Table Of ContentMathematical Modelling of
Contemporary Electricity
Markets
Mathematical Modelling
of Contemporary
Electricity Markets
Edited by
Athanasios Dagoumas
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Contributors
Numbers in parentheses indicate the pages on which the authors’ Nikolaos E. Koltsaklis (111, 317), Energy and Environ-
contributionsbegin. mental Policy Laboratory, School of Economics,
Business and International Studies, University of
Nicholas Apergis (289), University of Derby, Derby, Piraeus, Piraeus, Greece
United Kingdom
Evangelos Kotsakis (225), European Commission—Joint
A´ngel Arcos-Vargas (209), School of Engineering, Research Centre, Ispra, VA,Italy
DepartmentofIndustrialEngineeringandManagement
AgisKoumentakos(191),Energy&EnvironmentalPolicy
Science, UniversityofSeville, Seville, Spain
Laboratory, School of Economics, Business and Inter-
Vassilios Assimakopoulos (59), School of Electrical and nationalStudies,UniversityofPiraeus,Piraeus,Greece
Computer Engineering, National Technical University
StavrosLazarou(225),DepartmentofElectricalandElec-
ofAthens, Athens, Greece
tronic Engineering Educators, School of Pedagogical
Anastasios Bakirtzis (127), Department of Electrical and and Technological Education (ASPETE), Heraklion
ComputerEngineering,AristotleUniversityofThessa- Attikis, Athens, Greece
loniki (AUTH), Thessaloniki, Greece
Antonios Marinopoulos (127), European Commission,
Pablo Benalcazar (237), Mineral and Energy Economy JointResearchCentre(JRC),Petten, The Netherlands
Research Institute of the Polish Academy of Sciences,
Ilias Marneris (127), Department of Electrical and Com-
DepartmentofPolicyandStrategicResearch,Division
puterEngineering,AristotleUniversityofThessaloniki
ofEnergy Economics,Krako´w, Poland
(AUTH), Thessaloniki,Greece
PandelisBiskas(127),DepartmentofElectricalandCom-
Jesse Morris (279), Energy Web, Zug, Switzerland
puterEngineering,AristotleUniversityofThessaloniki
Andreas Ntomaris (127), Department of Electrical and
(AUTH), Thessaloniki, Greece
ComputerEngineering,AristotleUniversityofThessa-
DavidCanca(209),SchoolofEngineering,Departmentof
loniki(AUTH), Thessaloniki, Greece
Industrial Engineering and Management Science, Uni-
Fernando Nu´n˜ez (209), School of Engineering,
versity ofSeville,Seville, Spain
DepartmentofIndustrialEngineeringandManagement
StamatiosChondrogiannis(127),EuropeanCommission, Science, University ofSeville,Seville, Spain
JointResearchCentre(JRC),Ispra, Italy
IoannisPanapakidis(157,333),DepartmentofElectrical
Athanasios S. Dagoumas (349), Energy and Environ- and Computer Engineering, University of Thessaly,
mental Policy Laboratory, School of Economics, Volos,Greece
Business and International Studies, University of
Anthony Papavasiliou (173), UCLouvain, Louvain la
Piraeus, Piraeus, Greece
Neuve,Belgium
Haris Doukas (59), School of Electrical and Computer
JohnA.Paravantis(295),DepartmentofInternationaland
Engineering, National Technical University of Athens,
European Studies, University of Piraeus, Piraeus,
Athens, Greece
Greece
B.Hartmann(39),BudapestUniversityofTechnologyand
Theodosios Perifanis (3), Energy and Environmental
Economics,Budapest, Hungary
Policy Laboratory, Department of International and
SamHartnett (279), Energy Web, Zug, Switzerland European Studies, School of Economics, Business and
Jacek Kami(cid:1)nski (237), Mineral and Energy Economy International Studies, University of Piraeus, Piraeus,
Greece
Research Institute of the Polish Academy of Sciences,
DepartmentofPolicyandStrategicResearch,Division Fotios Petropoulos (59), School of Management, Uni-
ofEnergy Economics,Krako´w, Poland versity ofBath, Bath, UnitedKingdom
xi
xii Contributors
MichaelL.Polemis(289),DepartmentofEconomics,Uni- Konstantinos Tasiopoulos (95), Energy and Environ-
