IMPACT EVALUATION OF BGE’S SEP PILOT Ahmad Faruqui, Ph. D. Sanem Sergici, Ph. D. August 12, 2009 Technical Hearings Maryland Public Service Commission

2 The impact evaluation at a glance We used 2008 data from the SEP pilot to develop a price response impact simulation model (PRISM) for BGE PRISM has the following features ► Includes the demand response impact associated with several dynamic pricing rates and enabling technologies ► Identifies the impact of weather on price responsiveness ► Estimates hourly impacts during the critical event hours between 2 pm and 7 pm as well as 2 hours prior to the event and one hour after the event ► Tests whether customer price response varies by socio- demographic characteristics

3 BGE tested the impact of three different rates in conjunction with two different technologies Dynamic Peak Pricing (DPP) Rates Peak Time Rebate with a low rebate value (PTRL) Peak Time Rebate with a high rebate value (PTRH) The Energy Orb (ORB) A/C switch technology and Orb (ET_ORB)

4 “All-in” rates for the eight SEP programs All-in rates include electricity supply, transmission, distribution and customer charges Critical price for the PTR programs is inferred using the concept of opportunity cost

5 We estimated two different price elasticities to capture the impact of dynamic pricing in the SEP sample The elasticity of substitution measures the change in load shape caused by changing peak-to-off peak prices Percent change in the ratio of peak to off-peak consumption when there is a one percent change in the ratio of peak to off-peak prices The daily (price) elasticity measures the change in daily energy consumption caused by changing daily prices Percent change in the daily average consumption when there is a one percent change in the daily average price

6 Estimated price elasticities are weather dependent We employ two variables to capture the impact of weather in our analyses: 1- THI : Temperature humidity index 2- THI_DIFF: Difference between average peak and off-peak THI values Since the elasticities are based on the weather term, we identified three different levels for the weather variables to arrive at the elasticity values used in the PRISM simulations 1- Based on the Average Weather Uses the value of the weather term averaged over 10 Critical Peak Pricing (CPP) days (excludes 2 CPP days with low weather terms) 2- Based on the Minimum Weather Uses the value from the CPP day with the minimum THI_DIFF value (CPP 11~ 9/23/2008) 3- Based on the Maximum Weather Uses the value from the CPP day with the maximum THI_DIFF value (CPP 9~ 9/3/2008)

7 Substitution and daily price elasticities

8 Interpretation of the elasticities Substitution elasticity One percent increase in the ratio of peak to off-peak prices results in a percent decrease in the ratio of peak to off-peak consumption Daily (price) elasticity One percent increase in the daily average price results in a percent decrease in the daily average consumption

9 The elasticities rise with technology and hot weather

10 Comparison of BGE elasticities to those estimated in California (SPP) and New Jersey (PSE&G) BGE elasticities are midway between those estimated from SPP and PSE&G pilots

11 Comparison of substitution elasticities across programs

12 In the following slides, we report the program impacts Under PJM peak demand conditions (hour ending 17:00 with THI of 83.1), the reduction in critical peak demand ranges from 22 to 37 percent These impacts are based on the “hourly impact analysis” that will be introduced later in the presentation Based on the average CPP day weather scenario, average reduction in critical peak demand ranges from 18 to 33 percent In slides 13-15, we present demand response impacts averaged over the critical peak periods for three different weather scenarios

13 DPP Program- Critical Peak Demand Impacts Note: Impacts represent the averages over the critical peak period

14 PTRL Program- Critical Peak Demand Impacts Note: Impacts represent the averages over the critical peak period

15 PTRH Program- Critical Peak Demand Impacts Note: Impacts represent the averages over the critical peak period

16 Impact Summary- Demand Response Impacts (based on the average CPP day weather) Note: Impacts represent the averages over the critical peak period

17 Impact Summary - Total Monthly Consumption (based on the average CPP day weather)

18 Impact Summary - Demand Response and Total Monthly Consumption (based on the average CPP day weather) All impacts are statistically significant at the 5 percent level Note: Demand response impacts represent the averages over the critical peak period

19 We also estimated hour-specific substitution elasticities for the hours between 1 pm and 8 pm For each hour, we estimated a separate substitution equation Resulting substitution elasticity estimates are based on the hourly THI_DIFF averages from the first 10 CPP days The daily energy consumption equation was not re- estimated since it does involve hourly prices

20 Hourly Substitution Elasticity Equations Note: Other variables are also controlled for but suppressed here due to limited space. Note: Average CPP weather is based on the first 10 CPP days. The last two CPP days are omitted from the averages due to mild weather observed on those days.

