研究目的
To present a cost-effective system-level restoration scheme to improve power grids resilience by efficient response to the damages due to natural or manmade disasters.
研究成果
An efficient model was proposed to support the post-disaster decision making process for power grid restoration. The results demonstrate that the proposed model is able to find the optimal restoration schedule of damaged components in a cost-effective manner. The opportunity cost of lost loads, the repair cost, and the generation costs were considered as economic indices. It was demonstrated that economy of disaster needs to be an important part of the restoration plan.
研究不足
The technical and application constraints of the experiments include the limited restoration resources (repair crews) and the assumption that plenty of spare parts and equipment for restoration are available. Potential areas for optimization include the consideration of different skill levels for repair crews and the inclusion of more detailed economic measures in the model.
1：Experimental Design and Method Selection:
The methodology involves developing a post-disaster decision making model to find the optimal repair schedule, unit commitment solution, and system configuration in restoration of the damaged power grid. The model is formulated as a mixed-integer program and decomposed into an integer master problem and a dual linear subproblem to be solved using Benders decomposition algorithm.
2：Sample Selection and Data Sources:
The IEEE 118-bus system is used to analyze the proposed post-disaster restoration model. The system has 118 buses, 54 generation units, 186 branches, and 91 load sides.
3：List of Experimental Equipment and Materials:
The repair crew is considered to be the only limited resource that is allocated to repair the damaged components.
4：Experimental Procedures and Operational Workflow:
The model is implemented on the IEEE 118-bus system setup, composed of 162,481 decision variables, in which 22,308 of them are integer variables. The model is also constrained with 352,514 linear equations.
5：Data Analysis Methods:
The model is decomposed into an IP master problem and a dual linear subproblem, and is solved using Benders decomposition method.
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