Negative Electricity Prices in the German Electricity Market · 2017. 8. 30. · Negative...

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.


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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.


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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


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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


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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.


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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.


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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.


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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)


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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).


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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.


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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


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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).


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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


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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.


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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


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


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.


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


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)


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


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


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:


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.


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.


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.


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.


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


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


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


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.


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)


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.


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


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


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


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.


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).


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.


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-


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?


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


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).


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.


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


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.


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


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


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.


48

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PowerSystems:IEEETransactions,30(3):1624-1632.

Zhou, Y. S., Scheller-Wolf, A., Secomandi, N., Smith, S. (2014) Electricity Trading and

NegativePrice:Storagevs.Disposal,ManagementScience,62(3):880-898.


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


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


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


Table5

RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionof

ShareofRenewableEnergyPenetration 36

Table6

Regression Analysis Total Electricity Load as a function of Share of


Table7

Regression Analysis Net Electricity Load as a function of Share of


Table8

RegressionAnalysisProbabilityofNegativeElectricityPricesasafunction

ofShareofRenewableEnergyPenetration 41


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


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


56

TableA.3:RegressionAnalysisNetElectricityLoadasafunctionofTimeofDay

TableA.4:RegressionAnalysisDay-AheadElectricityPriceasafunctionofTimeofDay

TableA.5:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofTimeofDay


57

TableA.6:RegressionAnalysisDay-AheadElectricityPriceasafunctionofTotalLoad

TableA.7:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofTotalLoad

TableA.8:RegressionAnalysisDay-AheadElectricityPriceasafunctionofNetLoad


58

TableA.9:RegressionAnalysisNegativeDay-AheadElectricityPriceasafunctionofNetLoad

TableA.10:RegressionAnalysisDay-AheadElectricityPriceasa functionofShareofRenewable

EnergyPenetration


59

Table A.11: Regression Analysis Negative Day-Ahead Electricity Price as a function of Share of


TableA.12:RegressionAnalysisTotalElectricityLoadasafunctionofShareofRenewableEnergy

Penetration


60

TableA.13:RegressionAnalysisNetElectricityLoadasa functionofShareofRenewableEnergy

Penetration

TableA.14:RegressionAnalysisProbabilityofNegativeElectricityPricesasafunctionofShareof

Renewable


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")
























62



#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<-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"))


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


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"))


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)


66

#NewTimeUnit(1hour=>4x15minutesinonehour)

Tnew<-4


dt<-data.table(minutes=1:(24*396*4))

print(dt)


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]


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)


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)]


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)))


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


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',


y='NegativeElectricityPrice(€/MWh)'))+


geom_smooth(method="lm",se=FALSE,col='black')+


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))


stargazer(regression.1,

type="text",omit.stat=c('f'))



#DensityFunction

price01<-dt5[price>-1,price]

priceNeg<-dt4[price<0,price]

p0<-37/95

#Freqcounterfornegprice

install.packages("plyr")


72

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,


ggplot(Log.Freq.Total_new,aes(x=percentage,y=freq),ylim(c(-50,150)))+

labs(list(title='ProbabilityNegativeElectrictyPricevs\nShareRenewableEnergyPenetration',

x='ShareRenewableEnergyPenetration(%)',


73

y='ProbabilityofNegativeElectrictyPrice(log)'))+


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


dt7<-dt4[,list(hours)]


dt7[,Newres:=trunc((hours-1)/T2new+1)]

#Addingthethirdcolumn(TotalPredWind)

dt7[,ShareActRenew:=dt4$shareActRenew]

#Addingthefourthcolumn(TotalPredSun)

dt7[,price:=dt4$price]


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))+


74

labs(list(title='Day-AheadElectrictyPricevs\nShareRenewableEnergyPenetration',





annotate("text",x=0.25,y=0,label="P(€/MWh)≈56.540-65.117s")

#Onlyfournegativepricesintotal!




#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)',


75



geom_smooth(col='black')

summary(regression.4<-lm(price~TimeOfDay,data=dt4))



#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)',



76


geom_smooth(col='black')

summary(regression.5<-lm(price~TimeOfDay,data=dt5))



#ScatterplotTotalLoadvs.Price

ggplot(dt.load2,aes(x=TotalLoad,y=price),ylim=c(-50,150))+

labs(list(title='Day-AheadElectrictyPricevsTotalLoad',

x='TotalLoad(MWh)',



geom_smooth(method='lm',se=FALSE,col='black')

summary(regression.6<-lm(price~TotalLoad,data=dt.load2))



#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)'))+



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))



#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)',


geom_point(col='grey')

summary(regression.8<-lm(price.x~TotalLoad,data=dt.loadNeg))



#ScatterplotNetLoadvs.NegativePrice

ggplot(dt.loadNeg,aes(x=NetLoad,y=price.x))+

labs(list(title='NegativeDay-AheadElectrictyPricevsNetLoad',

x='NetLoad(MWh)',



summary(regression.9<-lm(price.x~NetLoad,data=dt.loadNeg))



#ScatterplotTimeofDayvs.TotalLoad

ggplot(dt.load2,aes(x=TimeOfDay,y=TotalLoad))+

labs(list(title='TotalLoadatTimeofDay',

x='TimeofDay(hours)',

y='TotalLoad(MWh)'))+


geom_smooth(se=FALSE,col='black')

summary(regression.10<-lm(TotalLoad~TimeOfDay,data=dt.load2))




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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_smooth(se=FALSE,col='black')

summary(regression.11<-lm(NetLoad~TimeOfDay,data=dt.load2))



#ScatterplotShareofRenewablesvs.TotalLoad

ggplot(dt.load2,aes(x=shareActRenew,y=TotalLoad))+

labs(list(title='TotalLoadvsShareRenewableEnergyPenetration',


y='TotalLoad(MWh)'))+


summary(regression.12<-lm(TotalLoad~shareActRenew,data=dt.load2))



#ScatterplotShareofRenewablesvs.NetLoad

ggplot(dt.load2,aes(x=shareActRenew,y=NetLoad))+

labs(list(title='ScatterplotShareofRenewabblesvsNetLoad',


y='NetLoad(MWh)'))+



annotate('text',x=0.7,y=25000,label='NL(MWh)=32964.890-31906.470s')

summary(regression.13<-lm(NetLoad~shareActRenew,data=dt.load2))




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#Summaryregression

stargazer(regression.1,regression.2,regression.3,regression.4,regression.5,

regression.6,regression.7,regression.8,regression.9,


#frequencydensitynegprice,negload

dt.loadNeg2<-dt.load2[dt.load2$NetLoad<0,]

dt.loadNeg22<-dt.loadNeg2[dt.loadNeg2$price<0,]

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#frequencydensitynegprice,posload

dt.load22<-dt.load2[dt.load2$NetLoad>0,]

dt.load222<-dt.load22[dt.load22$price<0,]

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Negative Electricity Prices in the German Electricity Market · 2017. 8. 30. · Negative...

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