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• W4225113968 endingPage "104719" @default.
• W4225113968 startingPage "104719" @default.
• W4225113968 abstract "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." @default.
• W4225113968 created "2022-05-01" @default.
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• W4225113968 date "2022-08-01" @default.
• W4225113968 modified "2023-09-26" @default.
• W4225113968 title "A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles" @default.
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• W4225113968 doi "https://doi.org/10.1016/j.est.2022.104719" @default.
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