ADRS home was calculated, for Super Peak and non-Super Peak weekdays –This instantaneous reduction was categorized into “High” (>30% drop), “Medium” (15-30% drop), and “Low” (< 15% drop) categories –Trends in load reductions (high, medium, low) were observed across the months for both Super Peak and non-Super Peak weekdays  The majority of homes (52%) varied their 2 p.m. load reductions widely across the summer months and between Super Peak and non-Super Peak weekdays  An additional 7% of ADRS homes made some minor adjustments with the technology  Approximately 41% of homes did not change their 2 p.m. load reductions significantly from month to month or between Super Peak and non-Super Peak weekdays.

47. R OCKY M OUNTAIN I NSTITUTE 46 Furthermore, about 10% of the ADRS population are “Supersavers,” reducing load at 2 p.m. by more than 30% consistently across the summer months on a daily basis  The Supersaver ADRS homes contributed ~20% of Super Peak reduction and ~24% non-Super Peak reduction across the summer months, in terms of instantaneous load shed at 2 p.m.  The Supersavers were not the only ones reducing significant load at 2 p.m., however. Approximately 50% of the population saved more than 30% of their 2 p.m. load on Super Peak weekdays, while 25% of the ADRS population saved more than 30% of their 2 p.m. load on non-Super Peak weekdays –The range of load reduction at 2 p.m. for high performance homes ranged from 30% to almost 100% on both Super Peak and non-Super Peak days  Ten percent of homes improved their performance across the summer months, gradually increasing their 2 p.m. load shed July-September on all weekdays  Seven percent of the population showed declining performance. –Three percent even increased their consumption during peak hours and on Super Peak days relative to off-peak hours and non-Super Peak days –Additional research is needed to determine whether these homeowners are consciously increasing their consumption during peak hours or whether, out of confusion, they are using the technology incorrectly

48. R OCKY M OUNTAIN I NSTITUTE 47 As an indication of the statistical quality of the results, the coefficient of variation allows us to compare relative variation between populations  The coefficient of variation (CV), which allows for comparison of the relative variation of values between populations, is defined as the standard deviation (SD) of a sample divided by the sample’s mean value  For ADRS, the mean and standard deviation was calculated for each time interval over the 5-hour peak period. A coefficient of variation was calculated for each consumption stratum according to Event and Non-Event days. –The mean, SD, and CV were calculated for total consumption of each group of homes: ADRS and control populations (A03 and A07) –The mean, SD, and CV were calculated for the load reduction between the control group (A03 or A07) and ADRS homes  A CV value greater than ~1 implies that the statistic is not significant (less than ~70% confidence)  A CV value less than ~0.5 implies that the statistic is significant within a ~95% confidence interval

49. R OCKY M OUNTAIN I NSTITUTE 48 Variation in total consumption is high across all groups of homes for both high- and (especially) low-consumption homes; ADRS load reductions relative to both control groups are statistically significant for the high consumption homes Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Coefficient of Variation in Load or Reduction (2pm-7pm) Population Non-Event Weekday Super Peak Day ADRS consumption A03 consumption A07 consumption Low Stratum 1.18 1.00 1.26 High Stratum 1.05 0.74 Low Stratum 1.16 1.18 0.83 1.26 High Stratum 0.99 0.87 0.93 A03-ADRS reduction A07-ADRS reduction 0.82 1.84 0.37 0.54 0.58 1.85 0.18 0.29 A03-A07 reduction 0.56 0.63 1.24 0.25

50. R OCKY M OUNTAIN I NSTITUTE 49 High CVs for load-reduction in low-consumption ADRS homes relative to A07 homes confirms our hypothesis that those results are not statistically significant, due to small and diverse samples  Variation in total consumption of ADRS and control groups is high for both high- and (especially) low-consumption homes.  The CV declines when we look at the difference in consumption between ADRS and each of the control groups, particularly for Super Peak days; this suggests that the variations among the ADRS homes loads and the control groups are not independent, but are correlated ( i.e., relatively high or low values tend to occur at similar times in each population  The CV values for load reductions of high-consumption ADRS homes relative to both control groups are substantially less than 0.5, suggesting that results are statistically significant at a 95% confidence level  Results of ADRS load reductions relative to control groups for low-consumption homes are mixed –The CV for low-consumption ADRS load reductions relative to the A03 control group and between low-consumption A03 and A07 homes are 0.6-0.8, suggesting that the results are statistically significant with ~80-90% confidence –The CV for low-consumption ADRS load reductions relative to the low consumption A07 control group confirms our hypothesis that these results are not statistically significant –Similarly, the low-consumption A07 control group load reductions relative to the low-consumption A03 control group on Super Peak days are not statistically significant –The small size of the low-consumption home populations seems to limit statistical quality

