Real-coded mixed-integer genetic algorithm for constrained optimal power flow

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic mutation schemes are proposed to deaf with continuous/discrete control variables, respectively. Simultaneously, because the length of the individual is short, it is easy to deal with the operation of control variables, and high computation efficiency can be achieved. The total generation cost of units with the prohibited operating zones is employed to evaluate the individual. The feasibility of the proposed method is demonstrated for a 26-bus system, and it is compared with the simple GA method in terms of solution quality and computation efficiency. The experimental results show that the MIGA method has the suitable mutation schemes, resulting in robustness and efficiency in solving non-convex OPF problems.