Research Article
Increasing Photovoltaic Self-consumption: An Approach with Game Theory and Blockchain
@INPROCEEDINGS{10.1007/978-3-030-45694-8_14, author={Matthieu Stephant and Dhaker Abbes and Kahina Hassam-Ouari and Antoine Labrunie and Beno\"{\i}t Robyns}, title={Increasing Photovoltaic Self-consumption: An Approach with Game Theory and Blockchain}, proceedings={Sustainable Energy for Smart Cities. First EAI International Conference, SESC 2019, Braga, Portugal, December 4--6, 2019, Proceedings}, proceedings_a={SESC}, year={2020}, month={6}, keywords={Photovoltaic self-consumption Game theory Blockchain}, doi={10.1007/978-3-030-45694-8_14} }- Matthieu Stephant
Dhaker Abbes
Kahina Hassam-Ouari
Antoine Labrunie
Benoît Robyns
Year: 2020
Increasing Photovoltaic Self-consumption: An Approach with Game Theory and Blockchain
SESC
Springer
DOI: 10.1007/978-3-030-45694-8_14
Abstract
In this paper, we present a distributed approach to optimise self-consumption on a university campus grid. The grid contains photovoltaic generators, electric vehicles, loads and a battery. We propose to solve the optimisation problem with a distributed method using game theory, where each element of the grid tries to reach its own objectives. In addition to this optimisation framework, we develop a physical model of the grid. This model uses real consumption and production data. We use it to simulate the production and consumption profiles obtained from the optimisation problem in order to check if these solutions respect the grid constraints. Finally, we propose to implement concretely this distributed approach using a private blockchain, which stores production and consumption data. In addition, a smart contract is deployed on the blockchain to transcribe the game theory framework. The smart contract collects the preferences of each element of the grid and launches the optimisation process. Then the blockchain gathers the results and replaces the role of a central optimisation supervisor. We present some preliminary results to illustrate our method.
