A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles

This study provides a tri-layer optimization framework in which the microgrid strategy for day-ahead market participation is determined by considering the uncertainties of load, RESs and EVs. Scenario-based method is utilized to deal with uncertainties. Besides, distribution feeder reconfiguration (DFR) and an incentive-based demand response (DR) program are used to enhance scheduling flexibility. In the first layer of the proposed model, the buy/sell bids of the microgrids are determined and sent to the pool market. In the second layer, the market clearing price (MCP) is determined according to the buy/sell bids of microgrids, and in the third layer, the microgrids are scheduled by a cooperative game theory approach. The proposed model is implemented on a 118-bus distribution system consisting of 4 microgrids and the results show that the dynamic topology improves the scheduling flexibility and thus reduces the total operating cost by 9.2%. The simulation results also show that EVs participation in scheduling leads to a reduction in the MCP during peak hours. Finally, the results illustrate that considering energy storage systems (EESs) and DR program leads to a reduction in the MCP and thus a 9.33% reduction in the total operating cost. •Providing an optimal three-layer framework to determine bidding strategy and day-ahead scheduling of microgrid•Employing a cooperative game theory approach to solve the scheduling problem of interconnected microgrids•Employing a scenario-based method to incorporate the uncertainties of load demand and RESs output power in the model•Reducing MCP through DFR implementation•Investigating the impact of EESs, EVs and DR program on MCP and operation cost of microgrids