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Demand Response and Energy Portfolio Optimization for Smart Grid using Machine Learning and Cooperative Game Theory

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Chis, Adriana
dc.date.accessioned 2018-08-24T09:03:07Z
dc.date.available 2018-08-24T09:03:07Z
dc.date.issued 2018
dc.identifier.isbn 978-952-60-8122-9 (electronic)
dc.identifier.isbn 978-952-60-8121-2 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/33598
dc.description.abstract Widespread availability of electricity is a hallmark of civilization. A reliable electricity supply is fundamental for the social and technological development of the world. To cope with the growing electricity demand and other challenges associated with energy delivery today, technological advancements towards a modern updated power grid are needed. The development of a smart grid is a solution to enable a more stable, reliable, efficient, economical and sustainable energy generation, transmission, distribution and usage. One drawback of the traditional power grid is the mismatch between energy supply and demand. The solution to this problem is the deployment of a more flexible energy generation system, together with a balanced electricity consumption. This could be achieved by means of demand side management (DSM). The focus of this thesis is to model efficient DSM methods for optimizing electricity consumption. In particular, price-based demand response (DR) methods that require the active participation of electricity users are developed. Price-based DR methods allow for energy users to optimize their energy consumption and reduce their costs. This occurs if they adjust and change their electricity consumption patterns in response to dynamic prices applied by utility companies. One problem tackled in this thesis is that of optimizing the charging of electric vehicles (EVs). More and more people are interested in purchasing EVs. The EVs however, will significantly increase their electricity consumption and cost. Using machine learning techniques, efficient methods that optimize the home charging of an EV and reduce the long term cost of charging for the owner are developed. The EV charging is scheduled by taking advantage of the time-varying electricity prices within a day, but also of the dynamic nature of prices on different days. In the traditional power grid, the role of the energy consumers was that of price takers with no other involvement in the energy sector. The smart grid however, will support consumers also in owning renewable energy sources (RESs) and energy storing systems (ESSs). Local energy generation and ownership of ESSs opens opportunities for new energy strategies and markets. By enabling cooperation among energy producers and consumers,  they would be able to manage and use their renewable energy resources and storage spaces more efficiently and reduce their electricity consumption costs even more. In this thesis, collaborative models for exchange and trade of energy within communities of households owning RESs and ESSs are developed. Using a mathematical model from cooperative game theory, the community energy portfolio optimization problem is formulated as a coalitional game for the households to minimize their costs, individually and collectively. Moreover, using a concept from microeconomics, a DSM method is also developed from the perspective of the utility company to balance the community's grid energy consumption.      en
dc.format.extent 100 + app. 68
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 148/2018
dc.relation.haspart [Publication 1]: A. Chis, J. Lundén, V. Koivunen. Scheduling of Plug-in Electric Vehicle Battery Charging with Price Prediction. In Proc. of the 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) conference, Copenhagen, Denmark, pp. 1-5, 6-9, Oct. 2013. DOI: 10.1109/ISGTEurope.2013.6695263
dc.relation.haspart [Publication 2]: A. Chis, J. Lundén, V. Koivunen. Optimization of plug-in electric vehicle charging with forecasted price. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, QLD, Australia, pp. 2086 - 2089, 19 - 24, Apr. 2015. DOI: 10.1109/ICASSP.2015.7178338
dc.relation.haspart [Publication 3]: A. Chis, J. Lundén, V. Koivunen. Reinforcement Learning-Based Plugin Electric Vehicle Charging With Forecasted Price. IEEE Transactions on Vehicular Technology, vol 66, no. 5, pp. 3674 - 3684, May 2017. DOI: 10.1109/TVT.2016.2603536
dc.relation.haspart [Publication 4]: A. Chis, V. Koivunen. Collaborative Approach for Energy Cost Minimization in Smart Grid Communities. In Proc. of the IEEE Global Conference On Signal And Information Processing (GlobalSIP), Montreal, Quebec, Canada, pp. 1115-1119 , 14 - 16, Nov. 2017. DOI: 10.1109/GlobalSIP.2017.8309134
dc.relation.haspart [Publication 5]: A. Chis, J. Lundén, V. Koivunen. Coalitional Game Theoretic Optimization of Electricity Cost for Communities of Smart Households. In Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New-Orleans, LA, USA, pp. 4726 - 4729, 5 - 9, Mar. 2017. DOI: 10.1109/TSG.2017.2784902
dc.relation.haspart [Publication 6]: A. Chis, V. Koivunen. Coalitional game based cost optimization of energy portfolio in smart grid communities. IEEE Transactions on Smart Grid, to appear.
dc.relation.haspart [Publication 7]: A. Chis, J. Rajasekharan, J. Lundén, V. Koivunen. Demand Response for Renewable Energy Integration and Load Balancing in Smart Grid Communities. In Proc. of the 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, pp. 1423-1427, 29 Aug.-2 Sept., 2016. DOI: 10.1109/EUSIPCO.2016.7760483
dc.relation.haspart [Errata file]: Errata for publications 1, 3 and 5
dc.subject.other Electrical engineering en
dc.subject.other Energy en
dc.title Demand Response and Energy Portfolio Optimization for Smart Grid using Machine Learning and Cooperative Game Theory en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Sähkötekniikan korkeakoulu fi
dc.contributor.school School of Electrical Engineering en
dc.contributor.department Signaalinkäsittelyn ja akustiikan laitos fi
dc.contributor.department Department of Signal Processing and Acoustics en
dc.subject.keyword smart grids en
dc.subject.keyword demand side management en
dc.subject.keyword demand response en
dc.subject.keyword machine learning en
dc.subject.keyword game theory en
dc.subject.keyword electric vehicles en
dc.subject.keyword smart community en
dc.identifier.urn URN:ISBN:978-952-60-8122-9
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Koivunen, Visa, Prof., Aalto University, Department of Signal Processing and Acoustics, Finland
dc.opn Huang, Yih-Fang, Prof., University of Notre Dame, USA
dc.opn Harjunkoski, Iiro, Dr., ABB Corporate Research, Germany
dc.contributor.lab Signal Processing Group en
dc.rev Kekatos, Vassilis, Asst. Prof., Virginia Tech, USA
dc.rev Harjunkoski, Iiro, Dr., ABB Corporate Research, Germany
dc.date.defence 2018-09-14
local.aalto.acrisexportstatus checked
local.aalto.formfolder 2018_08_24_klo_11_29
local.aalto.archive yes

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