A Hybrid Dynamic Optimization Approach for Stability Constrained Optimal Power Flow

Stability-constrained optimal power flow (SOPF) is an effective and economic tool to enhance stability performance by adjusting initial steady-state operating conditions, with the consideration of rotor angle and short-term voltage performance criteria. SOPF belongs to the category of dynamic optimization problems which are computationally expensive. In order to reduce its computational complexity, a hybrid dynamic optimization approach is proposed for efficient and robust solving SOPF problems. Based on the direct multiple shooting method, this approach combines the algorithmic advantages from existing direct sequential and simultaneous approaches. Coarse-grained parallelism among multiple shooting intervals is explored. A modular-based implementation architecture is designed to take advantage of the loose coupling between time-domain simulation and optimization. Case studies on various test systems indicate that the proposed approach is able to reduce computation time compared with other direct approaches for dynamic optimization. Also, the investigated parallelizations are effective to achieve acceleration on a symmetric multiprocessing platform.