Optimization of Point Absorber Wave Energy Parks

60 Pages·2017·2.32 MB·English

Optimization of Point
Absorber Wave
Energy Parks
MARIANNA GIASSI
UURIE 353-18L
ISSN 0349-8352
Division of Electricity
Department of Engineering Sciences
Licentiate Thesis
Uppsala, 2018
Abstract
Renewableenergiesarebelievedtoplaythekeyroleinassuringafutureofsustainableenergy
supplyandlowcarbonemissions.Particularly,thisthesisfocusonwaveenergy,whichiscrea-
tedbyextractingthepowerstoredinthewavesoftheoceans.
Inorderforwaveenergytobecomeacommercializedformofenergy,modulardeployment
ofmanywaveenergyconverters(WECs)togetherwillberequiredintheupcomingfuture.This
designwillthusallowtobenefit,amongothers,fromthemodularconstruction,thesharedelec-
tricalcablesconnectionsandmoorings,thereductioninthepowerfluctuationsandreductionof
deploymentandmaintenancecosts.
Whenitcomestoarrays, thecomplexityofthedesignprocessincreaseenormouslycom-
paredwiththesingleWEC,giventhemutualinfluenceofmostofthedesignparameters(i.e.
hydrodynamicandelectricalinteractions,dimensions,geometricallayout,waveclimateetc.).
UppsalaUniversityhasdevelopedandtestedWECssince2001,withthefirstoffshorede-
ployment held in 2006. The device is classified as a point absorber and consists in a linear
electric generator located on the seabed, driven in the vertical direction by the motion of a
floatingbuoyatthesurface.
Nowadays,oneofthedifficultiesofthesectoristhatthecostofelectricityisstilltoohigh
andnotcompetitive,duetohighcapitalandoperationalcostsandlowsurvivability.Therefore,
onesteptotrytoreducethesecostsisthedevelopmentofreliableandfastoptimizationtools
forparksofmanyunits.
Inthisthesis,afirstattemptofsystematicoptimizationforarraysoftheUppsalaUniversity
WEChasbeenproposed. Ageneticalgorithm(GA)hasbeenusedtooptimizethegeometry
ofthefloaterandthedampingcoefficientofthegeneratorofasingledevice. Afterwards,the
optimallayoutofparksupto14deviceshasbeenstudiedusingtwodifferentcodes,acontinuous
andadiscretevariablesrealcodedGA.Moreover,themethodhasbeenextendedtostudyarrays
withdevicesofdifferentdimensions. Adeterministicevaluationofsmallarraylayoutsinreal
waveclimatehasalsobeencarriedout. Finally,aphysicalscaletesthasbeeninitiatedwhich
willallowthevalidationoftheresults.
Amulti–parameteroptimizationofwavepowerarraysoftheUppsalaUniversityWEChas
been shown to be possible and represents a tool that could help to reduce the total cost of
electricity,enhancetheperformanceofwavepowerplantsandimprovethereliability.
List of papers
Thisthesisisbasedonthefollowingpapers,whicharereferredtointhetext
bytheirRomannumerals.
I GiassiM.,GötemanM.;Parameteroptimizationinwaveenergy
designbyageneticalgorithm;Proceedingsofthe32ndInternational
WorkshoponWaterWavesandFloatingBodies,Dalian,China,23-26
April,IWWWFB2017.
II GiassiM.,GötemanM.;Layoutdesignofwaveenergyparksbya
geneticalgorithm;UnderrevisionforOceanEngineering,2017.
III GiassiM.,GötemanM.,ThomasS.,EngströmJ.,ErikssonM.,Isberg
J.;Multi-parameteroptimizationofhybridarraysofpointabsorber
WaveEnergyConverters;Proceedingsofthe12thEuropeanWaveand
TidalEnergyConference,Cork,Ireland,27-31August,EWTEC2017.
IV ThomasS.,GiassiM.,GötemanM.,ErikssonM.,IsbergJ.,Engström
J.;Optimalconstantdampingcontrolofapointabsorberwithlinear
generatorindifferentseastates: comparisonofsimulationandscale
test;Proceedingsofthe12thEuropeanWaveandTidalEnergy
Conference,Cork,Ireland,27-31August,EWTEC2017.
V BozziS.,GiassiM.,MorenoMiquelA.,BizzozeroF.,GruossoG.,
ArchettiR.,PassoniG.;Wavefarmdesigninrealwaveclimate: the
Italianoffshore;Energy,122(378-389),January2017.
DOI:10.1016/j.energy.2017.01.094
Reprintsweremadewithpermissionfromthepublishers.

Contents
1 Introduction 7
..................................................................................................
