Optimal power flow by improved evolutionary programming

This article proposes an improved evolutionary programming (IEP) for solving optimal power flow (OPF) with nonsmooth and nonconvex generator fuel cost curves. Initially, the whole population is divided into multiple subpopulations, which are used to perform the parallel search in divided solution space. IEP includes Gaussian and Cauchy mutation operators in different subpopulations to enhance the search diversity, selection operators with probabilistic updating strategy to avoid entrapping in local optimum, and reassignment operator for every subpopulation to exchange search information. The proposed IEP was tested on the IEEE 30 bus system with three different types of generator fuel cost curves. It is shown that IEP total generator fuel cost is less expensive than those of evolutionary programming, tabu search, hybrid tabu search and simulated annealing, and improved tabu search, leading to substantial generator fuel cost savings. Moreover, IEP can easily facilitate parallel implementation to reduce the computing time without sacrificing the quality of solution.