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The wide use of renewable energy sources stimulates the implementation of microgrid (MG) systems. MG is an independent power system that consists of various distributed generations (DGs), storage devices, and loads. The optimization problems of MG have gained wide spread interest considering the economic and environmental operation management of the system. To address the multi-variable non-linear programming problem with multiple hybrid constraints, this paper proposes a novel hybrid multi-objective fireworks algorithm with gravitational search operator (MFAGSO). In the algorithm, the operator, as a new search mechanism, directs the sparks in the clustered region to exchange positional information with Pareto-optimal solutions at each iteration process for obtaining accurate results. The operation management of typical grid-connected MG, which comprises various DGs and storage battery, was optimized within 24 hours to evaluate the performance of the proposed algorithm. Considering operation cost and pollutant emission individually or simultaneously, 3 scenarios were involved in operation management for power market planning and practical considerations. The simulation results of the proposed algorithm were compared with those from other methods employed in previous literature and original fireworks algorithm. The results demonstrated the superior performance of the proposed method to solve operation management for optimizing problems of MG. [ABSTRACT FROM AUTHOR]

This paper proposes the new concept of preplanned microgrid islanding (PMI) in the smart distribution system. PMI is a special form of controlled islanding that can be used to provide demand-side service for electrical users under the scheduled distribution operation, especially for the preventive maintenance. A simulation-based multi-objective optimization framework is developed to decide the starting time and duration of PMI. The optimization objectives include the minimization of load curtailment, operating cost, and pollutant emission. On one hand, the stochastic short-term simulation method is developed to assess the performance of candidate PMI schemes considering multiple uncertainties and optimal scheduling of microgrid operation. On the other hand, the weighted-sum approach is used to compromise the conflicting objectives according to the decision preferences of users. Furthermore, the response surface methodology is adopted to improve the efficiency of scheme selection with the help of experimental design technique and regression analysis. Finally, the effectiveness and high efficiency of the proposed method is verified in case studies. Sensitivity analysis is also performed to investigate the impact of decision preference, season scenario, and sizing alternative on optimization result. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]