Large Wind Farm Layout Optimization Using Nature Inspired Meta-heuristic Algorithms

References

• Global Wind Statistics 2019 - GWEC. https://gwec.net/global-wind-report-2019. accessed: June 20, 2020.
   Google Scholar
• V. Nikolić, S. Shamshirband, D. Petković, K. Mohammadi, Ž Ćojbašić, T. A. Altameem, and A. Gani, “Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology,” Energy, Vol. 80, pp. 361–372, Feb. 2015.
   Web of Science ®Google Scholar
• S. Shamshirband, D. Petković, R. Hashim, S. Motamedi, and N. B. Anuare, “An appraisal of wind turbine wake models by adaptive neuro-fuzzy methodology,” Int. J. Electr. Power Energy Syst., Vol. 63, pp. 618–624, Dec. 2014.
   Web of Science ®Google Scholar
• Wind Farm Layout Optimization Competition. http://www.irit.fr/wind-competition/.
   Google Scholar
• 2014 Optimal Power Flow Problem Competition. https://www.uni-due.de/ieee-wgmho/competition2014.
   Google Scholar
• S. A. Khan, and S. Rehman, “Iterative non-deterministic algorithms in on-shore wind farm design: A brief survey,” Renew. Sustain. Energy Rev, Vol. 19, pp. 370–384, 2013.
   Web of Science ®Google Scholar
• J. S. González, M. B. Payán, J. M. Riquelme Santos, and F. González-Longatt, “A review and recent developments in the optimal wind-turbine micro-siting problem,” Renew. Sustain. Energy Rev, Vol. 30, pp. 133–144, 2014.
   Web of Science ®Google Scholar
• M. A. Lackner, and C. N. Elkinton, “An analytical framework for offshore wind farm layout optimization,” Wind Eng., Vol. 31, pp. 17–31, 2007.
   Google Scholar
• J. Park, and K. H. Law, “Layout optimization for maximizing wind farm power production using sequential convex programming,” Appl. Energy, Vol. 151, pp. 320–334, 2015.
   Web of Science ®Google Scholar
• S. D. O. Turner, D. A. Romero, P. Y. Zhang, C. H. Amon, and T. C. Y. Chan, “A new mathematical programming approach to optimize wind farm layouts,” Renew. Energy, Vol. 63, pp. 674–680, 2014.
   Web of Science ®Google Scholar
• R. Archer, G. Nates, S. Donovan, and H. Waterer, “Wind turbine interference in a wind farm layout optimization mixed integer linear programming model,” Wind Eng., Vol. 35, pp. 165–178, 2011.
   Google Scholar
• G. Marmidis, S. Lazarou, and E. Pyrgioti, “Optimal placement of wind turbines in a wind park using Monte Carlo simulation,” Renew. Energy, Vol. 33, pp. 455–1460, 2008.
   Web of Science ®Google Scholar
• L. Ekonomou, S. Lazarou, G. E. Chatzarakis, and V. Vita, “Estimation of wind turbines optimal number and produced power in a wind farm using an artificial neural network model,” Simul. Model. Pract. Th, Vol. 21, pp. 21–25, 2012.
   Web of Science ®Google Scholar
• M. Wagner, J. Day, and F. Neumann, “A fast and effective local search algorithm for optimizing the placement of wind turbines,” Renew. Energy, Vol. 51, pp. 64–70, 2013.
   Web of Science ®Google Scholar
• C. N. Elkinton, J. F. Manwell, and J. G. McGowan, “Algorithms for offshore wind farm layout optimization,” Wind Eng., Vol. 32, no. 1, pp. 67–84, 2008.
   Google Scholar
• Z. Changshui, H. Guangdong, and W. Jun, “A fast algorithm based on the submodular property for optimization of wind turbine positioning,” Renew. Energy, Vol. 36, pp. 2951–2958, 2011.
   Web of Science ®Google Scholar
• U. A. Ozturk, and B. A. Norman, “Heuristic methods for wind energy conversion system positioning,” Electr. Power Syst. Res, Vol. 70, pp. 179–185, 2004.
   Web of Science ®Google Scholar
• B. Du-Pont, and J. Cagan, “An extended pattern search approach to wind farm layout optimization,” in International Design Engineering Technical Conference and Computers and Information in Engineering Conference 2010, Montreal, Quebec, Canada.
