Research Article
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Year 2021, Volume: 5 Issue: 2, 89 - 94, 01.04.2021
https://doi.org/10.31127/tuje.698856

Abstract

References

  • Abdelsalam A M, El-Shorbagy M A (2018). Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy, 123, 748-755. DOI: 10.1016/j.renene.2018.02.083
  • Brusca S, Lanzafame R & Messina M (2014). Wind turbine placement optimization by means of the Monte Carlo simulation method. Modelling and Simulation in Engineering, 760934. DOI: 10.1155/2014/760934
  • Celik I, Yildiz C & Sekkeli M (2018). An optimization model for wind turbine micro-siting in wind power plant installation. Gazi University Science Journal: PART:C Design and Technology, 6(4), 898-908. DOI: 10.29109/gujsc.424155
  • Celik I, Yildiz C & Sekkeli M (2019). A model for evaluating the basic wake effect in the calculation of wind turbine power output on offshore wind power plant. The Black Sea Journal of Sciences, 9(1), 1-9. DOI: 10.31466/kfbd.531554 (in Turkish).
  • Changshui Z, Guangdong H & Jun W (2011). A fast algorithm based on the sub- modular property for optimization of wind turbine positioning. Renewable Energy, 36(11), 2951-2958. DOI: 10.1016/j.renene.2011.03.045
  • Chen Y, Li H, Jin K, Song Q (2013). Wind farm layout optimization using genetic algorithm with different hub height wind turbines. Energy Conversion and Management, 70, 56-65. DOI: 10.1016/j.enconman.2013.02.007
  • Chen Y, Li H, He B & Wang P (2015). Multi-objective genetic algorithm based innovative wind farm layout optimization method. Energy Conversion and Management, 105, 1318-1327. DOI: 10.1016/j.enconman.2015.09.011
  • 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, 96(A), 676–686. DOI: 10.1016/j.renene.2016.05.018
  • Emami A & Noghreh P (2010). New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renewable Energy, 35(7), 1559–1564. DOI: 10.1016/j.renene.2009.11.026
  • Engelbrecht A P (2005). Fundamentals of computational swarm intelligence. John Wiley & Sons, United Kingdom.
  • Feng J & Shen W Z (2015). Solving the wind farm layout optimization problem using random search algorithm. Renewable Energy, 78, 182–192. DOI: 10.1016/j.renene.2015.01.005
  • Gao X, Yang H, Lu L & Koo P (2015). Wind turbine layout optimization using multi- population genetic algorithm and a case study in Hong Kong offshore. Journal of Wind Engineering and Industrial Aerodynamics, 139, 89-99. DOI: 10.1016/j.jweia.2015.01.018
  • Garcia-Bustamante E, Gonzalez-Rouco J F, Jimenez P A, Navarro J & Montávez J P (2009). A comparison of methodologies for monthly wind energy estimation. Wind Energy, 12, pp. 640–659. DOI: 10.1002/we.315
  • González J S, Rodriguez A G G, Mora J C, Payan M B & Santos J R (2011). Overall design optimization of wind farms. Renewable Energy, 36(7), 1973–1982. DOI: 10.1016/j.renene.2010.10.034
  • González J S, Rodriguez A G G, Mora J C, Santos J R, Payan M B (2010). Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy, 35(8), 1671-1681. DOI: 10.1016/j.renene.2010.01.010
  • Grady S A, Hussaini M Y & Abdullah M M (2005). Placement of wind turbines using genetic algorithms. Renewable Energy, 30(2), 259–270. DOI: 10.1016/j.renene.2004.05.007
  • Hou P, Hu W, Soltani M, Chen Z (2015). Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE Transactions on Sustainable Energy, 6(4), 1272–1282. DOI: 10.1109/TSTE.2015.2429912
  • Karadöl I, Keçecioğlu O F, Açıkgöz H & Şekkeli M (2017). Examination of Solar and Wind Energy Hybrid System for Kahramanmaraş Region. KSU Journal of Engineering Sciences, 20(2), 89–96.
