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The installation of an energy storage system to smooth the fluctuations of wind power output at a certain wind farm can improve the electric quality of wind power connected to the grid. In order to reduce the capacity of the energy storage system and the loss of the battery and make full use of the advantages of the super-capacitor, a game theory-based coordination and optimization control methodology for a wind power-generation and storage system (WPGSS) is presented in this paper. Aiming to maximize the WPGSS's overall profit, the methodology, taking the smoothing effect of the active power, the cost of the hybrid energy storage system (HESS), and the earnings of wind power connected to grid into consideration, builds a coordination and optimization control model based on the ensemble empirical mode decomposition (EEMD) algorithm combined with game theory. In the model, the low-pass filtering signal obtained by the EEMD is used to smooth the fluctuations of wind power output, and the band-pass filtering signal and high-pass filtering signal obtained by the EEMD are used to achieve energy distribution among the HESS. Cooperative game theory is introduced to determine the filter order of the EEMD according to the state of charge (SOC) of the HESS and to achieve the coordination and optimization control of the WPGSS taking the maximization of the WPGSS's overall profit as the game's goal constraint conditions. The genetic algorithm (GA) and particle swarm optimization (PSO) are adopted to solve the model's optimal solution, and the simulation tests were realized to verify the effectiveness of the proposed method, which can provide a theoretical basis for the coordination and optimization control of the WPGSS.

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