A new hybrid algorithm for multi‐objective reactive power planning via facts devices and renewable wind resources

Rahmad Syah, Peyman Khorshidian Mianaei, Marischa Elveny, Naeim Ahmadian, Dadan Ramdan, Reza Habibifar, Afshin Davarpanah

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The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non‐linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi‐objective hybrid algorithm is proposed to solve this problem by considering the linear and non‐linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non‐dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

Original languageEnglish
Article number5246
Number of pages32
Issue number15
Publication statusPublished - 03 Aug 2021


  • Hybrid algorithm
  • Multi‐objective optimization
  • Particle swarm optimization
  • Reactive power (RP) planning
  • Virus colony search
  • Wind
  • Algorithms
  • Electricity
  • Goals


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