  • Kennedy J & Eberhart R (1995). Particle Swarm Optimization. International Conference on Neural Networks, Perth, WA, Australia, 1942-1948. DOI: 10.1109/ICNN.1995.488968
  • Kusiak A & Song Z (2010). Design of wind farm layout for maximum wind energy capture. Renewable Energy, 35(3), 685-694. DOI: 10.1016/j.renene.2009.08.019
  • Long H & Zhang Z (2015). A two-echelon wind farm layout planning model. IEEE Transactions on Sustainable Energy, 6(3), 863-871. DOI: 10.1109/TSTE.2015.2415037
  • Marmidis G, Lazarou S & Pyrgioti E (2008). Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renewable Energy, 33(7), 1455-1460. DOI: 10.1016/j.renene.2007.09.004
  • Mosetti G, Poloni C & Diviacco B (1994). Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. Journal of Wind Engineering and Industrial Aerodynamics, 51(1), 105-116. DOI: 10.1016/0167-6105(94)90080-9
  • Pagnini L C, Burlando M, Repetto M P (2015). Experimental power curve of small-size wind turbines in turbulent urban environment. Apllied Energy, 154, 112–121. DOI: 10.1016/j.apenergy.2015.04.117
  • Parada L, Herrera C, Flores P & Parada V (2017). Wind farm layout optimization using a Gaussian-based wake model. Renewable Energy, 107, 531–541. DOI: 10.1016/j.renene.2017.02.017
  • Pookpunt S & Ongsakul W (2016). Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand. Energy Conversion and Management, 108, 160-180. DOI: 10.1016/j.enconman.2015.11.002
  • Pookpunt S & Ongsakul W (2013). Optimal placement of wind turbines within wind farm using binary particle swarm optimization with timevarying acceleration coefficients. Renewable Energy, 55, 266–276. DOI: 10.1016/j.renene.2012.12.005
  • Sekkeli M, Keçecioğlu O F, Açıkgöz H & Yıldız C (2015a). A comparison between theoretically calculated and actually generated electrical powers of wind turbines: A case study in Belen wind farm, Turkey. Academic Platform Journal of Engineering and Science, 1(3), 41-47. DOI: 10.5505/apjes.2013.55265
  • Sekkeli M, Yildiz C, Karik F, Sözen A (2015b). Wind energy in Turkey electricity market. Gazi Journal of Engineering Science, 1(2), 253-264.
  • Song M X, Chen K, Wang J (2018). Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution. Journal of Wind Engineering & Industrial Aerodynamics, 172, 317-324. DOI: 10.1016/j.jweia.2017.10.032
  • Sun H, Yang H & Gao X (2019). Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines. Energy, 168, 637-650. DOI: 10.1016/j.energy.2018.11.073
  • Turner S D O, Romero D A, Zhang P Y, Amon C H & Chan T C Y (2014). A new mathematical programming approach to optimize wind farm layouts. Renewable Energy, 63, 674-680. DOI: 10.1016/j.renene.2013.10.023
  • Wang Y, Liu H, Long H, Zhang Z & Yang S (2018). Differential evolution with a new encoding mechanism for optimizing wind farm layout. IEEE Transactions on Industrial Informatics, 14(3), 1040-1054. DOI: 10.1109/TII.2017.2743761
  • Wan C, Wang J, Yang G & Zhang X (2010). Optimal micro-siting of wind farms by particle swarm optimization. Advances in swarm intelligence. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13495-1_25
  • Wind turbine model, https://en.wind-turbine-models.com/turbines/821-vestas-v63 (Accessed 19 April 2019).
  • Yang K, Kwak G, Cho K, Huh J (2019). Wind farm layout optimization for wake effect uniformity. Energy, 183, 983-995. DOI: 10.1016/j.energy.2019.07.019
  • Yang J, Zhang R, Sun Q & Zhang H (2015). Optimal wind turbines micrositing in onshore wind farms using fuzzy genetic algorithm. Mathematical Problems in Engineering, 1-9.

