ESTIMATES OF THE IMPACT OF THE 1981 DEMAND SUBSCRIPTION SERVICE PROGRAM

Transcription

1 ESTIMATES OF THE IMPACT OF THE 1981 DEMAND SUBSCRIPTION SERVICE PROGRAM Mark S0 Kumm Conservation/wad Management Department Southern california Edison Company The views expressed in this paper are the author's and not necessarily those of the Southern california Edison ABSTRACT 10 INTRODUCTION In this paper, estimates of the impact of the Demand Subscription Service (DSS) Program on the electricity load patterns of program participants are presented& The DSS Program is an ongoing direct load management program administered by the Southern California Edison Company (SCE) in which participants "subscribe" to a maximum kilowatt (kw) demand level that is enforced at SCE's option & Estimates of the impact of the DSS Program presented in this paper are obtained from descriptive and econometric analyses of the load patterns of a group of participants and a group of nonparticipants & In the descriptive analysis, comparisons are made between the hourly mean kilowatt demands of participants and nonparticipants@ In the econometric analysis, the kilowatt demands of participants and nonparticipants are compared after taking account of the effects of weather and differences in customer appliance and demographic characteristics~ In both the descriptive and econometric analyses, the kilowatt demands of ~.'~~~~~ ~~ are found to generally be than nonparticipants during the peak of days on which the SUbscription demand levels are enforced~ The strongest evidence for kw demand reductions are found in the "moderate" weather zone & In addition, the kilowatt-hour (kwh) ~~"'~~4'''~~~.'~U of participants is less than that of nonparticipants, with the strongest evidence for statistically significant reductions also found in the moderate weather zone0 During the past six years, the Southern California Edison Company (SCE) has sought ways of reducing a rapidly growing peak demand for electricity in its service territory~ Particular attention has been given to the residential sector in this regard0 In the latter part of 1980, SCE initiated the Demand Subscription Service Program (DSS) as a means of reducing peak demand in the residential sector6 In this program, participating customers "subscribe" to a maximum kilowatt (kw) demand level that is enforced at SCE's option0 In return, these customers receive reductions in their monthly electricity bil10 In this report, estimates of the impact of the DSS Program on the electricity usage patterns of participant customers relative to a group of nonparticipants are presented0 These impact estimates are made through an analysis of electricity usage data that were recorded by magnetic tape recorders installed on the electricity meters of approximately 300 program participants and approximately 100 nonparticipants~ Load data recorded during the period June 1, 1981 through September 15, 1981 are analyzed for this report~ This report is divided into four sections, including this introduction0 Following the introduction is Section 2, in which a detailed description of the DSS program is provided0 In Section 3, results from the anaysis of the load data are presented@ The presentation in this section is focused on a comparison of the mean kw demands of participants and nonparticipants, and on the results from -489-

2 an econometric analysis of the impact of the DSS Program on the kw demand and kwh consumption of participants relative to nonparticipantsg The fourth section is comprised of a summary of the conclusions from this report and suggestions for further research", 2", DESCRIPTION OF THE DSS PROGRAM The Demand Subscription service Program is an ongoing direct load management program that was initiated as a means of reducing peak demand in the SCE service territory. The DSS activities conducted during 1981 comprised the experimental phase of the program", For this phase, participation in the program was limited to approximately 2,000 custorners0 The experimental phase of the program was designed to provide preliminary information about the operation and impact of the program that is needed to plan for future program expansion. In the future, participation is expected to be offered to all customers in the SCE service territory~ In 1981, participation was offered to customers who live in newly constructed (less than one year old) single family dwellings $ The offer to participate was further restricted to customers who reside in four see service districts, Covina, Ontario, Fullerton, and San Joaquin Valley, and was also limited to customers with estimated annual electricity consumption of at least 4900 kwh 40 Participation in the program was voluntary~ Each individual participant in the DSS Program subscribes to a maximum level of kilowatt (kw) demand that is enforced at SCEms option& The kw subscription level is based upon the customer's appliance stock, and is calculated to allow the customer to use a reasonable number of appliances during all including the peak, while still maintaining a demand level that will contribute to system load reductions~ The option to enforce the kw subscription level is usually exercised during periods when see expects system demand to reach a large or new peak~ During when the kw subscription level is not enforced, no restrictions are placed on the customer's kvj demand", Enforcement of the kw subscription level is accomplished through a device attached to each individual participant's electricity meter. This device, which is called the "DSS device," is designed to receive an FM radio signal. During periods when SCE enforces the kw subscription levels, a signal is transmitted by SCE that activates the DSS devices. If the kw demand of a participating customer exceeds the kw subscription level during the activation period, the customer's electricity service is temporarily discontinuedo Service can be restarted immediately after the customer switches off a sufficient number of appliances to allow demand to remain within the subscription levele In return for participating in the DSS Program, an incentive payment is made to each participant that takes the form of a monthly electricity bill credit", The size of the incentive payment is inversely related to the kw subscription level, and is generally larger for participants with larger stocks of electric appliances", The DSS Program conducted during 1981 was designed to provide information regarding the effects of weather, length of activation period, and frequency of activation on the kw demand and k\fu consumption of program participants. The selection of customers to capture weather and activation impacts are discussed separately belowe The distribution of DSS Program participants across the four SCE service districts was done to capture the effects of weather upon demand and consumption 0 The four districts in which DSS participants reside (Covina, Ontario, Fullerton, and San Joaquin Valley) fall within three weather zones that were established for SCE's recent load management programs", The zones are designed according to an average summer temperature range. The specification of the weather zones included in the 1981 DSS Program is provided in Table 1, along with the relation of the DSS districts to these zones. To address issues relating to activation, customers were assigned to one of four possible activation scheduleso Each customer was given knowledge of only one -490-

