Factors Affecting the Optimal Sizing of Generators and Storage in a Stand-Alone Hybrid ...

142 Pages·2016·3.02 MB·English

Factors Affecting the Optimal Sizing of
Generators and Storage in a Stand-Alone
Hybrid Renewable Energy System
Mohammad Shafiqur Rahman Tito
A thesis submitted to
Auckland University of Technology
in fulfillment of the requirements for the degree of
Doctor of Philosophy (PhD)
2015
School of Engineering

Abstract
Thereisaneedtoprovidelowcostelectricpowersystemsinanumberoflocationsaroundthe
world. However,thesizeandcostofrenewableenergygeneratorsandstorageincreasewhen
they are used alone, due to the stochastic nature of sources. Reliability similar to the grid
powersupplycanonlybeachievedbycombiningmorecomplementaryenergysourcesin
thepresenceofstoragedevices;thuscreatingahybridrenewableordistributedenergysystem.
The optimal sizing of a hybrid renewable energy system (HRES) is important in order
to keep the system reliable with low investment cost and with adequate or full use of
resources. In this work, the stochastic nature of renewable generation and demand and
non-linear system characteristics are explored in the process of optimizing the size of a
HRES.Inachievingthis,ahybridoptimizationmethodologywasdevelopedfortheintention
of matching the renewable generation with the demand of a site. The results showed that
thedemandprofiledictatesthesizeofaHRESandisasimportantastheoptimizationmethod.
Inpreviousstudies,averagehourlydemandprofilesforaday,totalmonthlyload,seasonal
daily load profile, average hourly demand profile repeated throughout the year have been
iv
examined,however,thisworkdemonstratesthatthesedemandprofilesfailtorepresentreal
life electrical demand. As such, this work extends our understanding of the influence of
demand,usingmultiple“realworld”demandprofiles,onsizingaHRESthatresultinvarying
temporalpositionofloadsandthepeakenergydemand. Itshowsthatthetotaldailydemand
ofasitecanvarysignificantlyduetosocio-demographicfactorsandthatinsizingaHRES,
thevariationoftheannualdemandprofileduetothesefactorsmustbeconsidered.
Moreover, it furthers this by exploring the random day to day variations of peak demand
bothinmagnitudeand temporal positionthroughouttheyearthatoccurdue tothevarying
weatherconditionsandhabitsoftheuser. Itwasdeterminedthattheeffectofthisrandom
variation ofelectricalloadonthe optimalsizeofa HRESwassignificant and anadvanced
methodforsizingaHRESundertheseconditionswasdevelopedanddemonstrated.
Finally,aseriesofdemandsidemanagement(DSM)optionswereproposedfortheHRES
and incorporated with the sizing method. It was shown that the investment costs could be
significantly reduced with the introduction of each of the DSM options, in particular, by
utilizingexcessenergygeneratedbytheHREStoheatathermalstoragesystem.
Table of contents
Listoffigures ix
Listoftables xiii
Listofsymbolsandabbreviations xv
1 Introduction 1
1.1 Importanceofusingrenewableenergysources . . . . . . . . . . . . . . . . 1
1.2 Renewableenergysources . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Geothermalenergy . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Solarenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.3 Windenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.4 Hydroandmicro-hydro . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Stand-alonehybridrenewableenergysystem . . . . . . . . . . . . . . . . . 7
1.4 Energystoragesystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 SizingmethodforHRES . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6 Electricaldemand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
vi Tableofcontents
1.7 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.8 Scopeofresearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.9 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 SystemModel 23
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Systemconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Objectivefunctionformulation . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 Modelingofthesystemforoptimization . . . . . . . . . . . . . . . . . . . 27
2.4.1 Windturbinegeneratormodel . . . . . . . . . . . . . . . . . . . . 30
2.4.2 Photovoltaicmodulemodel . . . . . . . . . . . . . . . . . . . . . 32
2.4.3 Batterymodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5 Reliabilitymodelbasedonlossofpowersupplyprobability(LPSP) . . . . 38
2.6 OptimizationMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.6.1 Geneticalgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.6.2 Exhaustive(iterative)searchmethod . . . . . . . . . . . . . . . . . 42