21 Hourly substitution elasticities

22 Summary of Hourly Substitution Elasticities

23 Using the estimated hourly elasticities and a series of THI values, we calculated the hour-specific demand response impacts For Hour 17 and THI level of 83.1 degrees, the demand impacts range between 22 to 37 percent (details can be found in the Appendix)

24 Impact Summary- Demand Response Impacts (based on the PJM peak demand conditions) Note: Peak demand reductions are defined for HE 17:00 for THI of 83.1 degrees

25 Summary of SEP impacts Demand response impacts based on the average CPP day weather scenario and averaged over the critical peak period ranges from 18 to 33 percent Demand response at PJM’s peak demand conditions range from 22 to 37 percent ► Without enabling technologies, the reduction in critical peak period usage ranges from 22 to 26 percent ► Presence of both A/C switch and ORB almost doubles the impacts that are obtained from the rates alone ► Existence of the ORB increases the extent of demand response

26 In the next three slides, we demonstrate how a reference hourly load profile changes with demand response We use the 2008 average load profiles of R (non-heating) and RH (heating) customers weighted by their share in the population We select a CPP day for demonstration purposes (July 17, 2008) and zoom in on the load profile for that day We take the hourly percent CPP demand reduction figures resulting from our hour-specific elasticity estimations and apply these to the CPP period hourly demand For the off-peak hours, we use the average percent off- peak demand reduction figures to find out the load levels after demand response

27 Load profile on a CPP day- DPP

28 Load profile on a CPP day- PTRL

29 Load profile on a CPP day- PTRH

30 APPENDIX I.Elasticity Calculations II.Hourly Impact Estimations III.PRISM Simulation IV.Impact of Appliances and Demographic Variables V.Regression Models

31 I. Elasticity Calculations

32 We have estimated substitution and daily elasticities that are sensitive to the variations in weather Weather sensitive elasticities are estimated by introducing an interaction term between price and the weather variables (thi_diff and thi). These terms are demonstrated in the estimated equations below: Substitution EquationsDaily Equations

33 Here is an example of how we calculate the weather sensitive elasticity terms

34 We have generated three sets of elasticity values based on different values of the weather variables We employ two variables to capture the impact of weather in our analyses: 1- THI : Temperature humidity index 2- THI_DIFF: Difference between average peak and off-peak THI values Since the elasticities are based on the weather term, we identified three different levels for the weather variables to arrive at the elasticity values used in the PRISM simulations 1- Based on the Average Weather Uses the value of the weather term averaged over top 10 CPP days 2- Based on the Minimum Weather Uses the value from the CPP day with the minimum thi_diff (CPP 11~ 9/23/2008) 3- Based on the Maximum Weather Uses the value from the CPP day with the maximum thi_diff (CPP 9~ 9/3/2008)

35 Summary of Weather Data on CPP Days over all CPP Hours/ June 1- September 30, 2008

36 BGE Substitution and Daily Elasticity Values

37 II. Hourly Impact Estimations

38 We estimated hourly substitution elasticities

39 We estimated hour by hour regressions between load and THI on 12 CPP days for hours 13 through 20 We use these equations to predict the “load before demand response” load for a given hour and given temperature We use the average 2008 load profile for R (non- heating) and RH (heating) customers weighted by their share in the population to estimate the equations

40 Estimated Reference Load Values (kWh/hour) before Demand Response

41 DPP

42 DPP_ET_ORB

43 PTRL (Rebate level : $1.16 /kWh)

44 PTRL_ORB (Rebate level : $1.16 /kWh)

45 PTRL_ET_ORB (Rebate level : $1.16 /kWh)

46 PTRH (Rebate level : $1.75 /kWh)

47 PTRH_ORB (Rebate level : $1.75 /kWh)

48 PTRH_ET_ORB (Rebate level : $1.75 /kWh)

49 III. PRISM Simulation

50 PRISM Simulation of the SEP Program Types Using PRISM, we have simulated demand response to BGE’s eight program types at the customer level Our metrics include: Percent change in peak and off-peak consumption on critical days Percent change in peak and off-peak consumption on non-critical days Percent change in total monthly consumption

51 Steps in the PRISM simulation 1- Start with the load profile of a typical BGE customer 2- Identify the all-in existing rate this customer would pay under the current tariff regime 3- Identify the all-in rate this customer would pay if they participated in the SEP pilot program 4- Determine the price responsiveness of this customer by using a version of PRISM that includes the SEP price elasticities 5- Simulate demand response in the SEP

52 We simulated the impacts from the three rate designs and two technology types tested in the SEP pilot Under the PTRL and PTRH program designs, peak period consumption has a cost equal to the current rate plus the foregone rebate amount

