sistema control demanda calentar agua pd enero 2006 juana

388 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 21, NO. 1, JANUARY 2006

Innovative Approaches to Verifying DemandResponse of Water Heater Load ControlGrayson C. Heffner, Senior Member, IEEE, Charles A. Goldman, and Mithra M. Moezzi

AbstractThis study describes a pilot effort to measure load re-ductions from a residential electric water heater (EWH) load con-trol program using low-cost statistically based measurement andverification (M&V) approaches. This field experiment is describedwithin the larger framework of overcoming barriers to participa-tion of noninterval metered customers in Demand Response (DR)Programs. We worked with PJM Interconnection and a Curtail-ment Service Provider (CSP) to collect hourly load data for twosubstations and several hundred households over six weeks of loadcontrol testing. The experimental design reflected constraints im-posed by limited funding, manpower, equipment, and the routineoperation of the load control system by the CSP. We analyzed sub-station- and premise-level data from these tests in an attempt toverify several point estimates taken from the hourly diversifieddemand curves used by the CSP to establish aggregate load reduc-tions from their control program. Analysis of premise-level dataallowed for provisional verification that the actual electric waterheater load control impacts were within a 60 to+10% band ofthe estimated values. For sub-station level data, measured valuesof per-unit load impacts were generally lower than the CSP esti-mated values for Electric Cooperative #2, after accounting for con-founding influences and operational test problems. Based on thisexperience we offer recommendations to ISO and utility DR pro-gram managers to consider before undertaking further develop-ment of alternatives to the conventional but costly program-wideload research approach.

Index TermsDemand management, electricity markets, loadcontrol, load management, load shedding, statistics, substationmeasurements, water heating.

I. NOMENCLATURE

ADDF Adjusted Diversified Demand Factor.ALM Active Load Management.AMR Automatic Meter Reading.CSP Curtailment Service Provider.DR Demand Response.ELRP Economic and Emergency Load Response Pro-

grams.ETS Electric Thermal Storage.EWH Electric Water Heater.FERC Federal Energy Regulatory Commission.ISO Independent System Operator.M&V Measurement and Verification.

Manuscript received September 8, 2004; revised January 6, 2005. This workwas supported by the Assistant Secretary of Electric Transmission and Distribu-tion, Electric Markets Technical Assistance Program of the U.S. Department ofEnergy, under Contract DE-AC03-76SF00098. Paper no. TPWRD-00413-2004.

G. C. Heffner is with the Global Energy Associates, Inc, North Potomac, MD20878 USA (e-mail: [email protected]).

C. A. Goldman and M. M. Moezzi are with the Lawrence Berkeley Na-tional Laboratory, Berkeley, CA 94720 USA (e-mail: [email protected];[email protected]).

Digital Object Identifier 10.1109/TPWRD.2005.852374

PURPA Public Utility Regulatory Policy Act.SCADA Substation Control and Data Acquisition.

II. INTRODUCTION

I SOs that oversee and administer wholesale electricity mar-kets are making efforts to ensure that these markets providecomparable opportunities for participation of both supply-sideand demand-side resources, consistent with FERC policy di-rection. Demand Response (DR) programs allow for customer-operated resources, including on-site generators and end-useloads, to offer and receive payments for measurable reductionsin their power demands during emergency events or in responseto high real-time or forward market prices.1

Experience thus far has shown that larger and interval-metered customers can effectively participate in ISO-operatedemergency, real-time, and day-ahead wholesale energy markets[1]. Such mobilization of end-use loads in response to price orsystem emergency signals can be shown to have very significantbenefits both in terms of system reliability and dampening ofvolatile market clearing prices [2].

A difficult technical and program design issue is posed bysmall customers without interval meters, who are often excludedfrom participation by requirements for measurement and veri-fication (M&V) of their hourly load reductions. FERC has en-couraged ISOs to take steps to develop solutions to this problem,and PJM Interconnection has responded with a pilot DR pro-gram targeted at smaller, noninterval metered customers [3].

ISO-New England and New York ISO have adopted mea-surement and verification protocols that allow statistical sam-pling of small customer loads as a substitute for direct intervalmetering for certain DR programs [4]. However, the guidelinesfor statistically based M&V remain general, and there are veryfew documented examples of its successful application for ISO-based small customer DR programs [5]. The participation bar-riers faced by small, noninterval metered customers are likely topersist until there are standardized M&V protocols in place thatare acceptable to ISO administrators (and system dispatchers)and have been empirically validated.

