Optimal stochastic scheduling of a multi-carrier multi-microgrid system considering storages, demand responses, and thermal comfort

This paper focuses on the day-ahead scheduling of a multi-carrier multi-microgrid that delivers electricity, heat, and hydrogen to its customers. The microgrids are equipped with renewable sources, combined heat and power, gas boiler, electric boiler, multi-energy storage, and power-to-hydrogen facilities, and aim at minimizing their operational and environmental costs. Demand response for electrical and hydrogen loads as well as thermal comfort for heat demand have been considered to increase flexibility. The distribution company, which aims at maximizing its profit, imports electricity and natural gas from the main grid. Stochastic optimization is proposed, and the generated scenarios were reduced using the mixed-integer linear programming method. Considering that the problem possesses two conflicting objective functions, the problem was two-level with nonlinear terms. The Karush-Kuhn-Tucker conditions, duality theory, and Fortuny-Amat transformation were implemented to transfer the problem into a single-level mixed-integer linear programming problem. The effectiveness of the proposed framework in four cases has been tested on a hypothetical multi-carrier multi-microgrid. Energy storage, demand response, and thermal comfort resulted in a microgrid cost reduction of 12.31 % and a distribution company profit reduction of 17.25 %. According to the results, thermal comfort can reduce MGs costs remarkably since it adds more flexibility.