关键词: 电-氢协同, Copula理论, 核密度估计, 经济性, 优化调度

Abstract: The wind and solar energy resources are not well aligned with the load center in China, resulting in a temporal and spatial mismatch. Utilizing the correlation and complementarity between wind and solar within the same region, adopting an electric-hydrogen synergistic energy storage mode is an effective technical path to significantly mitigate the effects of renewable energy output on the power grid. In this paper, the non-parametric kernel density estimation method is employed to fit the wind and solar output data. By employing the Copula theory, a method for generating spatio-temporal scenarios considering the correlation of wind and solar power generations is proposed. By considering the correlation of wind and solar power generation, a model for the economic day-ahead optimization scheduling problem of the electric-hydrogen synergistic energy storage system is further developed, and the adaptive simulated annealing particle swarm optimization algorithm is adopted to solve the scheduling model. The simulation results indicate that compared with the basic particle swarm optimization algorithm, the adaptive simulated annealing particle swarm optimization algorithm of this model possesses higher solution speed and accuracy. Under the electric-hydrogen coupling mode, combined with the peak-valley time-of-sale price mechanism, the scheduling scheme based on the day-ahead economic optimization scheduling model saves approximately 19% of the full-day operation cost. Adopting this scheme can realize the on-site consumption of fluctuating new energy sources, and offer a flexible power-hydrogen matching approach for building friendly scale-up grids.

Key words: Electric-Hydrogen, Copula theory, kernel density estimation method, Economy, Optimize scheduling