53 PRISM Impacts are presented for the average BGE customer We calculated the typical load based on R and RH customer load data during the treatment period

54 The DPP rate design, for the average customer, leads to: 20.1 percent drop in critical peak usage 1.8 percent drop in peak usage 0.9 percent increase in monthly usage

55 The DPP rate enhanced with enabling technologies leads to: 32.5 percent drop in critical peak usage 4.4 percent drop in peak usage 1.2 percent increase in monthly usage

56 The PTRL rates lead to: 17.8 percent drop in critical peak usage 0.5 percent drop in the monthly consumption

57 The PTRL rates enhanced with orbs lead to: 23 percent drop in critical peak usage 0.5 percent drop in the monthly consumption

58 The PTRL rates enhanced with the enabling technologies lead to: 28.5 percent drop in critical peak usage 0.5 percent drop in the monthly consumption

59 The PTRH rates lead to: 21 percent drop in critical peak usage 0.6 percent drop in the monthly consumption

60 The PTRH rates enhanced with orbs lead to: 26.8 percent drop in critical peak usage 0.6 percent drop in the monthly consumption

61 The PTRH rates enhanced with the enabling technologies lead to: 33 percent drop in critical peak usage 0.6 percent drop in the monthly consumption

62 IV. Impact of Appliance and Demographic Variables

63 We tested whether price elasticities vary with socio- demographic (SD) variables The following SD variables were included in the testing : CAC ownership (CAC) Programmable thermostat ownership (PT) Pool ownership (POOL) Multi-family home (MULTI) Owner vs. renter (OWNER) College or above education level (COLLEGE) Over 75K income (OVER_75K) Below 25K income (BELOW_25K) For the substitution equation, only MULTI and COLLEGE showed statistical significance For the daily equation, POOL and OVER_75K showed statistical significance Other variables (including CAC) did not show any statistical significance in any of the regressions

64 Substitution Equations w/ and w/o SD Variables Note: Other variables are also controlled for but suppressed here due to limited space.

65 Substitution elasticities vary with SD variables

66 Daily Equations w/ and w/o SD Variables Note: Other variables are also controlled for but suppressed here due to limited space.

67 Daily elasticities also vary with SD variables

68 Conclusions from the SD Regressions Multi-family home residence tends to reduce substitution elasticity while having a college or above education level increases it Owning a pool or having an income level above 75K tend to increase daily price elasticity Despite the fact that we find some of the SDs to be significant, we focus on the models w/o SD variables ► Not all customers respond to the surveys; as a result we lose 20 percent of the analysis sample

69 V. Regression Models

70 Our regression models were constructed after a series of model and specification tests We started with a model that has unique substitution elasticity coefficients for each of the eight program types However, when we conducted joint significance tests, results showed that the substitution elasticities can not be distinguished from each other in statistical terms within the “price only”, “with Orb”, and “with Orb and A/C switch” groups (These results are shown in the next slide) This is the rationale for the final specifications for our models which are presented in slides 74-76

71 One substitution elasticity for each of the eight program types Other dummy variables and thi_diff interactions are also controlled for but suppressed here due to limited space.

72 The algebra of peak-to-off peak substitution : Logarithm of the ratio of peak to off-peak load for a given day :The difference between peak and off-peak THI. THI is defined as follows: THI= 0.55 x Drybulb Temperature x Dewpoint :Logarithm of the ratio of peak to off-peak prices for a given day :Interaction of THI_DIFF variable with monthly dummies :Dummy variable is equal to 1 when the period is June 2008 through September 30, 2008 : Interaction of with treatment customer dummy : Dummy variable that is equal to 1 when the month is k : Dummy variable that is equal to 1 on weekends :Interaction of ratio of peak to off-peak prices and THI_DIFF for a given day : Dummy variable that is equal to 1 on CPP days

73 The algebra of daily energy consumption : Logarithm of the daily average of the hourly load : Logarithm of the daily average of the hourly THI : Logarithm of the daily average of the hourly Price : Interaction of variable with monthly dummies : Dummy variable is equal to 1 when the period is June 2008 through September 30, 2008 : Interaction of with treatment customer dummy : Dummy variable that is equal to 1 when the month is k : Dummy variable that is equal to 1 on weekends : Interaction of price with ln (THI) : Dummy variable that is equal to 1 on CPP days

74 The parameters of the substitution equation Note: Other variables are also controlled for but suppressed here due to limited space.

75 The parameters of the daily energy equation Note: Other variables are also controlled for but suppressed here due to limited space.

76 Parameters of the hourly substitution equations Note: Other variables are also controlled for but suppressed here due to limited space.