   Google Scholar
• R. A. Rivas, J. Clausen, K. S. Hansen, and L. E. Jensen, “Solving the turbine positioning problem for large offshore wind farms by simulated annealing,” Wind Eng., Vol. 33, pp. 287–298, 2009.
   Google Scholar
• S. Salcedo-Sanz, D. Gallo-Marazuela, A. Pastor-Sánchez, L. Carro-Calvo, A. Portilla-Figueras, and L. Prieto, “Offshore wind farm design with the Coral Reefs Optimization algorithm,” Renew. Energy, Vol. 63, pp. 109–115, 2014.
   Web of Science ®Google Scholar
• A. Kusiak, and Z. Song, “Design of wind farm layout for maximum wind energy capture,” Renew. Energy, Vol. 35, no. 3, pp. 685–694, 2010.
   Web of Science ®Google Scholar
• A. S. Salcedo-Sanz, D. Gallo-Marazuela, A. Pastor-Sánchez, L. Carro-Calvo, A. Portilla-Figueras, and L. Prieto, “Evolutionary computation approaches for real offshore wind farm layout: A case study in Northern Europe,” Expert Syst. Appl., Vol. 40, pp. 6292–6297, 2013.
   Web of Science ®Google Scholar
• M. X. Song, K. Chen, Z. Y. He, and X. Zhang, “Bionic optimization for micro-siting of wind farm on complex terrain,” Renew. Energy, Vol. 50, pp. 551–557, 2013.
   Web of Science ®Google Scholar
• G. Mosetti, C. Poloni, and B. Diviacco, “Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm,” J. Wind Eng. Ind. Aerod, Vol. 51, pp. 105–116, 1994.
   Web of Science ®Google Scholar
• S. A. Grady, M. Y. Hussaini, and M. M. Abdullah, “Placement of wind turbines using genetic algorithms,” Renew. Energy, Vol. 30, pp. 259–270, 2005.
   Web of Science ®Google Scholar
• S. Rajper, and I. J. Amin, “Optimization of wind turbine micrositing: A comparative study,” Renew. Sustain. Energy Rev., Vol. 16, no. 8, pp. 5485–5492, 2012.
   Web of Science ®Google Scholar
• A. Emami, and P. Noghreh, “New approach on optimization in placement of wind turbines within wind farm by genetic algorithms,” Renew. Energy, Vol. 35, pp. 1559–1564, 2010.
   Web of Science ®Google Scholar
• J. S. González, ÁG González Rodríguez, J. C. Mora, M. B. Payán, and J. R. Santos, “Overall design optimization of wind farms,” Renew. Energy, Vol. 36, pp. 1973–1982, 2011.
   Web of Science ®Google Scholar
• C. Wan, J. Wang, G. Yang, and X. Zhang, “Optimal siting of wind turbines using real coded genetic algorithms,” in The european wind Energy Conference and exhibition (EWEC 2009), Marseille, France.
   Google Scholar
• J. S. Gonzalez, A. G. Gonzalez Rodriguez, J. C. Mora, J. R. Santos, and M. B. Payan, “Optimization of wind farm turbines layout using an evolutive algorithm,” Renew. Energy, Vol. 35, pp. 1671–1681, 2010.
   Web of Science ®Google Scholar
• J. C. Mora, J. M. Calero Baron, J. M. Riquelme Santos, and M. Burgos Payan, “An evolutive algorithm for wind farm optimal design,” Neurocomputing, Vol. 70, pp. 2651–2658, 2007.
   Web of Science ®Google Scholar
• S. Sisbot, O. Turgut, M. Tunc, and U. Camdali, “Optimal positioning of wind turbines on gokceada using multi-objective genetic algorithm,” Wind Energy, Vol. 13, no. 4, pp. 297–306, 2010.
   Web of Science ®Google Scholar
• F. G. Montoya, F. Manzano-Agugliaro, S. López-Márquez, Q. Hernández-Escobedo, and C. Gil, “Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms,” Expert Syst. Appl., Vol. 41, pp. 6585–6595, 2014.
   Web of Science ®Google Scholar
• W. Y. Kwong, P. Y. Zhang, D. Romero, J. Moran, M. Morgenroth, and C. Amon, “Wind farm layout optimization considering energy generation and noise propagation,” in International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2012), Chicago, IL, August 12–15, 2012, pp. 1–10.