1.1 Waveenergy 7
.....................................................................................
1.2 UppsalaUniversityconcept 8
.............................................................
1.3 Waveenergysectorchallenges 8
........................................................
1.4 Multi-unitsarrays 9
.............................................................................
1.5 Researchquestion 11
..........................................................................
1.6 Structureofthework 11
.....................................................................
2 Theory 13
........................................................................................................
2.1 Waveenergyfarmmodel 13
...............................................................
2.1.1 Linearwavetheory 13
..........................................................
2.1.2 Waves-structuresinteraction 16
...........................................
2.1.3 Dynamicequation 18
............................................................
2.2 Optimizationtheory 18
.......................................................................
2.2.1 Geneticalgorithm 19
............................................................
3 Methods 22
......................................................................................................
3.1 Simulations 22
.....................................................................................
3.1.1 Singledeviceoptimization 22
..............................................
3.1.2 Arraylayoutoptimization 22
...............................................
3.1.3 Multi-parametersarrayoptimization 24
..............................
3.1.4 Deterministicarrayevaluation-Acasestudy 27
...............
3.2 Wavetankexperiments 29
..................................................................
4 Results 31
........................................................................................................
4.1 Singledeviceoptimization 31
............................................................
4.1.1 FirstGAvalidation 31
..........................................................
4.2 Arraylayoutoptimization 32
..............................................................
4.3 Multi-parametersarrayoptimization 35
............................................
4.3.1 SecondGAvalidation 36
.....................................................
4.4 Deterministicarrayevaluation-Acasestudy 37
..............................
4.5 Wavetankexperiments 39
..................................................................
5 Discussion 41
..................................................................................................
5.1 Costfunction 41
..................................................................................
5.2 Computationaltime 50
........................................................................
5.3 GAparameterssensitivity 51
.............................................................
6 Conclusions 52
................................................................................................
7 Futurework 54
................................................................................................
8 Summaryofpapers 55
....................................................................................
9 Svensksammanfattning 57
.............................................................................
10 Acknowledgements 58
...................................................................................
References 59
........................................................................................................
1. Introduction
TheEUenergyandclimateactiongoalistoreducegreenhousegasemissions
by 40% by 2030, and that the proportion of renewable energy (RE) will have
tocoveratleast27%ofthetotalenergyuse[1].
According to the Swedish energy policy, the share of renewable energy shall
beatleast50%oftotalenergyuseby2020and100%by2040[2].
1.1 Wave energy
Renewable energies represent the solutions to a future of sustainable energy
supplyandnocarbonemissions. Oceanenergy,i.e. theenergythatcanbehar-
vestedfromseawater,isthegeneraltermwhichincludesthefollowingenergy
resources: tidal currents, ocean currents, tidal range, waves, ocean thermal
energy and salinity gradient. The overall potential of all these resources is
enormous.
The origin of wave energy is the unbalanced irradiation of the sun at dif-
ferent latitudes. Due to this temperature variation on the Earth’s surface, the
atmospheric pressure varies and induces motion of air masses from high to
lowpressureareas,creatingwinds. Asthewindblowsoverwater,someofthe
energy is transfered to the ocean, forming waves, which store this energy as
potentialenergy(inthemassofwaterdisplacedfromthemeansealevel)and
kinetic energy (in the motion of water particles). The wind speed, the length
oftimethewindblowsandthelengthofthegenerationareawillinfluencethe
height and the period of the resulting waves [3]. Waves usually travel long
distances without much energy loss and therefore are really efficient in the
energy transport. The theoretical potential of wave energy in the world has
been estimated to be around 3 TW [4]. Like other renewable energy sources,
waveenergyisavailablewithseasonalandgeographicalvariability. Theareas
with the highest incident wave potential are the western coasts of continents,
between40◦−60◦latitude,duetothefluxofregularwesterlywinds(Fig.1.1).
Technology to extract wave energy consists nowadays of many different
concepts, and they can be classified according to operational principle, loca-
tion, power take off (PTO) and directional characteristics, for example. Over
thelastdecades,ahugenumberofwaveenergyconverters(WECs)havebeen
developed, patented and tested. However, until now, there is no device that
has reached the required level of reliability for full scale commercialization.
7
Figure1.1. Annualmeanwavepowerdensity(colors)andannualmeandirectionof
thepowerdensityvectors(→)[5]
Nevertheless, differentcoastalareaswithdifferentwaveclimateswillrequire
different type of technologies, making it more likely that a small number of
deviceswillbeconqueringthemarket.
