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

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