Wind power plant layout optimization using particle swarm optimization

Year 2021, Volume: 5 Issue: 2, 89 - 94, 01.04.2021
https://doi.org/10.31127/tuje.698856

Abstract

The use of wind energy has rapidly increased in recent years. In parallel with this rapid increase, Wind Power Plant (WPP) installation has become an important research topic. The selection of wind turbine location in WPP installation effects turbine output power. If the appropriate turbine position is not selected, the total generation of WPP is decreased. The purpose of this study was to determine the locations that wind turbines can achieve the highest energy generation. In this study, an optimization model was proposed to achieve the best WPP layout. In the first stage, field data and Wind Atlas Analysis and Application Program (WAsP) software were used to obtain wind speed distributions in the region where the WPP will be installed. . These distributions were used in the developed optimization model in MATLAB. The actual power curve of a wind turbine was used in the model to calculate energy generation. In the second stage, the locations of the wind turbine were determined by particle swarm optimization (PSO) method. In the final stage, the results of developed MATLAB model were compared with WASP to check accuracy. The difference between MATLAB model and WAsP software was found as 0.04%. This result showed that this model performed a calculation with acceptable accuracy. In addition, it was seen that wind turbines were located to the high wind velocity regions with the solution of the developed optimization model.   

References

  • Abdelsalam A M, El-Shorbagy M A (2018). Optimization of wind turbines siting in a wind farm using genetic algorithm based local search. Renewable Energy, 123, 748-755. DOI: 10.1016/j.renene.2018.02.083
  • Brusca S, Lanzafame R & Messina M (2014). Wind turbine placement optimization by means of the Monte Carlo simulation method. Modelling and Simulation in Engineering, 760934. DOI: 10.1155/2014/760934
  • Celik I, Yildiz C & Sekkeli M (2018). An optimization model for wind turbine micro-siting in wind power plant installation. Gazi University Science Journal: PART:C Design and Technology, 6(4), 898-908. DOI: 10.29109/gujsc.424155
  • Celik I, Yildiz C & Sekkeli M (2019). A model for evaluating the basic wake effect in the calculation of wind turbine power output on offshore wind power plant. The Black Sea Journal of Sciences, 9(1), 1-9. DOI: 10.31466/kfbd.531554 (in Turkish).
  • Changshui Z, Guangdong H & Jun W (2011). A fast algorithm based on the sub- modular property for optimization of wind turbine positioning. Renewable Energy, 36(11), 2951-2958. DOI: 10.1016/j.renene.2011.03.045
  • Chen Y, Li H, Jin K, Song Q (2013). Wind farm layout optimization using genetic algorithm with different hub height wind turbines. Energy Conversion and Management, 70, 56-65. DOI: 10.1016/j.enconman.2013.02.007
  • Chen Y, Li H, He B & Wang P (2015). Multi-objective genetic algorithm based innovative wind farm layout optimization method. Energy Conversion and Management, 105, 1318-1327. DOI: 10.1016/j.enconman.2015.09.011
  • 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, 96(A), 676–686. DOI: 10.1016/j.renene.2016.05.018
  • Emami A & Noghreh P (2010). New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renewable Energy, 35(7), 1559–1564. DOI: 10.1016/j.renene.2009.11.026
  • Engelbrecht A P (2005). Fundamentals of computational swarm intelligence. John Wiley & Sons, United Kingdom.
  • Feng J & Shen W Z (2015). Solving the wind farm layout optimization problem using random search algorithm. Renewable Energy, 78, 182–192. DOI: 10.1016/j.renene.2015.01.005
  • Gao X, Yang H, Lu L & Koo P (2015). Wind turbine layout optimization using multi- population genetic algorithm and a case study in Hong Kong offshore. Journal of Wind Engineering and Industrial Aerodynamics, 139, 89-99. DOI: 10.1016/j.jweia.2015.01.018
  • Garcia-Bustamante E, Gonzalez-Rouco J F, Jimenez P A, Navarro J & Montávez J P (2009). A comparison of methodologies for monthly wind energy estimation. Wind Energy, 12, pp. 640–659. DOI: 10.1002/we.315
  • González J S, Rodriguez A G G, Mora J C, Payan M B & Santos J R (2011). Overall design optimization of wind farms. Renewable Energy, 36(7), 1973–1982. DOI: 10.1016/j.renene.2010.10.034