   Google Scholar
• X. Gao, H. Yang, L. Lu, and P. Koo, “Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore,” J. Wind Eng. Ind. Aerod, Vol. 139, pp. 89–99, 2015.
   Web of Science ®Google Scholar
• X. Gao, H. Yang, and L. Lu, “Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm,” Energy, Vol. 73, pp. 430–442, 2014.
   Web of Science ®Google Scholar
• J. S. Gonzalez, M. B. Payan, and J. M. Riquelme-Santos, “Optimization of wind farm turbine layout including decision making under risk,” Syst. Journal, IEEE, Vol. 6, no. 1, pp. 94–102, March 2012.
   PubMed Web of Science ®Google Scholar
• L. Wang, A. C. C. Tan, Y. Gu, and J. Yuan, “A new constraint handling method for wind farm layout optimization with lands owned by different owners,” Renew. Energy, Vol. 83, pp. 151–161, 2015.
   Web of Science ®Google Scholar
• C. Wan, J. Wang, G. Yang, and X. Zhang, “Optimal micro-siting of wind farms by particle swarm optimization,” in Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, Vol. 6145, Y. Tan, Y. Shi, and K. C. Tan, Eds. Berlin: Springer, 2010.
   Google Scholar
• C. Wan, J. Wang, G. Yang, and X. Zhang, “Optimal micro-siting of wind farms by particle swarm optimization,” in International Conference on Swarm Intelligence (ICSI 2010), Beijing, China, 2010, pp. 198–205.
   Google Scholar
• S. Pookpunt, and W. Ongsakul, “Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients,” Renew. Energy, Vol. 55, pp. 266–276, 2013.
   Web of Science ®Google Scholar
• A. Behnood, H. Gharavi, B. Vahidi, and G. H. Riahy, “Optimal output power of not properly designed wind farms considering wake effects,” Int. J. Electr. Power Energy Syst, Vol. 63, pp. 44–50, 2014.
   Web of Science ®Google Scholar
• S. Chowdhury, J. Zhang, A. Messac, and L. Castillo, “Unrestricted wind farm layout optimization (UWFLO): investigating key factors influencing the maximum power generation,” Renew. Energy, Vol. 38, pp. 16–30, 2012.
   Web of Science ®Google Scholar
• S. Chowdhury, J. Zhang, A. Messac, and L. Castillo, “Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions,” Renew. Energy, Vol. 52, pp. 273–282, 2013.
   Web of Science ®Google Scholar
• Y. Eroğlu, and S. U. Seçkiner, “Design of wind farm layout using ant colony algorithm,” Renew. Energy, Vol. 44, pp. 53–62, 2012.
   Web of Science ®Google Scholar
• B. Pérez, R. Mínguez, and R. Guanche, “Offshore windfarm layout optimization using mathematical programming techniques,” Renew. Energy, Vol. 53, pp. 389–399, 2013.
   Web of Science ®Google Scholar
• C. Wan, J. Wang, G. Yang, H. Gu, and X. Zhang, “Wind farm micro-siting by Gaussian particle swarm optimization with local search strategy,” Renew. Energy, Vol. 48, pp. 276–286, 2012.
   Web of Science ®Google Scholar
• Y. K. Wu, C. Y. Lee, C. R. Chen, K. W. Hsu, and H.-T. Tseng, “Optimization of the wind turbine layout and transmission system planning for a large-scale offshore wind farm by AI technology,” IEEE T. Ind. Appl., Vol. 50, no. 3, pp. 2071–2080, May-June 2014.
   Web of Science ®Google Scholar
• J. Feng, and W. Z. Shen, “Solving the wind farm layout optimization problem using random search algorithm,” Renew. Energy, Vol. 78, pp. 182–192, 2015.
   Web of Science ®Google Scholar
• B. Saavedra-Moreno, S. Salcedo-Sanz, A. Paniagua-Tineo, L. Prieto, and A. Portilla-Figueras, “Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms,” Renew. Energy, Vol. 36, no. 11, pp. 2838–2844, Nov. 2011.
   Web of Science ®Google Scholar
• D. H. Wolpert, and W. G. Macready, “No free lunch theorems for optimization,” IEEE Trans Evol. Comput, Vol. 1, no. 1, pp. 67–82, 1997.
   Google Scholar
• D. Simon, “Biogeography-based optimization,” IEEE Trans. Evol. Comput, Vol. 12, no. 6, pp. 702–713, 2008.