51. R OCKY M OUNTAIN I NSTITUTE 50  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analytical Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

52. R OCKY M OUNTAIN I NSTITUTE 51 Conclusions – ADRS homes with technology consume less on-peak energy than comparable homes* on standard rates or the CPP-F; the technology benefit is even stronger on Super Peak days  On non-event weekdays from July through September, average ADRS homes with technology consumed less on-peak energy (between 2 p.m. and 7 p.m.) than comparable homes* on standard tiered-rates (A03) or the SPP CPP-F (A07) –ADRS homes with technology used 3.7 kWh less on-peak electricity per home (34% lower) than comparable homes* on standard rates (A03) –ADRS homes used less on peak than CPP-F homes (A07) as well, 1.6 kWh lower on average (savings of 18%)  Over the twelve Super Peak days, technology-enabled ADRS homes consumed considerably less on-peak energy per home than their comparable control groups –ADRS homes consumed 7.4kWh (or 50%) less Super Peak energy per day than homes on standard rates (A03) –With ADRS technology, participants consumed 2.5 kWh less super peak electricity per day (26% savings) than comparable homes in the SPP on CPP-F (A07) Note: ADRS participants were enrolled on a first-come, first-served basis; results were not modified to address potential self-selection bias * Homes in the treatment and control groups are comparable in that they all lie in Climate Zone 3 and have central air conditioning; further, raw load data for the A03 and A07 control groups have been weighted according to the distribution of the ADRS population with respect to utility and historical consumption strata

53. R OCKY M OUNTAIN I NSTITUTE 52 Conclusions – Performance of ADRS homes with technology improved relative to both control groups from July to September  ADRS technology enabled homes reduced load by ~50% consistently across the summer Super Peak events relative to homes without technology or rates (A03)  Relative to CPP-F homes (A07), ADRS homes’ performance improved throughout the summer. Load reduction during the Super Peak hours increased from 25% in July and August to 31% in September  This observed improvement in ADRS performance does not seem to be explained by weather differences or other variables other than occupant behavior  Technology enabled ADRS homes’ reduction of Super Peak load decreased over the five-hour Super Peak period, but they still out-performed A07 homes on the CPP-F rate without technology. Performance again improved in September, when the load reduction was sustained better in the last 1-2 hours of the Super Peak events  Total daily energy consumption of ADRS houses was 5% lower than A03 homes on non-event weekdays and 12% lower on Super Peak days. Compared to A07 homes, ADRS homes’ total daily usage was 2% lower on Super Peak and non-event weekdays

54. R OCKY M OUNTAIN I NSTITUTE 53 Conclusions –ADRS proved particularly useful to pool owners and to moderate/high-consumption homes; less so for homes with modest consumption  Where present, pool pumps make a significant contribution to reduction of peak load vs. A03* –Relative to a control group of pools (from a Nevada Power load management program), ADRS pools reduce on-peak / Super Peak consumption by 2.8 kWh per day –For the average ADRS home with a pool, this 2.8 kWh reduction is 48% of the 5.8 kWh total reduction on non-event weekdays and 29% of the 9.5 kWh expected on Super Peak days –As just 44 of the ~175 ADRS have pools, reductions from pool loads comprise roughly 20% of total peak load reduction and 10% of the reduction in Super Peak consumption  Breaking down the population by energy-consumption stratum, technology appears to be an important driver in reducing Super Peak load for high-consumption homes, while the price signal appears to be a stronger driver of reduction in low-consumption homes  Household level analysis reveals that the majority of ADRS homes (52%) actively experimented with the technology to control home energy use, while an additional 7% made minor adjustments. Furthermore, about 10% of the ADRS population are “Supersavers,” reducing load at 2 p.m. by more than 30% consistently across the summer months on a daily basis * Total reduction of on-peak/Super Peak load by homes with pools is calculated algebraically rather than by direct measurement

