Energy Management of Grid Interconnected Multi-Microgrids Based on P2P Energy Exchange: A Data Driven Approach

• Published in: IEEE transactions on power systems 2021-03, Vol.36 (2), p.1546-1562
• Main Authors: Thirugnanam, Kannan, Moursi, Mohamed Shawky El, Khadkikar, Vinod, Zeineldin, Hatem H., Al Hosani, Mohamed
• Format: Article
• Language: English
• Subjects: Algorithms; Consumers; Deep neural network; Diesel fuels; Diesel generators; Distributed generation; distributed generators; Energy consumption; Energy exchange; Energy management; Energy storage; Energy transfer; Exchanging; Fossil fuels; fuzzy logic; Generators; Heuristic algorithms; microgrids; Peer to peer computing; peer-to-peer energy exchange; Power consumption; Pricing; Storage systems; Wind turbines
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2020.3025113

Grid interconnected multi-microgrids provides potential benefits to the consumers, where the microgrids (MGs) equipped with distributed generators (DGs), energy storage systems (ESSs), and diesel generators. However, intermittency of DGs, high cost of ESSs, and depleting fossil fuels are the major challenges for the economic operation of interconnected multi-microgrids. One potential way to address these challenges is to develop an energy management strategy (EMS) for the grid interconnected multi-microgrids. This paper proposes an EMS to reduce consumer energy consumption cost (ECC) using fuzzy-based peer-to-peer (P2P) energy exchange algorithm with dynamic pricing. In this context, the MGs consumers load power demand (LPD) and DGs output behaviors are modeled using random vector functional link network approach to predict future time slot values. Then, a fuzzy-based P2P energy exchange algorithm is developed to enable the surplus energy transfer to grid and/or MGs with dynamic pricing. Furthermore, an ESS charging/discharging energy control and diesel generator turn on strategies are developed based on the MGs deficit power. Then, the MGs consumer LPD reduction strategy is implemented based on the consumer ECC margin and energy consumption index. Finally, an EMS is proposed that includes on demand-supply strategy and consumer energy consumption cost reduction strategy based on the future time slot values. The novelty of the proposed work lies within the energy management of grid interconnected multi-microgrids and the reduction of consumers ECC through surplus energy transfer to grid and/or MGs using fuzzy-based P2P energy exchange algorithm with dynamic pricing. Historical data are used to demonstrate the effectiveness of the proposed EMS for grid interconnected multi-microgrids.