negative electricity prices in the german electricity market · 2017. 8. 30. · negative...
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Negative Electricity Prices in the
German Electricity Market
Athesissubmittedinlinewiththerequirementsoffulfillment ofaMasterinScienceBusiness InformationManagementDegree
June20,2016
AUTHORMilouJ.Saraber(366867)
COACHDr.YasharGhiassi-Farrokhfal
DepartmentofTechnologyandOperationsManagement
CO-READERPhd.DerckKoolen
DepartmentofTechnologyandOperationsManagement
NegativeElectricityPricesintheGermanElectricityMarket
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Preface
ThecopyrightoftheMasterthesisrestswiththeauthor.Theauthorisresponsibleforitscontents.
RSMisonlyresponsiblefortheeducationalcoachingandcannotbeheldliableforthecontent.
NegativeElectricityPricesintheGermanElectricityMarket
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Abstract
Renewableenergysourceshavethepotentialofbecominganimportantproviderofenergyinthe
future.TheEuropeanCommissionanditsMemberStatesarecurrentlyreviewingthepossibilities
ofincreasingthepenetrationofrenewableenergysourcesintotheelectricitymarket.However,
thisincreaseinpenetrationhighlightstheimportanceofasustainableelectricitymarketdesign.
Undercurrentmarketconditionsitispossibleforelectricitypricestodropbelow€0,00/MWh.
However,itremainsunclearhownegativeelectricitypricesoccur,howrenewableenergysupply
impacts the occurrence of negative electricity prices, and how this influences the current
electricitymarket. This research shows that negative electricity prices occurmore frequently
duringoff-peak loadhours. Inparticular,negativepricesoccur four timesasoftenduring the
nightincomparisontodayhours.Additionally,whennetelectricityloadisnegativethefrequency
of negative prices occurring is almost 16%, in comparison to 0.78% under a positive net
electricity load. For the impact of renewable energy sources, this research proves that the
probabilityofnegativeelectricitypricesincreasesexponentiallyfastastheshareofrenewable
energypenetrationpasses the25%.These resultspressurise the sustainability of the current
electricitymarket design and has ramifications for suppliers aswell as policymarkerswhen
renewable energy sourceswill contribute significantlymore to the total energy supply in the
future.
NegativeElectricityPricesintheGermanElectricityMarket
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TableofContents
Preface....................................................................................................................................................................1
Abstract..................................................................................................................................................................2
TableofContents...............................................................................................................................................3
IIntroduction......................................................................................................................................................5
IITheoreticalOverviewandProblemDefinition.................................................................................8I.TheGermanElectricityMarket............................................................................................................................8A.TheFuturesMarkets..........................................................................................................................................9B.TheControlReserveMarket...........................................................................................................................9C.TheBalancingMarket.....................................................................................................................................10
II.ElectricitymarketinpresenceofRenewableEnergy............................................................................11A.RenewableEnergyintheGermanElectricityIndustry....................................................................12B.StorageSystems.................................................................................................................................................13C.GeographicalDiversity....................................................................................................................................14
III.NegativePricing....................................................................................................................................................15A.PlausibleDriversofNegativePrices.........................................................................................................15B.FutureofNegativePrices..............................................................................................................................16
IIIDataandMethodology............................................................................................................................18I.DataDescription......................................................................................................................................................18II.DataDescriptives...................................................................................................................................................19A.RenewableShareandElectricityPrice......................................................................................................20B.TotalLoad..............................................................................................................................................................21C.TotalNetLoad......................................................................................................................................................22
III.ConceptualModel................................................................................................................................................23
IVResults............................................................................................................................................................25I.Descriptiveanalysis...............................................................................................................................................25A.Seasonality...........................................................................................................................................................25B.TimeofDay..........................................................................................................................................................27C.TotalLoad.............................................................................................................................................................29D.NetLoad................................................................................................................................................................31
II.Theeffectofrenewableenergy.......................................................................................................................34A.NegativePrices..................................................................................................................................................36B.TotalLoad............................................................................................................................................................37
NegativeElectricityPricesintheGermanElectricityMarket
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C.NetLoad................................................................................................................................................................39D.TheProbabilityofNegativeElectricityPrices.....................................................................................40
VManagerialImplications..........................................................................................................................43
VIConclusions..................................................................................................................................................45
VIILimitationsandFutureWork.............................................................................................................46
VIIIBibliography.............................................................................................................................................48
IXAppendix.......................................................................................................................................................52I.ListofFigures1–18..............................................................................................................................................52II.ListofTables1–6.................................................................................................................................................53III.TableofNotationEquations............................................................................................................................54IV.RegressionTables................................................................................................................................................55V.Rcode..........................................................................................................................................................................61
NegativeElectricityPricesintheGermanElectricityMarket
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IIntroduction
Volatilerenewableenergysourcesareexpectedtohaveanincreasinglyprominentroleincurrent
electricitymarkets.Europeisactivelytryingtotakeinternationalactiononclimatechange,for
examplethroughthe2015ParisClimateConference(COP21).Forthefirsttimeinhistory,195
countriesadoptedalegallybindingglobalclimatedeal(Ec.europa.eu,2016).Thisagreementhas
set a global plan to limit globalwarming to below2°C. For example,Germany is increasingly
focusingonan increaseof renewableenergy integration into theelectricitygrid.TheGerman
renewable energy targets for 2020 aim to reach an 18% share of the total final energy
consumptionanda35%shareofthetotalelectricityconsumption(Deloitte,2013).
ThequantityofrenewablesproducedintheEuropeanUnion(EU)increasedby84.4%duringthe
period2003-2013(Ec.europa.eu,2015).Renewableenergyassuredformorethan15%oftotal
energy consumption in the EU in 2013 (Ec.europa.eu, 2015). This growth requires an
improvement in the efficiency of integrating renewable energy to the power grid as, under
currentmarketconditions,electricitypricesareabletobecomenegative.Eventhoughwindand
solarsourcesarecontributingarelatively lowsharetothetotalenergyproduction, thesetwo
energysourcesareexpandingrapidly.Bothwindandsolarenergyrespectivelyaccounted for
10.5%and5.5%ofthetotalEUrenewableenergyproductionin2013(Ec.europa.eu,2015).
Anincreasingcontributionofvolatilerenewableenergysourcetotheconventionalpowergrid,
windandsolarinparticular,raisesanimportantproblem.Thecurrentelectricitymarketisnot
designedforahighintegrationofvolatileenergysources,asthesessourcesofenergyarehighly
volatileinnaturecausingsupplytoexcessdemandeasily.Asaconsequence,electricitypricesare
abletobecomenegative,policieshavebeensetintoplacebytheEuropeanCommissionin2008
allowingelectricitypricestodropbelow€0,00/MWh.
Theelectricitymarketisdesignedtoensureamarketequilibrium,wheresupplyanddemandare
balancedatanypoint in time.Duetodeviationsbetweenactualenergyoutputandday-ahead
predictionsofrenewableenergysources,producerswilltendtobidconservativelyinthemarket
toprotectthemselvesagainstreal-timerisks(Zhang,2015).Thisdeviationhasresultedinaday-
aheadpredictionerrorofawindfarm,approximately25%,ensuringthatproducersbidmuch
lessthantheyforecasted(GEEnergy,2010).Inaddition,theenergystoragesystemsarecurrently
notabletostoreelectricityonalargescale(Zhouetal.,2014).Allenergythatisnotconsumedon
thespotislost,resultinginpotentialimbalancesintheelectricitymarketandpotentiallynegative
electricityprices.
NegativeElectricityPricesintheGermanElectricityMarket
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As renewableenergy sourcesare contributingmore to the total energy supply, theelectricity
marketwillneedtobalancesupplyanddemandmoreoften.Thisiscausedbyadditionalenergy
supplyfromunpredictablerenewableenergysources,restrainingthecurrentelectricitymarket
andcanresultinanincreaseofnegativeelectricityprices.Negativeelectricitypricesoccurwhen
ahighinflexibleenergysourcemeetslowdemand,duetoadropinelectricitypricesasaresult
oflowdemand(EPEXSpotSE,sd).Asinflexiblepowersourcescannotbeshutdownandrestarted
in a quick and cost-efficient manner, the electricity market allows the prices to drop below
€0,00/MWh.Thisforcescompanieseithertocurtailthesupplyorpayfortheirenergysupply.
Currently,itremainsunclearwhateffectrenewableenergyhasonnegativeelectricityprices.
Thispaperanalyses theoccurrenceofnegativeelectricitypricesunderan increasingshareof
renewableenergyintegrationintheGermanelectricitymarket,usingGermanelectricitymarket
data. According to prior research (Cloete, 2014) an increased share of renewable energy
negatively impacts the electricity prices. However, this research considers the causation of
negativeelectricitypricesduetoanincreaseinrenewableenergyintegration.Thispaperproves
thatthereisnopatterninthevalueofnegativepricesasaconsequenceofincreasingrenewable
energyintegration.Onthecontrary,thevalueofnegativepricesasafunctionofanincreasing
shareofrenewableenergyismorelikelytodecreasethanincreases.Inaddition,Iprovethateven
thoughthereisnopatterninthevalueofnegativeelectricityprices,negativeelectricityprices
occurmoreoftenafterarenewableenergysharepenetrationof25%.
Currentstudiessolelyresearchtheeffectofanincreasingshareofinflexibleenergyonelectricity
prices.Althoughspeculationsexist that the shareof renewableenergy can stimulatenegative
prices(Zhouetal.,2014),itremainsunclearwhatthefactorsarethatleadtonegativeelectricity
prices.Thisgapincurrentliteraturehasneverbeenqualitativelynorquantitativelystudiedto
thebestofmyknowledge.Thisstudyisanattempttoaddressthisproblemthroughextensive
empirical analyses. It is of great importance to study the interplay between the share of
renewableenergyandelectricityprices,whiletakingotherexternalfactorsintoaccountsuchas
demandandseasonality,inordertogaininsightsonthedifferenteffectsofintegratingrenewable
energy into theelectricitygrid. I layouthownegativeprices changeas theelectricitymarket
movestoalargescaleofrenewableenergyintegration.Thisisanimportantfactorinassessing
whetherthecurrentdesignoftheelectricitymarketbecomesunstableandneedstobechanged.
NegativeElectricityPricesintheGermanElectricityMarket
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Furthermore, practical relevance can be obtained through the renewable energy integration
focus. Momentarily, the European Commission is analysing the option of a fully integrated
European Electricity Market. In May 2005 the European Commission Research Directorate-
General defined an initial scope for a European Integrated ElectricityMarket, to increase the
efficiency,safetyandreliabilityofEuropeanelectricitytransmissionanddistributionsystemsand
toremoveobstacles in increasing integrationof renewableenergysources (Hammons,2008).
Thispaperwillbehelpfulinanalysingthefeasibilityofanincreasedshareofrenewableenergy
intotheEuropeansElectricityMarket,andwhetherthecurrentmarketdesignissustainablein
achievingthisincreaseinrenewableenergypenetration.
Thispaperisorganizedasfollows.Section2reviewspriorresearchandefinesthegapincurrent
literature, the model is presented and described in Section 3 and the numerical analysis is
presentedinSection4.InSection5Ielaborateonmanagerialimplications.Lastly,inSection6I
concludeontheempiricalanalysis.
NegativeElectricityPricesintheGermanElectricityMarket
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IITheoreticalOverviewandProblemDefinition
I.TheGermanElectricityMarket
The core of this study is to analyse the effect of volatile renewable energy on the current
electricitymarket. In1996theEuropeanUnionliberalizedtheelectricitymarketsthroughthe
Directive 96/92/EC, in order increase the operational efficiency, maintain quality of the
electricity supplied, andminimize costs for the end customer (Conejo et al., 2010). After this
liberalizationthestructureoftheelectricitymarketschanged.Wherepreviouslythegeneration,
allocation, and sales where state owned, these are now unbundled and structured in a
competitivemarketplace.Onlytheallocationprocessremainsunderstatecontrol(GMBHand
GTAI,2016).
Theelectricitymarketconsistsoftwobasicmarketsthatconnectwithoneanother;thefutures
market,includingtheforwardmarket,theday-aheadmarket,andtheintra-daymarket,andthe
real-time market. Energy is being traded through bi-lateral long-term contracts, and on the
followingexchangemarkets;theEuropeanEnergyExchangeEEXinLeipzig,andtheEuropean
EnergyExchangeEPEXSPOTinParis(FederalMinistryforEconomicAffairsandEnergy(BMWi),
2014).Thestructureof theelectricitymarket isvisualized inFigure1.Besides tradingon the
exchangemarket,energy isbeingboughtdirectly fromsuppliers,basedonsocalled“overthe
counter”contracts(OTC).
Figure1:SubmarketsintheGermanelectricitymarket(BMWi,2014)
NegativeElectricityPricesintheGermanElectricityMarket
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A.TheFuturesMarkets
Ontheforwardmarketderivateproductsofenergyareboughtandsoldonaspecifiedfuturedate,
where companies hedge against the uncertainties in the day-ahead and intra-day markets
(Conejoetal.,2010).Ingeneral,marketparticipantsareabletoagreeoncontractsuptosixyears
inadvance.Thus,thismarketallowsfortradingproductsinthefutureatthecurrentprice.As
electricitypricesarehighlyvolatile,theforwardmarketisveryimportant.
Electricity deliveries for the next day are traded on the day-ahead market with decreasing
uncertainty,asparticipantsareabletoestimaterealconsumptioncloserintime(FederalMinistry
forEconomicAffairsandEnergy(BMWi),2014).Theday-aheadmarketclosesat12:00hours,
afterwhichnobidscanbeplacedfornextdayenergydeliveries.
Inordertodecreasetheoccurrenceofmarketimbalances,participantsareabletotradeinsame-
dayenergydeliveryproductsrangingfromquarterhourstohourblocksofenergyintheintra-
day market, which closes 45 minutes before actual energy delivery (Federal Ministry for
EconomicAffairsandEnergy(BMWi),2014).
B.TheControlReserveMarket
Besides theabove-mentionedelectricitymarkets,Germanyhas three control reservemarkets
thataremanagedbytheTSOs:(1)primary,(2)secondary,and(3)tertiary.Aftergate-closureof
the intraday market, the estimated electricity supply and demand should be in equilibrium.
However,despiteallefforts toperfectlypredict thedemand inorder tomatchthesupply, the
electricitymarketwillbeaffectedbyunforeseensituations(Möller,2010).Thereservecapacity
in the market is allocated by the TSO’s to account for such uncertainties between the real
electricitydemandandthepredictedelectricitydemandatgate-closure.
Theprimary control reservemarket is activated in30 secondsupuntil 5minutes and reacts
instantlywhenimbalancesinthemarketoccur,whereasthesecondarycontrolreservemarketis
activatedin5minutesuntil15minutes,andthetertiarycontrolreservemarketisactivatedin15
minutes up till one hour (Bayer, 2015). These markets needs to ensure energy equilibrium
betweensupplyanddemandaftertheintra-daymarkethasbeenclosed,duetohighvolatilityof
renewableenergygenerationandotherreasonssuchasmarketblackouts(Nicolosi,2010).The
fourlargestTSOsoperatethesemarketsandallocatetheirreservecapacitytoencounterforthese
discrepancies(Möller,2010).
