Load research and load estimation in electricity …

1 VTT PUBLICATIONS 289 load research and load estimationin electricity distributionAnssi Sepp l VTT EnergyDissertation for the degree of Doctor of Technology to be presentedwith due permission for public examination and debate in Auditorium S1at Helsinki University of Technology (Espoo, Finland)on the 29th of November, 1996, at 12 o clock research CENTRE OF FINLANDESPOO 1996 ISBN 951 38 4947 3 (soft back ed.)ISSN 1235 0621 (soft back ed.)ISBN 951 38 5200 8 (URL: )ISSN 1455 0849 (URL: )Copyright Valtion teknillinen tutkimuskeskus (VTT) 1996 JULKAISIJA UTGIVARE PUBLISHERV altion teknillinen tutkimuskeskus (VTT), Vuorimiehentie 5, PL 2000, 02044 VTTpuh. vaihde (09) 4561, faksi (09) 456 4374 Statens tekniska forskningscentral (VTT), Bergsmansv gen 5, PB 2000, 02044 VTTtel. v xel (09) 4561, fax (09) 456 4374 Technical research Centre of Finland (VTT), Vuorimiehentie 5, 2000, FIN 02044 VTT, Finlandphone internat.

2 + 358 9 4561, fax + 358 9 456 4374 VTT Energia, Energiaj rjestelm t, Tekniikantie 4 C, PL 1606, 02044 VTTpuh. vaihde (09) 4561, faksi (09) 456 6538 VTT Energi, Energisystem, Teknikv gen 4 C, PB 1606, 02044 VTTtel. v xel (09) 4561, fax (09) 456 6538 VTT Energy, Energy Systems, Tekniikantie 4 C, 1606, FIN 02044 VTT, Finlandphone internat. + 358 9 4561, fax + 358 9 456 6538 Technical editing Leena UkskoskiVTT OFFSETPAINO, ESPOO 19963 Sepp l , Anssi. load research and load estimation in electricity distribution . Espoo 1996,Technical research Centre of Finland, VTT Publications 289. 118 p. + app. 19 ( )Keywordselectric power generation, electric power distribution , electric loads, loadresearch, load estimation , electricity , distribution systems, customers,measurement, models, variations, analyzingABSTRACTThe topics introduced in this thesis are: the Finnish load research project, asimple form customer class load model, analysis of the origins of cus-tomer s load distribution , a method for the estimation of the confidence in-terval of customer loads and distribution load estimation (DLE) whichutilises both the load models and measurements from distribution developments bring new knowledge and understanding of electricitycustomer loads, their statistical behaviour and new simple methods of howthe loads should be estimated in electric utility applications.

3 The economicbenefit is to decrease investment costs by reducing the planning marginwhen the loads are more reliably estimated in electrc utilities. As the Fin-nish electricity production, transmission and distribution is moving towardsthe de-regulated electricity markets, this study also contributes to the devel-opment for this new Finnish load research project started in 1983. The project was initiallycoordinated by the Association of Finnish Electric Utilities and 40 utilitiesjoined the project. Now there are over 1000 customer hourly load record-ings in a simple form customer class load model is introduced. The model is de-signed to be practical for most utility applications and has been used by theFinnish utilities for several years. There is now available models for 46 dif-ferent customer classes.

4 The only variable of the model is the customer sannual energy consumption. The model gives the customer s average hourlyload and standard deviation for a selected month, day and statistical distribution of customer loads is studied and a model forcustomer electric load variation is developed. The model results in a4lognormal distribution as an extreme case. The model is easy to simulateand produces distributions similar to those observed in load research of the load variation model is an introduction to the further analy-sis of methods for confidence interval the `simple form load model , a method for estimating confidenceintervals (confidence limits) of customer hourly load is developed. The twomethods selected for final analysis are based on normal and lognormal dis-tribution estimated in a simplified manner.

5 The simplified lognormal esti-mation method is a new method presented in this thesis. The estimation ofseveral cumulated customer class loads is also class load estimation which combines the information from loadmodels and distribution network load measurements is developed. Thismethod, called distribution load estimation (DLE), utilises informationalready available in the utility s databases and is thus easy to apply. Theresulting load data is more reliable than the load models alone. One impor-tant result of DLE is the estimate of the customer class share to the distri-bution system s total study is one consequence of the load research project of Finnish elec-tric utilities started at the Association of Finnish Electric Utilities (AFEU)in 1983. Forty utilities joined the project and over 1000 customers hourlyloads have been recorded since then.

6 The work for this thesis started while Iwas working at the AFEU in 1993 and continued at VTT Energy from 1994as a part of the distribution automation research programme work has been supervised by professor Jorma M rsky. I am grateful tohim for the co-operation and support during the academic owe many thanks to Dr Matti Lehtonen in VTT Energy for research man-agement, enthusiasm and support while studying these new matters of elec-tric power systems and distribution automation. Also I want to thank MrTapio Hakola and Mr Erkki Antila in ABB Transmit Oy for giving the in-dustrial perspective to this study and associate professor Mati Meldorf fromTallinn Technical University for very important comments. For an inspiringwork environment I want to thank all my superiors and colleagues at Finnish load research project has been a huge team work of many peo-ple working in different organisations.

