A new approach for wind turbine placement problem using modified differential evolution algorithm

Energy use is increasing worldwide with industrialization and advancing technology. Following this increase,renewable energy resources are increasingly preferred to reduce the costs of energy production. Wind energy is preferredas a renewable energy resource because it is clean and safe. Wind turbines are used to meet the demand for windenergy. They are placed close to each other to generate higher amounts of energy. However, the wake effect problemarises in these types of layouts, and this hinders the turbines from producing the desired yield. A modified differentialevolution (MDE) algorithm was proposed in this study to solve the placement problem for wind turbines, and employeda binary-real-coded method – obtained by combining binary coding and real coding. The proposed method containsthree different modifications: generation of the initial population, regeneration, and mutation. The effective distributionof the wind turbines on land was achieved with a preliminary operation proposed to generate the initial population.In addition, with the MDE method, population regeneration and elitism were carried out to increase the diversity ofpopulation and to preserve the success of the method. Finally, a mutation operation was performed on the individualsto alternate the presence or absence of wind turbines. To investigate the performance of the MDE method in solving thewind turbine placement problem, the method was applied to a study area of 2 x 2 km. The results were compared withthose obtained with other methods used in the published literature for the wind turbine placement problem. The mostsuccessful and productive placement was achieved using the proposed method, and experimental results showed that theMDE is an efficient and successful tool to solve the wind turbine placement problem.

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[1] Beskirli M, Hakli H, Kodaz H. The energy demand estimation for Turkey using differential evolution algorithm. Sadhana-Acad P Eng S 2017; 42: 1705-1715.
[2] The Global Wind Energy Council, Global Wind Report 2016.
[3] Mittal P, Kulkarni K, Mitra K. A novel hybrid optimization methodology to optimize the total number and placement of wind turbines. Renew Energ 2016; 86: 133-147.
[4] Mahmudov EN. Approximation and Optimization of Discrete and Differential Inclusions. Elsevier: Boston, MA, USA; 2011.
[5] Mosetti G, Poloni C, Diviacco B. Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. J Wind Eng Ind Aerod 1994; 51: 105-116.
[6] Grady S, Hussaini M, Abdullah MM. Placement of wind turbines using genetic algorithms. Renew Energ 2005; 30: 259-270.
[7] Gao X, Yang H, Lin L, Koo P. Wind turbine layout optimization using multi-population genetic algorithm and a case study in Hong Kong offshore. J Wind Eng Ind Aerod 2015; 139: 89-99.
[8] Gao X, Yang H, Lin L. Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model. Appl Energ 2016; 174: 192-200.
[9] Emami A, Noghreh P. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renew Energ 2010; 35: 1559-1564.
[10] Wan C, Wang J, Yang G, Zhang X. Optimal siting of wind turbines using real-coded genetic algorithms. In: Proceedings of European Wind Energy Association Conference and Exhibition. Marseille, France: EWEC; 2009. pp. 3710-3715.
[11] Pookpunt S, Ongsakul W. Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients. Renew Energ 2013; 55: 266-276.
[12] Chen K, Song M, Zhang X. Binary-real coding genetic algorithm for wind turbine positioning in wind farm. J Renew Sustain Ener 2014; 6: 053-115.
[13] Beskirli M, Koc I, Hakli H, Kodaz H. A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm. Renew Energ 2018; 121: 301-308.
[14] Ituarte-Villarreal CM, Espiritu JF. Optimization of wind turbine placement using a viral based optimization algorithm. Procedia Comput Sci 2011; 6: 469-474.
[15] Rehman S, Ali SS, Khan SA. Wind Farm Layout Design Using Cuckoo Search Algorithms. Appl Artif Intell 2018; 32: 956-978.
[16] Jensen NO. A note on wind generator interaction, 1983.
[17] Wan C, Wang J, Yang G, Li X, Zhang X. Optimal micrositing of wind turbines by genetic algorithms based on improved wind and turbine models. In: Proceedings of the 48th IEEE Conference on Decision and Control. Shanghai, P.R.China: IEEE; 2009. pp. 5092-5096.
[18] Moorthy CB, Deshmukh M. A new approach to optimise placement of wind turbines using particle swarm optimisation. Int J Sust Energ 2015; 34: 396-405.
[19] Mittal A. Optimization of the layout of large wind farms using a genetic algorithm. MSc, Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Ohio, USA, 2010.
[20] Fister D, Fister I, Šafarič R. Parameter tuning of PID controller with reactive nature-inspired algorithms. Robot Auton Syst 2016; 84: 64-75.
[21] Storn R, Price K. Differential Evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley, USA: International Computer Science Institute; 1995.
[22] Gämperle R, Müller SD, Koumoutsakos P. A parameter study for differential evolution, Advances in intelligent systems, fuzzy systems. Evolutionary Computation 2002; 10: 293-298.
[23] Karaboğa D. Yapay Zeka Optimizasyon Algoritmalari. 6th ed. Ankara, Turkey: Nobel Akademik; 2014.
[24] Ronkkonen J, Kukkonen S, Price K. Real-parameter optimization with differential evolution. In: IEEE Congress on Evolutionary Computation. Edinburgh, Scotland: IEEE; 2005. pp. 506-513.
[25] Porfyri KN, Delis AI, Nikolos IK, Papageorgiou M. Calibration and validation of a macroscopic multi-lane traffic flow model using a differential evolution algorithm. In: Transportation Research Board 96th Annual Meeting. Washington DC, USA: TRB; 2017. pp. 1-17.
[26] Ilonen J, Kamarainen JK, Lampinen J. Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 2003; 17: 93-105.
[27] Bas E. The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. Journal of Artificial Intelligence and Soft Computing Research 2016; 6: 5-11.
[28] Dragoi EN, Curteanu S. The use of differential evolution algorithm for solving chemical engineering problems. Rev Chem Eng 2016; 32: 149-180.
[29] Samal P, Ganguly S, Mohanty S. Planning of unbalanced radial distribution systems using differential evolution algorithm. Energ Syst 2017; 8: 389-410.
[30] Gao W, Liu S. Improved artificial bee colony algorithm for global optimization. Inform Process Lett 2011; 111: 871-882.
[31] Bajer D, Martinović G, Brest J. A population initialization method for evolutionary algorithms based on clustering and Cauchy deviates. Expert Syst Appl 2016; 60: 294-310.
[32] Kitayama S, Arakawa M, Yamazaki K. Discrete differential evolution for mixed discrete non-linear problems. J Civil Eng Arch 2012; 6: 594.