Distributed Optimization for Networks of Battery Energy Storage Systems in Energy Communities with Shared Energy Incentives

In this paper we propose a distributed optimization framework for Italian energy communities, characterized by the possibility of receiving a monetary state incentive based on the amount of "shared energy"consumed. Renewable energy produced by members of the community can be "shared", i.e., other members of the community not physically connected to renewable energy sources can consume energy at a reduced cost due to state incentives according to specific rules. In our model we assume that users may have access to either, neither or both, battery energy storage systems (BESS) and renewable energy sources. We propose a cooperative distributed optimization framework which is the basis for distributed predictive control of a network of BESSs that changes the hourly amount of shared energy consumed in the community. The proposed approach can be used to preserve privacy of user consumption data. We provide numerical results indicating that the total cost of energy for a community is significantly reduced by the proposed approach and that cooperation among the BESS owned by the members of the energy community is beneficial.