55. R OCKY M OUNTAIN I NSTITUTE 54 Executive Summary – ADRS load reductions relative to both control groups are statistically significant for the high consumption homes  As an indication of the statistical quality of the results, the coefficient of variation allows us to compare relative variation between populations  The coefficient of variation (CV), which allows for comparison of the relative variation of values between populations, is defined as the standard deviation (SD) of a sample divided by the sample’s mean value  A CV value greater than ~1 implies that the statistic is not significant (less than ~70% confidence)  A CV value less than ~0.5 implies that the statistic is significant within a ~95% confidence interval Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Coefficient of Variation in Load or Reduction (2pm-7pm) Population Non-Event Weekday Super Peak Day ADRS consumption A03 consumption A07 consumption Low Stratum 1.18 1.00 1.26 High Stratum 1.05 0.74 Low Stratum 1.16 1.18 0.83 1.26 High Stratum 0.99 0.87 0.93 A03-ADRS reduction A07-ADRS reduction 0.82 1.84 0.37 0.54 0.58 1.85 0.18 0.29 A03-A07 reduction 0.56 0.63 1.24 0.25

56. R OCKY M OUNTAIN I NSTITUTE 55 High CVs for load-reduction in low-consumption ADRS homes relative to A07 homes confirms our hypothesis that those results are not statistically significant, due to small and diverse samples  Variation in total consumption of ADRS and control groups is high for both high- and (especially) low- consumption homes.  The CV declines when we look at the difference in consumption between ADRS and each of the control groups, particularly for Super Peak days; this suggests that the variations among the ADRS homes loads and the control groups are not independent, but are correlated ( i.e., relatively high or low values tend to occur at similar times in each population  The CV values for load reductions of high-consumption ADRS homes relative to both control groups are substantially less than 0.5, suggesting that results are statistically significant at a 95% confidence level  Results of ADRS load reductions relative to control groups for low-consumption homes are mixed –The CV for low-consumption ADRS load reductions relative to the A03 control group and between low- consumption A03 and A07 homes are 0.6-0.8, suggesting that the results are statistically significant with ~80-90% confidence –The CV for low-consumption ADRS load reductions relative to the low consumption A07 control group confirms our hypothesis that these results are not statistically significant –Similarly, the low-consumption A07 control group load reductions relative to the low-consumption A03 control group on Super Peak days are not statistically significant –The small size of the low-consumption home populations seems to limit statistical quality

57. R OCKY M OUNTAIN I NSTITUTE 56 Recommendations for possible future extension of the pilot  Due to the lack of depth in data populations, particularly for the low stratum, additional recruiting should be performed to increase confidence of results –Standard tiered rates (A03): low consumption PG&E and SDG&E homes and high consumption SDG&E homes –CPP-F homes without technology (A07): low consumption and high consumption SDG&E homes –ADRS homes with technology: low consumption SCE homes and high consumption SDG&E homes  Continue to provide information and educational materials to the ADRS participants, in order to provide a test of whether performance can improve in subsequent summers  Because ADRS home load reduction decreases relative to A07 homes in the later hours of the Super Peak period, a shorter duration event or later start may improve the consistency of load reductions and the cost effectiveness of the program

58. R OCKY M OUNTAIN I NSTITUTE 57  Executive Summary  Pilot Background and Overview of Experimental Design  Data Sources  Analysis Methodology  Load Impact Results  Conclusions and Recommendations  Appendix Table of Contents

59. R OCKY M OUNTAIN I NSTITUTE 58  Meter Data Comparison  Low-Strata Issues  Load Curves  Methodology Coefficient Appendix

60. R OCKY M OUNTAIN I NSTITUTE 59 Invensys meter data serves as a good proxy for utility meter data from ADRS homes, as demonstrated by the July 22nd Event Day below Time of Day Electric Load per Home (kWh/hr) Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis ADRS - Utility Data ADRS - Invensys Data July 22nd Event Day Load Profile - All Homes  Super Peak 

61. R OCKY M OUNTAIN I NSTITUTE 60 Weighting the raw data from A07 by the distribution of the ADRS sample runs the risk of skewing the results by placing undue emphasis on the behavior of a single contributor A07 Population by Utility - Low Consumption ADRS Population by Utility - Low Consumption Source: Utility Data, RMI analysis Note: ADRS and A07 strata classification based on historical ADU. Utility # of Homes % of ADRS Population

62. R OCKY M OUNTAIN I NSTITUTE 61 However, exclusion of potentially skewed SDG&E data does not change the result among modest energy users; the remaining technology-enabled homes still do not outperform A07 Average July Event Day - All Low Consumption Homes Low Stratum Electric Load per Home (kWh/hr) Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Average July Event Day - SDG&E Homes Omitted  Super Peak  Time of Day A03 A07 ADRS A03 A07 ADRS With a single SDG&E home comprising 32% of the weighted load, the sample is sensitive to a potential outlier (e.g., the demand spike at 8:15 a.m.) Yet, even excluding the SDG&E home, homes without GoodWatts (A07) consume less than homes with it (ADRS) at nearly every hour of the day

