Design Optimization of Renewable Energy Systems Using Advanced Optimization Algorithms

395 Pages·2022·6.572 MB·English

by  Venkata Rao Ravipudi, Hameer Singh Keesari

Green Energy and Technology
Venkata Rao Ravipudi
Hameer Singh Keesari
Design Optimization
of Renewable
Energy Systems
Using Advanced
Optimization
Algorithms
Green Energy and Technology
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·
Venkata Rao Ravipudi Hameer Singh Keesari
Design Optimization
of Renewable Energy
Systems Using Advanced
Optimization Algorithms
VenkataRaoRavipudi HameerSinghKeesari
DepartmentofMechanicalEngineering DepartmentofMechanicalEngineering
S.V.NationalInstituteofTechnology SreenidhiInstituteofScience
Surat,Gujarat,India andTechnology
Hyderabad,Telangana,India
ISSN1865-3529 ISSN1865-3537 (electronic)
GreenEnergyandTechnology
ISBN978-3-030-95588-5 ISBN978-3-030-95589-2 (eBook)
https://doi.org/10.1007/978-3-030-95589-2
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Preface
Global warming is a significant concern that raises a need for cleaner production
of energy. Renewable energy resources are significant sources of contribution to
such clean energy demands. Taking advantage of these renewable energy sources
providessignificantopportunitiesforhandlingenergy-relatedproblems.Inthelast
few decades, researchers have focused on renewable energy resources like solar
energy, bioenergy, wave energy, ocean thermal energy, tidal energy, geothermal
energy,andwindenergy.Thishasresultedinthedevelopmentofnewtechniquesand
tools that could harvest energy from renewable energy sources. However, to meet
energydemandsandreduceinvestment,arigorousstudyofenergyextractionsystems
is required. Identifying, analyzing, and optimizing the effect of various parame-
ters of a renewable energy system contribute significantly to assessing the system
performance.Furthermore,itisalwaysnotpreferabletopresenttheoptimumsystem
parametersconsideringonlyasingleobjectiveasthesesystemshavemultipleobjec-
tives such as power output, system efficiency, investment cost, and economic and
ecologicalfactors.Hence,researchershavedevelopedvariousoptimizationmodels
of these systems and presented optimum system parameters through single- and
multi-objectiveoptimizationusingadvancedoptimizationalgorithms.
All evolutionary computation and swarm intelligence-based optimization algo-
rithmsarepopulation-basedalgorithmsandhavecontrolparameterssuchaspopula-
tionsize,crossoverprobability,mutationprobability,scalingfactor,inertiaweight,
socialandcognitiveparameters,amongothers.Appropriateadjustmentofthecontrol
parametersdictatesthealgorithmconvergencetowardtheglobaloptimum.Inappro-
priate adjustment of the control parameters leads to premature convergence and
increased computational efforts. Also, selecting an appropriate population size for
differentoptimizationapplicationsisatediousjob.Additionally,inmulti-objective
optimization,selectingthemostsuitablesolutionfromnon-dominatedsolutionset
isdifficult.
Inthisbook,recentlydevelopedJayaandRao(Rao-1,Rao-2,andRao-3)algo-
rithmsaredescribedforsingle-andmulti-objectiveoptimizationofselectedrenew-
able energy systems. In addition, variants of the Jaya and Rao algorithms are
presented to show the improvement in performances. Furthermore, variants of the
v
vi Preface
Jaya algorithm, namely multi-team perturbation guiding Jaya (MTPG-Jaya) algo-
rithmandadaptivemulti-teamperturbationguidingJaya(AMTPG-Jaya)algorithm,
andvariantsoftheRaoalgorithms,namelyelitistRao(ERao-1,ERao-2,andERao-3)
algorithmsandself-adaptivepopulationRao(SAP-Rao)algorithm,aredemonstrated
for optimization of the selected renewable energy systems. These algorithms have
noalgorithm-specificparametersandrequireonlythecommoncontrolparameters.
Additionally,theapplicabilityofmulti-attributedecision-makingmethodsinmulti-
objective optimization problems is discussed, and a decision-making procedure is
recommendedbasedonaverageranktoidentifythebestsolutioninaPareto-front.
TheJayaandRaoalgorithmsandtheirvariantsaredevelopedbyourteam,and
thesearegainingwideacceptanceintheoptimizationresearchcommunity.Afterits
introduction in 2016, the Jaya algorithm is finding many applications in different
fieldsofscienceandengineering.Themajorapplications,asofDecember2021,are
foundinthefieldsofelectricalengineering,mechanicaldesign,thermalengineering,
manufacturingengineering,civilengineering,structuralengineering,computerengi-
neering,electronicsengineering,physics,chemistry,biotechnology,andeconomics.
Manyresearchpapershavebeenpublishedinvariousreputedinternationaljournals
ofElsevier,Springer-Verlag,Taylor&Francis,andIEEETransactions,inaddition
to those published in the proceedings of international conferences. The number of
researchpapersiscontinuouslyincreasingatafasterrate.TheJayaalgorithmandits
variantshavecarvedanicheinthefieldofadvancedoptimization,andmanymore
researchersmayfindthisasapotentialoptimizationalgorithm.TheRaoalgorithms
are developed by our team in 2020, and these are also gaining acceptance in the
optimizationresearchcommunity.
Thisbookpresentsacomprehensivereviewonlatestresearchanddevelopment
trendsatinternationallevelforparameteroptimizationofvariousrenewableenergy
systems.Usingexamplesofvariousrenewableenergysystems,thepossibilitiesfor
parameter optimization with Jaya and Rao algorithms including their variants are
demonstrated.Thebookpresentsrealcasestudies,resultsofapplicationsofthebasic
Jaya and Rao algorithms, and their variants and comparison with other advanced
optimization techniques and highlights the best optimization technique to achieve