   Web of Science ®Google Scholar
• H. Ma, and D. Simon, “Blended biogeography-based optimization for constrained optimization,” Eng. Appl. Artif. Intel, Vol. 24, pp. 517–525, 2011.
   Web of Science ®Google Scholar
• J. C. Bansal, and P. Farswan, “Wind farm layout using biogeography based optimization,” Renew. Energy, Vol. 107, pp. 386–402, 2017.
   Web of Science ®Google Scholar
• Github Repository. https://github.com/d9w/WindFLO/tree/master/Scenarios.
   Google Scholar
• Github Repository. https://github.com/d9w/WindFLO/tree/master/Wind%20Competition/2014.
   Google Scholar
• Open Wind. ‘Open Wind Theoretical Basis and Foundation’ (Available: http://software.awstruepower.com/openwind/downloads/).
   Google Scholar
• Open Wind. ‘Open Wind User Manual’ (Available at: http://software.awstruepower.com/openwind/downloads/).
   Google Scholar
• http://www.financeformulas.net/Annuity-Payment-Factor.html
   Google Scholar
• D. E. Goldberg. Genetic algorithm in search optimization and machine learning. MA: Addison-Wesley Publishing, 1989.
   Google Scholar
• D. Kalyanmoy. Optimization for engineering design algorithms and examples. New Delhi: Prentice-Hall of India, 2004.
   Google Scholar
• A. Czarn, C. MacNish, K. Vijayan, B. Turlach, and R. Gupta, “Statistical exploratory analysis of genetic algorithms,” IEEE Trans. Evol. Comput, Vol. 8, no. 4, pp. 405–421, 2004.
   Web of Science ®Google Scholar
• J. Grefenstette, “Optimization of control parameters for genetic algorithms,” IEEE Trans. Syst. Man Cybern, Vol. SMC -16, pp. 122–128, 1986.
   Web of Science ®Google Scholar
• M. A. Raouf Shafei, D. K. Ibrahim, E. El-Din Abo El-Zahab, and M. A. Aly Younes, “Biogeography-based optimization technique for maximum power tracking of hydrokinetic turbines,” in 3rd International Conference on renewable Energy research and applications (ICRERA 2014), Milwakuee, USA, 19–22 Oct 2014, pp. 789–794.
   Google Scholar
• A. Bhattacharya, and P. K. Chattopadhyay, “Application of biogeography-based optimization for solving multi-objective economic emission load dispatch problems,” Electr. Power Components Syst, Vol. 38, no. 3, pp. 340–365, 2010.
   Web of Science ®Google Scholar
• J. Kennedy, and R. Eberhart. Swarm intelligence. San Mateo, CA: Morgan Kaufmann, 2001.
   Google Scholar
• M. Dorigo. Optimization, Learning and Natural Algorithms. PhD thesis, Politecnico di Milano, Italy, 1992.
   Google Scholar
• M. Zlochin, M. Birattari, N. Meuleau, and M. Dorigo, “Model-Based search for combinatorial optimization: A critical survey,” Ann. Oper. Res, Vol. 131, no. 1-4, pp. 373–395, Oct. 2004.
   Web of Science ®Google Scholar
• P. K. Ammu, K. C. Sivakumar, and R. Rejimoan, “Biogeography-Based optimization - A survey,” Int. J. Electron. Comput. Sci. Eng, Vol. 2, no. 1, pp. 154–160, 2012.
   Google Scholar
• P. K. Roy, S. P. Ghoshal, and S. S. Thakur, “Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function,” Expert Syst. Appl., Vol. 37, no. 12, pp. 8221–8228, December 2010.
   Web of Science ®Google Scholar
• P. K. Roy, S. P. Ghoshal, and S. S. Thakur, “Multi-objective optimal power flow using biogeography-based optimization,” Electr. Power Components Syst., Vol. 38, no. 12, pp. 1406–1426, 2010.
   Web of Science ®Google Scholar
• Biogeography-based optimization. Article in Wikipedia, the free encyclopedia. Link: https://en.wikipedia.org/wiki/Biogeography-based_optimization.
   Google Scholar
• S. Milton, and J. C. Arnold. Introduction to probability and statistics: principles and applications for Engineering and Computer science. New York, NY: McGraw-Hill, 1990.
   Google Scholar