Research on optimization control strategy of DC microgrid based on PSA-PSO algorithm

DC microgrid with hydrogen-electric coupled characteristic is an effective solution for low-carbon development. To achieve the stable and efficient operation of different types of hydrogen and electrical equipment in the DC microgrid, this paper proposes a DC microgrid optimization control strategy based on parameter self-adaptive particle swarm optimization (PSA-PSO) algorithm. Firstly, this strategy takes economy as the optimization objective, and optimizes the operating state of hydrogen and electrical equipment with constraints of the equipment operation state and electricity balance. Then, in response to the problems existing in PSO algorithm, a PSA-PSO algorithm is proposed. This algorithm introduces Gaussian disturbance with adaptively adjusting the learning factor, inertia weight, and disturbance step size of the algorithm, thereby improving control performance. An example analysis was performed using real data, the results show that compared to traditional optimization control strategy, it can effectively improve the ability to search for optimal solutions, so that achieve adaptive coordinated optimization control of microgrid, and reduce operating cost.