temperature sensitivity analysis of system power profiles

8/8/2019 Temperature Sensitivity Analysis of System Power Profiles

1/6

Temperature Sensitivity Analysis of System Power ProfilesC. S. Chen J. C. Wang M. S. Kang J. C. Hwang C. W. Huang

Deportment of Electrical Engineering Department of Electrical Engineering Power Research InstituteNational Sun Yat-Sen University National Kaohsiung Institute of Technology Taiwan PowerCompany

Kaohsiung, Taiwan 804 Kaohsiung, Taiwan 804

Abstract- This paper proposes a novelmethodologyto estimatetheimpactof temperatureriseto the systempowerconsumption.The loadsurveystudyis performedto derivethe typical load patternsof theresidential,commercial,and industrial customer respectively.Byanalyzing the relationship of customer power consumption andtemperature,the temperaturesensitivityof power consumptionforeach customerclass is determined.By integratingthe typical loadpatternsand total energyconsumption,the dailypowerprofilesandload compositionof Taipowersystemhas been obtained.With theload compositionsancl temperature sensitivities of all customerclasses, the hourly increase of system power loading due totemperaturerise is solved,Accordingto the study,the peakloadingofTaipowersystemwillbe increasedby 585MWor 2.4%of the systempowerdemandfor each 1C temperaturerise, The actualTaipowersystemloading is usecl to verify the accuracyof the temperaturesensitivitysolvedby the proposedmethod.It is concludedthat thepowerincreasedue to temperaturerise has been mainlycontributedby the usage of air conditionersin the commercialand residentialcustomers.Keywords: load survey, power profile synthesis, typical loadpatterns,temperaturesensitivity

I. INTRODUCTIONWith the economic development and the increase ofnational GNP, more and more air conditioners are used in thecommercial and residential sections. The air conditioning load

has contributed more than 35%of the system peak loading inTaiwan Power Company (Taipower) to result in thedeterioration of system loading factor during recent years.Many main transformers in the substations, which serve theurban areas, have become over loaded in summer. A singlefault contingency in distribution system often causes seriousoutage problem for the service customers.To enhance the operation of Taipower system with such ahigh percentage of air conditioners more effectively, a loadsurvey study has been performed in Taipower since 1993. Thepower consumption clfmore than 968 customers over differentservice classes has been collected. By analyzing the powerconsumption of all test customers in the same class, the typicalload patterns of each type of customers can be derived. Thesystem load profiles and load composition are thereforederived according to the hourly load contribution of eachcustomer class. To assess the impact of temperature change tothe system power loading, the temperature sensitivity analysisof the power consumption is performed for each customer classby investigating the relationship of customer powerconsumption and temperature. Although the powerconsumption of air conditioners under different temperatureconditions has been estimated by field test [1-4], it is still atedious work to include the load models of end use components

in the study for the system wise load study. By the fieldmeasurement of power consumption for the sample customers,the temperature sensitivity of each customer class can beevaluated more effectively by statistics methodology.

*nstallation of intelligent rmtem to measure Ithe power comuq$bn of test custormrsJ

Create tk sequential files of cwtomx 7pxwrcmmqtionatkrtedata ~deteetionbyChi-squaremdysis ~

PYowereonwrrptloni byeachcustomr I -~L_.._.. Iconsunptknforeach~PmIsE-- LSyntksis o f power system prof iles ~ ~ custonsxclass ~.. ~..~--. ..-Me- the increase of system pwer !

comrnsption dueto temperaturechsngeforeachcustormrclassto updatek systemoadprotlle~

Fig. 1 Temperature sensitivity analysis of system power consumption

II. STRATIFIED RANDOM SAMPLING OF LOADSURVEY STUDY [5-7]For load survey, it is important to select the propercustomer size for load study so that the typical load patternsderived can effectively represent the load behavior of the class.Since the customers within the same service class oftenillustrate more similar load characteristics, the stratifiedrandom sampling strategy is used in this paper to select the testcustomers for meter installation to record the powerconsumption. The total sampling size of the test customers isdetermined according to the pre-defined confidential level andthe budget available for the load study. The total populationsize and variances of customer power consumption within eachclass are considered in (1) to determine the total sampling sizeof test customers. The total sampling size is then allocated toeach class as defined by nh in (2) according to the weightingfactors, which are determined by the variance of powerconsumption and total customer number in the class.