TheaverageSwedishenergyfluxalongthewestAtlanticcoastisestimated
tobearound5kW/m[6]. Suchwaveclimatesareconsidered"mild"andthey
requiresmallratedpowerWECscomparedtoopenAtlanticcoastlikeUKor
Portugal,forexample. Largescaleelectricityproductionwillbenefitfromthe
deploymentofthedevicesinmulti-unitsarraysorparks: costreductions,mo-
dularity,redundancy,powerquality,sharingoftheelectricalcablesandutility
scalepowergenerationarejustsomeexamplesoftheadvantagesprovidedby
thesesystems.
1.2 Uppsala University concept
Uppsala University has been developing a point absorber wave energy con-
verter since 2006, which consists of a linear generator located on the seabed,
connected via a rope to a floater on the surface (Fig. 1.2). The generator has
permanentmagnetsmountedonhissurface,whilethestatorcontainscoilwin-
dings. When waves lift the buoy, the relative movement of the magnets with
respecttothecoilsinduceelectricityaccordingtoFaraday’slaw.
1.3 Wave energy sector challenges
The research during the last decades has resulted in many important achie-
vements. However,tobecomecostcompetitivewithotherenergysourcesand
togetthesupportandinterestofinvestors, thewaveenergysectorhasstillto
faceandsolvemanychallenges. TheEuropeanUnionandtheSwedishEnergy
8
Figure1.2. UppsalaUniversityWECandprincipleofoperation(fromPaperII).
Agency have developed and funded specific regulations and action plans to
helpthedeliveryofoceanenergy[1],[7],accordingtowhichsomeofthemost
significantaspectsthathavetobeaddressedare:
• Thereductionofthecostofthetechnology; prototypedemonstrationis
difficultandexpensive,duetotheharshmarineenvironment. Moreover,
thenumberoftechnologiesunderdevelopmentdecreasesthecapitalcost
reductionprogress.
• TheEU’stransmissiongridinfrastructuresexpansionsonshoreandoffs-
hore to deliver the new generated power; in addition, other infrastruc-
turesimprovementsuchasportfacilitiesandspecializedvesselsforde-
ploymentoperationandmaintenance.
• More knowledge about the environmental impact to mitigate the nega-
tiveeffectonthemarineenvironment,aswellasthesocialacceptability.
• Development of systems, subsystems and components related to power
transmissionquality,controlandmonitoring.
• Deviceperformancedevelopment.
• Improveinstallation,operationandmaintenancestrategies.
• Improving reliability and durability through development of models of
predictions; increased knowledge is also needed when it comes to up-
scalingconceptualindividualunitstoparks.
1.4 Multi-units arrays
There are many ways to reduce the cost of the technology. One option is to
deploylargearraysofmanyunits(examplessketchinFig.1.3). Havingapark
of wave energy converters instead of one or few bigger units has a lot of ad-
vantages: the modular construction, sharing of the electrical cables connecti-
ons and moorings, quality and smoothness of the power output, redundancy,
maintenancecanbedonewithoutshuttingdowntheentireproduction,higher
reliabilitytofailures,higherpowerproductionandcost-effectivedeployment.
9
Figure1.3. Outlineexamplesofarrays. SeabasedAB(topleft), AquamarinePower
Oyster(topright),CarnegieCETO(bottomleft),LangleeWavePower(bottomright).
However,sincethesystembecomesmuchmorecomplexthanasingleWEC,
therearemanyaspectsthatneedtobestudiedandunderstoodbeforetheactual
physicalrealizationofthepowerplant,suchas:
• Multi-deviceinteractionanalysis(hydrodynamicalandelectrical).
• Layoutgeometryofthepowerplant.
• Effectofthewaveclimateonthelayout.
• Optimalutilizationoftheavailableoceanarea.
• Powertakeoffcharacteristicsandcontrolstrategies
• Effectsonmarinelifeandcoastalprocesses.
• Economicalanalysis(CapEXandOpEXcosts).
All the aforementioned aspects will have a direct or indirect influence on the
powerproductionoftheplant. Normally,problemswithmultiobjectivegoals
are solved by optimization routines. However, optimization of an array of
waveenergyconverterisnotaneasytask,althoughveryimportantandcrucial
atthisstageofthewaveenergydevelopment. Thecomplexityoftheproblem
can be understood by looking at Fig. 1.4, where some of the most important
variablesofanarraydesignarerepresented.
Arrows represent influence on the "box" or variable they are pointing at.
It can be seen that the mutual relations among variables are many and multi-
directional. Note that this diagram includes many simplification and that the
problem,inreality,canbemuchmorecomplexthanthat.
The ideal optimization routine would optimize all these variables simulta-
neously, taking in account that, if one variable is changed, automatically all
thevariablesthattheboxispointingatwillbemodified.
10