Comparative Application of Multi-Objective Evolutionary Algorithms to the Voltage and Reactive Power Optimization Problem in Power Systems

摘要: This study investigates the applicability of two elitist multi-objective evolutionary algorithms (MOEAs), namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and an improved Strength Pareto Evolutionary Algorithm (SPEA2), in voltage reactive power optimization problem. The problem has been formulated mathematically as a nonlinear constrained multiobjective where real loss, load bus deviations installation cost additional (VAR) sources are to be minimized simultaneously. To assess effectiveness proposed approach, different combinations objectives have simulation results showed that were able generate whole set well distributed Pareto-optimal solutions single run. Moreover, fuzzy logic theory is employed extract best compromise solution over trade-off curves obtained. Furthermore, performance analysis SPEA2 found better convergence spread than NSGA-II. However, NSGA-II more extended some cases required less computational time SPEA2.