versity of Piraeus, Greece and Hellenic Competition mental Policy Laboratory, University of Piraeus,
Commission, Athens, Greece Piraeus, Greece
Marta Poncela-Blanco (127), European Commission, Eleftherios C. Venizelos (259), Energy & Environmental
Joint Research Centre (JRC), Ispra, Italy Policy Laboratory, School of Economics, Business
and International Studies, University of Piraeus,
B.Sinkovics(39),BudapestUniversityofTechnologyand
Piraeus, Greece
Economics, Budapest,Hungary
IoannisVlachos (279), Energy Web, Zug,Switzerland
SymeoniSoursou(Eleni)(17),EnergyandEnvironmental
Policy Laboratory, Department of International and I. Vokony (39), Budapest University of Technology and
European Studies, School of Economics, Business and Economics, Budapest,Hungary
International Studies, University of Piraeus, Piraeus,
Shutang You (75), Department of Electrical Engineering
Greece
and Computer Science, The University of Tennessee,
Evangelos Spiliotis (59), School of Electrical and Com- Knoxville, TN, UnitedStates
puter Engineering, National Technical University of Yao Zhang (75), Shaanxi Province Key Laboratory of
Athens, Athens, Greece Smart Grid,Xi’an Jiaotong University,Xi’an, China
I.Taczi(39),BudapestUniversityofTechnologyandEco-
nomics, Budapest,Hungary
Preface
Thebookaimsatexaminingachallengingtask,namelymathematicalmodellingofcontemporaryelectricitymarkets.Lib-
eralization of electricity markets, technological and policy developments create needs for advanced and robust mathe-
matical models toward quantifying and tackling those developments. This book aims to provide different mathematical
formulations,developedforcapturingnewchallengesandapproachesincontemporaryelectricitymarkets,includingboth
newaswellastraditionalmethodologies.Thebookincludesdifferentapproaches(i.e.,optimization,econometrics,neural
networks, blockchain, cooptimization, energy system models, simulation) applied in modelling different problems/chal-
lenges of electricitymarkets.
Iwouldliketothankallthecontributorsforprovidingchaptersofhighqualityandrespondingintimeandprofessionally
toallmyrequests.Theengagementofseveralexperts,whohave strongandlong-lastingexperience inreal problemsof
electricitymarkets,enablesthedevelopmentofabookthatIhopewillbeusefultoenergyexpertsfortheirdailyoperations.
Theengagementofseveralstaff,fromtheEnergyandEnvironmentalPolicyLaboratoryoftheUniversityofPiraeusthat
Iamdirecting,enablestheprovisionofamorecoherentandstructuredbook,whichisthemainproblemincaseofbooks
consisting ofchaptersfrom different contributors.
Theperiod,whenthebookwasedited,aswellasthemajorityofthechapterswerewritten,wasalsochallenging,asthe
wholeworldfacedtheCOVID-19outbreak.Thisperiodchallengednotonlytheenergysectorandthewholeeconomy,but
alsopersonalbeliefs,asuncertaintyisevidentandextremelydifficulttobepredicted.Thisperiodenabledmetospendmore
hourswithmyfamily,mywifeMariaandmychildrenStergiosandPanagiota,aswellasunderstandingthecommitmentsof
myparents,myfatherStergiosandmyrecentlygonemotherAgoritsa,onthementalandmoralcultivationofmyselfand
mysiblings. Iam devoting this book tomyfamilyand myparents.
Finally,fewdaysagoIwaselectedfromaspecialcommitteeoftheHellenicParliamentasPresidentoftheHellenic
Regulatory Authority of Energy, where my new duties include responsibilities on regulation and market monitoring of
electricityandnaturalgasmarketsinGreece.Thelastchapter,writteninspring2020undermyscientificidentity,tackles
marketconcentrationissuesforthosemarkets.Iplan,ifthereistimeavailable,toimplementthisscientificdissemination
strategyforothermarketdesignissues,aswellasforintroducingmoderntheoriesinregulation,suchasnudgingtechniques.
We hope thatthe readers will find the book useful.
