Offshore Wind Power Energy Storage Optimization via K-Means++ and Grey Wolf Algorithm

The randomness and intermittency of the output of offshore wind power pose challenges to grid connection. Energy storage(ES) is the key to solving this problem, but the outlay of ES is high. In order to streamline the configuration of offshore wind power ES and take into account both the smoothing of output fluctuations and economic efficiency, this paper proposes a method combining the K-Means++ algorithm (KMPA) and the Grey Wolf Optimization algorithm (GWOA). The annual power response trajectory of the ES mechanism is procured through the employment of wavelet packet decomposition. The KMPA is employed to formulate typical situations. A model is constructed with the aim of cutting the yearly total expense, and the GWOA is used for solving. Through the simulation of an example, this method is compared with other methods. The results show that it can effectively suppress the output fluctuations, has a low configuration error, high calculation efficiency, and more economical cost. It can provide strong support for the ES arrangement and infrastructure-laying planning of offshore wind farms (OWF).