3 TABLE 1& DESCRIPTION OF THE WEATHER ZONES INCLUDED IN THE 1981 PROGRAM District Fullerton Cbvina Ontario San Joaquin Valley Weather Zone Moderate Hot Hot Very Hot Average Swmner Maximum Temperature F ~99 F F 105~ F TABLE 20 ACTIVATION STRATEGIES USED IN THE 1981 D.. S.. S.. PROGRMl Activation Strategies Frequency High High Low Low Duration Long Short Long Short schedu1e~ Participants were assigned to the four strategies in approximately equal numbers in order to allow a useful statistical analysis of the program.. The four strategies are outlined in Table 2.. A high frequency is specified as 60 days per year of experiencing DSS device activation. A low frequency is specified as thirty activation days per year.. A long duration is specified as eight continuous hours per activation, with activation occurring during the period 10 a.. m.. PST through 6 p.. m.. PST.. A short duration is specified as five continuous hours per activation, with activation occurring during 12 noon PST through 5 p.. m.. PST.. As mentioned earlier, the number of participants in the 1981 program was limited to approximately 2,000.. These participants were distributed across the three weather zones as follows~ Approximately 500 program participants were in the moderate weather zone, approximately 1000 participants were in the hot weather zone, and approximately 500 participants were in the very hot weather zone.. To obtain data needed for the analysis of the impact of the DSS Program, magnetic recorders were installed on the meters of approximately 300 of the 2,000 participants.. Magnetic tape recorders were also installed on an additional 100 nonparticipants who served as a control group. The members of the control group were not informed of the DSS Program. In the case of both the 300 DSS participants and the 100 nonparticipants, recording devices were distributed with respect to weather zone in the same manner in which the approximately 2,000 participants in the DSS Program are distributed. The DSS Program plan specified for the 300 participant customers with magnetic tape recorders to be distributed with regard to weather zone and activation strategy as shown in Figure 1.. The plan also specified for the luu nonpartlclpants with magnetic tape recorders to be comprised of 25 customers from the moderate weather zone, 50 customers from the hot weather zone, and 25 customers from the very hot weather zone.. Participating customers who had magnetic tape recorders were not treated differently from other participants in any respect. The data used to estimate the impact of the DSS Program on the electricity demand patterns of participants are obtained from the magnetic tape recorders.. For the approximately 300 participants and 100 nonparticipants, these recorders provided average IS-minute kilowatt demand readings during all hours of every -491-