Athanasios Dagoumas
xiii
Introduction
AthanasiosDagoumas
EnergyandEnvironmentalPolicyLaboratory,DepartmentofInternationalandEuropeanStudies,SchoolofEconomics,BusinessandInternational
Studies,UniversityofPiraeus,Piraeus,Greece
Any scientific book usually aims torespondto the followingmain questions:
l What is the need ofthe book? What is the aim and scope ofthe book?
l What problemsdoes the book solve?
l What are the featuresof the book?
l Whatisitscontributionintheliterature?Aretherecompetitiverecentbooks?Howthisbookdistinguishesitselffromthe
competition?
l Whatisthestructureofthebookandhowitwasselected? Doesitsnature(bookofchapters)affectitscoherenceand
quality?
1 Which is the need for the book? What are its aims and scope?
Theneedforthisbookderivesfromthedynamicevolutionofcontemporaryelectricitymarkets,duetoliberalizationpro-
cesses,technologicalandpolicydevelopments.Electricitymarketsarefacingrapidreformsandliberalizationprocessesall
overtheworldwhichcreatenewformsofmarkets,i.e.,day-ahead,intraday,balancing.Moreover,technologicalchallenges
lead to the penetration of new technologies both in the supply and the demand side, affecting electricity market needs.
Distributed energy sources, stochastic renewables, dynamic demand, electric storage, electric vehicles, artificial intelli-
gence, and internet of things create new challenges for the contemporary power markets. Moreover, policy challenges,
such as energy security, security of supply, market concentration, decarbonization due to raising of environmental
awareness, shocks in the markets, and issues related to energy planning, are important issues for electricity markets.
But,asincaseofanycommoditymarket,theexaminationofitsmarketfundamentals,aswellasitsinterlinkagewithother
markets, is always an important task.
Thequantificationofthosedevelopmentswithadvancedandrobustmodellingtechniquesisveryimportant.Thisbook
aims to provide different mathematical models developed for capturing new challenges and approaches in competitive
electricitymarkets.Thebookincludesdifferentapproaches(i.e.,optimization,econometrics,neuralnetworks,blockchain,
cooptimization,energysystemmodels,simulation)inmodellingelectricitymarkets.Thebookcapturesthedevelopments
mainlyintheEuropeanelectricitymarkets,asthevastmajorityofitscontributorsprovideapplicationsmainlyforEuropean
markets. However, the book does not focus on a specific market, i.e., European, as the contributions are invaluable for
anyone interested inmathematical modelling for electricity market.
2 What problems does the book solve?
Thebook provides solutionstothe following issues:
l Technology and policy developments are rapid in the electricity sector, so mathematical models for capturing those
developments are expandingtocapture them
l Thespecificproblemsfacedbyreadersareforecastingmarketfundamentalsofpowermarkets(demand,supply,prices),
forecasting renewables, integration of dynamic demand, optimization of energy and ancillary services, dispatching
xv
xvi Introduction
power units in different market types, market concentration, integration of electric vehicle and storage in electricity
markets,evolutionofnewmarkettypes,biddingunderuncertaintyinelectricitymarkets,interlinkagewithothercom-
modities markets. Those problemsare tackled bythe relevant mathematical models andapplications.
l Thebookisexpectedtofamiliarizereaderswithelectricitymarkets’models,toidentifytheimportancefordeveloping
robust quantitative tools, and to help readers on developing/using relevant models that are important for their daily
responsibilitiesand modelling developments.
3 What are the features and benefits of the book?
The main features andbenefitsof the book are asfollows:
l Providedifferent modelling techniques for electricity markets modelling
l Assess new challenges incontemporaryelectricity markets
l Assess different types ofelectricity markets
l Familiarize energyexperts with quantitative skillsneeded incompetitive electricity markets
l Familiarize energyinvestors with markets’ riskby stressingthe multidimensionality ofelectricity markets
4 Who is the target audience
Thebookcapturesmainlytheneedsofenergyexperts;however,itcouldalsobeusefulasatextbook,especiallyinpost-
graduate studies and for doctoral students. Thereforethe target audience is:
l Energy experts working with electricity companies,system operators, energyexchanges, and regulators
l Master/PhD studentsexpanding their quantitative skills for competitive electricity markets
l Analysts in the power sector and inacademia
l Electricity market analysts, electricity traders, heads of energy management and energy trading division, electricity
retailers, riskanalysts inbanking and inelectricutilities, energyinvestors
Thebookaimstoimprovetheunderstandingoftheabovementionedpersonnelinpowermarketsoperations,toimprove
theirmodellingcapabilitiesforelectricitymarketsaswellastheunderstandingofthemodellingoutputs,toimprovetheir
strategy in participation in electricity markets, and to improve their understanding in decision making in energy
investments.
