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The increasing concern of power systems toward distributed generation enables modern power grids and energy management systems to focus their concentration to derive an optimal operational planning with regard to energy costs minimization of Micro-Grid and better utilization of Renewable Energy Sources in the presence of Battery Energy Storage. This paper presents Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (SIMBO-Q) to minimize total operation cost of Micro-Grid considering optimal size of Battery Energy Storage. SIMBO-Q performs the optimization through quarantine and treatment loop based on probability. However SIMBO-Q algorithm takes large number of iterations to reach to the optimum solution if the system has large number of variables. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning concept is introduced in basic SIMBO-Q algorithm. The proposed algorithm is tested on a typical Micro-Grid and simulation results establish that the proposed approach outperforms several existing optimization techniques.

The increasing concern of power systems toward distributed generation enables modern power grids and energy management systems to focus their concentration to derive an optimal operational planning with regard to energy costs minimization of Micro-Grid and better utilization of Renewable Energy Sources in the presence of Battery Energy Storage. This paper presents Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (SIMBO-Q) to minimize total operation cost of Micro-Grid considering optimal size of Battery Energy Storage. SIMBO-Q performs the optimization through quarantine and treatment loop based on probability. However SIMBO-Q algorithm takes large number of iterations to reach to the optimum solution if the system has large number of variables. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning concept is introduced in basic SIMBO-Q algorithm. The proposed algorithm is tested on a typical Micro-Grid and simulation results establish that the proposed approach outperforms several existing optimization techniques. [ABSTRACT FROM AUTHOR]