One component of PJMs small customer pilot program seeksto identify and test new approaches for M&V of these customers.PJM approached the US Department of Energy (DOE) andLawrence Berkeley National Laboratory (LBNL) regarding acollaborativeeffort in thisarea. In thispaper,wepresentanoverallperspective on measuring and verifying demand reductionsfor noninterval metered customers together with initial resultsfor one (of many possible) novel approaches to the problem.

1Regional transmission organizations offering DR programs includeISO-New England, New York ISO, PJM Interconnection, ERCOT, and CAISO.

0885-8977/$20.00 2006 IEEE

HEFFNER et al.: INNOVATIVE APPROACHES TO VERIFYING DEMAND RESPONSE OF WATER HEATER LOAD CONTROL 389

III. M&V METHODS FOR SMALL CUSTOMER LOAD CONTROLEstimating the impacts of residential load control has been

of interest to utility planners and engineers since it became acommon industry practice [6]. Much effort has been devoted tothoroughly understanding the natural diversity of end-use loadsand their behavior under various load control regimens (7, 8,9). Much of this foundational work was accomplished usingload research methods, in particular end-use load research, andstandard experimental frames including comparison of averageload curves for a population on control (test) and normal(noncontrol) days [10], [13], [15]. In addition to premise- orappliance-level load measurement, some analysts have esti-mated the impacts of end-use load control at more aggregatelevels, notably feeder circuits [11], substations [12], [13], mu-nicipalities [14], and the utility system [15], [16]. These studiesoften used notch tests, nick tests, or SCRAM tests thatare designed to elicit the maximum possible instantaneousdemand drop from end-use load control strategies.

Each of these measurement approaches has potential draw-backs and implementation requirements that create challengesin different settings. For example, Davis et al. [13] note the dif-ficulty in performing side-by-side comparisons of circuit-leveldata for test and baseline days, especially for temperature-sensitive loads, as the circuit-level loads on a given day can beaffected by the temperature trends of several preceding days.Additionally, Reed et al. [11] discuss the need for frequent datascanning (short averaging intervals), as low as 50 scans persecond, when attempting to detect the impacts of load control atthe circuit level. Several analysts suggest the simultaneous ap-plication of load impact measurement using different methodsapplied at different estimation levels [11], [12], [15], as is at-tempted here.

Table I provides a framework for considering conventionaland alternative M&V approaches for DR programs targetingsmaller customers without interval meters. Table I highlights un-conventional sampling or experimental design strategies (shownin italics) that might be utilized given the availability of dataand the level at which impacts are being estimated (e.g., enduse, premise, utility system). For example, the conventional ap-proach used to measure and verify load impacts of nonintervalmetered customers in DR programs typically relies on a sampleof interval-metered end-use loads or premises. Average load re-ductions are calculated for the sample; results are then used torepresent the entire program population (see Table IGrid Lo-cations A4 and B4 [1], [4], [5], [15].2 A drawback of this con-ventional M&V approach is its costliness and the time requiredfor implementation, factors which are particularly limiting forload aggregators participating in pilot programs that may beshort-lived or whose economics are sensitive to fixed costs.3

This framework of possible M&V protocols suggests theremay be opportunities for cost-saving innovation in M&V ap-proaches. Several have been tried, notably extrapolation of in-terval metering results at substation [Grid Location C4] and

2The extrapolation algorithm depends on sample design, especially whetherthe sample was stratified according to a variable thought to significantly influ-ence load impacts (e.g., kWh monthly usage).

3A PURPA-compliant load research survey for a small customer load controlprogram comprising 50 000 participants can cost $50 000$75 000.

TABLE IM&V PROTOCOL FRAMEWORK FOR CUSTOMERS PARTICIPATING

IN DR PROGRAMS

Note: Bold lettering signifies the conventional M&VApproach; italic lettering signifies unconventional and pos-sibly innovative approaches.

system levels [Grid Location D4]. The work reported here ex-plores two unconventional and possibly innovative M&V ap-proaches: interval metering of a sample of premises drawn at thesubstation level and used to represent the entire program popula-tion [Grid Location B4]; and substation-level interval meteringused to measure the average per-unit diversified demand of theentire program [Grid Location C4].

The venue for this M&V experimentation was a PJM-spon-sored Small Customer Pilot Program. The objective was to ex-plore whether unconventional and less costly M&V approachesimplemented at the substation level might provide lower-cost,quicker-turn-around estimates of the diversified demand im-pacts of a water heater load control program. The experimentalM&V approach utilized interval metering to simultaneouslymeasure aggregate load impacts at the Medium-Voltage (MV)substation transformer bank and per-end use load impacts at thepremise-level.4 The intent was to develop some field experiencewith these two unconventional but promising approaches to mea-suring the load impacts of small customer DR programs, subjectto the practical constraints of working with distribution utilitiesand load aggregators that have limited manpower and equipmentresources and need to maintain normal operations of their loadcontrol system.