5 What is the contribution of the book in the literature?
Theliteratureisextensiveconcerningmathematicalmodellingonpowermarkets.Eachchapterincorporatesseveralsci-
entific papers, including review papers that organize recent developments. However, the literature in relevant books is
limited.Thereexistseveralbooksthattacklemodellingandotherchallengesforcontemporaryelectricitymarkets,from
whichtheauthorchoosesthemostrelevanttohisknowledge,inordertojustifytheusefulness,contribution,andcomple-
mentarityofthisbook.Kariniotakis(2017)focusesonrenewableenergyforecasting,providingextendedapplicationsand
methodologieswhichhowever focusonlyonrenewables.Thereforetheproposed bookcapturesawideraudienceinthe
powersector.Sioshansi(2008)examinesmarketdesign,implementation,andperformanceissuesofcompetitiveelectricity
markets. This book captures more recent challenges of contemporary electricity markets, while it also focuses more on
mathematicalmodelsratherthanondesignofcompetitiveelectricitymarkets.Thereforeitismoreusefultoenergyexperts
indailyoperations.Conejoetal.(2010)focusondecisionmakingunderuncertaintyinelectricitymarkets,providingrobust
methodologicalapproachestocrucialchallengesrelatedtotacklinguncertaintyinelectricitymarkets.Thisbookcaptures
newtechnologyandpolicydevelopments,aswellasmodellingtechniquesandapplications.Sioshansi(2013)providesnew
paradigms,newchallenges,andnewapproachesconcerningtheevolutionofglobalelectricitymarkets,focusingmoreon
thedesignofelectricitymarkets.Thisbookfocusesonmodellingnewchallengesandprovisionofnewtechniques.Biggar
andReza(2014)examineeconomicissuesofelectricitymarkets,standingasausefultextbookonmarketfundamentalsof
powermarkets.Thisbookfocusesonmathematicalmodelsforelectricitymarkets,thereforetheaudienceisdifferentasit
Introduction xvii
targetsmoreonenergyexperts.LinandMagnago(2017)examinetheoriesandapplicationsinelectricitymarkets,standing
asatextbookprovidingthebasicconceptsofelectricpowersystems,microeconomics,andoptimizationtechniques.This
bookfocusesonmathematicalmodelsforelectricitymarkets,thereforetheaudienceisdifferentasittargetsmoreonenergy
experts.Thereviewundertakenrevealsthatthebookcontributestotheliteraturebyexaminingmathematicalmodellingon
electricitymarkets,focusingonnewchallengesandapproaches.Finally,thebook,althoughbeingabookofchapters,has
engagedseveralstaff fromtheEnergy andEnvironmental Policy LaboratoryoftheUniversityofPiraeus, withpersonal
engagement of the editor, in order to enhance coherence of the book and create a structure that resembles a textbook.
Thereforeitsaudienceconcernsmainlyenergyexperts,butcouldbeusefulasatextbook,especiallyinpostgraduatestudies
anddoctoralstudents.
6 Whatisthestructureofthebook?Doesitsnature(bookofchapters)affectitscoherenceandquality?
Thestructurewasbasedonextensiveliteraturereviewmadeanddiscussedinprevioussection.Firstly,areviewhasbeen
undertakenonexistingbookstowardidentifyingtheneedforissuingthisbookanditsstructure.Moreover,theextensive
experienceoftheEnergyandEnvironmentalPolicylaboratorystaffoftheUniversityofPiraeusonenergymodellingand
provisionofmodels/softwaretoenergycompaniesenhancedthecapabilitytoenhancethecoherenceofthebook,providing
chapter contributions on different modelling techniques and for technological/policy challenges not covered by other
contributors.Therefore the structure was donewith adouble purpose:
l toassistenergyexpertsontheirspecificproblemsandtofamiliarizethemonusinganddevelopingmodelsfortheirneeds
l butalso,asatextbook,especiallyinpostgraduatestudies,doctoralstudents,andanalyststohelpthem understandthe
capabilityof each modelling technique.
Thebook is split intofour different parts examininghow:
l market fundamentals (Part 1),
l forms of electricity markets(Part2),
l technology challenges (Part 3), and
l policy challenges (Part 4)
of electricity markets are modeled, respectively.
