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Mixed-Integer Linear Programming-Based Splitting Strategies for Power System Islanding Operation Considering Network Connectivity

An efficient splitting strategy is important for the islanding operation of power systems. In this paper, a mixed-integer linear programming (MILP)-based splitting method is proposed. First, the graph theory is employed to transform the splitting problem into a graph partition problem. Then, the graph partition problem is modeled as an MILP optimization problem with consideration to the network connectivity of all subgraphs. The MILP-based splitting strategy can be efficiently computed with existing commercial solvers, and the multiple optimal solutions can be captured with the recursive process. Compared with the splitting methods in the literature, the proposed method is more flexible and efficient, such that the users can select the optimal solution from multiple solutions with different interests and heuristic splitting rules (e.g., with the minimum amount of switched lines). Finally, the effectiveness of the proposed method has been verified with IEEE 30-, 118-, and 300-bus test systems.
- Xi'an Jiaotong University China (People's Republic of)
- University of Tennessee at Knoxville United States
- Xi’an Jiaotong-Liverpool University China (People's Republic of)
- Tennessee State University United States
- Tennessee State University United States
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).70 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