4 High Frequency Duration Low Frequency Duration Long Short Long Short Moderate Hot Very Hot 'ICTAL Total Participants = 300 Figure 1$ Distribution of Participants with Magnetic Tape Recorders by Weather Zone and Activation Strategy day~ The data analyzed for this report were recorded during the period June 1, 1981 through September 15, 1981~ All weekend days and holidays were excluded from the analysis because on these days, system demand is usually much lower than on weekdays~ The results from the analysis of these data are reported in the next section of this reporte In this section of the report, the results from a descriptive and an econometric analysis of the load data are presentede The descriptive analysis consists of a comparison of the mean kilowatt demands of treatment and control customers during each hour of average activation dayse Activation days are defined as days during which an effort was made to activate the DSS devices.. In the econometric analysis, regression techniques are used to compare the average kilowatt demands and kilowatt-hour consumption totals of treatment and control customers on average activation after controlling for the effects of weather and for differences in customer appliance and demographic characteristics.. The comparisons made in this section are focused on the kw demands and kwh consumption totals of long duration and short duration treatment customers relative to control.. Comparisons are not made between high and low frequency treatment customers because during the the activation schedules of both high and low frequency treatment customers within a weather zone were identicale As noted previously, the objective of the DSS program is to reduce kilowatt demand during periods of peak system demand.. Because each participant's kilowatt demand is limited during the peak period, some electricity consumption may be shifted to off-peak periodse Consequently, the DSS Program may impact load patterns over all hours of the day~ In this section, preliminary estimates of the impact of the DSS Program on the load patterns of participants are obtained through a comparison of the mean kilowatt demands of treatment and control households over all hours of average activation days.. In Table 3 the mean hourly kilowatt demands of long duration treatment and control customers on average activation days are presented~ The mean hourly kilowatt demands of short duration treatment and control customers are in Table 3~ The same control group is used to calculate the control group means presented in both tables0 The means in the tables are presented separately for June, July, and August, and are presented by weather zone6 Also summarized in Tables 3 and 4 are the results of statistical comparisons between the means of treatment and control customers for each -492-

5 TABLE 3.. MEAN HOURLY KILQ\t\1A'IT DEMANDS OF LONG DURATION TREArrMENT AND CONTROL CUSTOMERS ON AVERAGE AcrIVATION DAYS June 1981 July 1981 Hour Period Moderate Hot Very Hot Moderate Hot Very Hot Moderate Hot Very Hot Trt Cntl Trt Cntl Trt Cntl Trt Cntl Trt Cntl Trt Cntl Trt Cntl Trt Cntl Trt Cnt1 1 f\dnact ** ** ** * 0,, ** ,,75 3 0,, ** ,, * ** ** * I ** ** :" ** 0,, * * \.0 v.> ** 0,, ** ** I ** ,, ** ** ** ** ** * ** ,, * ** ** * --~ ~--~--~-----~~--~--~-~-~--~-~---~-----~-----~ -----~ ~-~ ACT ** * ** ** ** ** * * ** ,,33* ,, ** ,, ** ** * ,, ** ,, ** ,, ** ** ,, ** ,, ** * ** ** ** * ** * * ** ** ** ,, ** ** ** ,,03** ** ,,34** ** ~~ ~ ~-~-~~ ~ ~~-~~~-~-~-~---~ ~----~~-----~ ~------~----~ f\dnact ** * * ** * ** ** ** ** ,, * ** ** ** ** ** ** ** * ** ,, * ** ** * ** * A single asterisk is used to denote the treatment and control means are stati,stically different from zero at the 90% confidence level. ** A double asterisk denotes statistically different means at the 95% confidence level.

6 TABLE 4~ MEAN HOURLY KILOWA'IT DEMANDS OF SHORr DURATION TREA'IM:ENT AND CONTROL CUS~IE.RS ON AVERAGE AcrIVATION DAYS August 1981 Hour Period Moderate Hot Very Hot Moderate Hot Very Hot Moderate Hot Very Hot Trt Cnt! Trt Cnt! Trt Cnt! Trt Cntl Trt Cnt1 Trt Cntl Trt Cnt1 Trt Cntl Trt Cnt! 1 f\dnact * 0,, ,, ** * ,,08 L ,, ,, ,, ,, ,, ,, *' * ** ,, , ,, ** ** ** I ,,37** : ** ** ** \0 11 0,, ** 1,, * , ** ** :-- I 12 1,, ** 1,,43 2,.16** ** ** ** ~-~~-~---~-----~----~----~----~~~~~~~~~~ ~-~ ~------~ ~-~--~~~~-~-~ ~- ---~~-~~-~ ~~~~~~----~ 13 ACT 1,, ** L ** ,, ** ** ** ** * 1., ** 1,, ** ** ** ** ** 1,, ** * ** ** ** ,,18** ** ** ** ** ** ** * ** ** * * ** ** ) ~-~------~~~-~----~ ~ ~~~~~~ ~ ~-~~~~~~ ~~--~-----~ ~~~ NONACT ** ** ** ** 3,, ** * ** * ** ** ** * ** ,, ** ** ,, * * ,, * * ,, ** , * ** ** ** ** * * A single asterisk is used to denote the treatment and control means are statistically different from zero at the 90% confidence level. ** A double asterisk denotes statistically different means at the 95~ confidence level.