NegativeElectricityPricesintheGermanElectricityMarket
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C.TheBalancingMarket
Theelectricitymarketcopeswithenergysurplusandshortagepriortoenergydelivery,inorder
tobalance theproductionandconsumptiononemore timeabalancingmarket is set inplace
(Conejoetal.,2010).AccordingtoMöller(2010)thebalancingmarkethastwomainpurposes.
Firstly,themarketisresponsibletosecureandminimizethecontinuousfluctuationsinenergy
supply as well as demand. Furthermore, this market is accountable for moderating the
preliminaryreal-timeenergydeliveryschedules.Theseschedulesarebasedonalltransactions
uptotheclosureoftheintra-daymarket.
All power that has been sold in the day-ahead, but cannot be delivered on the agreed upon
timeframehastobeboughtbackonthebalancingmarketbythesystemoperators.Ingeneral,
thebalancingmarket consistsof theprocurementofbalancing servicesand the settlementof
imbalances(Vandezande,2011).InthecurrentliberalizedstructureoftheEuropeanelectricity
markets, TSOs (Transmission System Operators) are no longer solely responsible for the
generation resources, but are forced to procure balancing services from Balancing Service
Providers(BSPs).
Thebalancingmarketconsistsofprimary,secondary,andtertiarycontrol.Theprimarycontrol
market, which is automatically controlled by an interconnected network, adjusts supply and
demandlevelsinordertostabilizethesystemontheshortterm,causingthisbalancingmarket
to contribute a relatively small amount of the total real-time power delivered. The most
importantroleofthesecondarycontrolmarketistobalanceoutinter-areaexchangeimbalances
to the set target valueswithin a certain timeframe that does not exceed the 15-minute limit.
Generatingunits,whichare locatedintheareawheretheimbalancehasoccurred,controlthe
secondary market. The tertiary control market assists the secondary control, restores the
secondarymarket,orre-dispatchessecondarycontrolpower(Vandezande,2011).
Since the liberalization, the TSOs have passed their balancing responsibilities on to Balance
ResponsibleParties(BRPs)partially.TheBRPsareaccountableforbalancingtheirownportfolio
foracertainagreedupontimeframethroughtheimbalancesettlementmechanism.Thisportfolio
consists of the generation, purchase, and import of energy, set against the industrial and
residentialusers,sales,andexportofenergy(Vandezande,2011).Oneimportantnote,afterthe
intra-day market has been closed only TSOs can solve imbalances in the electricity market
throughthecontrolmarketsorthereal-time/balancingmarket.
NegativeElectricityPricesintheGermanElectricityMarket
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II.ElectricitymarketinpresenceofRenewableEnergy
ThemaincomplicationswithrenewableenergysourcescanbedescribedaccordingtotheVRES
(VariableRenewableEnergySources) framework,whichcausedifficulties in integrating these
sourcestotheconventionalpowergrid(Sijm,2014).Variability,theoutputofpowerisdependent
on the availability of the energy source. Uncertainty, meaning it is highly difficult to predict
conditionsunderwhichtheenergysourceproducespower.Location-specificsources,renewable
energysourcesareoftennotevenlydividedovertheglobeandareverydifficulttotransportto
otherlocations.Lastly,renewableenergysourcesarelowin“short-run”cost(Sijm,2014).
Furthermore, renewable energy is characterised by high capital costs, but low fuel and
operationalcosts.Thischaracteristicchangesthewaypowerplantsaredispatchedandthetype
ofenergythatwillbesuppliedtothegridfirst.Generallyspeaking,powerplantsthatgenerate
energyatthelowestvariableproductioncostwillsupplythegridfirst(Blok,2006).Incontrast
torenewableenergysources,fossilfuelisproducedatasignificantlyhighervariableproduction
costthatresultsinachangeofthepowersupplystructure(Klessmannetal.,2008).Thischange
insupplyingorderdecreasesthemarginalenergypriceonshortterm.Otherdirectandindirect
effects of renewable energy supplied to the grid on wholesale prices are a decrease of CO2
allowanceprices,hedgingoffuelpricerisks,andashifttowardsmoreflexiblepowergenerators
thatproduceatlowercapitalcostsandhigherfuelcosts(Klessmannetal.,2008).
Duetothehighvolatilityandunpredictabilityofrenewableenergysourcesintheirgenerationof
power,theyinfluencethedistributionofconventionalpowerintheenergysystem(Klessmannet
al.,2008).Theseenergysourcesneedtobecombinedwithflexibleconventionalenergysources,
suchas fossilandgas, inorder tomakesure that thedemand in themarketcanbeprovided.
Matchingsupplyanddemandthroughconventionalsourcescausesanincreaseinenergypricing
asflexiblepowerplantshavehighervariablecosts.Furthermore,thismatchingstrategyincreases
CO2emission,decreasingthesustainabilityoftherenewablesources.Theprocessofmatching
supplywithdemandismostlydoneintheday-aheadmarketwhenforecastingerrorsarekeptto
aminimum.Inaddition,theintradaymarketisabletoreducetheunpredictabilityinsuchaway
thatitsupportssamedaydeliverywhereunforeseeneventsareminimal.
Previous research has discovered several power quality issues related to the above stated
characteristics of renewable energy sources. Beaudin et al. (2010) summarized the most
importantissuesasfollows:powerconvertershaveseveralundesiredovertonesthatmighthave
anegativeimpactonelectronictechnology,reconnectingwindturbinesafterundesirablewind
NegativeElectricityPricesintheGermanElectricityMarket
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speedsmightcause“brownouts”,solarpowerisreceptivetopowerspikesasobjectspassover
theareaofthepanels,andwindpowerfluctuationscanreducethestabilityofthesystem.
A.RenewableEnergyintheGermanElectricityIndustry
In Germany the generation of renewable energy has become an important factor, where the
governmenthasastrongfocusonR&Dfundingtoimprovecurrentrenewabletechnologies.From
1991 onwards Germany incentivised investments in renewable energy generation through a
numberofsupportprogrammescontainingsubsidies,taxincentives,orsoftloanstomakethis
possible(Grotz,2002).Forsolarenergyspecifically,theRenewableEnergyActwasinsinuated
providingdebtfinancingforphotovoltaicpanelstocommercializethegenerationofsolarpower
(Wustenhagen,andBilharz,2006).
Inordertointegraterenewablesourcesintothegrid,Germanyusesafeed-intariff,wherethe
generatorssellproducedenergytosuppliersatareducedpriceperKWhfora fixedperiodof
time.Dueto learningcurves, the feed-intariffsdecreaseeveryyear fornewtechnologiesbya
predeterminedpercentage.Thesefixedtariffsensuregeneratorsofrenewablesourcesatafixed
fee for their energy, which enables neglecting the electricity market prices. However, these
generatorsarefreetoselltheirenergyontheelectricitymarket,wheretheTSOisresponsiblefor
theintegrationoftherenewablesourcesintothegrid.TheDSOtransferstheenergytotheTSO
that transforms the fluctuating profiles to a standard profile (Klessmann et al., 2008).
Unfortunately,thistransformationprocessisnottransparent,asTSOarebothresponsibleforthe
operationsystemandthetradingfunctionsofrenewableenergyproduction.
With growing integration of renewable energy sources into the grid, several implications on
transmission planning and system operations will occur, such as increased challenges in
forecastingandbalancingtheproductionofenergyandcontrollingthesystem(Farahmandetal.,
2012).However, currently there isa limitedcapacityavailable for the transmissionofenergy
acrossbordersthatarenotdirectlynexttoeachother,disallowingtheallocationofallrequired
reserves, especially to the Nordic system. This can be solved though a fully integrated
transmissionsystemforenergyandreservecapacity.
Imbalancesintheelectricitymarketoccurduetoforecasterrorsonrenewableenergysources
and network outages, which result in increasing regulation costs (Jaehnert, 2012). These
regulationcostscanbeeitherupwardordownward,dependingonanexcessordeficitimbalance,
whereeachunitdeterminesthebalancingmarketpriceforregulationandtheTSOselectsthe
actualprice(Gebrekiros,2015).
NegativeElectricityPricesintheGermanElectricityMarket
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Concluding, increasing the share of renewable energy penetrated into themarket will cause
severalchallengesinthecurrentstructureoftheelectricitymarket.Inordertoresolveseveralof
thesechallenges,solutionshavebeenproposedinpreviousliterature.Astheshareofrenewable
energyof thetotalsupplyonthegrid isgrowing, increasingtheaccuracyof forecasting isnot
enough,storagesystemsneedtobeoptimized,thegeographicaldiversityneedstobeaggregated,
negativepricingpoliciesmightneedtoarise,orthedemandneedstobecomemorerobust.As
thisthesisfocusessolelyonthesupplysideoftheelectricitymarket,andspecificallyonnegative
energyprices,thefourthsolutionwillbeneglectedinthisresearch.
B.StorageSystems
Asthetotalproductionofrenewableenergyishighlyvolatileandunpredictable,thestorageof
energy becomes important in balancing the electricity market. Energy storage capacitates
decouplingofthegenerationofelectricityfromdemand(Carrascoetal.,2006).Meaning,energy
generatedatlow-demandandlow-generationcosts,canbereleasedattimesofhigh-demandand
high-generationcosts.Severalstoragetechnologieshavebeenimplementedinrenewableenergy
systems to improve the quality of the power generated and to support critical loads during
“mains’powerinterruptions”(Carrascoetal.,2006).Accordingtothesamearticlenewstorage
systems need to be developed to “optimize energy conversion and transmission, and control
reactive power, in order to minimize harmonic distribution, to achieve at a low cost a high
efficiencyoverawidepowerrange,andtohaveahighreliabilityandtolerancetothefailureofa
subsystemcomponent”.
Currently,ElectricalEnergyStorageSystems(EES)areabletoincreasethereliabilityofthegrid;
thesystemprovidesenergywhenit isneededattherightdemandlevel.TheElectricalenergy
storagetechnologyoptionsreport(2010), identifiedEESsystemsasanapplicablesolutionfor
theintegrationofrenewableenergysourcestothegrid.AccordingtoDunnetal.(2011)EESis
abletoregulatefrequencyload,shavepeaks,andshiftloads,whichmayimprovethereliability,
stability,andcostsofthegrid.
Thesesystemsshouldalsorequirelowmaintenanceandlonglife-cycle,reducingEEStotalcosts
(Beaudin et al., 2010). There are several storage systems that are applicable under these
requirements, such as batteries, flywheels, capacitors, and SMES, as these systems maintain
powerquality,gridstability,andarescalable,modular,durable,andlowinmaintenance(Beaudin
et al., 2010). Furthermore, these EES can be developed further without supply shortage
constraints.Therefore,theseEESsystemsmightbeasolutiontointegratingrenewableenergy
NegativeElectricityPricesintheGermanElectricityMarket
14
sourcesinanefficientwayintothecurrentelectricitymarket.However,EESsystemsneedtobe
scalableinordertocopewiththedistributednatureofrenewableenergysources.
C.GeographicalDiversity
InthecurrentEuropeanmarkets,balancingservicesarelimitedtocountrybordersduetovarying
gateclosure timesandbalancingrules.Anaggregategeographicalbalancingmarket increases
efficiency in the form of sharing balancing resources and reduces the amount of required
balancingactsby“nettingofimbalances”(ENTSO-E,2011).Nettingofimbalancesisknownasthe
cancelling out of upward imbalances in one geographical areawith downward imbalances in
anothergeographicalarea.AccordingtoGebrekiros(2015)thebalancingcostsarereducedina
scenarioofcross-borderbalancingmarketintegration.Furthermore,whenexchangeofbalancing
energyismadepossiblebetweenzones,nettingofimbalancesoccur.Onaverage,thereisa50%
decrease of imbalances as a result of cross-border balancing services in comparison to local
balancing.Thisdecreaseiscausedbythenettingofimbalancesandthepossibilitytousecheaper
balancingenergyfromcross-borderzones(Gebrekiros,2015).
Other advantages of cross-border integration are the creation of more competition in the
electricitymarket,animprovementofsupplysecurity,andincreasingopportunitiestointegrate
renewableenergytothegrid(1).However,disadvantageswillhavetobemanagedbythelocal
TSOs.In2009,theEuropeanNetworkofTransmissionSystemOperatorsforElectricity(ENTSO-
E)wasformed,inordertoensureanefficienttransmissionnetworkmanagement,allowingfor
tradeandsupplyofelectricityacrosscountryborders,andintegratingrenewableenergysources
tothegrid(ENTSO-E,2015).Since2006CentralWestEurope(CWE)issomewhatintegratedinto
onegeographicalelectricityexchangemarket,wheretheAPX,BPX,PowernextandEEXmarkets
arecoupled.AccordingtoFigueiredoanddaPereiradaSilva(2013)pricecouplingmechanisms
haveledtothestartofintegratingspotelectricitymarkets.
Integratingthebalancingmarketscanfacilitatemutualprocurementofbalancingservicesand
lead tomore flexible utilization of existing and future transmission capacities across country
borders (Farahmand et al., 2012). According to Farahmand et al. (2012) an integration of
balancingmarketsprovidesanopportunitytoreducetheactivationofthermalgeneration,and
thusreducethecostofreserveprocurement(by72%)andcostsofsystembalancing(by30%),
allowingforabetterintegrationofrenewableenergy.
Besides the optimization of storage systems and utilizing the opportunities of geographical
diversity,negativepricesareabletoensureahigherintegrationofrenewableenergysources.
NegativeElectricityPricesintheGermanElectricityMarket
15
III.NegativePricing
Renewableenergysourcescanhavevariousimpactsontheelectricitymarket,ofwhichoneisa
negativeelectricityprice.Supplyanddemandmatchinginreal-timeiscriticalintheelectricity
market,wherethestorageofenergyplaysanimportantrole.Insituationswheresupplyishigher
thandemand,powerplantswilldesire tosellgeneratedenergyata laterstagewhendemand
increases. Negative prices are driven through the logical continuation of the generalmarket-
principal,whichestablishesapriceatacertainsupplyanddemand(Gotz,2014).However,these
negativepricesplaceaconstraintontheincreaseofrenewableenergyshareoftotalsupply.Due
to the inflexibility of renewable energy sources, generationof energy cannotbe stopped, and
renewableenergyissoldeventhoughthepricesarebelownil.Thisstudyprovesthatnegative
pricesintheelectricitymarketwilloccuronamorefrequentbasisandtoalargerextent,when
theintegrationofrenewableenergytothegridincreases.
A.PlausibleDriversofNegativePrices
Due to market imbalances the electricity market has set negative price policies into place,
negativepricesoccurforashortperiodoftimewhenhighinflexibleenergygenerationmeetslow
demand, creating a supply surplus (EPEX Spot SE, 2015; Frauhofer Institute for Systems and
Innovation Research ISI, et al., 2015). As these inflexible energy sources, mostly renewable
sources,cannotbeshutdownorrestartedinaquickandcost-efficientway,asurplusoccursfor
alongerperiodoftime(EPEXSpotSE,2015).AccordingtoZhou,Y.etal.,(2014)negativeprices
canbecausedbyseveralfactorsintheenergymarket.
