In order to maximize the promotion effect of renewable energy policies, this paper proposes a capacity allocation optimization method of wind power generation, solar power and energy storage in power grid planning under different policy objectives. Firstly, based on the policy quantification, grey relation analysis 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 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 is considered. Finally, the proposed model is solved by NSGA-Ⅱ-PSO 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 ensure the economic and the stability of the power system. The paper can provide the reasonable basis and the valid analytical method for the policy formulation and the renewable energy development.

In the context of the energy crisis, the development of low-carbon integrated energy systems has become a prominent research area. This article addresses the challenges posed by high energy consumption and emissions in integrated energy systems by proposing a multi-stage planning method for low-carbon integrated energy that considers load time transfer characteristics. The first step involves examining the time transfer characteristics of demand response and analyzing the economic benefits of integrated energy systems participating in the electricity-carbon market. Subsequently, a multi-stage green low-carbon planning model for the integrated energy system is constructed. To validate the effectiveness of the proposed model, actual calculation results are obtained. These results demonstrate that demand response, specifically in data centers, can significantly reduce the operational costs of integrated energy systems. Furthermore, the multi-stage low-carbon planning approach is shown to be more reasonable and economically beneficial compared to single-stage planning. Overall, this research article provides insights into the development of low-carbon integrated energy systems within the context of the energy crisis. By considering load time transfer characteristics and employing a multi-stage planning method, the article highlights the potential for reducing costs and improving the overall efficiency of integrated energy systems.