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Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach

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Abstract

Nowadays, the increasing usage of renewable energy sources (RES) in modern power systems introduces new challenges in power system planning and operation. Specifically, a high penetration of RESs introduces additional complexity into the optimal power flow (OPF) problem, which has a highly nonlinear complex structure. Under this environment, this paper discusses a modified hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) integrated with chaotic maps (CPSOGSA) to apply the composite benchmark test functions and to solve the OPF problem with stochastic wind power and flexible alternating current transmission system (FACTS) devices. Numerical studies are used to illustrate effectiveness of the proposed CPSOGSA approach against other approaches such as moth swarm algorithm, grey wolf optimizer, and whale optimization algorithm. Additionally, to demonstrate the superiority and robustness of CPSOGSA algorithm, Wilcoxon signed-rank test is applied for all case studies. Case studies indicate the potential of CPSOGSA method in effectively solving OPF problem with stochastic wind power and FACTS devices.

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Acknowledgements

Dr. Serhat DUMAN would like to thank the support provided by Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB 2219 Postdoctoral Research Program under Application Number 1059B191700888.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Clarkson University, Potsdam, NY, 13699, USA

    Serhat Duman & Jie Li

  2. Electrical and Electronics Engineering, Technology Faculty, Duzce University, 81600, Duzce, Turkey

    Serhat Duman & Ugur Guvenc

  3. Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Lei Wu

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  1. Serhat Duman
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Correspondence to Serhat Duman.

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Duman, S., Li, J., Wu, L. et al. Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach. Neural Comput & Applic 32, 8463–8492 (2020). https://doi.org/10.1007/s00521-019-04338-y

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