  • González J S, Rodriguez A G G, Mora J C, Santos J R, Payan M B (2010). Optimization of wind farm turbines layout using an evolutive algorithm. Renewable Energy, 35(8), 1671-1681. DOI: 10.1016/j.renene.2010.01.010
  • Grady S A, Hussaini M Y & Abdullah M M (2005). Placement of wind turbines using genetic algorithms. Renewable Energy, 30(2), 259–270. DOI: 10.1016/j.renene.2004.05.007
  • Hou P, Hu W, Soltani M, Chen Z (2015). Optimized placement of wind turbines in large-scale offshore wind farm using particle swarm optimization algorithm. IEEE Transactions on Sustainable Energy, 6(4), 1272–1282. DOI: 10.1109/TSTE.2015.2429912
  • Karadöl I, Keçecioğlu O F, Açıkgöz H & Şekkeli M (2017). Examination of Solar and Wind Energy Hybrid System for Kahramanmaraş Region. KSU Journal of Engineering Sciences, 20(2), 89–96.
  • Kennedy J & Eberhart R (1995). Particle Swarm Optimization. International Conference on Neural Networks, Perth, WA, Australia, 1942-1948. DOI: 10.1109/ICNN.1995.488968
  • Kusiak A & Song Z (2010). Design of wind farm layout for maximum wind energy capture. Renewable Energy, 35(3), 685-694. DOI: 10.1016/j.renene.2009.08.019
  • Long H & Zhang Z (2015). A two-echelon wind farm layout planning model. IEEE Transactions on Sustainable Energy, 6(3), 863-871. DOI: 10.1109/TSTE.2015.2415037
  • Marmidis G, Lazarou S & Pyrgioti E (2008). Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renewable Energy, 33(7), 1455-1460. DOI: 10.1016/j.renene.2007.09.004
  • Mosetti G, Poloni C & Diviacco B (1994). Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm. Journal of Wind Engineering and Industrial Aerodynamics, 51(1), 105-116. DOI: 10.1016/0167-6105(94)90080-9
  • Pagnini L C, Burlando M, Repetto M P (2015). Experimental power curve of small-size wind turbines in turbulent urban environment. Apllied Energy, 154, 112–121. DOI: 10.1016/j.apenergy.2015.04.117
  • Parada L, Herrera C, Flores P & Parada V (2017). Wind farm layout optimization using a Gaussian-based wake model. Renewable Energy, 107, 531–541. DOI: 10.1016/j.renene.2017.02.017
  • Pookpunt S & Ongsakul W (2016). Design of optimal wind farm configuration using a binary particle swarm optimization at Huasai district, Southern Thailand. Energy Conversion and Management, 108, 160-180. DOI: 10.1016/j.enconman.2015.11.002
  • Pookpunt S & Ongsakul W (2013). Optimal placement of wind turbines within wind farm using binary particle swarm optimization with timevarying acceleration coefficients. Renewable Energy, 55, 266–276. DOI: 10.1016/j.renene.2012.12.005
  • Sekkeli M, Keçecioğlu O F, Açıkgöz H & Yıldız C (2015a). A comparison between theoretically calculated and actually generated electrical powers of wind turbines: A case study in Belen wind farm, Turkey. Academic Platform Journal of Engineering and Science, 1(3), 41-47. DOI: 10.5505/apjes.2013.55265
  • Sekkeli M, Yildiz C, Karik F, Sözen A (2015b). Wind energy in Turkey electricity market. Gazi Journal of Engineering Science, 1(2), 253-264.
  • Song M X, Chen K, Wang J (2018). Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution. Journal of Wind Engineering & Industrial Aerodynamics, 172, 317-324. DOI: 10.1016/j.jweia.2017.10.032
  • Sun H, Yang H & Gao X (2019). Investigation into spacing restriction and layout optimization of wind farm with multiple types of wind turbines. Energy, 168, 637-650. DOI: 10.1016/j.energy.2018.11.073
  • Turner S D O, Romero D A, Zhang P Y, Amon C H & Chan T C Y (2014). A new mathematical programming approach to optimize wind farm layouts. Renewable Energy, 63, 674-680. DOI: 10.1016/j.renene.2013.10.023
  • Wang Y, Liu H, Long H, Zhang Z & Yang S (2018). Differential evolution with a new encoding mechanism for optimizing wind farm layout. IEEE Transactions on Industrial Informatics, 14(3), 1040-1054. DOI: 10.1109/TII.2017.2743761
  • Wan C, Wang J, Yang G & Zhang X (2010). Optimal micro-siting of wind farms by particle swarm optimization. Advances in swarm intelligence. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13495-1_25
  • Wind turbine model, https://en.wind-turbine-models.com/turbines/821-vestas-v63 (Accessed 19 April 2019).
  • Yang K, Kwak G, Cho K, Huh J (2019). Wind farm layout optimization for wake effect uniformity. Energy, 183, 983-995. DOI: 10.1016/j.energy.2019.07.019
  • Yang J, Zhang R, Sun Q & Zhang H (2015). Optimal wind turbines micrositing in onshore wind farms using fuzzy genetic algorithm. Mathematical Problems in Engineering, 1-9.
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

