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An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators

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  • Suganthi, S.T.
  • Devaraj, D.
  • Ramar, K.
  • Hosimin Thilagar, S.

Abstract

Congestion management is imperative for reliable and secure system operation in restructured power systems. Since the installation of wind farms at proper locations offers the possibility of congestion relief, this paper investigates congestion management in power systems with specific consideration of wind energy sources. The optimal location of a wind farm is determined by the Bus Sensitivity Factor and the Wind Availability Factor. Differential Evolution is a population-based heuristics algorithm used for solving non-linear optimization problems. We propose an Improved Differential Evolution based approach to ease congestion in transmission lines by generator rescheduling and installation of new wind farms. In this approach, an enhanced mutation operator is introduced to improve the performance of the Differential Evolution algorithm. A standard IEEE-30 bus system is used to evaluate the proposed algorithm under critical line outages. The simulation results show that the proposed approach is more effective than other approaches.

Suggested Citation

  • Suganthi, S.T. & Devaraj, D. & Ramar, K. & Hosimin Thilagar, S., 2018. "An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 635-642.
    • Handle: RePEc:eee:rensus:v:81:y:2018:i:p1:p:635-642
    DOI: 10.1016/j.rser.2017.08.014
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    References listed on IDEAS

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    1. Sood, Yog Raj & Singh, Randhir, 2010. "Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources," Renewable Energy, Elsevier, vol. 35(8), pages 1828-1836.
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    Cited by:

    1. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    2. Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    3. Mingcan Li & Hanbin Xiao & Lin Pan & Chengjun Xu, 2019. "Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms," Energies, MDPI, vol. 12(5), pages 1-32, March.

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