Stability-constrained Optimal Power Flow For Power Svstems:Model,Algorithm And Parallelization

Stability-constrained optimal power flow (SOPF) is an important research topic in power system operation and control. It is able to minimize system operating cost, while enhancing system dynamic performance after disturbance, including transient stability and short-term voltage stability, by means of adjusting steady-state operating conditions. Mathematically, SOPF belongs to the category of dynamic optimization, which is essentially a nonlinear programming problem with differential algebraic equation constraints. The computational process of dynamic optimization is commonly complicated, especially for large-scale power systems with realistic dynamic component models, long simulation intervals and multiple contingencies, which leads to high CPU and memory usage. Computational complexity is the major theoretical and technical barrier in the research of SOPF. In this thesis, efforts have been made on the development of an optimization method and its corresponding implementation based on numerical optimization theory and high performance computing techniques. Major outcomes of this research work include:1) A unified SOPF model with both transient stability and short-term voltage stability constraints is proposed, which is described as a dynamic optimization formulation. Also, in order to consider various dynamic model of realistic power system components in SOPF problem, a systematic approach is developed for model integration for power system steady-state and transient-state analysis, based on object-oriented design and automatic differentiation technique. A modular-based framework is designed and implemented for SOPF, in order to increase the algorithm flexibility and application potential of this approach.2) An combination of direct multiple shooting method and reduced-space interior point method is proposed for the two stages of dynamic optimization solution, i.e. the conversion stage for differential algebraic equations and the solving stage for nonlinear programming problems. Compared with previous investigations in the literature, the proposed algorithm is able to utilize the characteristics and structures of SOPF problems, which greatly increases the convergence performance and computational efficiency. The effectiveness of the proposed two-stage optimization algorithm is verified by numerical experiments on a series of large-scale power system test cases.3) Four parallel decomposition strategies are proposed for different algorithmic aspects in the solving process of SOPF, including contingency decomposition, elemental decomposition, shooting interval decomposition and sensitivity parameter decomposition. These parallel approaches are able to fully utilize various high performance computing platforms, including multi-core CPU based computing clusters, symmetric multi-processing platforms and graphics processing units. Multi-level parallelization is achieved, in order to speed-up and scale-up the proposed SOPF approach.