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Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D

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  • Yin, Peng-Yeng
  • Wu, Tsai-Hung
  • Hsu, Ping-Yi

Abstract

Wake effect and wind uncertainty are the key factors resulting in low efficiency in wind energy extraction. Classic micro-siting approaches focus on reducing the wake effect to determine the best number and positions of the turbines. However, very little literature has addressed the issue of risk due to wind uncertainty which causes the expected production to be distantly deviated from what is actually produced. Multi-objective modeling is of particular interest due to its potential of managing risk. This paper proposes several multi-objective risk management (MORM) models which set the foundation on Monte Carlo simulation to conduct cost, benefit, and risk analyses. We develop an enhanced multi-objective evolutionary algorithm with decomposition (MOEA/D) algorithm by taking advantages of wind farm structure. The experiment result with real wind farm data shows the application differences in gauging the risks with various MORM models. The enhanced MOEA/D is compared with two state-of-the-art algorithms and the former produces the best frontier in the objective space in most of the simulations with mean absolute percentage improvement (API) of 46%. We demonstrate what-if analysis with various risk scenarios to assist the decision maker to realize his/her risk tolerance and to reach quality tradeoff decisions.

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  • Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:579-597
    DOI: 10.1016/j.energy.2017.09.103
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    Cited by:

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    2. Moreno, Sinvaldo Rodrigues & Pierezan, Juliano & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2021. "Multi-objective lightning search algorithm applied to wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    3. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    4. Zhang, Jingrui & Zhu, Xiaoqing & Chen, Tengpeng & Yu, Yanlin & Xue, Wendong, 2020. "Improved MOEA/D approach to many-objective day-ahead scheduling with consideration of adjustable outputs of renewable units and load reduction in active distribution networks," Energy, Elsevier, vol. 210(C).
    5. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2020. "Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines," Energy, Elsevier, vol. 205(C).
    6. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.
    7. Tan, Bifei & Chen, Haoyong, 2020. "Multi-objective energy management of multiple microgrids under random electric vehicle charging," Energy, Elsevier, vol. 208(C).

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