Transient stability constrained optimal power flow (TSCOPF) is able to reduce costs while keeping the operation point away from the stability boundary. Unacceptable computational time is one of the largest barriers in applying TSCOPF-based solutions. Based on reduced-space interior point method (RIPM), this paper introduces a parallel algorithm with high computing efficiency for multi-contingency TSCOPF problems. A two-level parallelism is developed to fully utilize the computing power of Beowulf clusters equipped with multi-core CPUs. Compute-intensive steps are decomposed according to different contingencies with mathematical equivalent transformations. Distributed computing tasks are accelerated using elemental decomposition on Jacobians, and multithreaded libraries are employed to exploit multi-core CPUs. The effectiveness of the proposed algorithm is benchmarked on a Beowulf cluster with 16 computing nodes with 128 CPU-cores using test cases including up to 2746 buses and 16 contingencies. Numerical results indicate that the proposed parallel approach has great capacity in accelerating TSCOPF solution.
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