Abstract
Optimal power flow (OPF) objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC) algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.
Highlights
Research on the optimal operation theory of power systems reached a new level after the optimal power flow (OPF) concept was proposed by the French scholar Carpentier in the 1960s [1,2]
The optimization mechanism of the artificial bee colony (ABC) algorithm was analyzed in [14], performance testing was done using some typical numerical functions, and the results demonstrated that the ABC algorithm possesses superior performance in terms of solving numerical optimization problems compared with the genetic algorithm (GA) algorithm, particle swarm optimization (PSO) algorithm and DE algorithm
In order to overcome the shortcomings of the ABC algorithm, the mutation operation, crossover operation and tent chaos mapping are introduced into the ABC algorithm to form an improved artificial bee colony (IABC) algorithm
Summary
Research on the optimal operation theory of power systems reached a new level after the optimal power flow (OPF) concept was proposed by the French scholar Carpentier in the 1960s [1,2]. Typical heuristic methods include the genetic algorithm (GA) [9,10], particle swarm optimization (PSO) [11,12] and others. These algorithms possess better global search abilities, aren’t restricted by the initial point position, and can effectively solve problems with discrete variables. In [15], the ABC algorithm was applied to solve an optimal reactive power flow problem of which the optimization objective was minimization of active power loss, and the simulation showed that the active power loss obtained by the ABC algorithm is lower than those obtained by other heuristic algorithms, and ABC algorithm had better convergence properties. The results are compared with those obtained by other methods and demonstrate that the proposed approach is efficient and superior
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