Multi-objective dynamic optimal power flow using improved artificial bee colony algorithm based on Pareto optimization

This paper presents an improved artificial bee colony algorithm based on Pareto optimization for solving the multi-objective dynamic optimal power flow (DOPF) problem. To simulate the dynamic operational situations of a power system in 24h, various load demands related to each hour were considered in an OPF problem. The multi-objective DOPF problem was solved by minimizing total generation cost, total emission, and total real power loss while meeting some constraints. When considering a multi-objective optimization problem, one of the objective functions is improved, and the other objective functions deteriorate. These functions cannot be improved simultaneously because they contradict each other. The global Pareto optimal front, which is composed of a set of nondominated solutions, was identified in the multi-objective optimization problem. Power system operators can appropriately select one of the nondominated solutions according to various situations. An improved artificial bee colony algorithm was used as optimal tool on the optimization update procedure. To avoid yielding local optimal solutions to the problem, a skill called chaos queues was added. To demonstrate the effectiveness of the proposed method, the multi-objective DOPF was performed on the IEEE 30-bus and 118-bus systems. The results of using the proposed method were compared with the results of using other algorithms, revealing the effectiveness of the proposed method in the multi-objective DOPF problem. Copyright (C) 2015 John Wiley & Sons, Ltd.