Demand Response and Energy Portfolio Optimization for Smart Grid using Machine Learning and Cooperative Game Theory
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School of Electrical Engineering |
Doctoral thesis (article-based)
| Defence date: 2018-09-14
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Authors
Date
2018
Major/Subject
Mcode
Degree programme
Language
en
Pages
100 + app. 68
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 148/2018
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.Description
Supervising professor
Koivunen, Visa, Prof., Aalto University, Department of Signal Processing and Acoustics, FinlandKeywords
smart grids, demand side management, demand response, machine learning, game theory, electric vehicles, smart community
Other note
Parts
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[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 View at publisher
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[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 View at publisher
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[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 View at publisher
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[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 View at publisher
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[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 View at publisher
- [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.
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[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 View at publisher
- [Errata file]: Errata for publications 1, 3 and 5