Big Data Application in Power Systems PDF
Preview Big Data Application in Power Systems
Big Data Application in Power Systems Big Data Application in Power Systems Edited by Reza Arghandeh Assistant Prof. in Electrical Engineering, Department of Electrical & Computer Engineering, Florida State University Yuxun Zhou PhD candidate, Department of Electrical Engineering and Computer Sciences, UC Berkeley Elsevier Radarweg29,POBox211,1000AEAmsterdam,Netherlands TheBoulevard,LangfordLane,Kidlington,OxfordOX51GB,UnitedKingdom 50HampshireStreet,5thFloor,Cambridge,MA02139,UnitedStates Copyright©2018ElsevierInc.Allrightsreserved. Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,electronicor mechanical,includingphotocopying,recording,oranyinformationstorageandretrievalsystem, withoutpermissioninwritingfromthepublisher.Detailsonhowtoseekpermission,furtherinformation aboutthePublisher’spermissionspoliciesandourarrangementswithorganizationssuchasthe CopyrightClearanceCenterandtheCopyrightLicensingAgency,canbefoundatourwebsite: www.elsevier.com/permissions. ThisbookandtheindividualcontributionscontainedinitareprotectedundercopyrightbythePublisher (otherthanasmaybenotedherein). Notices Knowledgeandbestpracticeinthisfieldareconstantlychanging.Asnewresearchandexperiencebroaden ourunderstanding,changesinresearchmethods,professionalpractices,ormedicaltreatmentmay becomenecessary. Practitionersandresearchersmustalwaysrelyontheirownexperienceandknowledgeinevaluatingand usinganyinformation,methods,compounds,orexperimentsdescribedherein.Inusingsuch informationormethodstheyshouldbemindfuloftheirownsafetyandthesafetyofothers,including partiesforwhomtheyhaveaprofessionalresponsibility. Tothefullestextentofthelaw,neitherthePublishernortheauthors,contributors,oreditors,assume anyliabilityforanyinjuryand/ordamagetopersonsorpropertyasamatterofproductsliability, negligenceorotherwise,orfromanyuseoroperationofanymethods,products,instructions,orideas containedinthematerialherein. LibraryofCongressCataloging-in-PublicationData AcatalogrecordforthisbookisavailablefromtheLibraryofCongress BritishLibraryCataloguing-in-PublicationData AcataloguerecordforthisbookisavailablefromtheBritishLibrary ISBN:978-0-12-811968-6 ForinformationonallElsevierpublications visitourwebsiteathttps://www.elsevier.com/books-and-journals Publisher:JoeHayton AcquisitionEditor:LisaReading EditorialProjectManager:AnaClaudiaA.Garcia ProductionProjectManager:VijayarajPurushothaman CoverDesigner:MarkRogers TypesetbySPiGlobal,India Contributors Reza Arghandeh UC Berkeleyand FloridaStateUniversity, Tallahassee, FL, United States Mohammad Babakmehr ColoradoSchool ofMines,Golden, CO, UnitedStates Ricardo J. Bessa INESC Technology and Science—INESCTEC, Porto, Portugal Saverio Bolognani Automatic Control Laboratory ETH Zu€rich, Zu€rich, Switzerland Angelo Cenedese University of Padova,Padova,Italy Michael Chertkov Los AlamosNational Laboratory,Los Alamos, NM, United States DeepjyotiDeka Los AlamosNationalLaboratory, Los Alamos, NM,United States Roy Dong University of California, Berkeley,Berkeley,CA, United States Feng Gao Tsinghua University Energy InternetResearch Institute,Beijing, China Madeleine Gibescu Eindhoven University of Technology, Eindhoven,The Netherlands Bri-MathiasHodge NationalRenewable Energy Laboratory, Golden, CO,United States GabrielaHug ETH Zurich,PowerSystemsLaboratory, Zurich, Switzerland Jeffrey S. Katz IBM,Hartford,CT, United States Stephan Koch ETHZurich; AdaptricityAG,c/o ETH Zurich,PowerSystemsLaboratory, Zurich, Switzerland Hanif Livani University of NevadaReno, Reno, NV, United States MehrdadMajidi University ofNevada, Reno, NV, United States John D. McDonald GEEnergy Connections-Grid Solutions,Atlanta,GA,United States Sadaf Moaveninejad Polytechnic University of Milan,Milan,Italy Elena Mocanu Eindhoven University of Technology, Eindhoven,The Netherlands Ingo Nader Unbelievable Machine,Vienna,Austria Behzad Najafi Polytechnic University of Milan, Milan, Italy Phuong H. Nguyen Eindhoven Universityof Technology,Eindhoven, The Netherlands Lillian J. Ratliff University of Washington, Seattle,WA, United States Fabio Rinaldi PolytechnicUniversityof Milan, Milan, Italy Marcelo G. Simoes ColoradoSchool of Mines, Golden, CO,United States Matthias Stifter AIT AustrianInstitute ofTechnology,Center of Energy, Vienna,Austria Carol L.Stimmel ManifestMind,LLC,Canaan, NY,United States Gian Antonio.Susto University of Padova,Padova,Italy Akin Tascikaraoglu MuglaSitki Kocman University, Mugla,Turkey Matteo Terzi Universityof Padova, Padova,Italy AndreasUlbig ETHZurich; AdaptricityAG,c/o ETH Zurich,PowerSystemsLaboratory, Zurich, Switzerland xi xii Contributors YangWeng Arizona StateUniversity, Tempe, AZ, UnitedStates RuiYang NationalRenewable Energy Laboratory, Golden, CO, United States Yingchen Zhang NationalRenewable Energy Laboratory, Golden, CO,United