Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II

Stability is an important constraint in power system operation and the transient stability constrained optimal power flow (OPF) has always received considerable attention in recent years. In this paper, the defects of the existing models and algorithms around this topic are firstly analyzed, on the basis of which, a multi-objective optimization method is proposed. The basic idea of the proposed method is to model transient stability as an objective function rather than an inequality constraint and consider classic transient stability constrained OPF (TSCOPF) as a tradeoff procedure using Pareto ideology. Second, a master-slave parallel elitist non-dominated sorting genetic algorithm II is used to solve the proposed multi-objective optimization problem, the parallel algorithm shows an excellent acceleration effect and provides a set of Pareto optimal solutions for decision makers to select. An innovative weight assigning technique based on fuzzy membership variance is also introduced for a more scientific and objective optimal solution decision. Case study results demonstrate the proposed multi-objective method has many advantages, compared with traditional TSCOPF methods.