4As the paper describes, in applying this approach it is critical to choose an MVnetwork serving a customer population with a high saturation of program partic-ipants, so that the aggregate effect of many small load impacts is observable.


TABLE IICHARACTERISTICS OF ELECTRIC COOPERATIVES

IV. SMALL CUSTOMER PILOT PROGRAM CHARACTERISTICSThe CSP participating in PJMs small customer pilot program

operates an integrated load management system that serves theneeds of rural electric cooperatives located throughout the PJMcontrol area. This integrated system comprises 45 000 load con-trol switches and delivers an estimated 35 MW of load reduc-tion in summer (50 MW in winter) through control of residen-tial electric water heaters, water pumps, and electric thermalstorage space heaters. Communications and dispatch is coordi-nated via a head-end computer system and an extensive tele-phone/radio communications system. The system has been inplace for years and is operated on a daily basis to managethe daily maximum demand presented by rural cooperatives towholesale suppliers.

LBNL worked with the CSP and two of its client electric co-operatives to identify target substations suitable for measuringthe aggregate impacts of load control and to develop a regimenof short-duration load control tests that met the following oper-ational criteria:

did not interfere with ongoing system operations; were not in the busiest seasons (summer and winter); and minimized the potential intervening effects of seasonal

nonresidential loads.The two substations selected are both important delivery

points (and therefore metering points) for wholesale servicefrom PJM-member generators. Table II5 provides summaryinformation on the two substations, including (1) the customermix and the number of households with electric water heating(EWH) load control devices; (2) the total coincident peak de-mand at each substation, with the residential component brokenout separately; (3) the number of premise-level load researchmonitoring devices that were utilized at each substation; and(4) the CSPs estimated load reduction per electric water heatercontrol point for each of the intended load control test periods,expressed as an Adjusted Diversified Demand Factor (ADDF).

The CSP uses these hourly ADDF values along with waterheater address groupings stratified by size and customer usage

5Note that the CSP-provided ADDF values can vary between substations forthe same hourly period. This reflects adjustment by the CSP to reflect householddemographics (EWH size and vintage, household size, etc.) particular to eachcooperative.

characteristics to form load control strategy tables that locallyoptimize dispatch according to network needs, time of day,season, and other variables. The ADDF values together withthe estimated restore demand function constitute the coreassumptions underlying the estimated diversified water heaterload control impacts, and were drawn from [9], [13] and [17].

In addition to exploring applications of unconventional,lower cost M&V approaches, the specific experimental ob-jective of this study was to measure and verify a selectionof point estimates taken from the ADDF curve, shown inFig. 1.6 This choice of M&V approachmeasuring individualpoint estimates from a daily seasonal end-use diversified de-mand curveverifies the overall estimation approach withoutmeasuring a particular (peak) summer or winter hour.

V. MEASUREMENT APPROACH

The experimental design and schedule for the load controltests was developed within the operational constraints im-posed by the CSP. A six-week period from early October tomid-November 2003 was selected to avoid the summer andwinter peak demands and take advantage of utility staff thatwere available during the shoulder season. Three separate loadcontrol notch tests, defined by start time and duration, wereundertaken to provide point estimates of load reductions forcomparison to the CSP ADDF values for electric water heater(see Table II). The load control tests were

Test A: 24 pm, Tuesdays and Thursdays in October;7 Test B: 79 am, Tuesdays and Thursdays in November;8 Test C: 68 pm, Tuesdays and Thursdays in November.

Wednesdays were designated as baseline days, with no loadcontrol scheduled.9 The CSP and two cooperatives collecteddata for the test period, which was forwarded to LBNL for anal-ysis. Operating difficulties were encountered during the tests, asthe CSP reported that parallel Electric Thermal Storage (ETS)load control and Voltage Control (VC) devices had been inad-vertently dispatched during many of the EWH load control tests.This seriously confounded efforts to analyze the substation leveldata, as the estimated demand impact of the ETS and VC pro-grams was comparable to or larger than the EWH program insome cases. Ultimately, LBNL was able to estimate load con-trol impacts only for Electric Cooperative #2 after making someadjustments to the sub-station level data.

VI. RESULTS

For the premise-level data LBNL estimated the load reductiondue to EWH load control by measuring the differences in hourlyusage patterns between the Load Control Test and Baselinedays. For substation-level data, Load Control Test day datawas examined to determine the demand drop at the time of con-trol and the demand recovery when control was relinquished.

6The ADDF values used by the CSP in formulating load control strategiesapplied Summer Season diversified demand data for MayOctober and WinterSeason diversified demand data for November through April.

