ABSTRACT
To improve the economic efficiency of wind farms, this paper proposes a method for simultaneously optimizing wind farm layout and cabling on complex terrain such as mountainous areas, which most previous studies have not considered. Multiple wind turbines should be placed to maximize energy production while minimizing the cable length (between wind turbines and between the substation and wind turbines). To optimize both, especially on complex terrain where wind speeds at a site are not constant, the proposed method combines a genetic algorithm (NSGA-II) and a capacitated minimum spanning tree approximation algorithm (Esau-Williams algorithm). For five sites with complex terrain, the proposed method is compared with the exact optimal solution obtained by the weighted sum method using the integer linear programming formulation. For a small number of candidate locations, the proposed method obtains a hyper-volume equivalent to the exact solution. In comparison, the proposed method can obtain a larger hyper-volume even in the case of many candidate locations where the weighted sum method is computationally infeasible in terms of practical resources and time. These results indicate that the proposed method effectively contributes to the wind farm design on complex terrain.
- Nicholas F Baker, Andrew P Stanley, Jared J Thomas, Andrew Ning, and Katherine Dykes. 2019. Best practices for wake model and optimization algorithm selection in wind farm layout optimization. In AIAA Scitech 2019 forum. AIAA, San Diego, CA, USA, 1--18. 0540.Google Scholar
- Naima Charhouni, Mehdi El Amine, Mohammed Sallaou, and Khalifa Mansouri. 2022. A preference-based multi-objective model for wind farm design layout optimization. International Journal on Interactive Design and Manufacturing (IJIDeM) 16, 1 (2022), 323--337.Google ScholarCross Ref
- Kalyanmoy Deb. 2014. Multi-objective optimization. In Search methodologies. Springer, New York, NY, USA, 403--449.Google Scholar
- Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197.Google Scholar
- Larry R. Esau and Kenneth C. Williams. 1966. On teleprocessing system design, Part II: A method for approximating the optimal network. IBM Systems Journal 5, 3 (1966), 142--147.Google ScholarDigital Library
- Ju Feng and Wen Zhong Shen. 2014. Wind farm layout optimization in complex terrain: A preliminary study on a Gaussian hill. In The Science of Making Torque from Wind. IOP Publishing, Copenhagen, Denmark, 012146.Google Scholar
- Ju Feng, Wen Zhong Shen, and Ye Li. 2018. An optimization framework for wind farm design in complex terrain. Applied Sciences 8, 11 (2018), 2053.Google ScholarCross Ref
- Stephen J Hartley and Aaron H Konstam. 1993. Using genetic algorithms to generate Steiner triple systems. In Proceedings of the 1993 ACM conference on Computer science. ACM, New York, NY, USA, 366--371.Google ScholarDigital Library
- José F Herbert-Acero, Oliver Probst, Pierre-Elouan Réthoré, Gunner Chr Larsen, and Krystel K Castillo-Villar. 2014. A review of methodological approaches for the design and optimization of wind farms. Energies 7, 11 (2014), 6930--7016.Google ScholarCross Ref
- Peng Hou, Weihao Hu, Mohsen Soltani, Cong Chen, and Zhe Chen. 2017. Combined optimization for offshore wind turbine micro siting. Applied energy 189 (2017), 271--282.Google Scholar
- Peng Hou, Jiangsheng Zhu, Kuichao Ma, Guangya Yang, Weihao Hu, and Zhe Chen. 2019. A review of offshore wind farm layout optimization and electrical system design methods. Journal of Modern Power Systems and Clean Energy 7, 5 (2019), 975--986.Google ScholarCross Ref
- Takeshi Ishihara, Atsushi Yamaguchi, and Yozo Fujino. 2002. A nonlinear model for predictions of turbulent flow over steep terrain. In The World Wind Energy Conference and Exhibition. WWEA, Berlin, Germany, 1--4. VB3.4.Google Scholar
- I Katic, Jørgen Højstrup, and Niels Otto Jensen. 1986. A simple model for cluster efficiency. In European wind energy association conference and exhibition, Vol. 1. A. Raguzzi, Rome, Italy, 407--410.Google Scholar
- Jim YJ Kuo, David A Romero, J Christopher Beck, and Cristina H Amon. 2016. Wind farm layout optimization on complex terrains-Integrating a CFD wake model with mixed-integer programming. Applied Energy 178 (2016), 404--414.Google ScholarCross Ref
- Sinvaldo Rodrigues Moreno, Juliano Pierezan, Leandro dos Santos Coelho, and Viviana Cocco Mariani. 2021. Multi-objective lightning search algorithm applied to wind farm layout optimization. Energy 216 (2021), 119214.Google ScholarCross Ref
- Robert Clay Prim. 1957. Shortest connection networks and some generalizations. The Bell System Technical Journal 36, 6 (1957), 1389--1401.Google Scholar
- Makbul AM Ramli, Houssem REH Bouchekara, and Ahmad H Milyani. 2023. Wind farm layout optimization using a multi-objective electric charged particles optimization and a variable reduction approach. Energy Strategy Reviews 45 (2023), 101016.Google ScholarCross Ref
- Carl Edward Rasmussen and Christopher K I Williams. 2006. Gaussian processes for machine learning. MIT press, Cambridge, MA, USA.Google ScholarDigital Library
- Rabia Shakoor, Mohammad Yusri Hassan, Abdur Raheem, and Yuan-Kang Wu. 2016. Wake effect modeling: A review of wind farm layout optimization using Jensen's model. Renewable and Sustainable Energy Reviews 58 (2016), 1048--1059.Google ScholarCross Ref
- Chandra Shekar and M R Shivakumar. 2019. Multi-objective wind farm layout optimization using evolutionary computations. International Journal of Advances in Applied Sciences 8, 4 (2019), 293--306.Google ScholarCross Ref
- M X Song, K Chen, Z Y He, and X Zhang. 2013. Bionic optimization for micro-siting of wind farm on complex terrain. Renewable Energy 50 (2013), 551--557.Google ScholarCross Ref
- M X Song, K Chen, Z Y He, and X Zhang. 2014. Optimization of wind farm micro-siting for complex terrain using greedy algorithm. Energy 67 (2014), 454--459.Google ScholarCross Ref
- M X Song, K Chen, X Zhang, and J Wang. 2015. The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain. Energy 80 (2015), 567--574.Google ScholarCross Ref
- Dennis Wilson, Silvio Rodrigues, Carlos Segura, Ilya Loshchilov, Frank Hutter, Guillermo López Buenfil, Ahmed Kheiri, Ed Keedwell, Mario Ocampo-Pineda, Ender Özcan, et al. 2018. Evolutionary computation for wind farm layout optimization. Renewable energy 126 (2018), 681--691.Google Scholar
- Laurence A Wolsey. 2020. Integer Programming. John Wiley & Sons, Hoboken, NJ, USA.Google Scholar
- Yuan-Kang Wu, Ching-Yin Lee, Chao-Rong Chen, Kun-Wei Hsu, and Huang-Tien Tseng. 2013. Optimization of the wind turbine layout and transmission system planning for a large-scale offshore windfarm by AI technology. IEEE Transactions on Industry Applications 50, 3 (2013), 2071--2080.Google ScholarCross Ref
- Jie Xu, Edward Huang, Chun-Hung Chen, and Loo Hay Lee. 2015. Simulation optimization: A review and exploration in the new era of cloud computing and big data. Asia-Pacific Journal of Operational Research 32, 03 (2015), 1550019.Google ScholarCross Ref
Index Terms
- Combined Layout Optimization of Wind Farm and Cable Connection on Complex Terrain Using a Genetic Algorithm
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