7 individual hour of average activation dayse A single asterisk is used to denote that the means are statistically different at the 90 percent level of confidence; double asterisks are used to denote statistically different means at the 95 percent level of confidence. A review of the means presented in Tables 3 and 4 indicates that during the activation period of average activation days in June, July, and August, the mean kw demands of control customers are consistently higher than those of treatment customers. The magnitudes of these differences are largest for customers in the moderate weather zone, where the mean kw for the control group during some hours of the activation period is as much as 3 k~l higher than that of the treatment group~ A brief review of the results from the hourly comparisons of treatment and control group means indicates that for almost all hours, the magnitude of the difference in means is statistically significant at the 95 percent level. For customers in the hot weather zone, approximately 50 percent of the differences are statistically different at the 90 percent confidence level. For customers in the very hot weather zone, where the differences in means are not as large, only 23 percent of the means are statistically different at the 90 percent level & Results In the previous section of this report, the mean kilowatt demands of treatment households were observed to generally be less than that of control households during the activation period of average activation days. Since these results are obtained from a comparison of means, the extent to which the observed differences are due to factors other than the DSS Program is not considered. In this regression is used to estimate the impact of the DSS Program on participants' kilowatt demand and kilowatt-hour consumption after controlling for the effects of weather and for differences in customer appliance and demographic characteristicse kw Demand To estimate the impact of the DSS Program on the kilowatt demand patterns of treatment households on average activation days, the parameters from the following set of 24 regression equations are estimated (1) kwt = aot + altt + b~ct + dttct + et t = 1,2,3'000,24. In this equation, aol and alt are skalar parameters, bt and dt are column vectors of parameters, and kwt T Ct TCt et mean kilowatt demand during the tth hour of average activation days a dummy variable equal to unity for treatment customers and zero for control customers a vector containing the tth period value of the appliance, demographic, and weather variables a vector formed by multiplying T and Ct a random error term for the tth hour 0 The observational units in each regression equation are treatment and control customers. These equations can be viewed as the relations suggested by a household production model in which it is assumed the household maximizes utility from the consumption of commodities that are produced in the household with electricity and appliance inputso The maximization of utility is assumed to be constrained by an income constraint and a production function. These equations can also be viewed as the relations suggested by an analysis of covariance model with interaction terms 0 Many of the appliance variables included as regressors reflect the estimated intensity of usage of each appliance instead of merely the presence or absence of the appliance. Among the appliances treated in the manner are electric ranges, refrigerators, freezers, washers, and dryers. Among the demographic variables included as regressors are household income, square footage of the dwelling, and the number of household mernberse To capture the effects of weather, a variable that incorporates both the amount of air conditioned living space and a measure of hourly cooling -495-

8 degrees is included as regressore The effect of the kilowatt demand ceiling is assumed to be captured by the parameter estimates for T, the treatment dummy variable, and TCtf the interaction terms between the treatment dummy variable and the appliance, demographic, and weather variables& These parameter estimates capture the effect of the DSS Program under the assumption that none of the variables included as regressors are correlated with the regression error terme The set of 24 kilowatt demand equations is estimated individually for each of three months, June, July, and August, and is also estimated for each individual weather zone for each month& Additionally, the regressions for each weather zone for each month are estimated twice, once using all long duration treatment and control customers, and once using all short duration treatment and control customerse To take account of the correlation of the error terms across the 24 regressions, a joint generalized least square procedure is usede With this estimation procedure, 18 sets of 24 regression equations are estimated: two sets for each of three weather zones, for three months0 Because the number of estimated parameters is large, they are not presented in this paper, but can be obtained from the author upon requeste The parameter estimates from the 24 hourly kilowatt demand equations are used to calculate estimates of the kilowatt demand reductions exhibited by treatment customers relative to control on average activation days These estimates are obtained by subtracting the mean predicted kilowatt demand of treatment customers from that of control customers & To simplify the calculation of the reduction estimates, each member of C, the vector of weather variables and customer and demographic variables, and each element of TC, the vector of interaction terms between the treatment dummy and C, is standardized to have a mean of zero before the regression analysis is performed& As a result, the difference between the mean tth period kw demand of treatment and control customers is alt0 In Tables 3 and 4, the reduction estimates obtained from the regression analysis are presentede Positive reduction estimates in these tables are obtained if the predicted mean kw demand of control households exceeds that of treatment customers, and negative estimates are obtained if the predicted mean klil demand of control customers is less than that of treatment customers~ The results reported in these tables can be summarized as followse First, the mean kilowatt demand of treatment customers is generally less than that of control customers during activation periods of average activation dayse This is true for both long and short duration treatment households in all weather zonese Second, for households in the moderate weather zone, all the estimated reductions during activation periods are statistically significant at the 95 percent confidence levele During the activation period of average activation days in August, the month containing the 1981 system peak day, the mean estimated reduction of long duration households is kw/customer, and of short duration households is kw/customer6 Third, both long and short duration treatment households in the moderate weather zone exhibit statistically significant reductions from control during nonactivation periods of average activation days6 Fourth, although treatment customers in the hot and very hot weather zones have lower predicted kilowatt demands than control customers, few of the estimated reductions during both the activation and nonactivation periods are statistically different from zero at the 95 percent confidence levelo This holds for all months, and for both long and short duration treatment households in these weather zones~ To estimate the DSS program's impact on the average activation day kilowatt-hour consumption of participants relative to nonparticipants, the parameters of the following equation are estimated, I (2) kwh = ao + alt + b C + d TC + e where ao and al are skalar parameters, band d are column vectors of parameters, -496-