States Jie Zhang University of Texas at Dallas, Richardson, TX, United States Yuxun Zhou UC Berkeleyand Florida StateUniversity, Tallahassee,FL, United States Thierry Zufferey ETH Zurich,PowerSystems Laboratory,Zurich, Switzerland About the Editors Reza Arghandeh is an Assistant Professor in the ECE Department in Florida State University. He is director of the Collaborative Intelligent Infrastructure Lab. He has been a postdoctoral scholar at the University of California, Berkeley’s California Institute for Energy and Environment 2013–15. He has 5years industrial experience in power and energy systems. He completed his PhDinElectricalEngineeringwithaspecializationinpowersystemsatVirginia Tech. He holds Master’s degrees in Industrial and System Engineering from Virginia Tech 2013 and in Energy Systems from the University of Manchester 2008.From2011to2013,hewasapowersystemsoftwaredesigneratElectrical DistributionDesignInc.inVirginia.Dr.Arghandeh’sresearchinterestsinclude, but arenot limitedto,data analysisand decision supportfor smartgrids and smart cities using statistical inference, machine learning, information theory, andoperationsresearch.HeisarecipientoftheAssociationofEnergyEngineers (AEE) Scholarship 2012, the UC Davis Green Tech Fellowship 2011, and the best paper award from the ASME 2012 Power Conference and IEEE PESGM 2015.HeisthechairoftheIEEETaskForceonBigDataApplicationforPower Distribution Network. YuxunZhouiscurrentlyaPhDcandidateatDepartmentofEECS,UCBerkeley. Prior to that, he obtained the Diplome d’Ingenieur in applied mathematics from Ecole Centrale Paris and a BS degree from Xi’an Jiaotong University. Yuxun has published more than 30 refereed articles, and has received several studentawards.Hisresearchinterestisonmachinelearningtheoriesandalgo- rithmsformodernsensorrich,ubiquitouslyconnectedcyber-physicalsystems, includingsmart grid, powerdistribution networks, smartbuildings, etc. xiii Preface: Objective and Overview of the Book The term “big data” is fairly new in power systems. Yet, its application and methodologies applied to massive data sets were developed a long time ago forelectricityloadconsumptionforecasting.Therecentdevelopmentsinmon- itoring,sensornetworks,andadvancedmeteringinfrastructure(AMI)dramat- ically increase the variety, volume, and velocity of measurement data in electricity transmission and distribution networks. Moreover, the progress in advancedstatistics,machinelearning(ML),databasestructure,anddatamin- ing methodologies marked by increasing the availability of open source plat- forms for data analytics is transforming the power system area and turning utilities into data-driven enterprises. In order to discuss the big data analytics applications for power systems, this bookbringstogetherexpertsfromallorganizationsandinstitutionsimpacted includingacademiaandindustry.Wefocusonrapidlymodernizingmonitor- ingandanalyticalapproachestoprocessthehighdimensional,heterogeneous, andspatiotemporaldata.Thisbookdiscusseschallenges,opportunities,success stories,andpathwaysforutilizingbigdatavalueinsmartgrids.Thedramatic changeinthefieldofscientificcomputing,microprocessors,anddatacommu- nicationsisaburdenforelectricutilitiestounderstand,follow,andadoptthe advancedstatistics,computerscience,andmathematicsconcepts.Today’sutil- ityengineersneedtobemoreinformedofthebasicconceptsandapplications formassivefielddataanalysis.Thisbook’sgoalistofacilitatethetransitionto data-driven utilities by providing a comprehensive view on big data issues, methodologies, and their various applicationsin the power systems area. Muchliketheauthorshipofthechaptersinthisvolume,theintendedaudience forthisbookextendsfromresearchers,graduatestudents,andfacultyworking in electricity networks and smart grid area to industrial scientists, engineers, data analysis experts, and software developers who are working on electricity networks and advanced technologies for smart grids. This book is also useful forpeoplewithlesstechnicalexpertiseinscientificcomputing.Weexpectthat thereaderwillhavesomeproficiencyinpowersystemsfundamentalsandthat xv xvi Preface he/shehashadatleastoneelementarycourseinstatistics.Thisbookcanalsobe useful for senior undergraduate students who have passed courses on power systems. Thisbookhasthreesectionsasfollows:I.HarnesstheBigDataFromPowerSys- tems,II.HarnessthePowerofBigData,andIII.PutthePowerofBigDataIntoPower Systems.Theopeningsectionisanoverviewoftheopportunitiesandchallenges fordata-drivenutilitiesintheeraofdistributedtechnologiesandresourcessuch asInternetofThings(IoT),flexibledemand,distributedgeneration,andenergy storage.Thesecond section reviewsresearchtrends onMLandartificialintel- ligence for the powersystem industry. The final section provides examples of theadvanceddataanalyticapplicationsforthegridoperation.Takentogether, thesethreebooksectionsprovideanoverviewoftheentirecycleofdataanalysis