Constrained optimal power flow by mixed-integer particle swarm optimization

This paper presents an efficient mixed-integer particle swarm optimization (MIPSO) for solving the constrained optimal power flow (OPF) with a mixture of continuous and discrete control variables and discontinuous fuel cost functions. In the MIPSO-based method, the individual that contains the real-value mixture of continuous and discrete control variables is defined, two mutation schemes are proposed to deal with the continuous and discrete control variables, respectively. Different objective functions with the valve-point loading effects constraints considered were employed to test the robustness of the proposed method. The feasibility of the proposed method is demonstrated for a 9-bus system and a 26-bus system, and it is compared with other stochastic methods in terms of solution quality, convergence property, and computation efficiency. The experimental results show that the MIPSO-based OPF method has suitable mutation schemes, resulting in robustness and effectiveness in solving constrained mixed-integer OPF problems.