Multi‐objective capacity estimation of wind ‐ solar ‐ energy storage in power grid planning consideration policy effect

Abstract

Numerous policies have been implemented to advance the growth of renewable energy. Nonetheless, certain policies have not yielded the anticipated impact on the progression of renewable energy development. In order to maximize the promotion effect of renewable energy policies, this study proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. First, based on the policy quantification, grey relation analysis (GRA) is used to calculate the correlation degree of the policy indicators on the planning capacity of renewable energy. Further, a multi‐objective capacity estimation model for wind, solar and energy storage is comprehensively presented. Some highly correlated policy indicators are transformed into the special constraints. And the economy and the stability of the power grid are integrated as the objective function. Meanwhile, the carbon trading and punishment for wind power and solar power abandonment are considered. Finally, the proposed model is solved by NSGA‐II‐PSO (particle swarm optimization) algorithm. The novelty of the algorithm is that the crossover operation of NSGA‐II is replaced by the position updating of particle swarm. The calculation result of the case study can effectively evaluate the optimal planning capacity of renewable energy under different policies, while ensuring the economic and the stability of the power system. The study can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.