Defence of dissertation in the field of Signal Processing Technology, M.Sc. (Tech.) Adriana Chis
The title of thesis is “Demand response and energy portfolio optimization for smart grid using machine learning and cooperative game theory”
A reliable electricity supply is fundamental for a good operation of every day industrial, commercial and residential activities. The deployment of a smart grid that enables a more flexible energy generation and an optimized energy consumption is necessary. Such grid will provide an efficient, sustainable and clean energy delivery.
The goal of this dissertation is to develop new machine learning and optimization methods that optimize the electricity consumption within the smart grid. Specifically, demand response methods that employ active participation of energy consumers are proposed. The electricity consumers may participate in the energy sector by adjusting their electricity consumption in response to dynamic prices applied by utility companies. Efficient machine learning-based methods that take advantage of the variations in the dynamic electricity prices are developed to schedule the home charging of electric vehicles. The proposed methods reduce the long term costs of charging for the owners.
Moreover, this thesis proposes cooperative game theory-based methods for renewable energy allocation and energy consumption optimization within communities of smart households. By enabling cooperation among households owning renewable energy sources and energy storing systems, the costs of electricity consumption are reduced for the households, both collectively and individually. The electricity consumption of the community is optimized also from the perspective of the utility company. Using a geometric programming-based optimization method, the community’s electricity consumption from the grid is optimized, obtaining a highly balanced energy consumption profile.
Opponents: Professor Yih-Fang Huang, University of Notre Dame, USA and Dr. Iiro Harjunkoski, ABB Corporate Research, Germany
Supervisor: Professor Visa Koivunen, Aalto University School of Electrical Engineering, Department of Signal Processing and Acoustics.
Thesis website
Notice of dissertation defence (pdf.)
Contact information: Adriana Chiș, +358504321708, adriana.chis@aalto.fi
Venue: Aalto University, hall AS2, TUAS building, Maarintie 8, Espoo