7 While the number of people is toolarge to mention individually I want to send thanks to all those who tookpart in the project and took responsibility for many important tasks in theelectric utilities and in the the financial support I want to thank VTT Energy, the Association ofFinnish Electric Utilities, TEKES Technology development centre, ABBT ransmit Oy and Imatran Voima the English language I want to thank Mr. Harvey Benson for hisfast and good service in checking the manuscript. The fine chart figures ofthe analysis of load data were possible thanks to Adrian Smith s Rain Post-Script graphics warmest thanks I want to address to my family. The writing of thiswork took much of my time at home. I am grateful for the patience and un-derstanding from my wife Ruut and daughters Anna and Pihla.

8 Their sup-port and engouragement made this work Sepp l 6 CONTENTSABSTRACT3 PREFACE5 CONTENTS6 SYMBOLS101 INTRODUCTION122 load INFORMATION IN electricity THE MEANING OF FACTORS INFLUENCING THE ELECTRIC Customer Time Climate Other electric Previous load AVAILABLE DATA IN ELECTRIC THE SIMPLE FORM CUSTOMER CLASS load MODEL FORDISTRIBUTION electricity distribution APPLICATIONS UTILISINGLOAD STATISTICAL ANALYSIS OF load MODEL PARAMETERS Sampling and Generalisation and bias233 load RECENT load research PROJECTS IN SOME The United THE FINNISH load research load research data Years of the Finnish load research project 1983 - THE EXPERIENCE OF THE FINNISH load Temperature Unspecified load distribution caused by load Linking the load models with the utility s customer Problems with seasonal variation in some Examples of load models compared with networkmeasurement Experience of the Finnish load research project compared toother countries364 DERIVATION OF STATISTICAL distribution FUNCTIONS FORCUSTOMER NORMAL distribution AND LOGNORMAL THE PHYSICAL BACKGROUND OF load DERIVATION OF CUSTOMER load distribution -BINOMIAL Independent small loads - additive binomial Interdependent loads - multiplicative binomial DERIVATION OF CUSTOMER load distribution -KAPTEYN S Definition of customer Customer s random action and reaction of Customer s random actions and reaction of customer s Definition of the reaction function with low Kapteyn s derivation of a skew Simulation of the customer load An example of the results of the Discussion5685 estimation OF CONFIDENCE

9 INTERVALS OF The measure for the accuracy of confidence The customer classes selected for this DESCRIPTION OF THE CONFIDENCE INTERVALESTIMATION Normal distribution estimation method: LogNormal distribution estimation method: LogNormal distribution estimation method Simplified LogNormal distribution Estimationmethod: Properties of The flow of computation estimating and verifying VERIFICATION OF THE ESTIMATORS WITH THE LOADRESEARCH Observed load distributions and estimated Verification of confidence interval Verification of % confidence interval Verification of confidence interval estimation of customer smaximum ESTIMATING CONFIDENCE INTERVALS OF THE DATAFROM THE APPLICATION OF THE CONFIDENCE INTERVALESTIMATORS TO PRACTICAL DISTRIBUTIONCOMPUTATION846 estimation OF CONFIDENCE INTERVALS OF DEVELOPMENT OF THE estimation METHODS FORSEVERAL The parameters of the sum of random Normal distribution confidence interval estimation NE forseveral Simplified lognormal distribution confidence intervalestimation SLNE for several VERIFICATION OF THE estimation OF SEVERALCUSTOMER S Verification of % confidence

10 Interval Verification of estimation of several customer s maximumloads937 distribution load estimation (DLE) THE estimation Definition of weighted least squares The formulation of Definition of the Application of A DLE EXPERIMENT WITH FOUR SUBSTATION load estimation WITH ONE UTILISATION OF distribution load ESTIMATION1088 DEVELOPMENT OF THE DEVELOPMENT OF UTILITIES DEVELOPMENT OF distribution AUTOMATIONPRODUCTS1119 CONCLUSIONS113 REFERENCES115 APPENDICES10 SYMBOLSAFEUA ssociation of Finnish Electric UtilitiesAPLA Programming LanguageDLED istribution load EstimationDSMD emand Side Management kcondition (0 or 1) if the time of use k of appliance k exceeds T( k)ichange of k in step i of a sequence of random changes (WT)ichange of WT in step i of a sequence of random changesd(t)day type at time t , e, vsymbols for random error of time, energy, {X}expected value of random variable X ()Xnormal distribution density functionF(X)normal distribution functionGa function representing the weighted sum of errors in DLEg(X)transformation function of sample datah(t)hour of day at time tk1, k2coefficients of Velander s formulaLNELogNormal distribution estimation method for confidenceintervalLNEALogNormal distribution estimation method for confidenceinterval, variation ALNEBLogNormal distribution estimation method for confidenceinterval, variation BL model of confidence interval L estimated model parameter of confidence interval , normaldistributionLc(m,d,h)ratio of hourly load to annual energy of class c, month = m,day = d, hour = h ()