Improved Genetic Algorithm-Based Optimization Approach for Energy Management Of Microgrid

In order to reduce the energy scheduling cost of microgrid, this paper presents an energy management strategy based on improved genetic algorithm. It not only coordinates the power output between different generator units in accordance with load fluctuation, but also adjusts dynamically the output of the power generation units according to the real-time electricity price. The improved genetic algorithm ensures that the best individual of each generation can be inherited to the next generation by replacing the worst individual with the best individual. Therefore, it is called optimal individual-based genetic algorithm (OIGA). Before population initialization, the method performs individual initialization, which can reduce the complexity of the population and the time required to execute the algorithm. Compared with genetic algorithm (GA) and MATLAB toolbox YALMIP, the proposed method performs better in convergence accuracy and speed.