Economic dispatch in microgrid with battery storage system using wild geese algorithm

The development of microgrid systems forces to integration of various distributed generators (DG) and battery energy storage (BES) systems. The integration of a BES system in MG provides several benefits such as fast response, short-term power supply, improved power quality, ancillary service, and arbitrage. The system constraints as power balance and the assets constraints as power limit of different DGs, energy, and charge/discharge power limit of BES increase the complexity of the original problem. Therefore, to tackle such a problem an efficient, robust, and strong optimization algorithm is required. In this paper, a recently developed optimization method known as the wild geese algorithm (WGA) has been applied to solve the problem. The WGA is a population-based metaheuristic approach inspired by the different aspects of the living behavior of wild geese. This algorithm has developed with the inspiration of different phases of wild geese's lives, such as their evolution, well-organized and coordinated long-distance group migration, and fatality. The WGA has tested on the MG problem and the obtained simulation results are validated by comparison of results obtained from the other methods. The result shows the WGA is efficiently able to handle the MG operational problem with numerous constraints and shows the potential to produce a high-quality solution in terms of cost reduction. The incorporation of BES reduces operating costs for MG's off-grid and on-grid operational modes by 5.91% and 8.62%, respectively. Further, the analysis for off-grid mode under different seasonality, reduction in the operational cost by 4.47%, 9.28%, 6.37%, and 7.22% was measured in the summer, autumn, winter, and spring seasons, respectively, with the integration of BES. Additionally, the integration of BES in on-grid mode results in a decrease in operating costs by 7.15%, 12.54%, 7.56%, and 11.07% in the summer, autumn, winter, and spring, respectively.