Multi-Constrained Optimal Power Flow by an opposition-based differential evolution

This paper proposes a robust method for solving the Multi-Constrained Optimal Power Flow (MCOPF) problem based on an opposition-based differential evolution (ODE) algorithm. The MCOPF problem, which considers transient stability, valve-point effects, prohibited operating zones, and branch flow thermal constraints, is a nonlinear, nonconvex, and nondifferentiable optimization problem in power system planning and operation, and is very difficult for conventional optimization methods to handle. The proposed ODE is an enhanced differential evolution (DE) method and employs the Opposition-Based Learning (OBL) for population initialization, production of new generations and also improving population's best fitness value. Numerical tests comparing conventional DE and ODE methods on the New England 10-generator, 39-bus system have validated the effectiveness and robustness of the proposed approach both in convergence speed and solution accuracy.