Part1concernsmodellingmarketfundamentalsofelectricitymarkets.Itconsistsofsixchapters.Thetermmarketfun-
damentalsmightnotbeaccurate,asreaderswouldexpectchaptersondemandandsupplyforecasting.Demandforecasting
isexaminedinChapters1,2,and3,withdifferentmethodologies,whilesupplyforecastingisexaminedindifferentaspects:
howsupply(fromnetworkassets)affectsnetworkdemandinChapter3,aswellasforecastinguncertaintyfromsupply(in
caseofwindassets)inChapter4.ThispartalsoincludesChapter5onforecastingelectricityprices,aswellasonlinking
electricitywithothercommoditypricesinChapter6.Thereforemarketfactorscouldbemoreappropriateasaterm,butwe
chose touse market fundamentals.
Chapter1,authoredbyTheodosiosPerifanis,focusesonenergydemandforecastingwitheconometrics.Demandfore-
castingisofcrucialimportanceintheliberalizedelectricitymarkets,bothforlong-andshort-termneeds.Short-termfore-
castingisdependableonmanyvariables,i.e.,weather,hour,season,workingorpublicholidayday,whichhoweverdonot
incorporateimportanteconomicindicators,suchasincome/economicactivity,asthosefiguresarenotmeasuredintheshort
term.Thoseindicatorsareusedforlong-termforecasting.Thisisthefocusofthischapter,applyingeconometricstoinves-
tigatetherelationshipbetweeneconomic(priceandGDP)andweatherconditions(numberofheatingandcoolingdegrees)
inthreeEuropeanmarkets:Denmark,France,andSpain.Demand’sreactionsareinvestigatedbothinthelongandshort
term, providing evidence on different elasticities on the examined countries, although facing similar market regulatory
environment.
Chapter2,authoredbySimoniSoursou,providesaneconometricapproachforGermany’sshort-termenergydemand
forecastingusingtraditionalmodellingtechniques.Thisstudyseekstoassessdifferenttimeseriesforecastingtechniques
for energy demand. Using an extensive sample of hourly observations for actual electricity load, under the Box-Jenkins
frameworkdifferentARMAandARMAXmodelsforGermany’senergydemandaredeveloped.Furthermore,aregression
analysis is realized tocompare ourestimates.
Chapter3,authoredbyB.Sinkovics,I.Taczi,I.Vokony,andB.Hartmann,providesanoveladaptiveday-aheadload
forecasting method, incorporating nonmetered distributed generation. The method provides a comparison of selected
European countries. This chapter reveals, similar to Chapter 1, the need for tailored solutions for each application, due
xviii Introduction
totheconsiderablyvaryingconditionsofeachcountry.Longshort-termmemorynetworksareasubtypeofrecurrentneural
networksusingauniquenodearchitecturecalledcells.Thisisoneofthemostwidelyusedstate-of-the-artmodelsforpre-
dictions,wheretemporaldependenciesareimportant.Thechaptermakesahypothesisthattheproliferationofnonmetered
residentialphotovoltaicplantssignificantlyaffectstheaccuracyofloadforecasts.Theintroducedlongshort-termmemory
loadpredictorwasappliedtotransmissionsystemoperatorfrequencycontrolareaswithvariousloadscalesandrenewable
penetration.Thedevelopedmodelhasoutperformednationalforecastsandhasidentifiedpotentialeffectsofnonmetered
distributedgeneration.Thiscomparisonreinforcedthehypothesisoftheauthorsthatresidentialphotovoltaicentitiesplay
animportantroleinthecharacteristicsofthedailyloadcurves,andtheperformanceofloadforecastscanbeimprovedby
taking such data into consideration, when developing forecasting algorithms.
Chapter 4, authored by Yao Zhanga and Shutang You, examines probabilistic forecasting toward modelling uncer-
taintiesinelectricitymarkets,byprovingawindpowerexample.Thisworkfirstdevelopsanenhancedk-nearestneighbor
(KNN)algorithmtofindsimilarweatherconditiondaysinhistoricaldataset.Thenanovelkerneldensityestimator(KDE)
isdevelopedandappliedtoderivetheprobabilitydensityofwindpowergenerationfromk-nearestneighbors.Logarithmic
transformationisutilizedtoreducetheskewnessofwindpowerdensity,andtheboundarykernelmethodisusedtoelim-
inatethedensityleakattheboundsofwindpowerdensity.Inaddition,anoutlierdetectiontoolisemployedtoremovethe
dataofwindpoweroutputbeingzeroinasuccessive48-hperiodduetowindturbinemaintenance.Anadvantageofthis
approach is that it could provide both point forecasts and probabilistic forecasts for wind power generation. Evaluation
results, on the public dataset from the 2014 Global Energy Forecasting Competition, demonstrate the high accuracy
and reliabilityofthe methodology.