Firstofall,nuclearandotherpowerplantsaretechnicallylimitedintheadjustmentofenergy
generation levels. Besides the technical limitation, nuclear power plants suffer from high
adjustmentcostsincasesofanenergysupplysurplus.Thesesituationscanleadtodecisionsto
payothermarketparticipantstopurchasetheoversupplyofenergywhendemandislow.Power
plantoperatorsmightacceptthesepricesinasituationwherestart-upcostsarehigh,positive
revenue can be generated, or when it is possible for these plants to offer capacity in other
markets, such as thebalancingmarket or thenuclearheatingmarket (Frauhofer Institute for
SystemsandInnovationResearchISI,etal.,2015).
Furthermore,accordingtoresearchdonebytheFrauhoferInstituteforSystemsandInnovation
ResearchISI,etal.(2015),powerplantsarealsopreparedtoacceptnegativepricesinasituation
whereitisrequiredinordertomanage“othercontractualobligationsthanplannedelectricity
NegativeElectricityPricesintheGermanElectricityMarket
16
production”.Thismightbethecasewhenaplanthasscheduledenergysupplytothebalancing
reservemarket.
Third,electricitypricescandropbelow€0,00/MWhwhenthereisalackoftransmissioncapacity
ofinter-areaelectricityexchange,whichcausesexcessinelectricitysupplylocally.Thisoccurs
whenelectricitygeneratedinoneareacannotbetransmittedtootherarea’s,wheretheremight
be an excess in demand. This relates back to the increasing dependency of a fully integrated
Europeanelectricitymarket,whereenergysupplyshouldbeabletotransmittedtogeographical
diversearea’sinordertoensurebalancedlocalelectricitymarkets.
Inaddition,ashortexcessofenergysupplycanbecausedbytheprioritizationof,forinstance,
wind-basedelectricity generationdue topolicies set intoplaceby theEuropeanCommission.
Thesepoliciesdisallowtherestrictionofrenewableenergygenerationunlessthiswillcausea
negativeeffectonreliability.Lastly,renewableenergygeneratorsarepreparedtoacceptnegative
pricesincaseswherethenegativepricedoesnotexceedthemarketpremiumtheyreceiveforthe
generationofeachKWhofrenewableenergy(Frauhofer Institute forSystemsand Innovation
ResearchISI,etal.2015).
Eventhoughexplanationsfornegativeelectricitypriceoccurrencearewidespread,thefocusof
this study lies on the effect of an increasing renewable energy integration into the German
electricity market on negative electricity prices. There are several factors that influence the
developmentofnegativeprices,sincetheintroductionofnegativepriceallowancein2008.The
main drivers behind the development of the negative prices are the increasing generation of
highlyfluctuatingelectricityfromrenewableenergysources,duetogovernmentincentivessuch
as subsidies on renewable energy generation, and the dependency of renewable sources on
weatherconditions.Bothdriversinfluencethefrequencyofnegativepricesoccurring(Frauhofer
InstituteforSystemsandInnovationResearchISI,etal.2015).Thepoliciesthatallownegative
electricitypricestooccur,setintoplacebytheEuropeanCommission,raisethequestionwhether
thisisasustainablemarketmodelforlargeintegrationrenewableenergysourceintegration.
B.FutureofNegativePrices
Itisexpectedthatthecontributionofrenewablesourcestothetotalenergysupplywillincrease
inthefuture,whichwillleadtoanexpansioninthetotalnumberofhourswithnegativeelectricity
prices.Thesenegativepriceswillreflectsituationswherethereisan(sudden)increaseinwind
andsolarenergygeneration,causingadditionalenergysupply(FrauhoferInstituteforSystems
and InnovationResearch ISI,etal.2015).However,according to thatsameresearch, the total
NegativeElectricityPricesintheGermanElectricityMarket
17
numberofhoursreflectingnegativeenergypricesheavilydependsontheabilityoftheenergy
systemtoadapttothegrowthofrenewableenergyandthe“weatheryear”.
Astheenergysupplyfromrenewableenergysourceswillincreaseinthenextyears,theenergy
systemwillneedtobecomemoreflexibletopreventnegativepricesfrombecomingastandard
instead of an exception (Gotz et al., 2014). Negative prices pressurize the current renewable
energysurcharges,increasingtherenewablesurchargeaccountsofgovernmentsasaresultfrom
renewablessoldatnegativeprices(Gotzetal.,2014).Gotzetal.(2014)proposeseveralmeasures
forregulators,gridoperatorsandplantoperatorstoincreasetheflexibilityoftheenergysystem
inavoiding(extreme)negativepricesinthefuture.First,regulatorsandgridoperatorsshould
reducetheminimumamountofenergygeneratedbyconventionalpowerplants,areductionof
theminimumenergy input of heat andpower plants through the expansion of the combined
heating and power plant law, and the balancing energy price system should be expanded to
increasebalancingscheduleperformanceandshort-termtrading.Additionally,plantoperators
shouldmakeconventionalandrenewableenergysourcesystemsmoreflexiblewhilegenerating
energy, eliminate operative obstacles, and try to reduce the minimal conventional energy
generationthroughaprovisionofsystemservicesusingrenewablesources.
Lastly, a fully integrated market could benefit from efficiency gains through geographical
diversity. These gains from an integrated market could easily be established, due to heavily
varyingweatherconditionsacrossEuropeandevenwithinMemberStates(Böckersetal.,2013).
Böckers et al. (2013) reveals that efficient allocation and transmission solely between two
European Member States, Germany and Spain, will result in additional electricity worth
approximately€740millionwithinoneyear,basedonphotovoltaics.Thisprovesthatadditional
savings could be established through a (fully) integrated European Electricity Market where
morethantwoMemberStateswilltradetheirenergyandotherrenewableenergysourceswill
beconsidered,suchaswind.
In conclusion, this study stresses the current market design for large integration of volatile
renewable energy sources. It is expected that the current market is not designed for the
integrationoflargetracesofrenewableenergy,andimplicationsneedtobesetintoplaceinorder
toestablishanIT-basedsolution.
NegativeElectricityPricesintheGermanElectricityMarket
18
IIIDataandMethodology
Themainpurposeofthispaperistoanalysetheoccurrenceofnegativeelectricitypricesinthe
Germanelectricitymarketwhentheshareofrenewableenergycontributingtothegridincreases
sufficiently.Inordertotesttheeffectofanincreaseinshareofrenewableenergyintegratedto
the grid on the electricitymarket prices, an empirical analysiswill be conducted. Relying on
observations from the German electricity market in order to answer how renewable energy
impact negative electricity prices, a quantitative analysis will be performed focussing on the
behaviourofelectricitypricesandexplainingthephenomenaofnegativeprices.Gaininginsight
innegativeelectricitypriceswillassesswhetherthecurrentmarketconditionsareoptimalfor
anincreaseofrenewableenergyshare.
Forthisresearch,currentelectricitymarketconditions,underwhichenergyisbeingtraded,must
be controlled inorder todetermine the relationshipbetween renewable energy andnegative
electricityprices.Furthermore,controllabilityisimportantinordertoestablishthecausaleffects
ofrenewableenergyonelectricityprices.Ascontrollingandmanipulatingthedataisbestdone
inanartificialsetting,anempiricalanalysiswillbetheresearchdesignofthisstudy.
I.DataDescription
This studyemploysanempirical analysis,where large tracesof renewableenergyneed tobe
penetratedintothemarketandnegativeelectricitypricingpoliciesneedtobeinplaceinorder
to analyse the interplay between volatile energy supply and negative prices. Data from the
GermanElectricitymarket,providedbytheEuropeanEnergyExchangeAGLeipzig, isusedfor
the analysis of this research. According to Deloitte (2013) Germany is the largest electricity
market inEurope,withover180GWof installedcapacity.TheGermanelectricitymarkethas
beencharacterisedbyconsiderablegrowthratesofrenewableenergygenerationoverthepast
decade,inordertodecreaseitsdependencyoncoal(Sensfuß,etal.,2008;Deloitte,2013).Since
2000theRenewableEnergySourcesAct(REA)hasbeensetintoplaceinordertostimulatethe
generationof renewableenergy. In2013renewableenergysourcesaccounted for50%of the
totalenergycapacityand29%ofthetotalenergyproduction,mostofwhichdrivenbywindand
photovoltaics(Deloitte,2013).BasedonthesegroundtheGermanmarketwasassessedasthe
mostsuitableenergymarkettobasethisresearchon.
During the period December 2012 and December 2013, the German electricity market
experienced97hourswhereelectricitywastradedfornegativeprices,onaverage-€40.97per
MWh(Gotzetal,2014).InthisperiodGermanelectricitypriceswerenegativemorethanallother
NegativeElectricityPricesintheGermanElectricityMarket
19
years up until today, therefore the above-mentioned timeframe is used in order to establish
whetherthesenegativepricesarespecificallycorrelatedwithanincreaseintheintegrationof
renewableenergy.Theempiricalanalysiswasconductedovera395-dayperiod,wherepricing
dataobservationsonanhourlybasisandrenewableenergygeneration,totalload,andrenewable
loadobservationsonaminutebasishavebeendownloaded.Thereafter,energygenerationand
loaddataobservationswhereconvertedfromminutetohourlytraces,inordertocompareboth
prices, demand, and supply based on the same time value.With this dataset, large traces of
electricitypricesandlargetracesofrenewableenergygenerationarecomparedwitheachother
inordertopredictacausalcorrelationbetweenelectricitypriceandrenewableenergysupply.
II.DataDescriptives
As outlined in previous literature on negative pricing, there seems to be an overall negative
influenceonelectricitypricesaslargertracesofrenewableenergyaresuppliedtothemarket.
AccordingtoCloete(2014)renewableenergygeneration,windandsolarenergyinparticular,
oftencauseadropinelectricitypricesbelow€0,00perMegawatthour(MWh)inGermany.The
drop in electricity prices indicates a quick reaction of themarket discounting an increase in
renewableenergygeneration.AsviewedinFigure1,anincreaseintheshareofrenewableenergy
generation on a daily basis (in percentages) causes the hourly spot price (€/MWh) to drop,
showing a 36% variance in the data. Cloete (2014) found a negative correlation, which will
increasinglyhamperthecompetitivenessofsolarandwind.Basedonthesefindings,additional
experimentssolelyanalysingnegativepricingunderdifferentcircumstanceswillbeconducted.
Figure2:EffectofRenewableEnergyShareonAverageSportPrice(Cloete,S.,2014)
NegativeElectricityPricesintheGermanElectricityMarket
20
The abovementioned dataset contains several main variables RenewableShare,
ElectricityPrice,TotalLoad,andTotalNetLoad.Inordertogetmoreinsightintheusefulnessof
the obtained dataset, general descriptions of these variables are touched upon. The dataset
containsbothpricingandtheshareofrenewableenergyforeveryhourofthedaybetweenthe
period1stofDecember2012and31stofDecember2013.
A.RenewableShareandElectricityPrice
Thetotalshareofrenewablesthatistradedontheelectricitymarket,percentagewiseforeach
tradinghour,iscalculatedaccordingthefollowingformula:
!" =$%$&
Where, st reflects the share of renewable energy penetrated into the grid, GR is the total
generationofrenewableenergyinMWh,andGTisthetotalgenerationofenergyinMWh.This
sharereflectsthecontributionofrenewableenergytothetotalelectricitysupplyduringaspecific
point in time t.Thasbeenconverted fromminute tohourlydata traces tobeable toanalyse
supplyandpriceonthesamelevel.
Energypricesarelargelydependentonthebalancebetweensupplyanddemand.Thepriceinthe
day-aheadmarket is setbasedon thesupplyanddemandequilibriumof the fullmerit-order-
curveatanhourlytimescale(Möller,2010).Thismerit-order-curvereflectsthebiddingpricesof
suppliersataspecificcapacity(MW),whereenergyistradedforthehighestbid.
Ingeneral, thepriceofenergy that is tradedon theelectricitymarket,perMegawatt foreach
tradinghour,isestablishedthroughthefollowingformula:
'" = ')" + '+"WherePtreflectsthetotaltradingpriceperMegawattofthatspecifictimeslot,treflectsaspecific
tradinghourof thedayontheday-aheadmarket,PSt is thehighestbiddingprice,andPNtthe
priceofthenetwork.Thepricingdatasetcontains24hpricingforeachdayoftradinginGermany
duringtheaboveestablishedtimeframe.
However,whendonegativepricesoccurintheelectricitymarket?Whatistheimpactofnegative
electricity prices? Normally, electricity prices drop below €0,00/MWhwhen there is a large
supplysurplusaimingtodecreasethissurplusinashortperiodoftime.Negativeelectricityprices
donotoccuroften,inthedatasetforthisstudyelectricitypriceswherenegativefor97trading
hoursovera396-dayperiodreflecting10%ofthetotaltradinghours.Inordertoclarifytheeffect
ofdailyelectricitypricesanditsrelationtorenewableenergy,thefollowingdescriptiveanalysis
NegativeElectricityPricesintheGermanElectricityMarket
21
has been conducted. Figure 2 plots electricity price values for different percentage points of
renewableenergypenetration.
Figure3:ScatterplotDailyDay-AheadElectricityPriceasafunctionofShareofRenewableEnergy
Penetration
Assuchthefollowingformulawasformulated:
,- € /01 ≈ 34. 36 − 43. 889:-
wherePtistheelectricitypriceandstintheshareofrenewableenergypenetratedintothegrid,
andtis24hours.
Daily negative prices occur more frequently as the value for the share of renewable energy
penetration increases significantly (P < 0.01). After a renewable energy penetration of 40%,
averagedailytradingpricescanbenegative.Astheshareofrenewables increases, theoverall
electricitypriceforthatspecificdaydecreasessignificantlyby€65.12/MWh.Whilethevalueof
renewableenergypenetrationrestsaround45%ofthetotalenergysupply,thevalueofthedaily
electricityprice(€/MWh)dropsbelow–€40,00.
B.TotalLoad
TheloadreflectsthetotaldemandintheelectricitymarketperMegawattofaspecificpointin
time.Asdemandimpactelectricitypricesdirectly,thisvariableistakenintoconsiderationwhen
assessing the effect of renewable energy on electricity prices. The total demand (or load) of
energythatisrequestedontheelectricitymarket,perMegawattforeachtradinghour,hasbeen
NegativeElectricityPricesintheGermanElectricityMarket
22
downloadeddirectlyfromTransmissionOperators.Inordertocomparepricedatatraceswith
demandtraces,thefollowingformulahasbeenusedtoconvertminutetohourlydata:
&;" =&; <=
>
whereTLtisthetotalelectricityloadandtthetimeofday.
Just as energy supply, demand fluctuates during the day. As presented in Figure 3, total load
dependsheavilyontimeofday.Astimeelapses,thetotalloadpenetratedintothegridincreases
significantly(P<0.01)with373,29MWhperunit,duetoanincreasingdemandforenergyas
timepassesby.Thislinearrelationshipcanbecapturedinthefollowingformula:
?@A BCD ≈ E9, 96G. 88 + G9G. EH3A
Duringmorninghours,thetotalloadislessthanlaterintheafternoonandevening.Thissame
patterncanbeobservedintheFigure12.Eventhoughtheminimumtotalloadisat6am,thereis
anoverallincreasingtrendtowardstheafternoonandevening,withhighsat3pmand9pm.On-
peak and off-peak hours are less visible for total load in comparison to the electricity prices
during the same time period. It can be concluded that there is a slight delay in total load,
electricitypricesreactsoonertotheestimationofon-andoff-peakhours.