(h&ihs~)2n= (1)(B~)+h$Nt#;.

0-7803-7031-7/01/$10.00 (C) 2001 IEEE-7803-7173-9/01/$10.00 2001 IEEE 854

Authorized licensed use limited to: Universitas Indonesia. Downloaded on May 17,2010 at 06:01:23 UTC from IEEE Xplore. Restrictions apply.


2/6

(2) created for each test customer, which will be used for statisticsanalysis to derive the typical load patterns for the customerNhSh ~h =n(Hi~,N isirN-n SB=2 N (J) (3)where n: total sampling size of test customersH: totallcustomer classesSh,si:chs v~iance of customer power consumptionNh,Ni:l;ota] customer number of class h

B:sampling error bound with (I-u) confidential levelAccording to (2), more customers have to be selected forthe class with larger variances of power consumption. On theother hand, only few customers are selected for the class withsmaller variance of power consumption because of theconsistency of power profiles among, the customers. By thismanner, the typical load patterns of all customer classes can besolvedwith the same level of accuracy. Table 1 shows the totalcustomer number to be selected for load study in each customerclass.Table 1 Sample anclpopulation size of custom,erlayers in Taipower district

I lowvoltagecompositecommercial3 (3- l)*220W I 986 I 35 II ~ lowvoltagecompositenoncommercial(3-d ~220V) I 441 I 16 I

E.,/lowvoltageindustrial(3-+ I 220V) 72,018 109~ highvoltagecompositecommercial(3-@ I 1IKV) 1,466 12s~ highvoltagecompositenoncommercial(3-@~1lKV) 1,254 1148 highvoltageindustrial(3-@ J 1lKV) 4,779 306g extrahighvckageindustrial(3-@ I 69KVor 161KV) 147 74sum 3,581,428 968III. PR.OCESS OF CUSTOMER POWERCONSUMPTION DATAIn this study, 968 customers are selected by the abovesamplingmethocland the intelligent meters have been installedto measure the customer power consumption within every 15minutes. The nc~tebookPC is used to retrieve the customerpower consumption once every 3 months, To prevent theanalysis bias due to abnormal power consumption, the Chi-squrtretest is perfom-sedon the customlerpower consumption toidentifi the existence of bad data in (4). Fig. 2a shows theoriginal power consumption of a test customer and three dataof abnormal power consumption are detected.

Prob(x ~2 x ~.,,a) = cx (4)where:~ ; _lfl Threshold value of Chi-square distribution with

ct signitkimtcelevelBy substituting the bad data with the mean powerconsumption for the corresponding hours, the daily loadprotiles are modified as Fig. 2b. After performing the bad datadetection, the sequential tile of power consumption is then

class


3/6

V.LOAD COMPOSITIONThe load composition of a power :systemcan be estimatedby the typical load patterns derived and total energyconsumption of all customers in the system. The monthlyenergy consumption of each customer can be obtained byretrieving the billing data from the customer informationsystem(CIS) in Taipower. The total energy consumption of theresidential, commercial, and industrial customers is calculatedby summing up the energy consumption of all customers withinthe same class. According to the ratio of power consumptionand number of the weekdays and weekends in a month, thetotal energy consumption by customer class i for all weekdaysE,,w is determined. Atler allocating the total energyconsumption to each hour according to the typical load patterni in Fig. 3, the mean value of power ccmsumptionfor customerclass i at hour j is then derived as (5).