9 TABLE 5 ESTIMATES OF KII1JV\1ATr DEMAND REDUcrIONS OF LONG DURATION TREATMENT HOUSEHOLDS ON AVERAGE AcrIVATION DAYS June 1981 July 1981 August 1981 Hour Period Moderate Hot Very Hot M:lderate Hot Very Hot t-bderate Hot Very Hot 1 MJNACT ** 0,, ** * 1.190** * ** ** ** 0.894** * ** ** * ** 0.838** * * * ** * * * ,, ** * I ** 0,, * ** ** ** po ** 0,, ** ** -0,, \0 ""'-..J 9 1.,432** -0,, ** ,, ** I ** ** ,,351** 0,, ~~~ ~--~ ~~------~ ~ ~-~~ ~-~ 11 ACT ** ** -0,, ** ,350** ** ,,178** 2.294** ,, ,748** ,,276 1,,270** ** ** ** ** ** 0,,958** ** ** 1.126* ** 0.890** 1.153** ** ** 3.246** ** 0.912** 1.121** ** 0.761* 0.994* ** ** 0.849** ** 0.,738* ** ~~~~----~~------~----~-----~ ~ ~~~ ~ ~----~-----~~~ 19 f\dnact 1.124* 0.886** * 0,, ** ** ,, ** ** ,,371** ** * ** 0,, ** 0.910* ,928* ,, ** ** * , ** ** * A single asterisk indicates the estimate is statistically different from zero at the 90% confidence level.. ** A double asterisk indicates significance at the 95% confidence level..

10 ~~_.~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TABLE 6.. ESTIMATES OF KILCWA'IT DN\WID REDUcrIONS OF SHORr DURATION TREA'n'lENT HOUSEHOIDS ON AVERAGE AcrIVATION DAYS JU'lS 1981 July 1981 August 1981 Hour Period Moderate Hot Very Ii.1t ~derate Hot Very Hot Moderate Hot Very Hot 1 NJNACT 0,,515...Q ** , *'" ,, * 0,,663** 0,, ** : ** * ** 0,, Ii 0,,363* -0,,026-0,,451** * ** 0.897** * ,, ** ,, * ** 0., * 6 0,,374** ,326 0.,518** ,,234** 0., ** 0., ** I ** D ** ** ,,042 +: ** ** Oe ** \0 co 10 2,,097** ** ,, ** ,243 I ** ,, ** ,,152 2,,273** ,, ** ** -0,, ** ~~~~~~~-~~~~~~~~~~-~~~~~~~~~~~~~~~-~~~~-~~ ~-~~~~~~-~~~~~~--~~~-~~~ ~~-~-~~~~-~--~-~~~-~-- 13 ACT ** ** ~747** ** 0,, ** ~245** "'{) ** ** ** ** ** 0,, ** " ** ** ~-~-~~~~~~~~--~~~~~~~~-~ ~----~~-~~---~~~~~---~~~ 18 N3NACT 2.068** e056** ** * ** Oe502 0,; ** ** ~546**...0,, ** -0,, * **' ** **...Q ** ** * ** -0,,091 -De * * A single asterisk indicates the estimate is statistically different from zero at the 90~ confidence level.. ** A double asterisk indicates slglificance at the 95% confidence level.