Figure4:ScatterplotTotalElectricityLoadasafunctionofTimeofDay
C.TotalNetLoad
Thetotalnetload,referringtothetotalenergydemandsubtractedbythetotalrenewableenergy
demandduring the sameperiod,measured inMegawattsperhours. Net load represents the
demandthatgridsuppliersmustmeetwithoutdispatchableenergysources,reflectingamore
stabledemand.Thetotalnetloadthatisrequestedfromtheelectricitymarket,foreachtrading
hour,willbecalculatedaccordingthefollowingformula:
NegativeElectricityPricesintheGermanElectricityMarket
23
+; > = &; − %;whereNLtisthetotalelectricityloadattthetimeofday.
Therenewableloadtracesareconvertedfromminutetohourlydatapoints.Thesameeffectof
timeontotalloadholdsforthetotalnetload,ascanbeobservedinFigure5.Thereisastrong
significanteffectofthetimeofdayonthenetelectricityload(P<0.01).Astimepassesby,more
electricityispenetratedintothegrid,withadailyhighbetween9pmand11pmatnight,which
canbeobservedinthegraphbelow.
Figure5:ScatterplotNetElectricityLoadasafunctionofTimeofDay
III.ConceptualModel
Aquantitativestudywillbedone,usingR,inordertoanalysetherelationbetweentheshareof
renewableenergyandtheelectricityprices,withrespecttotheindependentvariablestotalload,
netload,timeofday,andseasonality,usingthefollowingmodel.
NegativeElectricityPricesintheGermanElectricityMarket
24
Figure6:ConceptualModel
AsvisualisedinFigure6,theshareofrenewableenergyintegratedintotheGermanelectricity
market influences the electricity market price positively, effecting the day-ahead electricity
marketprice,mediatedbythetimeoftheday.Themeasureofreal-timerenewableenergysupply
hasasignificanteffectontheenergypricetradedforthatspecifichour.Statisticaltestsshowthat
theeffectofanincreaseintheshareofrenewableenergysignificantlyeffectstheday-aheadspot
priceof that samehournegatively.As theamountof renewableenergypenetration increases
from0to80%,theoverallaveragepriceontheday-aheadmarketdropsbymorethanahalf(Fig.
2).Whenthereisanincreaseinsupplyfrominflexiblepowersources(suchaswindandsolar)
anddemand remains at the same level, energypricesdrop.This increase in supplyoccurs as
generation from renewable sources cannot be stored nor shut down in a cost-efficient way,
resultinginapricedrop.Thisstudyshowsthatthissameeffectholdsfornegativeprices.
Anotheranalysisthatisconducted,istheprobabilityofnegativeelectricitypricesoccurringas
theshareofrenewableenergyincreases.Thestatisticalapproachusedrepresentsadiscreteand
continuousprobabilitydistributiondensityfunction:
I ' = 'J ∗ L −1 + (1 −'J)P(0, −221.99)whereP0reflectstherationegativepricesbetween€0.00and-€1.00ofallnegativepricesduring
the periodDecember 2012 andDecember 2013. The first term reflects themodel of discrete
negative pricing effecting the density. The second term reflects the continuous uniform
distributionofnegativeelectricitypricesbetween−€0.01and−€221.99.
NegativeElectricityPricesintheGermanElectricityMarket
25
IVResults
Motivatedbytheoccurrenceofnegativepricesinthecurrentelectricitymarket,Iinvestigatehow
renewableenergypenetrationcausestheday-aheadmarketpricestodropbelow€0,00/MWh.
Currently, the effects of generalmarketmechanisms and effects of fossil poweron electricity
pricing dominates empirical analysis. However, I argue that an increasing renewable energy
penetrationdirectlyinfluencestheday-aheadelectricityprices,andevendirectlyincreasesthe
probabilityofnegativeelectricitypricesoccurringintheelectricitymarket.Therefore,Iassesthe
directeffectofanincreasingrenewableenergypenetrationonreal-timeelectricitymarketprices.
Theresultsarestructuredasfollows,Istartwithageneralanalysisoftheelectricitymarket;how
thetimeofdayeffectselectricityprices,bothpositiveandnegative,totalload,andnetloadand
how total load and net load effect electricity prices, both positive and negative. Thereafter, I
analysethedirecteffectofanincreasingrenewableenergypenetrationonallelectricityprices
andzoomfurtherinonsolelynegativeelectricityprices.
I.Descriptiveanalysis
Besidesthedirectinfluenceofanincreasingshareofrenewableenergyontheelectricityprice,
otherfactorsinfluencethetradedelectricityprices.Externalmarketfactors,suchasseasonality,
timeofday,andenergydemand,haveanunpredictableinfluenceontheday-aheadtradingprices.
Theseeffectswillbehighlightedinthefollowingsection.
A.Seasonality
AsobservedinFigure3,thedatasetcontainsfourdaystradingelectricitynegatively,onaverage.
Tobetter contrast the values of these specific days,with twodays showing significantlyhigh
negativeelectricitypricesbelow–€40,00,Figure7visualisesalldailyaverageelectricityprices
duringthetrading-periodDecember2012andDecember2013inGermany.
NegativeElectricityPricesintheGermanElectricityMarket
26
Figure7:ScatterplotDailyDay-AheadElectricityPriceduringDecember2012–December2013
Figure7visualisesthatbothnegativepricesbelow–€40,00/MWhoccurredinDecember2012
aftereachotherduringChristmasonthe25thand26th,tradingelectricityfor–€56.87/MWhand
–€45.77/MWhonaveragerespectively.Additionally, itcanbeobservedthatatendof2012as
wellasatthebeginningof2013thereisasignificantdropintheaveragedailyelectricityprice.
Again during Christmas, on the 24th of December 2013, the electricity price dropped below
€0,00/MWh,tradingelectricityfor–€6,28/MWh.
In addition, during the summer period, the average daily electricity prices seem to be lower
comparedtocoldermonths,showingaslightdropduringtheperiodMay–August.Thiseffectis
theresultofan increasingrenewableenergysupplyandadecrease intheoverallenergy load
duringthatsameperiod.Besidesaslightdropduringsummerperiods,electricitypricesarenot
dependentonseasonality.
Over theperiod fromDecember2012tillDecember2013theredoesn’tseemtobeanoverall
negativetrend.Onthecontrary,thedaily-averageelectricitypriceistradedfor€40,00p/MWh
duringthisstudy.Therefore,itcanbeconcludedthattheeffectoftheshareofrenewableenergy
penetrated into the grid is not dependent on the time of year, considering the daily-average
tradingprices.
NegativeElectricityPricesintheGermanElectricityMarket
27
B.TimeofDay
Althoughtherearenosignificantseasonalityeffectsontheelectricityprice,therearedailyeffects
asthesupplyandloadperhourvariessignificantly.Asmentionedinpreviousresearch,electricity
isstronglydependentonsupplyanddemandateachspecificpointintime,effectedbylimited
storagepossibilities,weather conditions, andotheruncertainties (Lucia andSchwartz, 2002).
Due to these determinants in every precisemoment in time, off-peak and on-peak electricity
pricesoccur,wherepricescorrespondtospecifictimeperiodsduringtheday(Kaminski,1997;
EydelandandGeman,1998;Deng,2000).Basedontheseinsights,thedatasetusedforthisstudy
isbetestedinordertoestablishwhetheralargeintegrationofrenewableenergydirectlyeffects
theoff-peakandon-peaktradinghours.
Electricityprices(€/MWh)varythroughouttheday,showingageneraldailytrendthroughout
the year. At certain specific points in time during the day demand increases significantly,
reflectingon-peakhours,whichpositivelyeffectstheelectricitypricestradedduringtheseon-
peak hours. After these on-peak hours, electricity demand decreases resulting in a drop of
electricityprices.Thisistheeffectofanegativetrendintheamountofloadandtheelectricity
demand from certain hours of the day. This arises from the fact that people consumemore
electricityatalaterpointintheafternoonandintheeveningcomparedtomorninghoursand
duringthenight.ThistrendcanbeobservedinFigure8below.
Figure8:ScatterplotDay-AheadElectricityPriceasafunctionofTimeofDay
NegativeElectricityPricesintheGermanElectricityMarket
28
Bothon-peakandoff-peakhoursarevisibleinFigure8,whereon-peakhoursarefrom8till10
amandbetween6to8pm.Off-peakhoursarebetween11amto6pmandatmidnightfrom9pm
till7am.Additionally,itseemsthatnegativepricesoccurmorefrequentlyduringoff-peakhours,
from0amuntil7am.Asdemanddecreases,negativepricesseemtooccurmorefrequently.This
canbeexplainedthroughthevolatileandunpredictableimpactfromrenewableenergysources
duringoff-peakhourswhendemandislow.
Theeffectofthetimeofthedayonelectricityprice,whensolelytakingthenegativepricesinto
consideration,seemstobecorrelatedwitheachother.Whentakinga linearregressionmodel
into consideration this effect is positive and significant with an effect of €2.37/MWh as time
increases,stoppingatmidnight.Thelinearlineoftherelationshipbetweenthesetwovariables
canberepresentedasfollows:
,- € /01 ≈ −3T. 9G3 + E. G44-
wherePtistheelectricitypriceandtisacertainpointintimeduringtheday(a24-hourrange).
Meaning,thatpricesaremorenegativeduringnightandearlymorning,comparedtolaterhours
duringtheday.
WhenvisualizingthenegativeelectricitypricedatapointsinFigure9,aspecificpatterncanbe
observed.Apartfromsomeoutliersbetween14and16pm,pricesarebelow€0,00/MWhduring
midnightupuntil9am in themorning.During theday, thereareeithernonegativepricesor
negativepricesbetween€0,00and–€100,00/MWh.
Figure9:ScatterplotNegativeDay-AheadElectricityPriceasafunctionofTimeofDay
NegativeElectricityPricesintheGermanElectricityMarket
29
AsdescribedinSectionII,negativeelectricitypricesoccurwhenthemarketischaracterisedbya
significant increase in supply of renewable energy concurringwith significant lower demand
(Gotzetal.,2014).AccordingtoGotzetal.(2014)hourscharacterisedbylowlevelsofdemand
aremorelikelytooccuronSundaysandholidays,orduringthenight.Therefore,thepricesof
electricityarelower,andlikelytobenegativemorefrequently,duringnighthoursandduringthe
dayfrom14till17pmwhendemandissignificantlylower.
Theprobabilityofnegativeelectricitypricesoccurringduringthenight,from00:00till07:00am,
isover0.02(2%),whilethereisonlyaprobabilityof0.005(0.5%)thatnegativeelectricityprices
will occur during the day, from 08:00 am till 23 pm. Therefore, it can be concluded that the
chances of electricity prices dropping below €0,00/MWh are four times as high as negative
electricitypricesduringtheday.
C.TotalLoad
Besidesthesupplyofrenewableenergyandtime-relevanteffects,(renewable)energydemand
from consumers also effects the electricity price significantly, as a result from market
mechanisms also know as the Hicks-Hansen model or the Keynesian model (Hicks, 1937;
Hanssen,1953).Totalloadreflectsthetotalelectricitydemandtraded,forthisresearchtotalload
reflects total demand in the German electricity market during the period December 2012 –
December2013.
Table1:RegressionAnalysisDay-AheadElectricityPriceasafunctionofTotalElectricityLoad
As can be observed in Table 1, an increase in the electricity load (MWh) results in a slight
significantincreaseintheelectricityprice(P<0.01).However,thiseffectisonly€0.0002perMWh
foraunit increase in load (MWh).This slighteffect canbeexplainedas follows,whenconsumers
NegativeElectricityPricesintheGermanElectricityMarket
30
require additional energy from the market, demand increases while supply remains the same,
resulting inan increase intheelectricityprice.Thispositiverelationshipcanbetranslated intothe
followingfunction:
,- € /01 ≈ GU. 399 + U. UUUEVW
wherePtistheelectricityprice(€/MWh)andTListhetotalloadintheelectricitymarket(MWh).
Figure10:ScatterplotDay-AheadElectricityPriceasafunctionofTotalElectricityLoad
Thissamepatternholds,whenlookingateachindividualdatapointinFigure10.Thereisaslight
increase of electricity prices as the total load increases. In addition, negative prices below –
€100.00p/MWhoccurbetweenatotaldemandof23.500and36.000MWh.Incasewetakeacloser
lookintotheeffectofthetotalloadonnegativeelectricityprices,electricitypriceswillnotdropbelow
€0.00/MWhasaresultofanincreaseintotalload.Therefore,itcanbeconcludedthatnegativeprices
areindependentonthetotalelectricitydemand
Ipreviouslydeterminedthatnegativeday-aheadelectricitypricesoccurindependentfromthe
totalelectricitydemand.ThissamepatterncanbeobservedinFigure11,thereisnopatternnor
effectofelectricityloadontheday-aheadspot-price,eventhoughthefrequencyofnegativeprices
occurringismostprobablebetweenatotalloadrangeof23.500and36.000MWh.
NegativeElectricityPricesintheGermanElectricityMarket
31
Figure11:ScatterplotNegativeDay-AheadElectricityPriceasafunctionofTotalLoad
D.NetLoad
Netelectricityloadreferstothetotalenergydemandsubtractedbythetotalrenewableenergy
demand during the same period, measured in Megawatts per hour. Net load represents the
demandthatgridsuppliersmustmeetwithoutdispatchableenergysources,reflectingamore
stabledemand.Itisexpectedthatnetloadwillhaveapositivesignificanteffectontheday-ahead
electricity price, because traditional power is not effected by external factors apart from the
predictednetload.
Table2:RegressionAnalysisDay-AheadElectricityPriceasafunctionofNetElectricityLoad
Asthenetloadincreases,resultingfromanincreaseinminimumdemandand/oradecreasein
therenewableenergyload,theelectricitypriceincreasessignificantlywith€0.0005(P<0.01)
NegativeElectricityPricesintheGermanElectricityMarket
32
(Tab.2),althoughthedataisnotwell fittedtotheregressionline(R2=0,072).Foreveryunit
increaseinminimumtotaldemand(MWh),theelectricitypriceincreaseswith€0.0003morethen
whentakingthetotalnetloadintoconsideration,whichisdemonstratedinthefunctionbelow:
,- € /01 ≈ E4. U3T + U. UUU3XW
wherePTistheelectricitypriceandNLinthetotalnetloadintheelectricitymarket(MWh).
