Power Grid Optimal Topology Control Considering Correlations of System Uncertainties

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Abstract
This paper presents a probabilistic formulation and solution technique for the application of DC optimal power flow (DCOPF)-based network topology control through transmission line switching strategies. Efficient utilization of the point estimation method (PEM) is pursued to model the system uncertainties, i.e., the stochastic load profile and the intermittent renewable generation. In order to address the computational effectiveness of the suggested probabilistic methodology, the PEM formulation is harnessed by a scenario reduction approach to capture the correlations of the system uncertainties, thereby achieving a more robust and faster operation solution for day-ahead and real-time applications. The proposed approach is applied to a modified IEEE 118-bus test system, where it demonstrates its attractive performance under different test scenario. To further verify the efficiency and scalability of the proposed algorithms on largescale systems, the proposed analytics are applied to 200-bus synthetic grid of central Illinois. AC feasibility and transient stability checks are performed on both test systems and the results are extensively analyzed.

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