11 and kwh T C TC e mean daily kilowatt-hour consumption on average activation days a dummy variable equal to unity for treatment customers and zero for control customers a vector of customer appliance and demographic variables, and weather variables a vector formed by multiplying T and C a vector of random error terms& The observational units in this regression are treatment and control customers& As in the kilowatt demand regression equations, weather variables and customer income, appliance, and demographic characteristics are included as regressors & In general, the appliance variables included in these regressions reflect the estimated intensity of usage of each appliance, not merely the presence or absence of the appliance& The effect of the kilowatt demand ceiling on the kilowatt-hour consumption of the treatment group relative to control is assumed to be captured by the parameter estimates for T, the treatment dummy variable, and for Te, the vector of interaction terms between the treatment durnmny variable and the appliance, demographic, and weather variables $ It is further assumed that none of the variables included as regressors are correlated with the regression error term~ The regression equation in equation (2) is estimated with ordinary least squares individually for June, July, and August, and is also estimated for each weather zone for each month@ Additionally, the regression equation for each weather zone is estimated twice, once using all long duration treatment and control customers, and once all short duration treatment and control customers As a result, the parameters of a total of 18 regression equations are estimated These parameter estimates are not in this paper, however, can be obtained from the author upon request~ The parameter estimates from the kilowatt-hour equations are used to calculate the predicted daily kilowatt-hour consumption reductions exhibited by treatment customers relative to control on average activation days~ These estimates are obtained by subtracting the mean predicted kilowatt-hour consumption of treatment customers from that of control customers@ To simplify the calculation of the reduction estimates, each element of C, the vector of weather and customer appliance and demographic variables, and each element of TC, the vector of interaction terms between the treatment dummy and C, is standardized to have a mean of zero before the regression analysis is performed~ As a result, the difference between the mean kwh consumption of treatment and control customers is a10 The consumption reduction estimates obtained from the regression analysis are presented in Table 7.. positive reduction estimates in these tables are obtained if the predicted mean kwh consumption of control customers exceeds that of treatment customers, and negative estimates are obtained if the predicted mean kvfu consumption of control customers is less than that of treatment customers~ The results presented in this table can be summarized as follows& First, the mean daily kilowatt-hour consumption of treatment customers is generally less than that of control during average activation days in June, July, and August.. This holds for all weather zones, with the exception of long duration customers in the very hot weather zone in AUgust0 Second, for customers in the moderate weather zone, all the estimated k~vh consumption reductions are statistically different from zero at the 95 percent confidence level & The mean esti.mated reduction of long duration treatment customers on average activation days in June, July, and August is kwh/customer, and of short duration treatment customers is 38&64 ~Vh/customer~ Third, for both long and short duration treatment households in the hot and very hot weather zones, none of the estimated kwh consumption reductions are statistically different from zero at the 90 percent confidence level~ -499-

12 TABLE 7 $ ESTIMATED KILOWATT-HOUR CONSUMPTION REDUcrIONS OF TREATMENT CUSTOMERS vs $ CONTROL ON AVERAGE AcrIVATION DAYS Control vso Long Duration Weather Zone July 1981 August 1981 Moderate Hot Very Hot ** OQ460 25<1546** ** Weather Zone June 1981 July 1981 August 1981 Moderate Hot Very Hot ** ,, ** ** e CONCLUSIONS AND DIRECTIONS FOR FURTHER ANALYSIS In summary, the results from the regression analysis of hourly kw demand indicate that after controlling for the effects of weather and differences in customer appliance and demographic characteristics, the mean kw demands of treatment customers are generally lower than control during the activation periods of average activation dayso These results also indicate that the strongest evidence for statistically significant reductions are found in the moderate weather zonee Both of these findings support the results from the comparisons of the hourly mean kw demands of treatment and control customerso which the estimates of the program's impact are influenced by having participants who are volunteers" This additional research is important because the voluntary nature of the nss Program can lead to misleading regression estimates of the program's impact 0 As yet, the influence of the voluntary nature of the program has not been addressed 0 In addition, the results from the regression analysis of daily kilowatt-hour consumption indicate that treatment customers have lower k~~ consumption on average activation The strongest evidence for significant reductions was again found in the moderate weather zone 0 As a result, the DSS Program seems to reduce total kwh consumption on average activation days" In a large part of the additional analysis that will be ~,~,~~,,~~\~rl with data from the 1981 DSS Program will be focused on the extent to -500-