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Cooperative multiagent optimization method for wind farm power delivery maximization

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  • Gu, Bo
  • Meng, Hang
  • Ge, Mingwei
  • Zhang, Hongtao
  • Liu, Xinyu

Abstract

Reducing wake losses and improving the overall power output of wind farms have become a research focus in attempts to optimize wind farm power generation. A cooperative multiagent optimization method (CMAOM) for wind farm power delivery maximization has been proposed in this paper. In the CMAOM, a wind farm wake distribution calculation model, based on the Jensen wake model, was constructed, and each turbine was then assigned as an agent; the CMAOM was used to reduce wake losses and improve the overall wind farm power output. The agent, multiagent objective function and grid environment were defined in this study using wind turbine characteristics, and the CMAOM, including the neighborhood competition operator, mutation operator, and self-learning operator, were calibrated using wind turbine aerodynamic correlation characteristics. The Danish Horns Rev wind farm was selected as a case study, and the CMAOM and particle swarm optimization (PSO) algorithm were used to conduct analyses there. The results showed that the CMAOM proposed in this paper was more effective than the PSO algorithm and that the wind farm overall power output was increased by 7.51% for a 270° incoming wind direction and an incoming wind speed of 8.5 m/s.

Suggested Citation

  • Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:energy:v:233:y:2021:i:c:s0360544221013244
    DOI: 10.1016/j.energy.2021.121076
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    References listed on IDEAS

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    Cited by:

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    2. Fathy, Ahmed & Rezk, Hegazy & Yousri, Dalia & Kandil, Tarek & Abo-Khalil, Ahmed G., 2022. "Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system," Energy, Elsevier, vol. 249(C).
    3. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    4. Wang, Hao-ran & Feng, Tian-tian & Xiong, Wei, 2022. "How can the dynamic game be integrated into blockchain-based distributed energy resources multi-agent transactions for decision-making?," Energy, Elsevier, vol. 254(PB).

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