İbrahim Çelik 0000-0001-5923-554X

Ceyhun Yıldız

Mustafa Şekkeli

Publication Date April 1, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Çelik, İ., Yıldız, C., & Şekkeli, M. (2021). Wind power plant layout optimization using particle swarm optimization. Turkish Journal of Engineering, 5(2), 89-94. https://doi.org/10.31127/tuje.698856
AMA Çelik İ, Yıldız C, Şekkeli M. Wind power plant layout optimization using particle swarm optimization. TUJE. April 2021;5(2):89-94. doi:10.31127/tuje.698856
Chicago Çelik, İbrahim, Ceyhun Yıldız, and Mustafa Şekkeli. “Wind Power Plant Layout Optimization Using Particle Swarm Optimization”. Turkish Journal of Engineering 5, no. 2 (April 2021): 89-94. https://doi.org/10.31127/tuje.698856.
EndNote Çelik İ, Yıldız C, Şekkeli M (April 1, 2021) Wind power plant layout optimization using particle swarm optimization. Turkish Journal of Engineering 5 2 89–94.
IEEE İ. Çelik, C. Yıldız, and M. Şekkeli, “Wind power plant layout optimization using particle swarm optimization”, TUJE, vol. 5, no. 2, pp. 89–94, 2021, doi: 10.31127/tuje.698856.
ISNAD Çelik, İbrahim et al. “Wind Power Plant Layout Optimization Using Particle Swarm Optimization”. Turkish Journal of Engineering 5/2 (April 2021), 89-94. https://doi.org/10.31127/tuje.698856.
JAMA Çelik İ, Yıldız C, Şekkeli M. Wind power plant layout optimization using particle swarm optimization. TUJE. 2021;5:89–94.
MLA Çelik, İbrahim et al. “Wind Power Plant Layout Optimization Using Particle Swarm Optimization”. Turkish Journal of Engineering, vol. 5, no. 2, 2021, pp. 89-94, doi:10.31127/tuje.698856.
Vancouver Çelik İ, Yıldız C, Şekkeli M. Wind power plant layout optimization using particle swarm optimization. TUJE. 2021;5(2):89-94.
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