Chapter5,authoredbyEvangelosSpiliotis,HarisDoukas,VassiliosAssimakopoulos,andFotiosPetropoulos,focuses
onforecastingofweek-aheadhourlyelectricitypricesinBelgiumwithstatisticalandMachineLearning(ML)methods.
Electricitypriceforecastingisinfluencedbynumerousvariables,suchasweatherconditions,electricityconsumption,and
seasonal factors. This chapter provides an overview of both statistical and ML approaches. It compares the forecasting
performanceoftwopopularMLmethods,namelyneuralnetworksandrandomforest,tothatoftraditional,statisticalones
byconsideringtheelectricitymarketinBelgium.Theresultsindicate thatMLmethods provide betterforecasts,bothin
termsofaccuracyandbias.ExternalvariablesimprovetheperformanceofbothstatisticalandMLapproaches;however,
the improvements in the latter groupare relatively moresubstantial.
Chapter6,authoredbyKonstantinosTasiopoulos,focusesonmodellinginterlinkedcommodities’prices,examiningthe
linkageofelectricitypriceswithnaturalgasprices.Severaleconometricmethodologiesareappliedtoexamineboththe
pricetransmissionmechanismandthevolatilityspilloversbetweentheTitleTransferFacility(TTF)naturalgaspriceand
the wholesale electricity day-ahead prices regarding the case of Netherlands. Evidence of an asymmetric cointegrating
relationshipfoundwithnaturalgaspricescausingelectricitypricesbothinthelongandintheshortrun.Theasymmetric
relationshipsuggestedthatTTFpricedecreasescausefasteradjustmentstoELCpricesthanincreases,whereaselectricity
priceadjustmentsdonotseemtoinfluenceTTFprices.Furthermore,thechapterstudiesthepartialcovolatilityspillovers
with the implementation of a diagonal BEKK model. Results signified that covolatility spillovers do exist between two
variables with the spillovers effectsmoving towarddifferent directions.
Part2concernsmodellingdifferentformsofelectricitymarkets.Itconsistsoffivechapters.Itconsidersdifferentforms
ofelectricitymarkets,namelyday-ahead,examinedinChapters7and8;intraday,examinedinChapter8;andbalancing
markets, examined in Chapters 8 and 9. Moreover, it examines markets from the perspective of market participants.
Chapter10examinesportfoliooptimizationfromtheretailers’perspective,whileChapter11examineselectricityportfolio
optimization for amarket perspective with vertical integration.
Chapter7,authoredbyNikolaosE.Koltsaklis,describesanoptimizationmodelfortheeconomicdispatchproblemin
power exchanges. The chapter considers recent developments in the process of European power markets’ integration
towardadoptingacommonclearingalgorithmamongEuropeanpowerexchangesunderthetitleEUPHEMIA(acronym
for Pan-European Hybrid Electricity Market Integration Algorithm),which provides several options and products tothe
market participants. This chapter proposes an optimization-based methodological framework for the optimal economic
dispatchprobleminpowerexchanges,furtherenhancingblockorderoptionsoftheEUPHEMIAalgorithm.Morespecif-
ically,throughthedevelopmentofamixedintegerlinearprogramming(MILP)modelandutilizinganiterativeprocess,it
quantifiestheimpactsoftheproposednewoptionstotheoptimalpowermix,theresultingmarketclearingprices,andon
the welfares achieved by the market participants. The model incorporates all the current market products of the
EUPHEMIA algorithm, as well as introduces new market products such as the flexibility provision of the main aspects
ofblockorders(minimumacceptanceratio,priceoffer,andblocktimelimits).Theapplicabilityoftheproposedapproach
hasbeenassessedintheHellenicpowersystemanditsinterconnectionsinSoutheastEurope,providingarobustandsys-
tematicmethodologicalformation,beingabletoaddressthenewmarketoperatingchallengesoflowcarbonflexiblepower
systems.