Figure12:ScatterplotDay-AheadElectricityPriceasafunctionofNetLoad
Electricityloadgeneratedbytraditionalpowerplantsispredictableinthesensethatsupplyand
demandcanbematchedefficiently,without the influence fromunpredictableexternal factors
suchasweatherconditionsandstoragelimitation.Inascenariowherethedemandincreases,the
totalnon-renewableloadwillincrease,resultinginanincreaseintheday-aheadelectricityprice
(Fig.12)
Inaddition,Figure12showsthatthefrequencyofnegativepricesduringnegativenetload(MWh)
occursmoreoftenincomparisontothefrequencyofnegativeelectricitypricesduringpositive
netload(MWh).DuringtheperiodDecember2012–December2013negativenetloadoccurs
during154tradinghours,comparedto9.325positivenetloadtradinghours.Additionally,during
the154negativenetloadtradinghours,theelectricitypricedroppedbelow€0.0024times.This
resultsinaprobabilityofnegativeprices,duringnegativenetloadtradinghoursof0.15584(15.584%
ofthetime).Onthecontrary,only73hoursofnegativeelectricitypricesoccurduringpositivenetload
tradinghours.
NegativeElectricityPricesintheGermanElectricityMarket
33
Theprobabilityofnegativeprices,whennetloadispositive,isonly0.00783(0.783%ofthetime).Itis
morelikelythatnegativeelectricitypriceswilloccurwhenthenetloadisnegative,thusnegativeprices
aredependentonnetload.Inconclusion,netloadisakeyfactoreffectingtheday-aheadelectricity
prices.ThissamepatterncanbeobservedinFigure13.
Figure13:ScatterplotNegativeDay-AheadElectricityPriceasafunctionofNetLoad
However,thestrongdependencyofnetloadonnegativeelectricitypricesdoesnotholdwhen
performing a linear model. No significant effect can be found on the relationship between
negativeelectricitypricesandthetotalnetloadduringthesameperiod(Tab.3).Thismightbe
thecausedduetoalowfrequencyofnegativepricesintheoveralldataset.Inordertoanalysethe
effectof(negative)netloadonnegativeelectricitypricesalongerperiodwithahigherfrequency
ofnegativepricesshouldbetakenintoconsideration.
Table3:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofNetElectricity
Load
NegativeElectricityPricesintheGermanElectricityMarket
34
In conclusion, there are several external factors that influence electricity prices without
consideringtheimpactofrenewableenergy.Aspredicted,boththetimeoftheday(witha24-
hourrange)andnet loadhavesignificanteffectson theelectricityprices.The timeof theday
causes the electricity prices to reflect on-peak and off-peak hours, where electricity prices
(€/MWh)become four timesmore likely todropbelow€0,00/MWhduringoff-peakhours in
comparisontooff-peakhours.Interestingly,theprobabilityofnegativepricesoccurringduringa
negativenetloadincreasesincomparisontoapositivenetload.Implyingthatvolatilerenewable
energy sources that cannot be stored when load is negative, influence the electricity prices
negativelyaselectricitywillbesuppliedwhendemandisnegative.
II.Theeffectofrenewableenergy
Inthissectionthegeneraleffectofanincreasingrenewableenergypenetrationwillbeassessed,
inordertoconcludewhetherornotthereisadirecteffectofrenewableenergyontheday-ahead
electricity price. If the share of renewable energy influences the electricity price negatively,
electricitygeneratorswillmakealossoneveryKWhsold,whereaselectricityconsumerswillgain
perKWhbought.However,whenapositiveeffectwillbeobserved,electricitygeneratorswill
increasetheirprofits,asthepriceperKWhsoldwillincreaseastheshareofrenewableenergy
penetratedintothegridincreases.Inordertoidentifythiseffect,Ianalysearegressionanalysis
onthesetwovariables,ElectricityPriceandShareofRenewableEnergy,ascanbeseeninTable
4.
Table4:RegressionAnalysisDay-AheadElectricityPriceasafunctionofShareofRenewableEnergy
Penetration
Theshareofrenewableenergypenetrationhasasignificantnegativeinfluence(P<0.01)onthe
day-aheadelectricityprices.Thisconclusionisinlinewithpriorresearchshowingthatday-ahead
NegativeElectricityPricesintheGermanElectricityMarket
35
pricesdropasthepenetrationofrenewableenergyintothegridincreases(Cloete,etal.,2014;
Gotz,etal.,2014).Basedonthisinformationthefollowingequationcanbeestablished:
,- € /01 ≈ 69. UG4 − GE. GHG:-
wherePtisthehourlyelectricitypriceandsTintheshareofrenewableenergypenetratedinto
thegridatacertainpointintime(t).
Figure 14: Scatterplot Day-Ahead Electricity Price as a function of Share of Renewable Energy
Penetration
Thissameeffectcanbeconcludedfromthefigurepresentedabove(Fig.14)wheredatapoints
onelectricitypricesareplottedagainstanincreasingshareofrenewableenergypenetratedinto
the electricity grid. There is a general downward trend of electricity prices as the share of
renewableenergypenetrationincreases.Thiscanbeexplainedthroughanincreaseinfluctuating
supplywhiledemandremainsthesame,resultinginanimbalancebetweensupplyanddemand.
Inordertobalancetheelectricitymarket,electricitypricesdrop.Furthermore,itisobservedthat
negativeelectricitypricesarehigherwhentheshareofrenewableenergypassesthe30%ofthe
totalenergysupply(MWh).
In conclusion, the general correlation between renewable energy and electricity prices is
negative.However, itremainsunclearwhetheranincreasingrenewableenergyshareexplains
the occurrence of negative electricity prices. In the next section the correlation between
renewableenergyandnegativeelectricitypricesislaidout
NegativeElectricityPricesintheGermanElectricityMarket
36
A.NegativePrices
Whatistheeffectofanincreaseinrenewableenergysupplyonsolelynegativeelectricityprices?
Whenzoominginandreviewingnegativeelectricitypricesasafunctionoftheshareofrenewable
energypenetrated into theelectricitymarket,adifferenteffectseemstooccurasshowin the
resultsbelow(Tab.5)basedon97hoursofnegativeelectricityprices.
Table 5: Regression Analysis Negative Day-Ahead Electricity Price as a function of Share of
RenewableEnergyPenetration
Astheshareofrenewableenergypenetrationincreases,thereisasignificantpositiveeffectof
approximately€154.03perpercentpointincrease(P<0.01).Meaning,asmorerenewableenergy
willbesuppliedtotheelectricitymarket,theheightofthenegativepriceswilldecrease.Basedon
thefactthatanincreaseinrenewableenergyhasapositiveeffectonthetradedelectricityprice,
thefollowingequationcanbedeveloped.
,YZ[,- € /01 ≈ −8G8. U3T + 836. UGU:-
wherePneg,tisthehourlyelectricitypriceandstintheshareofrenewableenergypenetratedinto
thegridatacertainpointintime(t).
However, it must be noted that during the period December 2012 till December 2013 the
electricitymarkettradedelectricityundernegativepricesforonly97hoursoutofatotalof9480
hours. This reflects a 0,01% ratio of negative prices occurring during a 486-day time span.
Therefore,thesesignificanteffectsmightdifferwhentakingalargertime-spanintoconsideration
withrelativelymorenegativeprices.
NegativeElectricityPricesintheGermanElectricityMarket
37
Figure15: ScatterplotNegativeDay-AheadElectricityPrice as a functionof ShareofRenewable
EnergyPenetration
Addingtotheweaksignificance,whentakingacloserlookintothenegativepricesdatapoints,it
canbeconcludedthattheredoesnotseemtobealinearmodelfittingthedata(Fig.12).Most
negativepricesarewithinthe–€0,01and–€1,00pricingrange.Thevalueofnegativepricesisnot
effectedbytheshareofrenewableenergy;negativepricesarealmostindependentfromtheshare
ofrenewables.However,astheshareofrenewableenergypenetratedintotheelectricitymarket
surpasses30%,negativepricesseemtooccurmoreoftenwithanincreasingtrend,implyingan
exponentialrelationshipbetweenthenegativeelectricitypriceandtheshareofrenewables.
B.TotalLoad
UpuntilthispointIhaveproventhatseveralelements,mainlytimeofdayandshareofrenewable
energypenetratedintotheelectricitymarket,influencetheelectricitypriceseitherpositivelyor
negatively.However,itremainsuncertainhowdemandisinfluencedbyanincreasingrenewable
energypenetration.Totalloadseemstobehighlydependentontheshareofrenewableenergy
penetratedintotheelectricitymarket,whenconsideringalinearmodel(Tab.6).
NegativeElectricityPricesintheGermanElectricityMarket
38
Table 6: RegressionAnalysis Total Electricity Load as a function of Share of Renewable Energy
Penetration
BasedonthelinearmodelpresentedinTable6,itcanbeconcludedthataunitincreaseinthe
shareofrenewablesresults inthetotalenergyloadincreasingby1,452MWsignificantly(P<
0.01).Thisrelationcanbeattributedtotheeffectofuncertaintyandvariabilitythatcharacterize
renewableenergysources.Renewableenergysourcescannotbestorednorregulatedinorderto
influencethemarketpower.Therefore,allpowergeneratedthroughrenewableenergysources
willbeloadedandsuppliedtothemarket,effectingthetotalloadpositively.
However,suchatrendcannotbeobservedintherelationshippresentedinFigure16.Ingeneral,
thetotalnetloadishighlyvariableforeverypercentageofrenewableenergypenetratedintothe
electricitymarket.Meaning,intheGermanelectricitymarketthereisnoregulationforlimiting
the penetration of renewable energy between a certain range of total energy load (Fig. 16).
Therefore, it canbe said that the total load is independenton the shareof renewable energy
penetratedintothemarket.
NegativeElectricityPricesintheGermanElectricityMarket
39
Figure16:ScatterplotTotalElectricityLoadasafunctionofShareofRenewableEnergyPenetration
C.NetLoad
Eventhoughthetotalloadactsindependentlyfromthetotalload,itisexpectedthatthetotalnet
loadisnegativelyeffectedbyanincreasingpenetrationofvolatileenergysources(Tab.7).
Table 7: Regression Analysis Net Electricity Load as a function of Share of Renewable Energy
Penetration
In contrast to total electricity load, the share of renewable energy penetration has a strong
significantnegativeeffectonthenetelectricityload(P<0.01).Astheshareofrenewableenergy
increases,lessnetelectricityisloadedintothegrid,asillustratedintheformulabelow
XW- /01 ≈ GE, T46. HT − G8, TU4. 69U-
NegativeElectricityPricesintheGermanElectricityMarket
40
whereNLtisthenetelectricityloadandstintheshareofrenewableenergypenetratedintothe
gridduringacertainpointintime(t).
Aspreviouslyexplainednet loadexcludesthetotalrenewableenergyload,whichexplainsthe
negativetrendastheshareofrenewableenergyincreases.Asthedemandforrenewableenergy
increases,therenewableenergygenerationwilldecreaseaccordingly.Thissamepatterncanbe
observed inFigure17, for an increase in the total net electricity load the shareof renewable
energy decreases. Interestingly, net load becomes negative as the share of renewable energy
penetration increases from 23% onwards. Meaning, negative net load only occurs when
renewableenergysuppliesasignificantamountofMWhtothegrid.This istheresult froman
increaseinrenewableenergyload,directlyincreasingthesupplyofrenewableenergysources,
increasingtheshareofrenewableenergypenetration,andresultinginadownwardtrendinthe
netload.Figure17demonstratesthedemandprofileexcludingrenewableenergy,matchingthe
smootherproductiontrendoftraditionalenergysources.
Figure17:ScatterplotNetElectricityLoadasafunctionofShareofRenewableEnergyPenetration
D.TheProbabilityofNegativeElectricityPrices
Ihaveproventhattheshareofrenewableenergysignificantlyeffectselectricitypricesintheday-
ahead spot market negatively. However, the share of renewable energy penetrated into the
electricitymarketdoesnotdirectlyeffectthenegativeelectricityprice.Itremainsunclearwhat
effect renewable energy has on negative electricity prices traded in the market, raising the
questionwhethertheshareofrenewableenergypenetratedintothemarketresultinanincrease
intheprobabilityofnegativeelectricitypricesoccurring?
NegativeElectricityPricesintheGermanElectricityMarket
41
Table 8 presents the linear relationship for the probability for negative prices occurring as a
functionoftheshareofrenewableenergy.
Table 8: Regression Analysis Probability of Negative Electricity Prices as a function of Share of
RenewableEnergyPenetration
DuringtheperiodDecember2012–December2013atotalnumberof97negativetradinghours
occurred as the electricity prices dropped below €0,00/MWh. Consequently, the number of
negative electricity prices and the probability of those prices being negative per share of
renewable can be calculated.When analysing the probability of negative prices per share of
renewable(with1%increments),itcanbeconcludedthatthereisahighlyincreasingsignificant
effect(P<0.01)fortheprobabilityofnegativepricesoccurringastheshareofrenewableenergy
penetrationincreases(Tab5).Theseresultsindicatea0,063%increaseinthechanceofnegative
pricesoccurringintheelectricitymarketwhenincreasingtheshareofrenewableenergyby1%,
reflectedinhighlycorrelatedrelationship(R2=0.787).
The relationship based on an exponential model results in the construct of the following
relationship:
,U :% ≈ −4. TTZ]U.U4G:- whereP0istheprobabilitynegativeelectricitypricesshareofrenewableenergyattimetandst
intheshareofrenewableenergypenetratedintothegridattimet.
Foranincreaseintherenewableenergypenetrationthenumberofnegativepricesincreasesand
the probability of negative prices occurring increases (Fig. 18). Furthermore, the highest
frequencyofnegativepricesoccursbetweenarenewableenergypenetrationof25%and75%
(Fig.18).
NegativeElectricityPricesintheGermanElectricityMarket
42
Figure18:ScatterplotExponentialProbabilityofNegativeElectricityPricesasafunctionofShare
ofRenewableEnergyPenetration
Concluding, in the current setting of the electricity market electricity prices drop below
€0,00/MWhwith an increasing probability as the share of renewable energy penetrated into the
market increases (Fig. 18). This can be explained through the conservative set-up of the current
electricitymarket, not being able to act on volatile and unpredictable increases and decreases in
energysupplywhilethetotaldemandremainsthesame.
In2015almost194TWhofelectricitywassuppliedbyrenewableenergysources,totallyalmost
33% of the total electricity consumption in Germany (Quaschning, 2016). The probability of
negativeelectricitypricesfromoccurringundera33%shareofrenewableenergyisonly0.0014.
Meaning,undercurrentmarketconditionsitremainsmanageabletopermitnegativeprices in
theelectricitymarket.However,astheshareofrenewableenergywillincreaseinthenextdecade
thechancesofnegativeelectricitypricesincrease,restrainingtheelectricitymarket.
NegativeElectricityPricesintheGermanElectricityMarket
43
VManagerialImplications
Asrenewableenergyhasbeenintegratedintothemarketonalargerscaleforoverthelastcouple
of years, it becomes important for both research and business to gainmore insights inwhat
implicationsariseandcreateabetterunderstandingofthenatureofnegativeelectricitypricesin
particular.
Astheenergysuppliersareandwillbefocusingmoreonvolatilerenewableenergysources,they
havetobecomeawareofthelimitationsofrenewableenergyandtheireffectonthemarket. I
haveillustratedhownegativepricesoccur,whentheyoccur,andwhytheyoccurmorefrequently
asaconsequenceofanincreasingpenetrationofrenewableenergyintotheelectricitymarket.