inpowersystems.Thebookbeginswiththeutilityenterprisestructure,business model, and privacy issues, then delves into research trends in advanced data analysis, and ends full circle with real-world examples of actual applications of data analytics used daily by utilities. SECTION ONE: HARNESS THE BIG DATA FROM POWER SYSTEMS To providea big picturefor electric utilities, this section describes the current and future trends for data mining and data processing in electric utilities. Themovetowarddata-drivenutilityispossiblebyafundamentalshiftinorga- nizational culture and business processes, as well as data-related technology andpractices.Moreover,enrichingelectricutilitieswithdatarequiresinterop- erabilityacrossalloperationalandenterpriseunitsandrecognitionthatmain- taining the data privacy, security, and the seamless data flow is highly challenging.Theinteroperabilityinholisticdata-drivenutilitiesexpandstocus- tomersthroughtheirengagementandcontinuesdemand-sidemanagementfor higherreliability,servicequality,andefficiency.Aligningcustomers’needsand expectationswithutilities’businessdriverswillshapetheroadmaptogenerate, process,and access the data inutilities. Theinformationandcommunicationtechnology(ICT)platformisattheheart oftheroadmaptodata-drivenutilitieswhichsupportsthedataflowfromcus- tomersallthewaytothetransmissionandgenerationoperators.Utilitiesmade aggressive steps toward smartness by adopting the distribution automation (DA) solutions followed by AMI platforms. DA and AMI made a revolution in grid operation. However, the data flood from DA and AMI has created a nightmare for the utilities’ ICT infrastructure. A holistic approach for data- driven utilities is needed to openly discuss and clarify the foundational ICT requirements toserveall functions of the electric grid. Preface xvii Becomingadata-driven utility isinevitablein theage ofinternet, cloud com- puting, smart phones, and distributed resources. The advanced data analytics makecontinuousinnovationpossiblebyunlockinginsightsneverseenbefore. The ML, deep learning, and statistical inference are tools that help utilities to keep up with the torrent of data from different resources. Advanced big data analyticsprovideestimation,predication,diagnostics,andprognosticsconclu- sionsfromhistoricalandreal-timedataflows.Asmoredatabecomesavailable toutilitiesovertime,theMLalgorithmsprovidemorerefinedinsightsongrid operationplanning.However,thesynergiesbetweenICTnetworks,gridcom- ponents,operators,andcustomersrunthepowersystemintoacomplexgiant for ad hoc data-driven approaches and policies. This section of the book endeavors todeliver themessagethataholisticapproach based on afounda- tionofopenarchitectureandstandardswillensuretheopenflowofdataand interoperability between devices, systems, databases, and people in order to make data-driven utilities. The all-inclusive approaches to generate, transfer, andhandledataalsobringtremendousopportunitiestobreaktraditionalbar- riersinutilityorganizationsfordeliveringsafe,reliable,andaffordablepower totheir customers. Chapter 1 by John McDonald introduces these concepts through three case studies.Heexploresthevalueofadata-drivenutilityintermsofassetmanage- ment and safety, the fundamentals of standards and interoperability, and the enterprises of increased visibility into the transmission and distribution net- work.Thechapterillustratestheholisticdata-drivenutilityanditsfundamental business drivers to establish information and communications technology foundation, human resource, customer relation, and data-oriented organiza- tional cultural data in the grid operation and planning. The data-driven utilities now face greater and more frequent risk of intrusion and/orinterruptionduetothefactthatthesenetworksaremergingwithcyber networks,resultinginsociotechnicalandcyber-physicalsystemsthatarecreat- inganinfrastructuralIoTwhereallgridcomponentscaninteractandcollabo- rate. Integrating cyber components into the electric grid also means an incredible increase in security vulnerability and interdependencies among infrastructurecomponentsthatcreatetheriskofcascadingeffectsafterattacks. Moreover, the enhanced observability of the grid thanks to the smart meters’ high granular data is making more customers concerned and uncomfortable about data privacy. Carol L. Stimmel discusses state-of-the-art data privacy andsecurityinChapter2.Shelistsanumberofactualcasesforcybersecurity attacks on the grid and explains the impact of data-driven approaches in enhancingthedatasecurityandprivacy.Data-drivenutilitiesfunctionasmuch more than the operators of the physical grid; utilities are also responsible for massive enterprise systems with financial information, customer data, and a growing network of digital operations under human control. Thus, security
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