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

Serhat Duman, Jie Li, Lei Wu, Ugur Guvenc

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)8463-8492
Number of pages30
JournalNeural Computing and Applications
Volume32
Issue number12
DOIs
StatePublished - Jun 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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