7October is considered part of the Summer Season8November is considered part of the Winter Season9Mondays and Fridays were avoided, as the pre- and post-weekend weekdays

often have a different load shape and/or magnitude.


Fig. 1. Summer season hourly diversified demand curve.

TABLE IIILOAD CONTROL TEST RESULTS FOR ELECTRIC COOPERATIVE #1

A. Premise-Level ResultsLBNL applied a normalization technique to the raw premise-

level observations in order to account for load shape or magni-tude differences between the Test and Baseline day [6]. Normal-izing is a straightforward process of shifting the load shape inthe hour just before the test period so that the test day and base-line day load shapes coincide exactly in that hour. Normaliza-tion reduces the bias and variability in load reduction estimates,

TABLE IVLOAD CONTROL TEST RESULTS FOR ELECTRIC COOPERATIVE #2

without resorting to complex models adjusting for weather orother variables, and helps adjust for any skew in Baseline Dayload profile or load level. The observed and normalized resultsprovide a good estimate of the effect of load control while at-tempting to account for some intervening influences and effects.Tables III and IV summarize the premise-level results for Elec-tric Cooperatives # 1 and 2, respectively. Each row represents


a comparison of a Load Control Test Day and a Baseline Dayin sequential order of testing. The first eight rows summarizemeasurement results for the 24 pm summer period. The sub-sequent twelve rows show measurement results for the 79 amand 68 pm winter period tests. Column (2) shows the observedmean kilowatt value of the difference between each pair of LoadControl Test and Baseline Days and is calculated for the entirepopulation (N) of premise-level interval meters available at eachelectric cooperative. A negative value signifies a load reduction.Column (4) provides the corresponding normalized mean dif-ference for each test period. LBNL calculated a -value (using apaired t-test) for both the observed and normalized differencesin load between each test and baseline period (see Columns 3and 5). Premise-level data comparisons considered statisticallysignificant ( -value less than 0.1) are shown in italics.10

B. Substation-Level ResultsSubstation-level load impact analysis can be based on either

a simple time series analysis of loads over the test period orby a comparison of load values between baseline and testdays. Because of inadvertent intervening load control opera-tions (e.g., ETS), we opted for the simplest possible analysisschemeusing the load values immediately preceding the loadcontrol as the baseline against which the effects of load con-trol are measured. This simple method yields estimates of theinstantaneous Diversified Demand Drop as well as the subse-quent Demand Recovery, from which a measure of diversifiedduty cycle for the end use can be derived [12].

Interval (15 min.) metered data was available at the substa-tion level for both Electric Cooperatives 1 and 2. We discardedresults for Electric Cooperative # 1 because of the inadvertentoperation of an electric thermal storage (ETS) space heater loadblock during all of the test periods which was in aggregate largerthan the corresponding electric water heater load block (755versus 631 units).

The magnitude of the confounding effect of other load con-trol operations was much smaller at Electric Cooperative #2. Atthis coop, two load blocks besides the EWH load block were dis-patched during the October and November load control testsaload block comprising a 2.5% conservation voltage reduction(CVR), and a load block comprising 15 ETS units. We adjustedfor the CVR operation by subtracting 2.5% of the substationvoltage values at the start and the end of the tests from the cal-culations of Diversified Demand Drop and Demand Recovery.The ETS load block was small and considered negligible rela-tive to the EWH load block (i.e., 15 ETS units versus 243 EWHunits). We included the ETS units along with the EWH units

10The pairwise t-test assesses differences between paired observations, viaa one-sample t-test on differences computed for each pair. The estimated dif-ference (i.e., estimated load reduction) is the result of simple averaging of theobserved pairwise differences. The p-value is the statistical significance of at-test on the distribution of differences; a p-value of less than 0.1 means there isa 90% confidence that the magnitude and sign of the mean is not the result of arandom distribution but is due to our experimental design. Though we think thet-tests are a good description of these differences, note that the formal require-ments for the t-test are not met by this configuration of data, and the p-valuesand estimated differences should be taken as heuristic rather than as formal sta-tistical estimates. For example, the pairs are not independent observations norare the data necessarily normally distributed.

TABLE VSUBSTATION LOAD CONTROL TEST RESULTS FOR ELECTRIC COOPERATIVE #2

in calculating per-unit demand reductions.11 The results of thisanalysis of adjusted substation load data for Electric Coopera-tive # 2 are shown in Table V.

VII. DISCUSSION

A. Electric Cooperative #1: Premise-Level ResultsElectric Cooperative # 1 was selected for load control testing

because of the large number of EWH program participants (215)fitted with hourly interval meters. This large sample producedstatistically robust results that were not tainted by the inadver-tent operations problem, as none of the interval-metered EWHcustomers were part of the ETS load block.

As shown in Table III, 15 of 18 observed mean differencecomparisons and 18 of 18 normalized difference comparisonswere signed correctly (i.e., signifying a measured load reduc-tion), and eleven of twelve mean difference comparisons werestatistically significant at a 90% confidence interval based on

11Our rationale was that electric water heaters and thermal storage spaceheaters are intrinsically energy storage devices and, according to the CSP, havecomparable connected loads.


Fig. 2. Observed load reduction vs estimated ADDF value (electric cooperative # 1).

Fig. 3. Observed load reduction vs estimated ADDF value (electric cooperative # 2).

the normalized results. Fig. 2 compares the average load reduc-tions with the CSP-estimated ADDF values for each period; av-erage load impacts in each period that are statistically significantare shown with a dashed bar. Fig. 2 shows the underlying vari-ability in the observed usage data across comparison periods,as well as the effect of normalization on this variability. Nor-malization had the effect of reducing the load impact valuesfor each control strategy, especially in those cases where theobserved impacts were significantly higher than the estimated(ADDF) values. However, in several cases where the observedvalues were incorrectly signed or very low, the normalizationadjusted the impact value sufficiently closer to the estimated(ADDF) value to at least have the correct sign. The overalleffects of normalization were to 1) reduce the variability ofthe load impact values, and 2) produce average load impactsthat were lower than the estimated (ADDF) values. The av-erage observed load reduction over the 18 control-versus-base-line comparison periods is kW/participant with a stan-dard error of 0.041, while the average normalized load reduction

is kW/participant, with a standard error of 0.05. Thesevalues are much lower than the ADDF values provided by theCSP.

B. Electric Cooperative #2: Premise-Level ResultsOnly nine interval load recorders were available for place-

ment on EWH program participants served by this substation;none of these participated in the ETS program. The observedand normalized premise-level results of the load control testingare shown in tabular format in Table IV, while Fig. 3 comparesthe observed and normalized average load reductions for thesenine premises with the estimated ADDF values. Only six ob-served load impact results and one normalized load impact re-sult were statistically significant, which is primarily a resultof the small sample size. The normalized mean difference re-sults are signed correctly in 15 out of 18 load control tests. Thenormalization step does appear to modulate both the very lowand very high individual load impact values. In fact, for Sub-station # 2 the normalized impact values are very close to the


TABLE VICOMPARISON OF LOAD IMPACT MEASUREMENTS AT THE SUBSTATION AND

PREMISE LEVEL FOR ELECTRIC COOPERATIVE #2 (IN KW PER UNIT)

estimated (ADDF) values, both for the individual load controlstrategies and for the overall average. The average observed loadimpact over the 18 control-versus-baseline comparison periodsis kW/participant, with a standard error of 0.195 whilethe average normalized load reduction is kW/participantwith a standard error of 0.22. On average, the observed and nor-malized results tend to support the ADDF values provided bythe CSP for each load control period.

C. Electric Cooperative #2: Substation-Level ResultsTable V summarizes the adjusted substation level measure-

ments of Diversified Demand Drop for the 18 load control tests.Dividing the Demand Drop by the units controlled (includingboth EWH and ETS devices) yields a per-unit demand impact,which can be compared to the ADDF load curves (see Fig. 1)and the normalized premise-level mean difference results (seeFig. 3). Following Heffner and Kaufman [12], the Demand Re-covery, or Rebound Effect, can be calculated over the period im-mediately before and after the time when control is relinquished.The quotient of these two terms provides a rough estimate of thediversified device duty cycle for that particular load control testperiod and is a proxy for the percentage of devices that are ac-tive over a given test period. For water heaters, this calculationof duty cycle will vary according to time of day, length of thecontrol test period, and other variables, such as inlet water andpossibly ambient temperature.12

Table VI compares the load impacts from the CSPs engi-neering estimates (ADDF) with normalized premise-level andadjusted substation level data for the three load control teststrategies. The measured values of per-unit load impact fromsub-station level data are generally lower than the normalizedload impacts from premise-level data for Electric Cooperative#2, after adjusting for the intervening effects of CVR and ETSoperations. The measured load impacts for Test Period B fromthe two methods are reasonably in line with one another. ForTest Period A, the premise-level and substation-level resultsare both much lower than the ADDF value, with the substation

12Additional data from load control tests of differing duration and conductedduring other times of the day would make it possible to fully characterize theaggregate characteristics of this EWH program, including hourly diversified de-mand and the net restore demand function [9].

level results less than half of the ADDF value. The sub-stationlevel results for Test Period C are skewed by conditions in theevening hours of 4 of 6 November test days when either theDemand Drop, Demand Recovery, or Duty Cycle is signedincorrectly or cannot be calculated.