The overall electricity price decrease as the share of renewable energy increases, shown in
previous literature as well as in Figure 3, enforcing suppliers to bid more controversially.
Suppliersshouldaccountforthisdecreasingelectricityprice,tradedonthemarket,whichwill
effecttheoverallcompanyrevenueandprofitstreamsforenergysuppliers.When,forinstance,
taking theParisClimateConference intoaccount, theshareof renewableenergywill increase
overthecomingyears,requiringenergysupplierstochangetheirbusinessmodelsinorderto
retainasustainablebusiness.
Furthermore,Ihaveproventhattheprobabilityofnegativeelectricitypricesoccurringincreases
astheshareofrenewableenergypenetratedtotheelectricitymarket increasesexponentially.
Thismeansthatsuppliersofrenewableenergyneedtobecomeawareofthefactthat,asthey
supply more renewable energy, the day-ahead market price is more probable of becoming
negative formore trading hours, again impacting the revenue stream of suppliers generated
through tradingenergyon the electricitymarket.However, asprovenbyZhanget al. (2015),
acquiringenergywastersgivessupplierstheopportunitytoretainearningsthroughcontributing
to the total energy load, receivingmoney for the amount of loadwhen electricity prices are
negative,atthecostofdirectlywasting“green”energy.
Third,suppliersneedtobecomeawareofthefactthatadailyaveragenegativepriceismorelikely
tooccurinthewintermonthswheretotalenergydemandishighandrenewableenergysupply
ismorevolatile.Eventhoughitisexpectedthatrenewableenergywillbecomemoreimportant
intheelectricitymarket, itremainsvolatileandrequires fortheback-upof traditionalenergy
power plants. A lower supply of renewable energy during winter months and a growing
dependency on renewable energy in comparison to an overall higher demand duringwinter
NegativeElectricityPricesintheGermanElectricityMarket
44
months, forces suppliers to adapt their generation and bidding strategies dependent on the
season.
Anothereffectofanincreasingrenewableenergypenetration,negativepricesoccurmoreoften
duringnightincomparisontodaytime.Thisistheeffectoflowdemandduringtheseoff-peak
hours,impactingthebalanceofdemandandsupply.Supplierswillneedtoshavethesupplyof
electricity during night hours in order to prevent electricity prices from dropping below
€0,00/MWhanddecreasingtheimpactofnegativepricesonthecompany’soverallrevenueandprofit
streams.
Inconclusion, thecurrentelectricitymarketdesignshowsseveral flaws.Energysupplierswill
havetoadapttheirstrategybasedontheabovementionedeffectsofanincreasingdependency
on renewable energy, in order to stay in business. Though, these implications raise the
importanceofalteringthecurrentdesignoftheelectricitymarketandrequirenewpolicieson
theoccurrenceofpermittingnegativeelectricityprices.Thisstudyhasproventhatthecurrent
electricitymarket cannot integrate large traces of renewable energy. Albeit the fact that the
tradedelectricityprice,structurally,willneverdropbelow€0,00/MWh,theelectricitypricedoes
decreaseandnegativepriceswilloccuronamoreregularbasis.Thisconsequencepressurises
the EuropeanCommission to research prospectivemarket structures and newnegative price
policies.Thesenewmarketstructureswillneedtobecome(more)flexibleandabletointegrate
largetracesofrenewableenergy.Thefoundationofthesenewmarketstructuresshouldbebased
ontheeffectsproveninthisstudy.Asregardstonewmarketpolicies, itshouldbequestioned
whether negative price structures have achieved the initial intended effect andwhether this
structureissustainableforthefuture.
NegativeElectricityPricesintheGermanElectricityMarket
45
VIConclusions
Thisresearch,basedondetaileddatafromtheGermanelectricitymarket,presentsanextensive
analysisontheconceptofnegativepricingasaresultofanincreasingshareofrenewableenergy
integratedintotheelectricitygrid.Acrucialfactorinmarketsareabalancedsupplyanddemand,
howeverthevolatilityofrenewableenergysourcesimpactsthisbalanceandthereforeaffectsthe
electricitymarketinseveralways.Inaddition,Iresearchedanelementintheelectricitymarket
that remains uncertain and unknown in the currentmarket, namely the occurrence negative
electricityprices.Inlinewithpreviousresearch,thisstudyshowsthatthereisadirectnegative
effectbetweenanincreasingshareofrenewableenergypenetrationonelectricityprices.Where
previousresearchstoppedanalysingnegativepricing,anextensiveprobabilityanalysisrevealed
thatoncetheshareofrenewableenergypassesthe25%(Fig.13)oncontributionoftotalenergy
supply, the probability of negative prices occurring in the electricity market increases
exponentially.
Additionalfactorsthateffectelectricitypricingandtheamountofrenewableenergypenetrated
into the market, seasonality, time of day, total load, and net load, have been taken into
consideration. Evidence suggests that the total electricity load in themarket has no effect on
negativeelectricitypricingnoronthetotalshareofrenewableenergypenetratedintothemarket.
Thisimpliesthatthecurrentmarketmodeldoesnotrestrict(renewable)energysuppliersinthe
supplyofvolatileenergytothemarket,onthecontrary.Therearenoobligationsinsupplying
extra renewable energy, even when renewable energy encounters for over 80% of the total
energy supply. When excluding the renewable energy load from the total load, and solely
considering the net load in the electricitymarket, evidence suggests that negative electricity
pricesoccurmore frequently andwithahigher chancewhen the totalnet load isnegative in
comparisontopositivenetload(15.58%versus0.78%respectively).
Furthermore,different timeframesaccount fordifferenteffectsonnegativeelectricitypricing.
When considering daily timeframes, seasonality effects in negative electricity prices become
visible. During winter, negative prices occur more often as such that the total average daily
tradingprice forelectricitycanbenegativeduringwintermonths.Whenfurther investigating
dailyelectricitypricingtrends,itisproventhatnegativepricesoccursolelyduringoff-peakhours
andmoreoftenduringnighthours.
Inconclusion,thisresearchcontributestocurrentliteraturebasedonthreeconcepts.First,the
probabilityofnegativeelectricitypricesoccurringincreasesexponentiallyfastinthepresenceof
NegativeElectricityPricesintheGermanElectricityMarket
46
anincreasingshareofrenewableenergy.Secondly,theprobabilityofnegativepricesoccurringis
dependentontheshareofrenewableenergypenetratedintothemarket,thetotalnetload,and
thetimeoftheday.Thiscanbecalculatedthroughmultiplyingtheprobabilityofnegativeprices
occurringunder separately an increasing shareof renewableenergy, increasingnet load, and
varying time of day. Lastly, this study proves that negative prices occur mostly on off-peak
demandhoursandduringnight.
VIILimitationsandFutureWork
There remain some important limitations to this analysis, when taking the abovementioned
conclusionintoaccount.Itmustbenotedthatthisanalysis issolelyconductedontheGerman
electricitymarket.Thismarketisknownforanoverallhighintegrationofrenewableenergyinto
theelectricitygrid.Inorderforthepresentedresultsinthisresearchtoholdforgeneralizability,
other electricitymarkets should be researched such as the Dutch, French, and United States
electricity markets. Furthermore, taking different demographical variables such as average
country demand and supply ratio’s into account, might change the significance levels of the
resultspresentedinthisstudy.
In addition, due to the complex structure of negative pricing and time limitations, itwas not
possibletocontrolforotherfactorsimpactingtheelectricitymarket,suchastheefficiencyofthe
transmissionanddistributionsystem,theoverallpoliticalenvironmentinEurope,andpricesof
fossil fuels. As the energy supply highly depends on prices of fossil fuels, the fluctuations in
electricitypricescouldalsooccurasaresultfromtheseprices.Thissameconclusionholdsfor
politicalintervention,wheretheEuropeanCommissioncurrentlypressurisesitsMemberStates
toincreasetheoverallcontributionofrenewableenergysourcesofthetotalenergygeneration
and supply. Other European obligations aswell as local political incentivesmight change the
resultsinthisstudy.Itstillremainsuncertainwhetherthesefactorsinfluencetheoccurrenceof
andthepatterninnegativeelectricityprices.
Finally,eventhoughreal-timedatahasbeenusedinthisresearch,theempiricalsettingofthis
studymighthavenot fullyreflected thereal-worldelectricitymarket.Consequently, real-time
direct effects of renewable energy increases on the (negative) electricity price could not be
measured.Anotherconcernwiththedatausedforthisstudy,istheshorttimeperiodthathas
been chosen. All results are based on 13months of electricity and price traces, affecting the
significanceofseveralresultssuchasthedirecteffectsonnegativepricesingeneral.Inorderto
increasethesignificancelevelsandrepresentativenessoftheeffectsonnegativeprices,itwillbe
NegativeElectricityPricesintheGermanElectricityMarket
47
necessary to increase the time horizon of the research and research more than 97 negative
electricitytradinghours.
While this studymakes several contributions to existing literature, negative electricity prices
remain a newphenomenon in the electricitymarket and is insufficiently researched up until
today.Thesustainabilityofnegativeelectricitypricesinthecurrentelectricitymarketstrategy
needstobeassessedthroughanincreasingflexibilityanalysis,inordertoestablishwhetherit
remainslucrativetoallowelectricitypricestobenegative.Assuchthisresearchsetsthestone
forfurtherresearchontheoccurrenceofnegativepricesunderanincreasingrenewableenergy
penetrationandtheanalysisonthesustainabilityofpermittingnegativeprices.
Furthermore,thestrategyofallowingfornegativeelectricitytradinghoursmustbeassessedin
comparisontootherstrategiesthatareabletoencounterformarketimbalancesthatarisefrom
anincreasingrenewableenergypenetration.Severalstrategiesthatcanbetakenintoaccountare
peak-shaving,fromademand-sideperspective,geographicaldiversity,andstoragepossibilities,
fromasupply-sideperspective.
Lastly, including new sources of electricity supply, such as access electric vehicle electricity
generation,andassesstheeffectofthesenewsourcesofenergygenerationonnegativeprices
willbeinterestingtoanalyse.Thesenewsourcesofenergycaneffectasupplier’sbusinessmodel
(Kahlen,2013).However, itmustbe furtherresearchedwhether thesenewsourcesofenergy
penetrated into the electricity market also effects the occurrence of negative pricing in the
electricitymarketingeneral.
NegativeElectricityPricesintheGermanElectricityMarket
48
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http://www.nera.com/publications/archive/2015/kommentierung-des-24-eeg-
.html[Accessed16January2016].
Huntowski,F.A.,Patterson,A.,Schnitzer,M., (2012)Negativeelectricitypricesandthe
productiontaxcredit–Whywindproducerscanpayustotaketheirpowerandwhy
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Jaehnert, S. (2012) Integration of RegulatingPowerMarkets inNorthernEurope, PhD,
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Nicolosi,M.,(2010)Windpowerintegrationandpowersystemflexibility–Anempirical
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NegativeElectricityPricesintheGermanElectricityMarket
52
IXAppendix
I.ListofFigures1–18
Name Title Page
Figure1 SubmarketsintheGermanElectricityMarket 8
Figure2 EffectofRenewableEnergyonAverageSpotPrice 19
Figure3
Scatterplot Daily Day-Ahead Electricity Price as a function of Share of
RenewableEnergyPenetration 21
Figure4 ScatterplotTotalElectricityLoadasafunctionofTimeofDay 22
Figure5 ScatterplotNetElectricityLoadasafunctionofTimeofDay 23
Figure6 ConceptualModel 24
Figure7
Scatterplot Daily Day-Ahead Electricity Price during December 2012 –
December2013 26
Figure8 ScatterplotDay-AheadElectricityPriceasafunctionofTimeofDay 27
Figure9
ScatterplotNegativeDay-AheadElectricityPriceasafunctionofTimeof
Day 28
Figure10 ScatterplotDay-AheadElectricityPriceasafunctionofTotalLoad 30
Figure11 ScatterplotNegativeDay-AheadElectricityPriceasafunctionofTotalLoad 31
Figure12 ScatterplotDay-AheadElectricityPriceasafunctionofNetLoad 32
Figure13 ScatterplotNegativeDay-AheadElectricityPriceasafunctionofNetLoad 33
Figure14
ScatterplotDay-AheadElectricityPriceasafunctionofShareofRenewable
EnergyPenetration 35
Figure15
ScatterplotNegativeDay-AheadElectricityPriceasafunctionofShareof
RenewableEnergyPenetration 37
Figure16
Scatterplot Total Electricity Load as a function of Share of Renewable
EnergyPenetration 39
Figure17
ScatterplotNetElectricityLoadasafunctionofShareofRenewableEnergy
Penetration 40
Figure18
Scatterplot Exponential Probability of Negative Electricity Prices as a
functionofShareofRenewableEnergyPenetration 42
NegativeElectricityPricesintheGermanElectricityMarket
53
II.ListofTables1–6
Name Title Page
Table1
Regression Analysis Day-Ahead Electricity Price as a function of Total
ElectricityLoad 29
Table2
Regression Analysis Day-Ahead Electricity Price as a function of Net
ElectricityLoad 31
Table3
RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionof
NetElectricityLoad 33
Table4
RegressionAnalysisDay-AheadElectricityPriceasafunctionofShareof
RenewableEnergyPenetration 34
Table5
RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionof
ShareofRenewableEnergyPenetration 36
Table6
Regression Analysis Total Electricity Load as a function of Share of
RenewableEnergyPenetration 38
Table7
Regression Analysis Net Electricity Load as a function of Share of
RenewableEnergyPenetration 39
Table8
RegressionAnalysisProbabilityofNegativeElectricityPricesasafunction
ofShareofRenewableEnergyPenetration 41
NegativeElectricityPricesintheGermanElectricityMarket
54
III.TableofNotationEquations
Variable Explanation
st ShareofRenewableEnergyPenetration
GR GenerationofRenewableEnergy(MWh)
GT GenerationofTotalEnergy(MWh)
Pt Day-AheadElectricityPrice(€/MWh)
Pneg,t NegativeDay-AheadElectricityPrice(€/MWh)
PSt Highestbiddingpricesupplier(€/MWh)
PNt Priceinthenetwork(€/MWh)
t Isthespecificchosentimerange
TLt TotalElectricityLoad(MWh)
TL TotalLoad(MW)
NLt NetElectricityLoad(MWh)
NL NetLoad(MW)
RL RenewableLoad(MW)
P0 ProbabilityNegativePrices
NegativeElectricityPricesintheGermanElectricityMarket
55
IV.RegressionTables
Idetermineallresultsbasedonregressionanalysesonthecollecteddataset.Belowallsummary
results are laid out with corresponding significance levels for all regressions. The tables are
orderedfollowingthesequenceofresultsinchapter4.
