Emergency backup power robust planning for urban agglomeration power grids with a high proportion of new energy sources in extreme disaster scenarios

This study introduces a novel power supply and network model designed for large-scale power grids with a high proportion of new energy sources in urban agglomerations, applicable to extreme disaster scenarios. By enhancing the IEEE 30 basic calculation examples, the research develops a comprehensive framework that emphasizes computational performance and engineering practicality. A massive number of DC power flow calculation scenarios over 110000 and corresponding evaluation indices are proposed, effectively addressing grid states under extreme climate-induced disconnections. Compared to traditional AC power flow methods, this approach demonstrates superior computational efficiency and engineering value. Furthermore, an integrated optimization algorithm that combines two advanced evolutionary algorithms is presented. Compared to 7 commonly used or state-of-the-art algorithms, the novel algorithm proposed in this paper demonstrates superior performance in solving sparse and constrained multi-objective optimization problems. This algorithm is highly effective at managing sparsity and constraints during the optimization process, yielding 8 four-objective Pareto frontier solutions that offer professionals robust and economical decision-making options.