bestperformance.Thebookalsoincludesthevalidationofdifferentvariantsofthe
JayaandRaoalgorithmsthroughapplicationtocomplexsingle-andmulti-objective
unconstrainedbenchmarkfunctions.Thealgorithmsandcomputercodesofdifferent
versionsofJayaandRaoalgorithmsareincludedinthebookthatwillbeverymuch
usefultothereaders.Thisbookisexpectedtobecomeavaluablereferenceforthose
wishingtodoresearchontheuseofadvancedoptimizationtechniquesforsolving
single-/multi-objectivecombinatorialoptimizationproblemsrelatedtotherenewable
energysystems.
WearegratefultoDr.AnthonyDoyleandhisteamofSpringerfortheirsupportand
helpinproducingthisbook.Wewishtothankvariousresearchersandthepublishers
ofinternationaljournalsforpublishingtheresearchworksofourteam.Ourspecial
thanks are due to the director and the colleagues at Sardar Vallabhbhai National
InstituteofTechnology,Surat,India.Whileeveryattempthasbeenmadetoensure
thatnoerrors(printingorotherwise)enterthebook,thepossibilityofthesecreeping
Preface vii
intothe book is always there.We willbe grateful tothe readers ifthese errors are
pointed out. Suggestions for further improvement of the book will be thankfully
acknowledged.
Surat,India VenkataRaoRavipudi
Hyderabad,India HameerSinghKeesari
December2021
Contents
1 IntroductiontoRenewableEnergySystems ....................... 1
1.1 SolarEnergySystems ....................................... 1
1.2 WindEnergySystems ....................................... 2
1.3 HydroenergySystems ....................................... 3
1.4 OceanThermalEnergySystems .............................. 4
1.5 GeothermalEnergySystems ................................. 4
1.6 BioenergySystems ......................................... 5
1.7 NuclearEnergySystems ..................................... 6
1.8 OtherEmergingRenewableEnergyTechnologies ............... 7
References ..................................................... 8
2 SelectedRenewableEnergySystemsandFormulationofTheir
Problems ...................................................... 11
2.1 WindFarmLayout ......................................... 11
2.1.1 WakeModel ........................................ 11
2.1.2 PowerGenerationModel .............................. 13
2.1.3 CostModel ......................................... 13
2.1.4 Objective Function of the Wind Farm Layout
Optimization ........................................ 14
2.2 SolarAssistedEnergySystems ............................... 14
2.2.1 Solar-AssistedBraytonHeatEngineSystem ............. 14
2.2.2 Solar-AssistedStirlingHeatEngineSystem .............. 17
2.2.3 Solar-AssistedCarnot-LikeHeatEngineSystem .......... 22
2.3 Bio-EnergySystems ........................................ 23
2.3.1 Single-CylinderDirect-InjectionDieselEngine ........... 24
2.3.2 TurbochargedDIDieselEngine ........................ 25
2.3.3 CompressionIgnitionBiodieselEnginewithanEGR
System ............................................. 26
2.3.4 Microalgae-BasedBiomassCultivationProcess .......... 28
2.4 HydroEnergyandGeothermalEnergySystems ................. 29
2.4.1 HydropowerGenerationandReservoirOperation ......... 29
ix
x Contents
2.4.2 Ground Source Heat Pump-Radiant Ceiling Air
ConditioningSystem ................................. 29
References ..................................................... 30
3 AdvancedEngineeringOptimizationTechniquesandTheir
RoleinEnergySystemsOptimization ............................ 33
References ..................................................... 46
4 WorkingofJayaandRaoOptimizationAlgorithmsandTheir
Variants ....................................................... 53
4.1 WorkingoftheJayaAlgorithmandItsModifiedVersions ........ 53
4.1.1 JayaAlgorithm ...................................... 53
4.1.2 Multi-teamPerturbation-GuidingJaya(MTPG-Jaya)
Algorithm ........................................... 55
4.1.3 Adaptive Multi-team Perturbation-Guiding Jaya
(AMTPG-Jaya)Algorithm ............................ 61
4.1.4 Multi-objective Jaya and Multi-objective
AMTPG-JayaAlgorithms ............................. 66
4.2 WorkingoftheRaoAlgorithmsandTheirModifiedVersions ..... 69
4.2.1 RaoAlgorithms ...................................... 69
4.2.2 Multi-objectiveRaoAlgorithms ........................ 70
4.2.3 ElitistRaoAlgorithms ................................ 71
4.2.4 Multi-objectiveElitistRaoAlgorithms .................. 74
4.2.5 Self-AdaptivePopulationRao(SAP-Rao)Algorithm ...... 74
4.2.6 Multi-objectiveSAP-RaoAlgorithms ................... 79
4.3 PerformanceIndicators ...................................... 80
4.3.1 Coverage ........................................... 80
4.3.2 Spacing ............................................. 80
4.3.3 Hypervolume ........................................ 81
4.3.4 InvertedGenerationalDistance(IGD) ................... 81
4.4 Computational Results Analysis on Single-objective
OptimizationofUnconstrainedBenchmarkProblems ............ 82
4.4.1 ComputationalResultsAnalysison30Unconstrained
StandardBenchmarkProblems ......................... 82
4.4.2 ComputationalResultsAnalysisonUnconstrained
Unimodal and Multimodal Standard Benchmark
Problems ........................................... 93
4.5 Computational Results Analysis on Multi-objective
OptimizationBenchmarkProblems ........................... 114
References ..................................................... 127
5 Multi-attribute Decision-Making Methods and Their
ImplementationinEnergySystems .............................. 131
5.1 SimpleAdditiveWeighing(SAW) ............................ 132
5.2 WeightedProductMethod(WPM) ............................ 132