P q ,= E),W+DW *-,,, +5 (5)p, ,,4Where D~ :totalweekdays

pi,jtypical load patterns of customer class i at hourjBy integrating the power consumption profiles of allcustomer classes, the system daily power profile and loadcomposition can be obtained. Fig. 4 shows the synthesizeddaily power profile and load composition of Taipei district inTaipower for the summer season. It is found that both theresidential and commercial customers contribute most of thepower consumption while the industrial customers consume theleast power in the service area. The district peak load demandis 2019 MW at 2 PM and the power demands of thecommercial and residential customers are 1038 MW and 928MW respectively. The daily power consumption is varied withthe load behaviors of the service customers. Fig. 5 shows thesynthesized daily power profiles and load composition ofKaohsiung district. With the development of the heavy industryin the service area, the industrial customers have consumed50?40of the total district power demand. The residential andcommercial customers contribute 31A,and 1gO/O of the districtpower demand respectively. The peak load demand of thewhole district is 1758 MW, which occurs at 3 PM. Bycomparing Fig. 4 and Fig. 5, it is found that the significantdifferent daily power profiles have been illustrated because ofthe difference of load composition between Taipei andKaohsiung districts. Besides, the power consumption byindustrial customers remains very stable over the daily period,while the commercial class consumes most of the power duringdaytime period.To verifi the accuracy of the synthesized daily powerprofiles and Ioaclcomposition in this study, the actual powerconsumption of the Kaohsiung district has been recorded asshown by the solid line in Fig, 6. It is found that the averagemismatch between the actual and synthesized load profile is1.7%, which implies that the typical IIoadpatterns derived forcustomer classes can represent the system load behavior veryaccurately.

Lodd(MW) K Residential I Industriril K ncommerc ial

:~10008006004002000 1 3 5 7 9 II 13 Is 17 19 21 13Time(hour)

Fig,4 Synthesized daily power profiles of Taipei district for the summerseasonLoad(M W) EIResidentisl I Industrial K ICommercial2000

!80016001400120010008006004002000

1 2 3 4 5 6 7 8 9101112 [3141516171S19 2021222324Time(hour)Fig. 5 Synthesized daily power profiles of Kaohsiung district for the summer

season

1s001600140012001000Soo6004002000-200

Iactual pro file(SCADA)-- =-- synthesized profileaverage mismatch

123456789101 1121314151617 !8192021 222324T!me(hour)

Fig.6 Comparison of the actual and synthesized load curves (Kaohsiungdistrict)VI. TEMPERATURE SENSITIVITY OF POWERCONSUMPTIONDue to the usage of air conditioners in the commercial andresidential customer classes, the temperature rise alwaysintroduces dramatic increase of system power demand inTaipower. The interruptible load control has been implementedto reduce the system peak loading during the summer season toprevent the shortage of system capacity reserve. It is importantto investigate the temperature sensitivity of the powerconsumption for each customer class in Taipower system.The Pearson product moment analysis [8] is applied in thisstudy to find the relationship among the customer powerconsumption, temperature and humidity. The two tail t-test in(6) is then used to determine whether the relationship betweenthe above variables has reached the significant level.

~= rJEl-r2 (6)where K is the degree of freedom and r is the correlationcoefficientTable 2 shows the correlation coefllcients and t values of

0-7803-7031-7/01/$10.00 (C) 2001 IEEE-7803-7173-9/01/$10.00 2001 IEEE 856



4/6

the actual Taipower system demand, temperature and humidity.In this paper, the significant threshold t22,,j.ozjis set as 2,074with 95/0 confidential level. The correlation coefficient ofpower consumption and temperature is solved as 0,87 and thecorresponding t value is 8.3, which is mulch larger than thepredefmed threshold value, This implies that the temperaturechange will have significant impact to the system powerdemand. The correlation coefficient of power consumption andhumidity is -0.22 with t value as 1.07, which means that thevariation of humidity will not cause too much change of powerconsumption.Table 2 Correlation coetllcients and t values of power consumption,temperature and humidity.

