Power grid partition with improved biogeography-based optimization algorithm

Abstract

With the continuous expansion of the scale of the interconnected power grid and the large amount of new energy access, the power grid suffers from a wider range of faults. Unlocking the electromagnetic ring network and performing layered and partitioned operation of the power grid is the future development trend of the power grid. Grid partition operation can effectively enhance the controllability within each sub-partition of the power grid, the decoupling between the partitions and the robustness of cascade failure. Depending on the power grid topology and electrical characteristics, this paper proposes a new power grid partition algorithm based on the improved biogeography-based optimization (BBO) algorithm, which improves the algorithm's calculation speed and partition quality. The algorithm, first, analyzes the physical and operational characteristics of the power network, and determines the zoning principle based on the actual situation; next combined with the definition of relevant parameters of the complex network theory, constructs a topology model that conforms to the characteristics of the power grid; at the same time, considering the small-world characteristics of the power grid topology, the original biogeography is improved the update method of algorithm node information exchange reduces the time complexity of the algorithm and makes it more suitable for modern power grids. Then, according to the value of the improved modularity function and the grid zoning principle, the optimal zoning plan is output. Finally, the IEEE-39 node system and the IEEE-118 node system are designated as calculation examples. The extensive calculation example results demonstrate that the method of community network analysis considering the physical characteristics of the power grid is reasonable to a certain extent. The calculation method is simple and fast, which meets the needs of complex power grid analysis and engineering calculation.