TableA.1:RegressionAnalysisDailyDay-AheadElectricityPriceasafunctionofShareofRenewable
EnergyPenetration
TableA.2:RegressionAnalysisTotalElectricityLoadasafunctionofTimeofDay
NegativeElectricityPricesintheGermanElectricityMarket
56
TableA.3:RegressionAnalysisNetElectricityLoadasafunctionofTimeofDay
TableA.4:RegressionAnalysisDay-AheadElectricityPriceasafunctionofTimeofDay
TableA.5:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofTimeofDay
NegativeElectricityPricesintheGermanElectricityMarket
57
TableA.6:RegressionAnalysisDay-AheadElectricityPriceasafunctionofTotalLoad
TableA.7:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofTotalLoad
TableA.8:RegressionAnalysisDay-AheadElectricityPriceasafunctionofNetLoad
NegativeElectricityPricesintheGermanElectricityMarket
58
TableA.9:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofNetLoad
TableA.10:RegressionAnalysisDay-AheadElectricityPriceasa functionofShareofRenewable
EnergyPenetration
NegativeElectricityPricesintheGermanElectricityMarket
59
Table A.11: Regression Analysis Negative Day-Ahead Electricity Price as a function of Share of
RenewableEnergyPenetration
TableA.12:RegressionAnalysisTotalElectricityLoadasafunctionofShareofRenewableEnergy
Penetration
NegativeElectricityPricesintheGermanElectricityMarket
60
TableA.13:RegressionAnalysisNetElectricityLoadasa functionofShareofRenewableEnergy
Penetration
TableA.14:RegressionAnalysisProbabilityofNegativeElectricityPricesasafunctionofShareof
Renewable
NegativeElectricityPricesintheGermanElectricityMarket
61
V.Rcode
library(data.table)
library("SDSFoundations",lib.loc="~/Library/R/3.2/library")
library(igraph)
library(ggplot2)
library(curl)
library(stargazer)
library(data.table)
library(ggplot2)
#ThesisGridLoadData
#Transnet_BW
mnetzlast_ist_prognose_2012_12 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2012_12.csv")
mnetzlast_ist_prognose_2013_01 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_01.csv")
mnetzlast_ist_prognose_2013_02 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_02.csv")
mnetzlast_ist_prognose_2013_03 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_03.csv")
mnetzlast_ist_prognose_2013_04 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_04.csv")
mnetzlast_ist_prognose_2013_05 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_05.csv")
mnetzlast_ist_prognose_2013_06 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_06.csv")
mnetzlast_ist_prognose_2013_07 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_07.csv")
mnetzlast_ist_prognose_2013_08 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_08.csv")
mnetzlast_ist_prognose_2013_09 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_09.csv")
mnetzlast_ist_prognose_2013_10 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_10.csv")
mnetzlast_ist_prognose_2013_11 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_11.csv")
NegativeElectricityPricesintheGermanElectricityMarket
62
mnetzlast_ist_prognose_2013_12 <-
read.csv("~/Downloads/mnetzlast_ist_prognose_2013_12.csv")
#Amprion
Amprion <- as.data.table(read.csv("~/Documents/Business Information
Management/Thesis/Data/Net_Load_Amprion.csv"))
setnames(Amprion, old = c("Datum", "Uhrzeit", "Vertikale.Netzlast"), new = c("Date", "Time",
"NetLoad"))
#mergeTransnet_BW
Transnet_BW <- as.data.table(rbind(mnetzlast_ist_prognose_2012_12,
mnetzlast_ist_prognose_2013_01))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_02))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_03))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_04))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_05))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_06))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_07))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_08))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_09))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_10))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_11))
Transnet_BW<-as.data.table(rbind(Transnet_BW,mnetzlast_ist_prognose_2013_12))
Transnet_BW<-Transnet_BW[order(Date.from,Time.from,Date.to,Time.to),]
Transnet_BW$Date.from<-NULL
Transnet_BW$Time.from<-NULL
Transnet_BW$Date.to<-NULL
Transnet_BW$Time.to<-NULL
Transnet_BW$Projection..MW.<-NULL
Transnet_BW$X<-NULL
Transnet_BW[,Date:=Amprion$Date]
Transnet_BW[,Time:=Amprion$Time]
setnames(Transnet_BW,old=c("Actual.value..MW.",“Actual.value.MW.net”,"Date","Time"),new
=c(“TotalLoad”,"NetLoad","Date","Time"))
NegativeElectricityPricesintheGermanElectricityMarket
63
#Tennet
TenneT <- as.data.table(read.csv("~/Documents/Business Information
Management/Thesis/Data/Net_Load_TenneT.csv"))
TenneT[,Date:=Amprion$Date]
TenneT[,Time:=Amprion$Time]
TenneT$Position<-NULL
setnames(TenneT,old=c("Vertical.grid.load..MW.",“Vertical.grid.net.load.MW”,"Date","Time"),
new=c(“TotalLoad”,"NetLoad","Date","Time"))
#50Hertz
`50Hertz` <- read.csv("~/Documents/Business Information
Management/Thesis/Data/Net_Load_50Herzt_correct.csv",sep=";")
`50Hertz`$Time<-paste(`50Hertz`$Von,`50Hertz`$bis,sep="-")
`50Hertz`$Von<-NULL
`50Hertz`$bis<-NULL
setnames(`50Hertz`,old=c("Datum",“Vertikale.Last”,"Vertikale.Netzlast..MW.","Time"),new=
c("Date",“TotalLoad”,"NetLoad","Time"))
NetLoad<-merge(TenneT,Transnet_BW,by=c("Date","Time"))
setnames(NetLoad, old = c("Date", "Time", "NetLoad.x", "NetLoad.y", “TotalLoad.x”,
“TotalLoad.y”), new = c("Date", "Time", "NL_TenneT", "NL_Transnet_BW", "TL_TenneT",
"TL_Transnet_BW"))
NetLoad<-merge(NetLoad,`50Hertz`,by=c("Date","Time"))
setnames(NetLoad, old = c("Date", "Time", "NL_TenneT", "NL_Transnet_BW", "TL_TenneT",
"TL_Transnet_BW", "NetLoad", “TotalLoad), new = c("Date", "Time", "NL_TenneT",
"NL_Transnet_BW","TL_TenneT","TL_Transnet_BW","NL_50Hertz","TL_50Hertz"))
NetLoad<-merge(NetLoad,Amprion,by=c("Date","Time"))
setnames(NetLoad, old = c("Date", "Time", "NL_TenneT", "NL_Transnet_BW", "TL_TenneT",
"TL_Transnet_BW", "NL_50Hertz", "TL_50Hertz", "NetLoad", “TotalLoad”), new = c("Date",
"Time", "NL_TenneT", "NL_Transnet_BW", "TL_TenneT", "TL_Transnet_BW", "NL_50Hertz",
"TL_50Hertz","NL_Amprion",“TL_Amprion”))
#TotalLoad
NetLoad<-as.data.frame(NetLoad)
NetLoad["TotalLoad"]<-NA
NegativeElectricityPricesintheGermanElectricityMarket
64
NetLoad$TotalLoad <- (NetLoad$TL_TenneT + NetLoad$TL_Transnew_BW +
NetLoad$TL_`50Hertz`+NetLoad$TL_Amprion)
NetLoad$NetLoad <- (NetLoad$NL_TenneT + NetLoad$NL_Transnew_BW +
NetLoad$NL_`50Hertz`+NetLoad$NL_Amprion)
NetLoad$Date<-as.character(NetLoad$Date)
NetLoad$Date<-as.Date(NetLoad$Date,"%m.%d.%Y")
NetLoad$Date<-format.Date(NetLoad$Date,"%d/%m/%Y")
NetLoad<-as.data.table((NetLoad))
#TimeHorizon(15minutes)
T<-(24*396*4)
#NewTimeUnit(1hour=>4x15minutesinonehour)
Tnew<-4
#Tablewiththefirstcolumntime
dt.load<-data.table(minutes=1:(24*396*4))
#Addingthesecondcolumn(creatinghoursfrom15minutes)
dt.load[,Newres:=trunc((minutes-1)/Tnew+1)]
#Addingallothercolumns
dt.load[,TotalLoad:=NetLoad$TotalLoad]
#Changingthetimeresolution
dt.load[,TotalLoad_New:=mean(TotalLoad),by=Newres]
#Creatingthefinaldatatablewithnon-reduntantvalues
dt.load2<-data.table(hours=dt.load$Newres)
dt.load2[,TotalLoad:=dt.load$TotalLoad_New]
dt.load2<-unique(dt.load2)
dt.load2[,TimeOfDay:=Price_Data$TimeOfDay]
dt.load2<-merge(dt4,dt.load2,by=c("hours","TimeOfDay"))
NegativeElectricityPricesintheGermanElectricityMarket
65
dt.load2[,`:=`(totalPredWind=NULL,totalPredSun=NULL,totalActWind=NULL,totalActSun=
NULL,
totalActRenew = NULL, totalPredRenew = NULL, volume = NULL, sharePredWind =
NULL,
sharePredSun=NULL,shareActWind=NULL,shareActSun=NULL,sharePredRenew=
NULL)]
dt.load2[,LoadRenew:=dt3$totalActRenew]
dt.load2[,NetLoad:=TotalLoad-LoadRenew]
#Opendatasets
Final_Data <- read.delim("~/Documents/Business Information
Management/Thesis/Data/Final_Data.txt")
Price_Data <- read.delim("~/Documents/Business Information
Management/Thesis/Data/Price_Data.txt")
Volume_Data <- read.delim("~/Documents/Business Information
Management/Thesis/Data/Volume_Data.txt")
#TotalPredicted&Actualvariables
Final_Data$totalPredWind <- Final_Data$TenneT_Predicted.Wind..MW. +
Final_Data$TransnetBW_Predicted.Wind..MW. + Final_Data$X50Hertz_Predicted.Wind..MW. +
Final_Data$Amprion_Predicted.Wind..MW.
Final_Data$totalPredSun <- Final_Data$TenneT_Predicted.Sun..MW. +
Final_Data$TransnetBW_Predicted.Sun..MW. + Final_Data$X50Hertz_Predicted.Sun..MW. +
Final_Data$Amprion_Predicted.Sun..MW.
Final_Data$totalActWind <- Final_Data$TenneT_Actual.Wind..MW. +
Final_Data$TransnetBW_Actual.Wind..MW. + Final_Data$X50Hertz_Actual.Wind..MW. +
Final_Data$Amprion_Actual.Wind..MW.
Final_Data$totalActSun <- Final_Data$TenneT_Actual.Sun..MW. +
Final_Data$TransnetBW_Actual.Sun..MW. + Final_Data$X50Hertz_Actual.Sun..MW. +
Final_Data$Amprion_Actual.Sun..MW.
#TimeHorizon(15minutes)
T<-(24*396*4)
NegativeElectricityPricesintheGermanElectricityMarket
66
#NewTimeUnit(1hour=>4x15minutesinonehour)
Tnew<-4
#Tablewiththefirstcolumntime
dt<-data.table(minutes=1:(24*396*4))
print(dt)
#Addingthesecondcolumn(creatinghoursfrom15minutes)
dt[,Newres:=trunc((minutes-1)/Tnew+1)]
#Addingthethirdcolumn(TotalPredWind)
dt[,totalPredWind:=Final_Data$totalPredWind]
#Addingthefourthcolumn(TotalPredSun)
dt[,totalPredSun:=Final_Data$totalPredSun]
#Addingthefifthcolumn(TotalActWind)
dt[,totalActWind:=Final_Data$totalActWind]
#Addingthefourthcolumn(TotalActSun)
dt[,totalActSun:=Final_Data$totalActSun]
#Changingthetimeresolution
dt[,totalPredWind_New:=mean(totalPredWind),by=Newres]
dt[,totalPredSun_New:=mean(totalPredSun),by=Newres]
dt[,totalActWind_New:=mean(totalActWind),by=Newres]
dt[,totalActSun_New:=mean(totalActSun),by=Newres]
#Creatingthefinaldatatablewithnon-reduntantvalues
dt2<-data.table(hours=dt$Newres)
dt2[,totalPredWind:=dt$totalPredWind_New]
dt2[,totalPredSun:=dt$totalPredSun_New]
dt2[,totalActWind:=dt$totalActWind_New]
dt2[,totalActSun:=dt$totalActSun_New]
setkey(dt2,NULL)
NegativeElectricityPricesintheGermanElectricityMarket
67
#Creatinganewdatatablewithuniquevalues
dt3<-unique(dt2)
dt3[,TimeOfDay:=Price_Data$TimeOfDay]
#AllshareActRenewNAvaluesto0
for(iinseq_along(dt3))set(dt3,i=which(is.na(dt3[[i]])),j=i,value=0)
#Deletingthelastdayofthedatatable
dt3<-dt3[-c(9481:9504),]
#AddingafithcolumnwiththetotalpredeictedamountofRenewables
dt3[,totalPredRenew:=(dt3$totalPredWind+dt3$totalPredSun)]
#AddingasixthcolumnwiththetotalactualamountofRenewables
dt3[,totalActRenew:=(dt3$totalActWind+dt3$totalActSun)]
#Addingaseventhcolumnwiththepricedata
dt3[,price:=Price_Data$Price]
#Addingaeighthcolumnwiththevolumedata
dt3[,volume:=Volume_Data$Volume]
#Addinganinethcolumn(%PredWind)
dt3[,sharePredWind:=(dt3$totalPredWind/dt3$volume)]
#Addingatenthcolumn(%PredSun)
dt3[,sharePredSun:=(dt3$totalPredSun/dt3$volume)]
#Addingaeleventhcolumn(%ActWind)
dt3[,shareActWind:=(dt3$totalActWind/dt3$volume)]
#Addingatwelvethcolumn(%ActSun)
dt3[,shareActSun:=(dt3$totalActSun/dt3$volume)]
#Addingathirteenthcolumn(%PredRenewables)
dt3[,sharePredRenew:=(dt3$totalPredRenew/dt3$volume)]
NegativeElectricityPricesintheGermanElectricityMarket
68
#Addingafourtheenthcolumn(%ActRenewables)
dt3[,shareActRenew:=(dt3$totalActRenew/dt3$volume)]
#Solvemissingdata,anewdatasetwithoutmissingdata
dt4<-na.omit(dt3)
#Summaryanalysis
summary(dt4)
#GraphicalSummary
plot(sort(dt4$totalPredWind))
hist(dt4$totalPredWind)
plot(density(dt4$totalPredWind))
plot(sort(dt4$totalPredSun))
hist(dt4$totalPredSun)
plot(density(dt4$totalPredSun))
plot(sort(dt4$totalActWind))
hist(dt4$totalActWind)
plot(density(dt4$totalActWind))
plot(sort(dt4$totalActSun))
hist(dt4$totalActSun)
plot(density(dt4$totalActSun))
#ScatterplotshareActWindvsPrice