Pn

.B

Tn HnPn ; 1.00 0.87126 -0.2220803 8.32598 -1.06833Tn : 0.87126 1.00 -0.44208.32598 -2.31118Hn : -0.22208 -0.4420-1.06833 -2.31118.4By performing the statistic regression analysis [9] of thepower consumption with respect to the temperature as shown in

Fig. 7, the power consumption can therefore be represented bya polynomial fimction of temperature.Resklentialustortms

m!L~.65 0,70 0.75 0,80 0.85 0.90 YG-----,95 1050TnModel: Pn=O26 +0.38 Tn +0.59 Tn*Tn Frequency : 94F=10S.268 Prob>F=O.000 I R-square=O.698 P-mean= 164.94 KWT Pr.ab> iTl

3NTER 0.369 0.7126Tn 0,12 0,9046Tn*Tn 0,729 0,4679comrr-3xisl CustottmslllL~.2 0.3 0,4 0.5 0.6 0,7 0.8 ,9 I ,0 - I .2Tn

Model. Pn=0.87 -0.13 Tn +0.30 TnTn Frequency : II73F=252. 844 Prob>F=O.0001 R-square=O.7411P.mea.n=6304,01 KW

T Prob > ITI3NTER 17.343 0.0001Tn -1,044 0,2982Tn*Tn 3.937 0,0001

Industrial coatormram!iwe,65 0,70 0.75 0.80 0,85 0.90 0.95 I .00 1.05 1,10ml

Model Pn=2.06 -2.37 Tn +1 52 Tn*Tn Frequency 86F-3.37 I Prob>F=O.0247 R-square=O.085 P-mean=3528.28 KW

T Prob ~ ITIINTER 2,99 0,0037Tn -1,701 0,0928Tn*Tn 1.842 0.069

7 The statisticegressionnalysis of the power consumption with respect to thetemperature

With large R*value and small 95% confidential interval,the power consumption of the residential and commercialcustomers can be expressed as quadratic functions oftemperature in (7) and (8) respectively. On the other hand, thepower consumption of the industrial customers is randomlydistributed with temperature and less effect of temperaturechange to the power consumption is concluded in (9).PR=0.26+O.18T +0.59T# (7)Pc=0.87- 0.13Tn+0.30T; (8)PI=2.06- 2.57T +1.52T (9)Atter solving the power consumption as functions oftemperature, the temperature sensitivity (TS) of powerconsumption, which is defined as the percentage change ofpower consumption for 1VOemperature rise, can therefore bederived. By differentiating the power consumption with respectto temperature, the temperature sensitivities of powerconsumption for the residential, commercial, and industrialcustomers are obtained as follows.TS~ = 0.18 +1.18T (lo)TSC = 0.13 + 0.60Tn (11)TS, = -2.57 + 3.04T (12)Fig, 8 shows the hourly temperature sensitivities of thepower consumption for three customer classes. It is found thatthe average power consumption will be increased by 1.6?40nd1.1% for the residential and commercial customers when thetemperature rises by 1XO.t is interesting to note that the largestTS of commercial customers occurs at 8 AM instead of 2 PMwhen temperature reaches the highest. With high temperatureat 2 PM, the duty cycle of must WC units will approach to 1.0,The temperature rise will not cause so much powerconsumption increase as compared to the low duty cycle at 8AM when the commercial customers begin their business.

3.532.521.510.50

TS Residential Industrial Commercial

123456789101 112131415161718192021 222324Time(hour)

Fig. 8 Temperature sensitivity of power consumption for customer classesWith the temperature sensitivity solved previously for eachcustomer class, the increase of system power consumption for 1

C temperature rise is derived as (13),~P=(TsR*pR /+TSC*PC+TSI *P,) T_ (13)

Because the power consumption of different customer classhas different temperature sensitivity the power consumptionincrease of a service district due to temperature rise has to beevaluated according to the load composition in the district.Fig, 9 and Fig. 10 show the variation of system power profilesfor Taipei district and Kaohsiung district respectively when thetemperature changes by *2C. With very high percentage of0-7803-7031-7/01/$10.00 (C) 2001 IEEE0-7803-7173-9/01/$10.00 2001 IEEE 857



5/6

commercial loading, the power consumption of Taipei districtwill be increased Iby175MW for 2C temperature rise at 3 PM.For Kaohsiung district which serves high percentage ofindustrial customers with low TS, the power consumptionincrease for the same temperature rise is only 85MW.

