Advancing Fairness in Microgrid Energy Transaction: An Alternative Approach

In this paper, we present a peer-to-peer (P2P) energy trading system designed for equitable energy exchanges within a microgrid. Our objective is to enhance the fairness of energy distribution among all microgrid members. To achieve this, we introduce a novel objective function that enables us to simultaneously maximize overall welfare and minimize disparities among microgrid participants, thus promoting equitable transactions. The core of our system is an agent proficiently trained via deep reinforcement learning, ensuring efficient and equitable energy distribution across the network. We simulated the energy consumption of 8 houses, and their energy production to assess the feasibility and efficiency of the proposed system. The utilization of the proposed approach has yielded promising results. The intelligent agent was able to execute transactions and improve the fairness of energy trading within the microgrid. The overall average welfare of the microgrid over a day increased by 99.8%, while the disparities among the members were reduced.