Hierarchical optimization of policy and design for standalone hybrid power systems considering lifecycle carbon reduction subsidy

To achieve the target of carbon neutrality, the present work proposes a lifecycle carbon reduction subsidy policy to promote the exploitation of renewable energy technologies in remote areas. The hierarchical optimization of policy and design for a standalone hybrid renewable energy system is further conducted based on the proposed subsidy policy. The upper-level policy optimization model aims to maximize the renewable energy fraction and minimize the subsidy expenses borne by the government simultaneously, and the lower-level design optimization model aims to minimize the net present cost borne by the system operator. Four meta-heuristic algorithms coupled with a standard iterative method are employed to solve the hierarchical optimization problem, and their performances are comprehensively assessed in terms of convergence, robustness, and computational efficiency. Besides, an improved K-means clustering algorithm along with a tailored optimal K-value selection strategy is employed for scenario reduction. The results of the case study show that: (ⅰ) teaching-learning-based optimization performs the best convergence and robustness as it achieves the balance of exploration and exploitation. (ⅱ) the nexus between policy and system design presents a piecewise pattern, implying that the system configuration will change significantly only when the subsidy policy exceeds a certain threshold. (ⅲ) the proposed subsidy policy is expected to increase the renewable energy fraction to 99.23% and reduce the lifecycle carbon emissions by 80.7%. Overall, the proposed subsidy policy and its impact mechanism on system design can provide theoretical support for the government to formulate tailored policies for different preferences, and the proposed optimization framework and methodology can be directly extended to practical applications.