Post-Event Restoration Programming for Power System: A Case Study of Yunnan, China

When the scale of disrupted power networks is large, the optimal restoration programming problem for multiple pairs of shortest paths recovery games is often intractable. This work addresses this challenge by proposing a novel approach to optimize post-event power system restoration that considers the complexity of shortest-path restoration. A multi-party cooperative game model is formulated to capture the need for coordinated resource allocation and task execution among various stakeholders involved in the restoration process. To tackle the inherent problem complexity, we propose a Lagrange relaxation-based solution methodology, which decomposes the problem into two tractable sub-games appreciatively. We then find some properties for these two subgames, which can help to solve their optimal cost allocations efficiently. The effectiveness of the proposed approach is evaluated through experiments on systems of varying scales and a simulation mimicking power system recovery after an earthquake in Yunnan Province. The results show that our algorithm is both efficient and effective in generating multiple deployment strategies for post-event restoration.