A novel peer-to-peer energy trading strategy for multi-microgrid loads scheduling based on chance-constrained

Abstract

Facilitating producer-consumer P2P energy exchange is a viable paradigm in the era of decentralized energy. Energy trading requires the development of a fair pricing mechanism, but when numerous energy systems are involved in the transaction, the problem can get complicated. Through the decentralized coordination of distributed microgrid energy systems and shiftable microgrid appliances, this article introduces a decentralized EMS that facilitates P2P energy trading among prosumers in community. This lowers the energy costs per microgrid compared to operating each microgrid separately. A Chance-Constrained cooperative model connecting manufacturing, commercial, and residential prosumers with guaranteed trade fairness serves as foundation for suggested approach. The model is expanded to take into account several demand-side management strategies and widely utilized energy supply systems. This study offers a more succinct method for figuring out fair prices for multi-energy trading than earlier research. A comparison between chance-constrained optimization outcomes obtained results is implemented utilizing Improved Sparrow Search Algorithm (ISSA), and without optimization techniques. The results show that recommended strategy for microgrid demand control is appropriate and workable. Fair electricity pricing practices are used to minimize energy costs for prosumers in residential, commercial, and industrial sectors. The suggested solution improves overall electricity bills for the home, company, and factory by 80.34%, 61.429%, and 54.069%, respectively.