Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm

Xiaohui Yuan, Binqiao Zhang, Pengtao Wang, Ji Liang, Yanbin Yuan, Yuehua Huang and Xiaohui Lei

Energy, 2017, vol. 122, issue C, 70-82

Abstract: An improved strength Pareto evolutionary algorithm is proposed to solve the multi-objective optimal power flow problem. The fuel cost and emission are considered as two objective functions for the optimal flow problem. In the proposed algorithm, there are three aspects of improvements in the original strength Pareto evolutionary algorithm. First, the external archive population is only composed of the variable size of non-dominated individuals in environmental selection operator. Secondly, the Euclidean distance between the elite individuals and its k-th neighboring individuals is adopted to update the external archive population. Thirdly, the local search strategy is embedded into strength Pareto evolutionary algorithm. The performance of the proposed method has been tested on the IEEE 30-bus and IEEE 57-bus systems. The simulation results show that the proposed method is able to produce well distributed Pareto optimal solutions for the multi-objective optimal power flow problem. Compared with the results obtained by other methods, the superiority of the proposed method is verified.

Keywords: Strength Pareto evolutionary algorithm; Optimal power flow; Emission; Local search strategy; Environmental selection strategy (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:122:y:2017:i:c:p:70-82

DOI: 10.1016/j.energy.2017.01.071

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