Load(M W)200016001200800

o l-----, I123456789101 112131415161718192021 222324

Time(hour)Fig.9 The vuiation of power consumptiort with temperature changeby +2C forTaipei districtLoad(MW)

800 t =-Average Temperature--zc Io~. I

1234$6789101112 131415161718192021222324Time(hour)Fig. 10 The variation of power consumption with temperature changebyG2c for Kaohsiung districtAccording to the typical load patterns derived for eachcustomer class and total energy consumption by all customersin the same class, the system load prc~filecan be obtained by

integrating the power profiles of all customer classes. Fig. 11shows the synthesized power profile ?md load composition ofTaipower systeml for July, 2000. The industrial customersconsumed 53A of the total energy consumption while theresidential and commercial customers consumed 31Aand 160/0of the total system load respectively. The peak loading ofTaipower system is solved as 24765 MW at 3 PM.Load(MW)

;----z~fl

o~ I1234567891011 1213141516171819202122 2324Time(hour)

Fig. 11 The typical daily power profile and load composition of Taipower forJuly, 2000After solving the typical power profiles and temperaturesensitivity for e~ch customer class, the increases of power

consumption. For the residential, commercial, and industrialcustomers when the temperature rises by 2C has been solvedas Fig. 12 The power consumption of residential customers isincreased by 1346 MW at 10 PM when most of the A/C unitsare committed. During the daytime peak period, the powerconsumption of both commercial and residential customers isincreased by 536 MW respectively. For the industrialcustomers, the impact of temperature rise to the powerconsumption is much less significant as compared to the otherclasses.

AP(MW) + Residential + Industrial ommercial1600140012001000Soo600400200

0

t At /-

1234 S6789101 112131415161718192021 222324Time(hour)

Fig. 12The increase of power consumption by each customer class for 2Ctemperature rise

Fig. 13 shows the daily power profile and load compositionof Taipower systemwith temperature rise by 20C. The systempeak loading at 3 PM is increased from 24827 MW to 25995MW due to the temperature rise. The peak loading ofresidential customers is 9180 MW at 10 PM while the peakloading of commercial and industrial customers occurs at 3 PMwith magnitudes of 5239 MW and 12910 MW respectively.By comparing Fig, 11 and Fig 13, it is found that verysignificant effect of temperature rise to the increase of powerconsumption for both commercial and residential customers.In this paper, the actual system power consumption ofTaipower is also used to perform the statistic analysis. Thetemperature sensitivity of system power consumption has beenobtained and the increase of system peak loading for 2Ctemperature rise is solved as 1204 MW for July, 2000. Bycomparing to the power consumption increase of 1168MWsolved for the same temperature rise in this study the mismatchis less than 3Aand the accuracy of the proposed methodologyhas therefore been verified.

Mw + Resi&ntial + Imkrstial + Commrcial

14(DI-lm -K?XQmrcCOo I123456789101112131415 161718192021222324Hour

Fig. 13 The daily power profile and load composition of Taipower systemwith temperature rise 2C.

0-7803-7031-7/01/$10.00 (C) 2001 IEEE-7803-7173-9/01/$10.00 2001 IEEE 858



6/6

VII. CONCLUSIONIn this paper, a systematic approach has been proposed tosolve the temperature effect to the system power consumption.By performing the load survey study, the typical load patternsof residential, commercial and industrial customers have beenderived, According to the energy consumption of all customers,the hourly power demand of each customer class has beensolved to determine the load composition and daily power

profile of Taipower system. By statistic regression analysis, thetemperature sensitivity of power (consumption for eachcustomer class is derived. The power consumption increasecaused by temperature rise has been evaluated by the loadcomposition and temperature sensitivity. It is found thatTaipower system peak loading during summer season will beincreased by 1168MW when temperMure rises by 2C, whichhas been verified by the actual system power consumption.The system power loading increase is mainly contributed bythe commercial and residential customers due to the usage ofair conditioners. The system spimling reserve has beensignificantly deteriorated by the summer peak loading. Tosolve the problem, the load management of A/C cycling controlfor the commercial customers has been investigated byTaipower. This paper does provide a good reference forTaipower to determine the system load composition andidenti~ the potential of system peak load reduction by airconditioner load management for each customer class.

[1]

[2]

[3]

[4]

[5][6]

[7][8][9]

REFERENCESM.L. Chars and W.H. Crouch, An Integrated Load MarsagemenLDistribution Automation and Distribution SCADA System for OldDominion Electric Cooperative, IEEE Trnns. on Power Delivery, Vol. 5,No. 1, hrruary 11990,pp 384-390.M.L. Chrm, Cl]. Lin, C. W. Hurmg and T.G.Lu, A Load ManagementMaster Plan for Taiwan Power Company, Proceedings of the 7th annualPOWER-GENConference, Singapore, September 1999.C.S.Chen, J.C.HwangandC.W.Huang,Determinationf CustomerLoadCharacteristicsyLoadSurveySystematTaipower:IEEETrans.on Power Delivery, Vol. 11,No. 3, July 1996, pp 1430-1435.C.S.Chen, J.C.:Hwang and C.W.Huang, Application of Load SurveySystems to Proper Tariff Design; IEEE Trans. on Power Systems, Vol.12,No. 4, November 1997, pp.1746-175~.Load Research Manual, Association of Edison IlluminatingCompanies, Feb. 1990.Cochrsn, W.G., Sampling Techniques, 3rd edition., JohnWileyandSons,June.1997.Hansen, M,H., W.H.Hurwifz, and W.G.Madow, Sample SurveyMethods andTlheory,JohnWiley& Sons, 1953.David, R.A., Dennis J.S. and ThomasA.W.Statistics For Business AndEconomic, 7rcl edition., Anderson Sweeney Williams, 1999.Draper,N.R. and H. Smith, Applied Rer?TessionAnalysis, John Wiley& Sons, Inc., New York (1966).

BIOGRAPHIES~ reeeivedJreB.S.degreeromNaticmalaiwan University in 1976and the M.S, Ph.D. degree in Electrical Engineering from the University ofTexas at Arlington in 1981sad 1984respectively. From 1984 to 1994he wasa professor of Elec@ical Engineering department at National Sun Yat-Sen

University. Since 1994, he works as the deputy director general of Departmentof Kaohsiung Mass Rapid Transit. From Feb.1997 to July 1998, he was withthe National Taiwan University of Science and Technology as a professor.From August 1998, he is with the National Sun Yat-Sen University as a tirllprofessor. His majors are computer control of power systems and distributionautomation.MZM!aag received the B.S. degree in Electrical Engineering tlom NationalTaiwan University of Science in 1991 and 1993 and the MS. degree inElectrical Engineering from National Cheng Kung University in 1993 and1995 respectively. He has been a lecturer in Kao YuarrInst. of Tech. and isworking for his Ph.D. degree inNational Sun Yat-Sen University. His researchinterest is in the area of load management and demand subscription serviae.M-MQti3 received the B.S. ~d M.S. degree in Electrical Engineering fromNational Taiwan University of Science and Technology and National SunYat-Sen University in 1990 and 1993 respectively. He has been a lecturer inKao Yuan Inst. of Tech, and is working for his Ph.D. degree in National SunYat-Sen University. His research interest is in the area of load managementand demand subscription service.J&Hwaag received the M.S. and Ph.D. degree in Electrical Engineeringfrom National Taiwan University and National Sun Yat-Sen University in1987 and 1995 respectively. He has been a associate professor in KaoshiungInst. of Tech. His research interest is in the area of load management anddistribution automation.~ received the B.S degree in Electronic Engineering ffomNationalTaiwrrrsOcean University in 1972. He is a senior research engineer of PowerResearch Institute of Taipower and works as the project leader of the Taipowersystem load survey and the development of master plans for demand-sidemanagement and integrated resource planning.

0-7803-7031-7/01/$10.00 (C) 2001 IEEE0-7803-7173-9/01/$10.00 2001 IEEE 859

A th i d li d li it d t U i it I d i D l d d M 17 2010 t 06 01 23 UTC f IEEE X l R t i ti l

temperature sensitivity analysis of system power profiles

Documents