IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v141y2017icp579-597.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217316237
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.09.103?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Wu, Yuan-Kang, 2016. "Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1048-1059.
    2. Li, Y.Z. & Wu, Q.H. & Li, M.S. & Zhan, J.P., 2014. "Mean-variance model for power system economic dispatch with wind power integrated," Energy, Elsevier, vol. 72(C), pages 510-520.
    3. Abdulrahman, Mamdouh & Wood, David, 2017. "Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation," Renewable Energy, Elsevier, vol. 102(PB), pages 267-278.
    4. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    5. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    6. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    7. Fred Glover & Saïd Hanafi, 2010. "Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization Part I: Exploiting Proximity," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(1), pages 1-15, January.
    8. Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
    9. Marco Better & Fred Glover & Gary Kochenberger & Haibo Wang, 2008. "Simulation Optimization: Applications In Risk Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 571-587.
    10. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    11. Nigim, K.A. & Parker, Paul, 2007. "Heuristic and probabilistic wind power availability estimation procedures: Improved tools for technology and site selection," Renewable Energy, Elsevier, vol. 32(4), pages 638-648.
    12. Wagner, Markus & Day, Jareth & Neumann, Frank, 2013. "A fast and effective local search algorithm for optimizing the placement of wind turbines," Renewable Energy, Elsevier, vol. 51(C), pages 64-70.
    13. Yin, Peng-Yeng & Wang, Tai-Yuan, 2012. "A GRASP-VNS algorithm for optimal wind-turbine placement in wind farms," Renewable Energy, Elsevier, vol. 48(C), pages 489-498.
    14. Amin Ibrahim & Farid Bourennani & Shahryar Rahnamayan & Greg F. Naterer, 2013. "Optimal Photovoltaic System Design with Multi-Objective Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 4(4), pages 63-89, October.
    15. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Rasheed, Nadia, 2016. "Wind farm layout optimization using area dimensions and definite point selection techniques," Renewable Energy, Elsevier, vol. 88(C), pages 154-163.
    16. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Yan, Gary Kai & Gu, Michelle Dao & Moran, Joaquin & Morgenroth, Michael & Amon, Cristina H., 2016. "The impact of land use constraints in multi-objective energy-noise wind farm layout optimization," Renewable Energy, Elsevier, vol. 85(C), pages 359-370.
    17. Fred Glover, 2014. "Exterior Path Relinking for Zero-One Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 5(3), pages 1-8, July.
    18. Saavedra-Moreno, B. & Salcedo-Sanz, S. & Paniagua-Tineo, A. & Prieto, L. & Portilla-Figueras, A., 2011. "Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms," Renewable Energy, Elsevier, vol. 36(11), pages 2838-2844.
    19. Serrano González, J. & Burgos Payán, M. & Riquelme Santos, J., 2013. "Optimum design of transmissions systems for offshore wind farms including decision making under risk," Renewable Energy, Elsevier, vol. 59(C), pages 115-127.
    20. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    21. Fred Glover & Saïd Hanafi, 2010. "Metaheuristic Search with Inequalities and Target Objectives for Mixed Binary Optimization – Part II: Exploiting Reaction and Resistance," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 1(2), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2021. "Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition," Energy, Elsevier, vol. 215(PB).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Azlan, F. & Kurnia, J.C. & Tan, B.T. & Ismadi, M.-Z., 2021. "Review on optimisation methods of wind farm array under three classical wind condition problems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. 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.
    3. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
    4. Dinçer, A.E. & Demir, A. & Yılmaz, K., 2024. "Multi-objective turbine allocation on a wind farm site," Applied Energy, Elsevier, vol. 355(C).
    5. Kyoungboo Yang & Kyungho Cho, 2019. "Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study," Energies, MDPI, vol. 12(23), pages 1-15, November.
    6. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    7. Guirguis, David & Romero, David A. & Amon, Cristina H., 2017. "Gradient-based multidisciplinary design of wind farms with continuous-variable formulations," Applied Energy, Elsevier, vol. 197(C), pages 279-291.
    8. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.
    9. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    10. Wang, Longyan & Cholette, Michael E. & Zhou, Yunkai & Yuan, Jianping & Tan, Andy C.C. & Gu, Yuantong, 2018. "Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization," Renewable Energy, Elsevier, vol. 126(C), pages 819-829.
    11. Parada, Leandro & Herrera, Carlos & Flores, Paulo & Parada, Victor, 2018. "Assessing the energy benefit of using a wind turbine micro-siting model," Renewable Energy, Elsevier, vol. 118(C), pages 591-601.
    12. Guirguis, David & Romero, David A. & Amon, Cristina H., 2016. "Toward efficient optimization of wind farm layouts: Utilizing exact gradient information," Applied Energy, Elsevier, vol. 179(C), pages 110-123.
    13. 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).
    14. Chen, K. & Song, M.X. & Zhang, X. & Wang, S.F., 2016. "Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm," Renewable Energy, Elsevier, vol. 96(PA), pages 676-686.
    15. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization," Renewable Energy, Elsevier, vol. 107(C), pages 508-521.
    16. Cuadra, L. & Ocampo-Estrella, I. & Alexandre, E. & Salcedo-Sanz, S., 2019. "A study on the impact of easements in the deployment of wind farms near airport facilities," Renewable Energy, Elsevier, vol. 135(C), pages 566-588.
    17. Dalibor Petković & Siti Ab Hamid & Žarko Ćojbašić & Nenad Pavlović, 2014. "Adapting project management method and ANFIS strategy for variables selection and analyzing wind turbine wake effect," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 463-475, November.
    18. Rodrigues, S. & Bauer, P. & Bosman, Peter A.N., 2016. "Multi-objective optimization of wind farm layouts – Complexity, constraint handling and scalability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 587-609.
    19. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    20. Shakoor, Rabia & Hassan, Mohammad Yusri & Raheem, Abdur & Rasheed, Nadia, 2016. "Wind farm layout optimization using area dimensions and definite point selection techniques," Renewable Energy, Elsevier, vol. 88(C), pages 154-163.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:141:y:2017:i:c:p:579-597. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.