D. Possible Causes of Discrepancies Between EngineeringEstimates (ADDF Values) and Measured Results

There are a number of possible explanations for the discrep-ancies between the CSPs ADDF estimates of hourly diversi-fied demand and the corresponding field measurements. First,since no end-use load meters were available, both the intervalmeter data from premises and substation are susceptible to in-tervening factors that affect the level and shape of the measuredloads. These electric cooperatives serve rural households withother significant end-uses at both the premise and network levelswhose on- and off-cycles could mask or distort the measuredEWH load impact. Some measured diversified demand impactsare considerably closer to the ADDF estimates (see Test PeriodB, which are winter morning results) than others (see Test PeriodA which is the summer afternoon). These larger discrepanciesmay be caused by the relatively small number of load controltest data points, especially when one or two tests are strongly af-fected by exogenous factors such as unseasonably cold or warmweather. Finally, there is the possibility that the engineering esti-mates are indeed overstated in at least a few cases. Other utilitiesuse much lower diversified demand values for their summertimeelectric water heater loads, reflecting the warmer piped watersupply during summer. In contrast, the Summer and Winter di-versified demand curves used by the CSP are not very muchdifferent, based on an assumed prevalence of well water versuspiped water supply in the rural areas served by Electric Coop-eratives # 1 and 2.

E. Qualitative Load Shape AnalysisFig. 4 shows a typical average load curve derived from the

premise-level data for Electric Cooperative # 1. Comparing theload curves of the two Test Days (October 7 and 9) with theBaseline Day (October 8) it is easy to see the characteristics ofelectric water heater load controla sharp reduction in load at2 pm followed by a rebound effect after control is released at4 pm.

Fig. 5 illustrates some of the practical issues associatedwith load impact measurements taken at the substation level.During the 79 am load control tests on November 11 and 13we can clearly see the impacts of electric water heater loadcontrolrapidly reduced load at the time control is institutedfollowed by a rebound effect after control is relinquished.This expected pattern is reflected in the values of Table V. Therelationship between EWH/ETS control and substation loadis much less clear for the evening load control testa largerebound effect is apparent but there is little or no demand dropat the time that EWH/ETS control is initiated. Accounting forthe intervening factors that distort or obscure the expected loadcurve behavior is very difficult short of extensive time-seriesanalysis and multivariate regression analysisboth of whichare outside the scope of this modest experimental effort.


Fig. 4. Premise-level load shapeelectric cooperative # 1.

Fig. 5. MV-level load shapeelectric cooperative # 2.

VIII. LIMITATIONS OF THE PILOT M&V APPROACHES

The adjusted substation-level data from Coop # 2 yields loadimpact measurements much lower than the ADDF values pro-vided by the CSP or the premise-level data, except for Test B(Winter Morning). Using substation-level load reduction mea-surements alone would suggest that the ADDF values are toohigh for Summer Afternoon and Winter Evening but about rightfor Winter Morning (see Table V).

The premise-level measurements indicate that load impactsare on average lower than the ADDF values, but not uniformlylower (see Table VII). The overall load impact measurementfrom normalized premise-level data is lower than theaverage ADDF value for Electric Cooperative #1, if we considerthe normalized results, but is within 10% of the average ADDFvalue for Electric Coop # 2.

The M&V results reported here use a nonconventional ap-proach that has not yet been approved or adopted for measure-ment and verification applications by PJM. A primary purposeof this study is to help the CSP, PJM, and other ISOs consider

how legacy EWH load control programs (i.e., that have oper-ated for many years but cant provide recent PURPA-compliantM&V studies) may provide verifiable, customer load resourcesfor wholesale electricity markets in a manner acceptable to ISOsand other stakeholders. Based on our analysis, it is not yet clearwhether there are any shortcuts to a conventional PURPA-com-pliant load research approach as the basis for measuring andverifying the load impacts of noninterval metered customers.

IX. RECOMMENDED NEXT STEPSDespite limitations on the usefulness of the pilot M&V re-

sults, they form a basis for considering which novel approachesmay have relatively more promise, and the types of technical is-sues that should be accounted for in future efforts. Should PJMor another ISO wish to go forward with additional M&V experi-mentation along the lines described in this study, we recommendthe following improvements and suggestions:

1) Intervening Operations: The present analysis was ham-pered by both inadvertent load control and normal CSPload control operations that interfered with the planned


TABLE VIIESTIMATED, OBSERVED AND NORMALIZED LOAD REDUCTION VALUES

load control tests. This problem could be averted in fu-ture if the CSP set up and documented customized loadcontrol test strategies that would reside on the CSPs headend computer and could be implemented as required inorder to verify load impacts.

