a compromise solution based on fuzzy decision making for multi-objective hourly planning in clustered microgrids considering uncertainty of renewable energy resources

This paper proposes a multiobjective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. a fuzzy decisionmaker is used in this study to select the best solution from the optimally set goals by the pareto front beam method. in this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and mg clusters, with consideration for the uncertainty of renewable energy resources (ress), is examined. an hourly robust energy management approach is presented for distribution systems under the penetration of renewable energybased mgs. in addition to wind turbines and photovoltaics as ress, the mgs are equipped with energy storage systems and microturbines. the uncertainty of renewable generation is demonstrated via the information gap decision theory (igdt) technique. to validate the effectiveness of the proposed model, it is tested on a 94 bus distribution test system using the general algebraic modeling system (gams) software. the results show the prominence of mgs clustering in improving the technoeconomic characteristics of the distribution system and indicate the important consequences of clustered microgrids in optimal power transaction and distribution system operation.

A Compromise Solution based on Fuzzy Decision Making for multi-objective Hourly Planning in Clustered Microgrids Considering Uncertainty of Renewable Energy Resources

This paper proposes a multiobjective function for hourly optimization of microgrids performance through minimizing the operating costs, losses and, voltage deviation index. A fuzzy decisionmaker is used in this study to select the best solution from the optimally set goals by the Pareto Front beam method. In this paper, the collections of microgrids are separated into several clusters and power transaction between clusters as well as between distribution system and MG clusters, with consideration for the uncertainty of renewable energy resources (RESs), is examined. An hourly robust energy management approach is presented for distribution systems under the penetration of renewable energybased MGs. In addition to wind turbines and photovoltaics as RESs, the MGs are equipped with energy storage systems and microturbines. The uncertainty of renewable generation is demonstrated via the information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed model, it is tested on a 94 bus distribution test system using the general algebraic modeling system (GAMS) software. The results show the prominence of MGs clustering in improving the technoeconomic characteristics of the distribution system and indicate the important consequences of clustered microgrids in optimal power transaction and distribution system operation.