Optimal Power Flow Solutions Using Algorithm Success History Based Adaptive Differential Evolution with Linear Population Reduction

Abstract

Optimal power flow (OPF) is one of the highly non-linear, complex and challenging optimization problems in power system. The steady state parameters of an electrical network are determined in the process of solving OPF such that if recommended settings are followed, the operation of the network becomes economical and efficient. During earlier days, primary objective of OPF was minimization of fuel or generation cost. Due to growing importance on greenhouse gas emissions, power quality and system losses, numerous evolutionary algorithms (EAs) had been tried in the last couple of decades with several other objectives of OPF. This paper solves OPF with single objectives of minimizing fuel cost, emission and real power loss in the system. In addition, voltage stability enhancement is also set as an objective. Penalty function approach is adopted to deal with the system constraints which must be satisfied during the process of optimization. A state-of-the-art form of differential evolution (DE) algorithm, called L-SHADE, applied in solving the problem of OPF with different objectives. Success history-based parameter adaptation technique of DE is termed as SHADE. L-SHADE improves the performance of SHADE by linearly reducing the population size in successive generations. The algorithm is tested on standard IEEE 30-bus test system. Simulation results are analyzed and compared with some of the recent studies.