Optimal power flow solution with stochastic wind power using the Lévy coyote optimization algorithm

Abstract

Optimal power flow (OPF) is one of the most fundamental single/multi-objective, nonlinear, and non-convex optimization problems in modern power systems. Renewable energy sources are integrated into power systems to provide environmental sustainability and to reduce emissions and fuel costs. Therefore, some conventional thermal generators are being replaced with wind power sources. Although wind power is a widely used renewable energy source, it is intermittent in nature and wind speed is uncertain at any given time. For this reason, the Weibull probability density function is one of the important methods used in calculating available wind power. This paper presents an improved method based on the Levy Coyote optimization algorithm (LCOA) for solving the OPF problem with stochastic wind power. In the proposed LCOA, Levy Flights were added to the Coyote optimization algorithm to avoid local optima and to improve the ability to focus on optimal solutions. To show the effect of the novel contribution to the algorithm, the LCOA method was tested using the Congress on Evolutionary Computation-2005 benchmark test functions. Subsequently, the solution to the OPF problem with stochastic wind power was tested via the LCOA and other heuristic optimization algorithms in IEEE 30-bus, 57-bus, and 118-bus test systems. Eighteen different cases were executed including fuel cost, emissions, active power loss, voltage profile, and voltage stability, in single- and multi-objective optimization. The results showed that the LCOA was more effective than the other optimization methods at reaching an optimal solution to the OPF problem with stochastic wind power.