A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

• Published in: Energy conversion and management Vol. 92; pp. 149 - 161
• Main Authors: Kefayat, M., Lashkar Ara, A., Nabavi Niaki, S.A.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2015
• Subjects: Algorithms; Ant colony optimization; Ant colony optimization (ACO); Artificial bee colony (ABC); electric power; emissions; energy costs; Formicidae; fuel cells; Hybrid ACO–ABC; Multi-objective optimization; Optimal placement; Optimization; Point estimate method (PEM); Probabilistic methods; Probability theory; Renewable energy; Sizing; Swarm intelligence; system optimization; uncertainty; Wind energy; wind power; Point estimate method (PEM); Renewable energy; Artificial bee colony (ABC); Optimal placement; Hybrid ACO–ABC; Multi-objective optimization; Ant colony optimization (ACO)
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2014.12.037

•A probabilistic optimization framework incorporated with uncertainty is proposed.•A hybrid optimization approach combining ACO and ABC algorithms is proposed.•The problem is to deal with technical, environmental and economical aspects.•A fuzzy interactive approach is incorporated to solve the multi-objective problem.•Several strategies are implemented to compare with literature methods. In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods.