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Operational optimization of a stand-alone hybrid renewable energy generation system based on an improved genetic algorithm

In a hybrid renewable energy power generation system, optimization and control is a challenging task because the behaviors of the system are becoming unpredictable and more complex. After the system is built, optimization and control of its operation is important for utilizing the renewable energy efficiently and economically. In the paper, an improved genetic algorithm is developed for achieving the optimization of the hybrid RE system by considering its operation during its life-time. The proposed algorithm is validated by performing a scenario simulation and the results show that the improved genetic algorithm has better convergence speed or accuracy than those of the standard genetic algorithm.