2.6.3 Hybrid(GA-exhaustivesearch)optimizationmethod . . . . . . . . 44
2.7 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.7.1 OptimalsizeofaHRESfortypicalannualdemandprofile . . . . . 46
2.8 Concludingremarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3 OptimalSizingofaHRESConsideringSocio-Demographicfactors 51
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Tableofcontents vii
3.2 Demandprofile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3 Resultsanddiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.1 Optimalsizeconsideringsocio-demographicfactors . . . . . . . . 59
3.3.2 Reliabilityandsystemcostanalysis . . . . . . . . . . . . . . . . . 63
3.4 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4 OptimalSizingofaHRESunderTimeVaryingLoad 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 Demandprofile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.3 SizingMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3.1 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . 73
4.4 Concludingremarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5 OptimalSizeIntegratingDemandSideManagement 79
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Demandprofile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3 DSMOptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5.4 OptimizationMethodology . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.5 ResultsandDiscussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.5.1 Excessenergyusedforhotwaterandspaceheating . . . . . . . . . 84
5.5.2 Peakdemandreducedthroughouttheyear . . . . . . . . . . . . . . 88
5.5.3 PeakdemandreducedwhenSOCislow . . . . . . . . . . . . . . . 90
5.5.4 Fractionofpeakloadshiftedtooffpeakhours . . . . . . . . . . . 93
viii Tableofcontents
5.6 Concludingremarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6 ConclussionandFutureWork 97
References 101
AppendixA 115
A.1 InvestigationonTimeVaryingload . . . . . . . . . . . . . . . . . . . . . . 115
List of figures
1.1 Solarradiationthatreachestheearthsurface. . . . . . . . . . . . . . . . . 5
1.2 Hourlywinterandsummerloadoftypicalhouseofaday. . . . . . . . . . 16
1.3 Classifiedsixdifferentprofilesofasitebasedonsocio-demographicfactors 17
2.1 AWind-Photovoltaic-Batteryhybridrenewableenergysystem . . . . . . . 25
2.2 Hourlywindspeed(m/s)ofayearinAuckland,NewZealand. . . . . . . . 28
2.3 Hourlyglobalhorizontalradiation(W/m2)ofayearinAuckland,NewZealand. 28
2.4 Hourlytemperature(°C)ofatypicalyearinAuckland,NewZealand. . . . 29
2.5 Averagehourlyelectricalloadprofileofaday. . . . . . . . . . . . . . . . . 29
2.6 Powercharacteristicofatypicalwindturbinegenerator(WG). . . . . . . . 30
2.7 Flowchartoftheimplementedgeneticalgorithm(GA). . . . . . . . . . . . 41
2.8 Flowchartoftheimplementedexhaustivesearch(iterative)method . . . . 43
2.9 Flow-chartofhybridoptimizationmethod . . . . . . . . . . . . . . . . . . 45
3.1 DemandprofileofClass#1(highnightuse). . . . . . . . . . . . . . . . . . 54
3.2 Demand profile of Class #2 (Similar morning and evening peak with low
middayuse). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
x Listoffigures
3.3 DemandprofileofClass#3(highdayusewithmorningpeak). . . . . . . . 56
3.4 DemandprofileofClass#4(extendedeveningpeak). . . . . . . . . . . . . 57
3.5 DemandprofileofClass#5(mediumflatdayusewithearlyeveningpeak). 58
3.6 DemandprofileofClass#6(lowdayusewitheveningpeak). . . . . . . . . 59
3.7 Levelanddurationofthedemandofaday. . . . . . . . . . . . . . . . . . . 64
3.8 VariationofbatterySOCandgeneratedrenewableenergyfrom4000hrsto
5000hrsofayear. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.9 TotalsystemcostvariationwiththechangeofLPSP. . . . . . . . . . . . . 66
3.10 VariationofbatterySOCforLPSPof0.25percent. . . . . . . . . . . . . . 66
4.1 Hourlyelectricalloadprofileofaday. . . . . . . . . . . . . . . . . . . . . 71
4.2 Flowchartforgeneratingapproximatecriticalcombinationofannualdemand
profile. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.1 Hourlywinterandsummerloadofaday. . . . . . . . . . . . . . . . . . . . 82
5.2 ThevariationoftheSOCthroughouttheyearforsummeronlydemand. . . 86
5.3 The additional energy demand in winter session as compared to summer
demand. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.4 Theexcessenergygenerationfromthesystemoptimizedonlyforsummerload. 88
5.5 Themodifiedwinterloadfrom4140hrsto4280hrofayear. . . . . . . . . 89
5.6 ElectricalloadismodifiedasperminimumSOC. . . . . . . . . . . . . . . 91
5.7 Modifiedloadfromhours4140to4280ofayear. . . . . . . . . . . . . . . 92