63. R OCKY M OUNTAIN I NSTITUTE 62 July 14th Event Day July 14th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day  Super Peak  Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.79 kWh/hr1.25 kWh/hrAverage 1.10 kWh/hr1.54 kWh/hrMax A03-A07A03-ADRS 4.0 kWh6.3 kWh5-hr Total 0.46 kWh/hr 0.72 kWh/hr A07-ADRS 2.3 kWh A03 ADRS A07

64. R OCKY M OUNTAIN I NSTITUTE 63 July 22nd Event Day July 22nd Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 1.33 kWh/hr1.85 kWh/hrAverage 1.73 kWh/hr2.30 kWh/hrMax A03-A07A03-ADRS 6.7 kWh9.2 kWh5-hr Total 0.51 kWh/hr 0.89 kWh/hr A07-ADRS 2.6 kWh A03 ADRS A07  Super Peak 

65. R OCKY M OUNTAIN I NSTITUTE 64 July 26th Event Day July 26th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 1.39 kWh/hr1.81 kWh/hrAverage 1.74 kWh/hr2.21 kWh/hrMax A03-A07A03-ADRS 7.0 kWh9.1 kWh5-hr Total 0.42 kWh/hr 0.80 kWh/hr A07-ADRS 2.1 kWh A03 ADRS A07  Super Peak 

66. R OCKY M OUNTAIN I NSTITUTE 65 July 27th Event Day July 27th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.78 kWh/hr1.24 kWh/hrAverage 1.01 kWh/hr1.56 kWh/hrMax A03-A07A03-ADRS 3.9 kWh6.2 kWh5-hr Total 0.46 kWh/hr 0.64 kWh/hr A07-ADRS 2.3 kWh A03 ADRS A07  Super Peak 

67. R OCKY M OUNTAIN I NSTITUTE 66 August 9th Event Day Aug 9th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 1.01 kWh/hr1.53 kWh/hrAverage 1.25 kWh/hr1.98 kWh/hrMax A03-A07A03-ADRS 5.1 kWh7.6 kWh5-hr Total 0.51 kWh/hr 0.90 kWh/hr A07-ADRS 2.6 kWh A03 ADRS A07  Super Peak 

68. R OCKY M OUNTAIN I NSTITUTE 67 August 10th Event Day Aug 10th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 1.19 kWh/hr1.59 kWh/hrAverage 1.62 kWh/hr2.09 kWh/hrMax A03-A07A03-ADRS 5.6 kWh7.9 kWh5-hr Total 0.40 kWh/hr 0.74 kWh/hr A07-ADRS 2.0 kWh A03 ADRS A07  Super Peak 

69. R OCKY M OUNTAIN I NSTITUTE 68 August 11th Event Day Aug 11th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 1.09 kWh/hr1.50 kWh/hrAverage 1.50 kWh/hr2.04 kWh/hrMax A03-A07A03-ADRS 5.4 kWh7.5 kWh5-hr Total 0.41 kWh/hr 0.78 kWh/hr A07-ADRS 2.1 kWh A03 ADRS A07  Super Peak 

70. R OCKY M OUNTAIN I NSTITUTE 69 August 27th Event Day Aug 27th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.56 kWh/hr1.11 kWh/hrAverage 0.83 kWh/hr1.50 kWh/hrMax A03-A07A03-ADRS 2.8 kWh5.5 kWh5-hr Total 0.55 kWh/hr 0.79 kWh/hr A07-ADRS 2.8 kWh A03 ADRS A07  Super Peak 

71. R OCKY M OUNTAIN I NSTITUTE 70 August 31st Event Day Aug 31st Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.85 kWh/hr1.25 kWh/hrAverage 1.36 kWh/hr1.74 kWh/hrMax A03-A07A03-ADRS 4.2 kWh6.2 kWh5-hr Total 0.40 kWh/hr 0.60 kWh/hr A07-ADRS 2.0 kWh A03 ADRS A07  Super Peak 

72. R OCKY M OUNTAIN I NSTITUTE 71 September 8th Event Day Sept 8th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.80 kWh/hr1.51 kWh/hrAverage 1.22 kWh/hr1.90 kWh/hrMax A03-A07A03-ADRS 4.0 kWh7.5 kWh5-hr Total 0.71 kWh/hr 0.90 kWh/hr A07-ADRS 3.5 kWh A03 ADRS A07  Super Peak 

