Welfare Optimization in Energy Communities with P2P Markets

We address the requirement for Energy Communities (ECs) to integrate efficient Energy Management Systems (EMSs) that optimize resource operation and maximize the benefits for participants. In this work, we implement an EMS that considers the supply and demand profiles of agents, fostering their engagement and ensuring their continued involvement within the community. We establish a mathematical model of an EC composed of prosumers with different types of distributed energy resources and pure consumers. The EMS integrates game theory and optimization techniques to coordinate and schedule energy transactions using welfare functions. Through the developed algorithm, the maximization of community welfare is ensured. This method is compared with the traditional Interior-Point Method (IPM). The results indicate a normalized error average of 0.23%. We simulate a community with six agents and analyze two case studies. The results show that the EMS promotes agent participation by optimizing their resources and achieving more competitive buy and sell prices compared to the main grid. Furthermore, the EMS prioritizes energy dispatch within the EC over transactions with the main grid and accounts for generation costs. The implementation of the EMS improves community welfare, thus contributing to the sustainability of the EC.