plot(price~shareActWind,dt4,main='ScatterplotShareWindPowerPenetrationvsDay-Ahead
ElectrictyPrice',xlab='ShareWindPowerPenetration',ylab='ElectricityPrice($/MWh)',xlim
=c(0.0,0.25),ylim=c(-50,150),col='grey')
abline(lm(dt4$price~dt4$shareActWind),col='black')
summary.lm(price~schareActWin,dt4)
attributes(summary(lm(dt4$price,dt4$schareActWin)))
NegativeElectricityPricesintheGermanElectricityMarket
69
#CorrelationshareActWindvsPrice
cor(dt4$shareActWind,dt4$price)
#ScatterplotshareActSunvsPrice
plot(price~shareActSun,dt4,main= 'ScatterplotShareSunPowerPenetrationvsDay-Ahead
ElectrictyPrice',xlab='ShareSunPowerPenetration',ylab='ElectricityPrice($/MWh)',xlim=
c(0.0,0.8),ylim=c(-50,150),col='grey')
abline(lm(dt4$price~dt4$shareActSun),col='black')
#CorrelationshareActSunvsPrice
cor(dt4$shareActSun,dt4$price)
#ScatterplotshareActRenewvsPrice
ggplot(dt4,aes(x=shareActRenew,y=price),xlim=c(0.0,0.8),ylim(c(-50,150)))+
labs(list(title='Day-AheadElectrictyPricevs\nShareRenewableEnergyPenetration',
x='Share(%)RenewableEnergyPenetration(x100)',
y='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(method="lm",se=FALSE,col='black')+
annotate("text",x=0.75,y=50,label="P(€/MWh)≈47.036-32.383s")
#LinearregressionshareActRenewvsPrice
linFit(dt4$shareActRenew,dt4$price)
#PolynomialregressionsshareActRenewvsPriceasindexofvolume
y<-dt4$price
x<-dt4$shareActRenew
x2<-x^2
x3<-x^3
x4<-x^4
lm(y~x)
anova(lm(y~x))
lm(y~x+x2)
anova(lm(y~x+x2))dt
NegativeElectricityPricesintheGermanElectricityMarket
70
xv<-seq(min(x),max(x),0.01)
yv<-predict((lm(y~x+x2)),list(x=xv,x2=xv^2))
lines(xv,yv,col="green")
lm(y~x+x2+x3)
anova(lm(y~x+x2+x3))
xv<-seq(min(x),max(x),0.01)
yv<-predict((lm(y~x+x2+x3)),list(x=xv,x2=xv^2,x3=xv^3))
lines(xv,yv,col="red")
plot((lm(y~x+x2)),which=1)#nooveralstrongpatternintheresiduals
plot((lm(y~x+x2)),which=2)#distributionappearsrelativelynormal
summary(lm(y~x))
summary(lm(y~x+x2))
#Currentmodels
ylm<-47.04-(32.25*x)
ypr<-45.71-(20.87*x)-(16.52*sqrt(x))
#dtwithsolelynegativeprices
dt5<-dt4[dt4$price<0,]
dt5[,`:=`(totalPredWind=NULL,totalPredSun=NULL,totalActWind=NULL,totalActSun=NULL,
totalActRenew = NULL, totalPredRenew = NULL, volume = NULL, sharePredWind =
NULL,
sharePredSun=NULL,shareActWind=NULL,shareActSun=NULL,sharePredRenew=
NULL)]
#Scatterplotsharevspricesolelynegatives
ggplot(dt5,aes(x=shareActRenew,y=price),xlim=c(0.0,0.8))+
labs(list(title='NegativeDay-AheadElectrictyPricevs\nShareRenewableEnergyPenetration',
x='Share(%)RenewableEnergyPenetration(x100)',
y='NegativeElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(method="lm",se=FALSE,col='black')+
NegativeElectricityPricesintheGermanElectricityMarket
71
annotate("text",x=0.6,y=-75,label="P(€/MWh)≈-131.059+154.030s")
#PolynomialregressionsshareActRenewvsPriceasindexofvolume
ggplot(dt5,aes(x=price))+geom_histogram()
yn<-dt5$price
xn<-dt5$shareActRenew
xn2<-xn^2
xn3<-xn^3
xn4<-xn^4
lm(yn~xn)
anova(lm(yn~xn))
lm(yn~xn+xn2)
anova(lm(yn~xn+xn2))
#Negativepricingmodels
ynlm<--130.6+(153.7*xn)
ynpr<--81.90-(28.37*xn)+(160.17*sqrt(xn))
summary(regression.1<-lm(price~shareActRenew,data=dt4))
summary(regression.2<-lm(price~shareActRenew,data=dt5))
stargazer(regression.1,
type="text",omit.stat=c('f'))
stargazer(regression.2,
type="text",omit.stat=c('f'))
#DensityFunction
price01<-dt5[price>-1,price]
priceNeg<-dt4[price<0,price]
p0<-37/95
#Freqcounterfornegprice
install.packages("plyr")
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library(plyr)
dt6<-dt4[,list(price,shareActRenew,TimeOfDay)]
dt6<-dt6[order(shareActRenew)]
dt6[,count:=0]
price<-dt6$price
share<-dt6$shareActRenew
freq<-numeric(100)
total_no<-numeric(100)
for(iin1:nrow(dt6)){
k=ceiling(share[i]*100)
total_no[k]=total_no[k]+1
freq[k]=freq[k]+(price[i]<0)
}
freq<-as.data.frame(freq)
total_no<-as.data.frame(total_no)
Freq.Total<-as.data.frame(freq/total_no)
Freq.Total<-as.data.table(rapply(Freq.Total,f=function(x)ifelse(is.nan(x),0,x),how="replace"))
Freq.Total[,percentage:=seq(1,100)]
Log.Freq.Total <- as.data.table(rapply(log(Freq.Total), f=function(x) ifelse(is.infinite(x),0,x),
how="replace"))
Log.Freq.Total[,percentage:=seq(1,100)]
Log.Freq.Total_new<-Log.Freq.Total[Log.Freq.Total$freq!=0.0000000,]
#exponentialmodelsharerenew>freqnegativepricespershare
summary(exp.model<-lm(freq~percentage,data=Log.Freq.Total_new))
stargazer(exp.model,
type="text",omit.stat=c('f'))
ggplot(Log.Freq.Total_new,aes(x=percentage,y=freq),ylim(c(-50,150)))+
labs(list(title='ProbabilityNegativeElectrictyPricevs\nShareRenewableEnergyPenetration',
x='ShareRenewableEnergyPenetration(%)',
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73
y='ProbabilityofNegativeElectrictyPrice(log)'))+
geom_point(col='grey')+
geom_smooth(method='lm',se=FALSE,col='black')+
annotate("text",x=40,y=-2.5,label="Pneg(R%)≈-6.990e^-0.063(R%)")
#datatablefornetloadvsnegpricefrequency
#TimeHorizon(1hour)=>mustbeconvertedtooneday
T2<-(24*396)
#NewTimeUnit(1day=>24hoursinoneday)
T2new<-24
#Tablewiththefirstcolumntime
dt7<-dt4[,list(hours)]
#Addingthesecondcolumn(creatinghoursfrom15minutes)
dt7[,Newres:=trunc((hours-1)/T2new+1)]
#Addingthethirdcolumn(TotalPredWind)
dt7[,ShareActRenew:=dt4$shareActRenew]
#Addingthefourthcolumn(TotalPredSun)
dt7[,price:=dt4$price]
#Changingthetimeresolution
dt7[,ShareActRenew_New:=mean(ShareActRenew),by=Newres]
dt7[,price_new:=mean(price),by=Newres]
dt7$hours<-NULL
dt7<-unique(dt7,by="Newres")
dt7$ShareActRenew<-NULL
dt7$price<-NULL
setnames(dt7, old = c("Newres", "ShareActRenew_New", "price_new"), new = c("day",
"shareActRenew","price"))
#ScatterplotshareActRenewvsPrice,fortimeres=day
ggplot(dt7,aes(x=shareActRenew,y=price),xlim=c(0.0,0.8),ylim=c(-50,150))+
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74
labs(list(title='Day-AheadElectrictyPricevs\nShareRenewableEnergyPenetration',
x='Share(%)RenewableEnergyPenetration(x100)',
y='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(method='lm',se=FALSE,col='black')+
annotate("text",x=0.25,y=0,label="P(€/MWh)≈56.540-65.117s")
#Onlyfournegativepricesintotal!
summary(regression.3<-lm(price~shareActRenew,data=dt7))
stargazer(regression.3,
type="text",omit.stat=c('f'))
#ScatterplotdayvsPrice,fortimeres=day
install_github("ellisp/ggseas/pkg")
library(ggseas)
ggplot(dt7,aes(x=day,y=price),ylim=c(-50,150))+
labs(list(x='DayofYear',y='ElectricityPrice(€/MWh)'))+
ggtitle("ElectricityPrice(€/MWh)\nPerdiodDec2012-Dec2013")+
geom_line(col='grey')+
stat_smooth(col='black')+
scale_colour_manual(guide=guide_legend(),breaks=c("Day","Average"),
values=c("grey","black"))+
theme(legend.position=c(0,300))
#Endofyearpricedrops(Dec2012vsDec2013)
#ScatterplotTimeOfDayvsPrice
ggplot(dt4,aes(x=TimeOfDay,y=price),ylim=c(-50,150),xlim=c(1,24))+
labs(list(title='Day-AheadElectrictyPriceperHour',
x='TimeofDay(inhours)',
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75
y='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(col='black')
summary(regression.4<-lm(price~TimeOfDay,data=dt4))
stargazer(regression.4,
type="text",omit.stat=c('f'))
#Freqcounterfornegprice
install.packages("plyr")
library(plyr)
dt8<-dt4[,list(price,TimeOfDay)]
dt8<-dt8[order(TimeOfDay)]
dt8[,count:=0]
PriceNeg<-dt8$price
TimeDayNeg<-dt8$TimeOfDay
FreqNeg<-numeric(24)
Total_NoNeg<-numeric(24)
for(iin1:nrow(dt8)){
k=ceiling(TimeDayNeg[i])
Total_NoNeg[k]=Total_NoNeg[k]+1
FreqNeg[k]=FreqNeg[k]+(PriceNeg[i]<0)
}
FreqNeg<-as.data.frame(FreqNeg)
Total_NoNeg<-as.data.frame(Total_NoNeg)
Freq.Total.Neg<-as.data.frame(FreqNeg/Total_NoNeg)
#ScatterplotTimeofdayvsNeg.price
ggplot(dt5,aes(x=TimeOfDay,y=price))+
labs(list(title='NegativeDay-AheadElectrictyPriceperHour',
x='TimeofDay(hour)',
y='NegativeElectricityPrice(€/MWh)'))+
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76
geom_point(col='grey')+
geom_smooth(col='black')
summary(regression.5<-lm(price~TimeOfDay,data=dt5))
stargazer(regression.5,
type="text",omit.stat=c('f'))
#ScatterplotTotalLoadvs.Price
ggplot(dt.load2,aes(x=TotalLoad,y=price),ylim=c(-50,150))+
labs(list(title='Day-AheadElectrictyPricevsTotalLoad',
x='TotalLoad(MWh)',
y='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(method='lm',se=FALSE,col='black')
summary(regression.6<-lm(price~TotalLoad,data=dt.load2))
stargazer(regression.6,
type="text",omit.stat=c('f'))
#ScatterplotNetLoadvs.Price
ggplot(dt.load2,aes(x=NetLoad,y=price),ylim=c(-50,150))+
labs(list(title='Day-AheadElectrictyPricevsNetLoad',
xlab='NetLoad(MWh)',
ylab='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')+
geom_smooth(method='lm',se=FALSE,col='black')+
annotate("text",x=30000,y=60,label='P(€/MWh)=26.059+0.0005l')+
geom_vline(xintercept=0,col='gray44')+
geom_hline(yintercept=0,col='gray44')
summary(regression.7<-lm(price~NetLoad,data=dt.load2))
stargazer(regression.7,
type="text",omit.stat=c('f'))
#ScatterplotTotalLoadvs.NegativePrice
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dt.loadNeg<-merge(dt.load2,dt5,by="hours")
dt.loadNeg[,`:=`(TimeOfDay.y=NULL,price.y=NULL,shareActRenew.y=NULL)]
ggplot(dt.loadNeg,aes(x=TotalLoad,y=price.x))+
labs(list(title='NegativeDay-AheadElectrictyPricevsTotalLoad',
x='TotalLoad(MWh)',
y='NegativeElectricityPrice(€/MWh)'))+
geom_point(col='grey')
summary(regression.8<-lm(price.x~TotalLoad,data=dt.loadNeg))
stargazer(regression.8,
type="text",omit.stat=c('f'))
#ScatterplotNetLoadvs.NegativePrice
ggplot(dt.loadNeg,aes(x=NetLoad,y=price.x))+
labs(list(title='NegativeDay-AheadElectrictyPricevsNetLoad',
x='NetLoad(MWh)',
y='ElectricityPrice(€/MWh)'))+
geom_point(col='grey')
summary(regression.9<-lm(price.x~NetLoad,data=dt.loadNeg))
stargazer(regression.9,
type="text",omit.stat=c('f'))
#ScatterplotTimeofDayvs.TotalLoad
ggplot(dt.load2,aes(x=TimeOfDay,y=TotalLoad))+
labs(list(title='TotalLoadatTimeofDay',
x='TimeofDay(hours)',
y='TotalLoad(MWh)'))+
geom_point(col='grey')+
geom_smooth(se=FALSE,col='black')
summary(regression.10<-lm(TotalLoad~TimeOfDay,data=dt.load2))
stargazer(regression.10,
type="text",omit.stat=c('f'))
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#ScatterplotTimeofDayvs.NetLoad
ggplot(dt.load2,aes(x=TimeOfDay,y=NetLoad))+
labs(list(title='NetLoadatTimeofDay',
x='TimeofDay(hours)',
y='NetLoad(MWh)'))+
geom_point(col='grey')+
geom_smooth(se=FALSE,col='black')
summary(regression.11<-lm(NetLoad~TimeOfDay,data=dt.load2))
stargazer(regression.11,
type="text",omit.stat=c('f'))
#ScatterplotShareofRenewablesvs.TotalLoad
ggplot(dt.load2,aes(x=shareActRenew,y=TotalLoad))+
labs(list(title='TotalLoadvsShareRenewableEnergyPenetration',
x='Share(%)RenewableEnergyPenetration(x100)',
y='TotalLoad(MWh)'))+
geom_point(col='grey')
summary(regression.12<-lm(TotalLoad~shareActRenew,data=dt.load2))
stargazer(regression.12,
type="text",omit.stat=c('f'))
#ScatterplotShareofRenewablesvs.NetLoad
ggplot(dt.load2,aes(x=shareActRenew,y=NetLoad))+
labs(list(title='ScatterplotShareofRenewabblesvsNetLoad',
x='Share(%)RenewableEnergyPenetration(x100)',
y='NetLoad(MWh)'))+
geom_point(col='grey')+
geom_smooth(method='lm',se=FALSE,col='black')+
annotate('text',x=0.7,y=25000,label='NL(MWh)=32964.890-31906.470s')
summary(regression.13<-lm(NetLoad~shareActRenew,data=dt.load2))
stargazer(regression.13,
type="text",omit.stat=c('f'))
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#Summaryregression
stargazer(regression.1,regression.2,regression.3,regression.4,regression.5,
regression.6,regression.7,regression.8,regression.9,
type="text",omit.stat=c('f'))
#frequencydensitynegprice,negload
dt.loadNeg2<-dt.load2[dt.load2$NetLoad<0,]
dt.loadNeg22<-dt.loadNeg2[dt.loadNeg2$price<0,]
24/154
#frequencydensitynegprice,posload
dt.load22<-dt.load2[dt.load2$NetLoad>0,]
dt.load222<-dt.load22[dt.load22$price<0,]
73/9325