2) More Observations: The load control test regimen waskept to a minimum to avoid placing undue burdens oneither the CSP or electric cooperative staff. Any subse-quent effort should be designed to provide additional con-trol-baseline observations.

3) Include Shorter and Longer Duration Load ControlTests: A mix of load control test durations will allow forbetter characterization of the Demand Drop, Demand Re-covery, and Diversified Duty Cycle for the EWH programon an aggregate and premise-level basis. The net restoredemand function for the program could also be explicitlymeasured.

4) Additional 15-minute interval meter load recorders:Electric Cooperative # 2 fielded nine 15-minute intervalload recorders. While not statistically representative, ithas been a very useful data source. The most desirableexperimental design would be a formal sample design de-signed to provide known precision and confidence inter-vals given the variability within the participant populationserved by each cooperative or each MV network.

5) Additional post-test analysis, including rudimentarymodel development: The variability of hourly loads be-tween test and baseline days is the real challenge to thistype of analysis. Some (but not nearly all) of this vari-ability should be possible to control for with a model-based approach incorporating key variables influencingday-to-day load levels, notably temperature, day of theweek, and a community activity index of some type.Time-series analysis of substation level data is anotherpromising possibility.

REFERENCES[1] B. Neenan et al., How and Why Customers Respond to Electricity

Prices: A Study of NYISO and NYSERDA 2002 PRL Program Per-formance, Lawrence Berkeley Nat. Lab., Berkeley, CA, LBNL-52 209,2003.

[2] J. W. Black, J. M. Ilic, and J. L. Watz, Potential benefits of imple-menting load control, in Proc. IEEE Power Eng. Soc. Winter Meet., vol.1, Jan. 2002, pp. 177182.

[3] PJM Interconnection,, Norristown, PA, 99 FERC 61 139 and 99FERC 61 227, 2002.

[4] ISO-NE Load Response Program Manual, Appendix E: DevelopingMeasurement and Verification Plan, ISO New England, 2004.

[5] NY ISO Day Ahead Demand Response Program Manual, Revised: 07.25. 2003, Para. 216: Small Customers.

[6] Fundamentals of Load Management. IEEE Tutorial Course. Palo Alto,CA: Electric Power Res. Inst., 1988.

[7] W. E. Mekolites, R. M. Murphy, and J. L. Laine, AEP water heater loadmanagement test, in Proc. Amer. Power Conf., 1981.

[8] J. H. Reed, J. C. Thompson, R. P. Broadwater, and A. Chandrasekaran,Analysis of water heater data from athens load control experiment,IEEE Trans. Power Del., vol. 4, no. 2, pp. 12321238, Apr. 1989.

[9] S. H. Lee and C. E. Wilkins, A practical approach to appliance loadcontrol analysis: A water heater case study, IEEE Trans. Power App.Syst., vol. PAS-102, Apr. 1983.

[10] J. H. Reed and R. P. Broadwater, Analysis of energy usage data forwater heaters under load control, in Proc. Southeastern IEEE Conf.,Apr. 1988.

[11] J. H. Reed, W. R. Nelson, G. R. Weatherington, and E. R. Broadaway,Monitoring load control at the feeder level using high speed monitoringequipment, IEEE Trans. Power Del., vol. 4, no. 1, pp. 694703, Jan.1989.

[12] G. C. Heffner and D. A. Kaufman, Distribution substation load impactsof residential air conditioner load control, in Proc. IEEE PES WinterPower Meet., Jan. 1985.

[13] M. W. Davis, T. J. Krupa, and M. J. Diedzic, The economics of directcontrol of residential loads on the design and operation of the distributionsystem, in Proc. IEEE PES Summer Power Meet., Jul. 1982.

[14] C. Forlee. Water Heating in South Africa: Facts and Figures From the1997 Notch Testing Program. [Online]. Available: http://www.ctech.ac.za/conf/due/SOURCE/Web/Forlee/Forlee.html

[15] R. E. Stephens, Load control demand reduction estimation, IEEETrans. Power Syst., vol. 3, no. 1, pp. 5966, Feb. 1988.

[16] G. H. Weller, Managing the instantaneous load shape impacts causedby the operation of a large-scale direct load control system, IEEE Trans.Power Syst., vol. 3, no. 1, pp. 197199, Feb. 1988.


[17] B. F. Hastings, Ten years of operating experience with a remote con-trolled water heater load management system at detroit edison, in Proc.IEEE PES Summer Power Meet., Jul. 1979.

