A multi-objective optimization method for efficiency and fairness in P2P electricity trading model
Sho Akiyama, Norihiko Shinomiya · 2023
Recently, there has been a gradual shift from thermal energy to renewable energy, prompted by Feed-in Tariff (FIT) policy. However, the sustainability of FIT has come into question due to a lack of adequate funding. Then, the Peer-to-peer (P2P) energy system has attracted attention as an economically viable solution. This study proposes a method to enhance the efficiency and fairness of the entire electricity market by optimizing transaction partners and volumes among general households. Efficiency refers to the reduction of wasteful resources in the market, while fairness aims to prevent monopolistic practices. To achieve this, we conduct simulation experiments employing single-objective and multi-objective optimization methods for transaction matching. The results are then compared and verified to identify the most effective approach.