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The development of local energy communities and collective self-consumption framework at a large scale requires new control methods that take into account users preferences. This article presents a model of such a community, with diverse actors (photovoltaic generators, electric vehicles, storage system and tertiary buildings). Game theory is used to model the preferences of each user and to build a mathematical framework where each user optimizes individually his power profile according to these preferences. An ADMM distributed algorithm (Alternating Direction of Method of Multipliers) is employed for practical implementation. Thus, a central agent is no longer needed to reach the system equilibrium, in which all users are satisfied while ensuring that the local energy community does not import more power from the grid than allowed. The simulations performed on real data for different scenarios representing diverse users behaviors show that the developed approach converges to a stable state, and leads to a maximization of local energy exchanges.

INTRODUCTION: This paper presents a distributed approach to optimise self-consumption on a local energy community containing photovoltaic generators, electric vehicles, loads and a storage system. OBJECTIVES: The goal is to maximise energy sharing between users while preserving the indivual objectives of each user. METHODS: Game theory is employed to model users’ behavior and preferences. A distributed algorithm is used to solve the optimisation problem. In addition, a physical model of the grid is built to verify if the solutions respect grid constraints. Finally, a private blockchain environnement is deployed to concretely implement this distributed framework with a smart contract. RESULTS: It is shown that the proposed approach effectively leads to an increase of self-consumption rate on the local grid. CONCLUSION: The proposed distributed framework, combining game theory and blockchain, shows real potential to improve energy sharing on energy communities.

In this paper, we present a distributed approach to optimise self-consumption on a university campus grid. The grid contains photo- voltaic 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 dis- tributed 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 con- tract 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.

This paper gives a definition of collective self- consumption and introduces the current regulatory framework in some European countries. It proposes a review of relevant Demand Side Management (DSM) methods applicable to improve the collective self-consumption rate. It also introduces the concept of blockchain and its possible applications to collective self-consumption, with a focus on some current experimentations. Current blockchain applications include validation of measured data and energy transactions. New architectures propose a completely decentralized energy market and grid control based on the blockchain technology. However, a deeper analysis of the benefit of blockchain is required. The legal framework will also play a role on the future deployment of these applications.