73. R OCKY M OUNTAIN I NSTITUTE 72 September 9th Event Day Sept 9th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.89 kWh/hr1.52 kWh/hrAverage 1.26 kWh/hr1.91 kWh/hrMax A03-A07A03-ADRS 4.4 kWh7.6 kWh5-hr Total 0.63 kWh/hr 0.83 kWh/hr A07-ADRS 3.2 kWh A03 ADRS A07  Super Peak 

74. R OCKY M OUNTAIN I NSTITUTE 73 September 10th Event Day Sept 10th Event Day Load Profile - All Homes Electric Load per Home (kWh/hr) Time of Day Source: Utility Data, Invensys GoodWatts Reports Server, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Difference in Super Peak Usage 0.89 kWh/hr1.53 kWh/hrAverage 1.28 kWh/hr1.94 kWh/hrMax A03-A07A03-ADRS 4.5 kWh7.6 kWh5-hr Total 0.64 kWh/hr 0.81 kWh/hr A07-ADRS 3.2 kWh A03 ADRS A07  Super Peak 

75. R OCKY M OUNTAIN I NSTITUTE 74 Methodology—As an indication of the statistical quality of the results, the coefficient of variation allows us to compare relative variation between populations  The coefficient of variation (CV), which allows for comparison of the relative variation of values between populations, is defined as the standard deviation (SD) of a sample divided by the sample’s mean value  For ADRS, the mean and standard deviation was calculated for each time interval over the 5 hour peak period. A coefficient of variation was calculated for each consumption stratum according to Event and Non-Event days. –The mean, SD, and CV were calculated for total consumption of each group of homes: ADRS and control populations (A03 and A07) –The mean, SD, and CV were calculated for the load reduction between the control group (A03 or A07) and ADRS homes  A CV value greater than ~1 implies that the statistic is not significant (less than ~70% confidence).  A CV value less than ~0.5 implies that the statistic is significant within a ~95% confidence interval

76. R OCKY M OUNTAIN I NSTITUTE 75 Variation in total consumption is high across all groups of homes for both high- and (especially) low-consumption homes; ADRS load reductions relative to both control groups are statistically significant for the high consumption homes Coefficient of Variation in Load or Reduction (2pm-7pm) Population Non-Event Weekday Super Peak Day ADRS consumption A03 consumption A07 consumption Low Stratum 1.18 1.00 1.26 High Stratum 1.05 0.74 Low Stratum 1.16 1.18 0.83 1.26 High Stratum 0.99 0.87 0.93 A03-ADRS reduction A07-ADRS reduction 0.82 1.84 0.37 0.54 0.58 1.85 0.18 0.29 A03-A07 reduction 0.56 0.63 1.24 0.25 Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population.

77. R OCKY M OUNTAIN I NSTITUTE 76 High CVs for load-reduction in low-consumption ADRS homes relative to A07 homes confirms our hypothesis that those results are not statistically significant, due to small and diverse samples  Variation in total consumption of ADRS and control groups is high for both high- and (especially) low-consumption homes.  The CV declines when we look at the difference in consumption between ADRS and each of the control groups, particularly for Super Peak days. This suggests that the variations among the ADRS homes loads and the control groups are not independent, but are correlated, i.e., relatively high (or low) values tend to occur at similar times in each population.  The CV values for load reductions of high-consumption ADRS homes relative to both control groups are substantially less than 0.5, suggesting that results are statistically significant at a 95% confidence level.  Results of ADRS load reductions relative to control groups for low-consumption homes are mixed –The CV for low-consumption ADRS load reductions relative to the is 0.6-0.8, suggesting that the results are statistically significant with ~80-90% confidence. –The CV for low-consumption ADRS load reductions relative to the low consumption A07 control group confirms our hypothesis that these results are statistically insignificant. –Similarly, the low-consumption A07 control group load reductions relative to the low-consumption A03 control group are statistically insignificant. –The small size of the low-consumption home populations seems to limit statistical quality

78. R OCKY M OUNTAIN I NSTITUTE 77 It appears that ADRS homes are conserving energy in addition to shifting some load to off-peak hours. Most of the conservation effect occurs during the peak hours, however. Source: Utility Data, RMI analysis Note: A07 and A03 data scaled to match the ADRS customers’ population by utility and strata population. Consumption (kWh) Average Consumption, July-September Non-Event Weekdays Average Consumption, July-September Super Peak Event days A07 A03 ADRS