[18] M. L. Goldberg and G. K. Agnew, Protocol Development for DemandResponse CalculationFindings and Recommendations, KEMA-Xen-ergy, Calif. Energy Comm., Burlington, MA, Rep. 400-02-017F, 2003.

Grayson C. Heffner (SM04) received the B.S. degree in electrical engineeringand the Master of Engineering degree in mechanical engineering from the Uni-versity of California, Berkeley, and a Doctor of Public Administration degreefrom George Washington University, Washington, DC.

He specializes in energy technology R&D and project management, with anemphasis on power systems planning and customer end-use issues. He has exten-sive electric utility experience with the Electric Power Research Institute, PaloAlto, CA, and Pacific Gas and Electric Company (PG&E), San Francisco, CA.

Dr. Heffner is a past San Francisco Chapter Chair of the IEEE Power Engi-neering Society.

Charles A. Goldman received the M.S. degree from the Energy and ResourcesGroup from the University of California, Berkeley.

He is Staff Scientist and Group Leader of the Electricity Markets and PolicyGroup at Lawrence Berkeley National Laboratory, Berkeley. He has publishedover 80 articles and reports on energy efficiency and demand response policy,programs, and technology analysis, utility integrated resource planning, retailenergy services, and electric industry restructuring.

Mithra M. Moezzi received the Ph.D. degree from the University of California,Berkeley.

She is a Staff Research Associate at Lawrence Berkeley National Laboratory.

tocInnovative Approaches to Verifying Demand Response of Water HeatGrayson C. Heffner, Senior Member, IEEE, Charles A. Goldman, andI. N OMENCLATUREII. I NTRODUCTIONIII. M&V M ETHODS FOR S MALL C USTOMER L OAD C ONTROL

TABLE I M&V P ROTOCOL F RAMEWORK FOR C USTOMERS P ARTICIPATING ITABLE II C HARACTERISTICS OF E LECTRIC C OOPERATIVESIV. S MALL C USTOMER P ILOT P ROGRAM C HARACTERISTICSV. M EASUREMENT A PPROACHVI. R ESULTS

Fig.1. Summer season hourly diversified demand curve.TABLE III L OAD C ONTROL T EST R ESULTS FOR E LECTRIC C OOPERATIA. Premise-Level Results

TABLE IV L OAD C ONTROL T EST R ESULTS FOR E LECTRIC C OOPERATIVB. Substation-Level Results

TABLE V S UBSTATION L OAD C ONTROL T EST R ESULTS FOR E LECTRIC VII. D ISCUSSIONA. Electric Cooperative #1: Premise-Level Results

Fig.2. Observed load reduction vs estimated ADDF value (electriFig.3. Observed load reduction vs estimated ADDF value (electriB. Electric Cooperative #2: Premise-Level Results

TABLE VI C OMPARISON OF L OAD I MPACT M EASUREMENTS AT THE S UBSC. Electric Cooperative #2: Substation-Level ResultsD. Possible Causes of Discrepancies Between Engineering EstimateE. Qualitative Load Shape Analysis

Fig.4. Premise-level load shape electric cooperative # 1.Fig.5. MV-level load shape electric cooperative # 2.VIII. L IMITATIONS OF THE P ILOT M&V A PPROACHESIX. R ECOMMENDED N EXT S TEPS

TABLE VII E STIMATED, O BSERVED AND N ORMALIZED L OAD R EDUCTIONB. Neenan et al., How and Why Customers Respond to Electricity PJ. W. Black, J. M. Ilic, and J. L. Watz, Potential benefits of i

PJM Interconnection,, Norristown, PA, 99 FERC 61 139 and 99 FEISO-NE Load Response Program Manual, AppendixE: Developing MeasNY ISO Day Ahead Demand Response Program Manual, Revised: 07. 25Fundamentals of Load Management. IEEE Tutorial Course . Palo AltW. E. Mekolites, R. M. Murphy, and J. L. Laine, AEP water heaterJ. H. Reed, J. C. Thompson, R. P. Broadwater, and A. ChandrasekaS. H. Lee and C. E. Wilkins, A practical approach to appliance lJ. H. Reed and R. P. Broadwater, Analysis of energy usage data fJ. H. Reed, W. R. Nelson, G. R. Weatherington, and E. R. BroadawG. C. Heffner and D. A. Kaufman, Distribution substation load imM. W. Davis, T. J. Krupa, and M. J. Diedzic, The economics of diC. Forlee . Water Heating in South Africa: Facts and Figures FroR. E. Stephens, Load control demand reduction estimation, IEEE TG. H. Weller, Managing the instantaneous load shape impacts causB. F. Hastings, Ten years of operating experience with a remote M. L. Goldberg and G. K. Agnew, Protocol Development for Demand

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