Multi-Objective Optimal Power Flow Using Efficient Evolutionary Algorithm

A novel efficient multi-objective optimization (MOO) technique for solving the optimal power flow (OPF) problem has been proposed in this paper. In this efficient approach uses the concept of incremental power flow model based on sensitivities and some heuristics. The proposed approach is designed to overcome the main drawback of conventional MOO approach, i. e., the excess computational time. In the present paper, three objective functions i. e., generation cost, system losses and voltage stability index are considered. In the proposed efficient MOO approach, the first half of the specified number of Pareto optimal solutions are obtained by optimizing the fuel cost objective while considering other objective (i. e., system loss or voltage stability index) as constraint while the second half is obtained in a vice versa manner. After obtaining the total Pareto optimal solutions, they are sorted in the ascending order of fuel cost objective function value obtained for each solution leads to the Pareto optimal front. The proposed efficient approach is implemented using the differential evolution (DE) algorithm. The proposed efficient MOO approach can effectively handle the complex non-linearities, discrete variables, discontinuities and multiple objectives. The effectiveness of the proposed approach is tested on standard IEEE 30 bus test system. The simulation studies show that the Pareto optimal solutions obtained with proposed efficient MOO approach are diverse and well distributed over the entire Pareto optimal front. The simulation results indicate that the execution speed of proposed efficient MOO approach is approximately 10 times faster than the conventional evolutionary based MOO approaches. © 2017 Walter de Gruyter GmbH, Berlin/Boston.