Abstract
This paper presents a modified artificial bee colony (MABC) algorithm to solve optimal power flow (OPF) problem. In the proposed MABC algorithm, the searching operation for new food source of artificial bee colony (ABC) algorithm is replaced with mutation and crossover operation of differential evolution (DE) algorithm to improve exploitation capacity. The OPF objective functions involve minimization of total fuel cost of generating units, minimization of emission of atmospheric pollutants, minimization of active power losses, and minimization of voltage deviations. The fuzzy satisfaction-maximizing method is utilized to convert the multiobjectives problem into single objective problem. The proposed approach is applied to the OPF problem on IEEE 30-bus test system. And the results are compared with those obtained by other heuristic algorithms, which demonstrate that the MABC algorithm not only has a better exploration capacity but also possesses stronger exploitation capacity and can effectively solve the OPF problem.
Highlights
Optimal power flow (OPF) was first proposed by French scholar Carpentier in the 1960s
Because the active power loss caused by transmission and distribution and the emission cost brought by the some generators affect the economics of the power system [4], the OPF objective functions which are established from the aspect of system economics need to consider these two factors
The results demonstrate that the modified artificial bee colony (MABC) can solve the OPF problem effectively, and the optimal value obtained by MABC algorithm is better than other algorithms
Summary
Optimal power flow (OPF) was first proposed by French scholar Carpentier in the 1960s. The OPF problem is considered as complex multiconstraints, nonlinear, and noncontinuous optimization problem regardless of OPF model with single objective or multiobjective function Many classical algorithms such as linear programming [8], quadratic programming [9], simplified gradient method [10], Newton method [11], and interior point method [12] have been widely applied to solve the OPF problem. The typical heuristic methods include genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), artificial immune algorithm, and artificial bee colony (ABC) algorithm [13,14,15,16,17] These algorithms are based on multipoint stochastic searching, and they can effectively solve optimization problem with discrete variables. The simulation results are compared with those obtained by other heuristic methods, and they demonstrate that MABC algorithm possesses better exploration capacity and stronger exploitation capacity
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