JACIII Vol.22 No.2 pp. 256-270
doi: 10.20965/jaciii.2018.p0256


Multi-Objective Solar Farm Design Based on Parabolic Collectors

Zakiya Alfughi, Shahryar Rahnamayan, and Bekir Yilbas

Department of Electrical, Computer, and Software Engineering, University of Ontario Institute of Technology (UOIT)
2000 Simcoe Street, North Oshawa, Ontario L1H 7K4, Canada

May 10, 2017
January 16, 2018
March 20, 2018
photovoltaic, parabolic trough collector, multi-objective optimization, NSGA-II, evolutionary computation

The configuration of solar farms, in which solar collectors are arranged in rows, is related to field and collector characteristics and solar radiation data. The main parameters considered during the optimization of solar farm designs include the number of collector rows, the center-to-center distance between collectors, collector inclination angles, and the rim angles. Solar collectors can be subjected to shading depending on the spacing between the collector rows, collector height and angle, row length, and latitude of the solar field. This study aims to optimize solar farm design by ensuring the farm receives the maximum incident solar energy and incurs the minimum deployment cost. The proposed mathematical model for photovoltaic panels is presented in detail. A multi-objective evolutionary algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II), is used to achieve an optimum solar farm design that incorporates parabolic trough panels. The performances of the parabolic and flat panels are also compared, and the findings are discussed in detail. Based on the obtained results, we can verify that the parabolic PV model could generate more energy than the flat model. However, at the same cost, the flat PV model generated more energy than the parabolic model. There is a trade-off between the absolute values of the various objectives, and a solution can be selected based on the customer’s requirements and desires.

  1. [1] D. Sheen, “Born-Again Ziontist’ Revolutionizing Solar Energy Field,” 2011.
  2. [2] D. Weinstock and J. Appelbaum, “Optimal Solar Field Design of Stationary Collectors,” ASME J. of Solar Energy Engineering, Vol.126, No.3, pp. 898-905, 2013.
  3. [3] F. Bourennani, R. Rizvi, and S. Rahnamayan, “Optimal Photovoltaic Solar Power Farm Design Using the Differential Evolution Algorithms,” Int. Conf. on Clean Energy (ICCI ’10), pp. 1-8, 2010.
  4. [4] M. Vasile and L. Summerer, “Multi-Objective Optimisation of Integrated Space-Based and Terrestrial Solar Energy Systems,” 61st Int. Astronautical Congress, IAC 2010, pp. 1-10, 2010.
  5. [5] V. Garg and M.N. Murty, “Simulated Evolution and Learning, EPIC: Efficient Integration of Partitional Clustering Algorithms for Classification,” K. Deb, A. Bhattacharya, N. Chakraborti, P. Chakroborty, S. Das, J. Dutta, S. Gupta, A. Jain, V. Aggarwal, J. Branke, S. Louis, and K. Tan (Eds.), Lecture Notes in Computer Science, Vol.6457, pp. 706-710, Springer Berlin Heidelberg, 2010.
  6. [6] A. Khalkhali, M. Sadafi, J. Rezapour, and H. Safikhani, “Pareto Based Multi-Objective Optimization of Solar Thermal Energy Storage Using Genetic Algorithms,” Trans. of the Canadian Society for Mechanical Engineering, Vol.34, pp. 463-474, 2010.
  7. [7] K. Deb, F. Ruiz, M. Luque, R. Tewari, J. M. Cabello, and J. M. Cejudo, “On the Sizing of a Solar Thermal Electricity Plant for Multiple Objectives Using Evolutionary Optimization,” Applied Soft Computing, Vol.12, No.10, pp. 3300-3311, 2012.
  8. [8] A. Kornelakis, “Multiobjective Particle Swarm Optimization for the Optimal Design of Photovoltaic Grid-Connected Systems,” Solar Energy, Vol.84, No.12, pp. 2022-2033, 2010.
  9. [9] B. Myers, M. Bernardi, and J. C. Grossman, “Three-Dimensional Photovoltaic,” Applied Physics Letters, Vol.96, pp. 1-8, 2010.
  10. [10] H. Nasiraghdam and S. Jadid, “Optimal Hybrid PV/WT/FC Sizing and Distribution System Reconfiguration Using Multi-Objective Artificial Bee Colony (MOABC) Algorithm,” Solar Energy, Vol.86, pp. 3057-3071, 2012.
  11. [11] D. Suchitra, R. Utthra, R. Jegatheesan, and B. Tushar, “Optimization of a PV-Diesel hybrid Stand-Alone System Using Multi-Objective Genetic Algorithm,” Int. J. of Emerging Research in Management and Technology, Vol.2, pp. 68-76, 2013.
  12. [12] A. Ibrahim, F. Bourennani, S. Rahnamayan, and G. F. Naterer, “Optimal Photovoltaic System Design with Multi-Objective Optimization,” Int. J. of Applied Metaheuristic Computing (IJAMC), Vol.4, No.4, pp. 63-89, IGI Global, 2013.
  13. [13] S. S. Rao, H.-G. Lee, and Y. Hu, “Optimal Design of Compound Parabolic Concentrator Solar Collector System,” J. of Mechanical Design, Vol.136, No.9, pp. 1-10, 2014.
  14. [14] W. B. Stine and M. Geyer, “Power from the Sun,” Chapter 8, John Wiley and Sons publication Inc., 2001.
  15. [15] E. A. Mohamed, “Design and Testing of a Solar Parabolic Concentrating Collector,” Int. Conf. on Renewable Energies and Power Quality (ICREPQ ’03), No.13, pp. 1-8, 2013.
  16. [16] M. Günther, M. Joemann, and S. Csambor, “Chapter 5: Parabolic Trough Technology,” A. Guizani, D. Krüger, and T. Hirsch (eds.), Advanced CSP Teaching Materials, pp. 1-106, 2010.
  17. [17] V. M. Sharma, J. K. Nayak, and S. B. Kedare, “Shading and Available Energy in a Parabolic Trough Concentrator Field,” Solar Energy, Vol.90, pp. 144-153, 2013.
  18. [18] D. Weinstock and J. Appelbaum, “Optimization of Solar Photovoltaic Fields,” ASME J. of Solar Energy Engineering, Vol.131, No.3, pp. 1-9, 2009.
  19. [19] Z. Yi, Q. Zhong, L. Peng, G. Wenwen, L. Qiming, and H. Jia, “Calculating the Optimum Tilt Angle for Parabolic Solar Trough Concentrator with the North-South Tilt Tracking Mode,” Fourth Int. Conf. on Digital Manufacturing and Automation (ICDMA), pp. 329-334, 2013.
  20. [20] O. Solomon, “Energy Assessment of a Parabolic Trough Collector in North Cyprus,” Master Thesis, Eastern Mediterranean University, 2010.
  21. [21] E.-G. Talbi, “Metaheuristics: From Design to Implementation,” Wiley Series on Parallel and Distributed Computing, John Wiley and Sons publication Inc., pp. 308-320, 2009.
  22. [22] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II,” IEEE Trans. on Evolutionary Computation, Vol.6, No.2, pp. 182-197, 2002.
  23. [23] NCDIA, “Monthly Averaged Hourly Solar Angles Relative to the Horizon and Solar Azimuth Angles Due South in Degrees, National Climate Data and Information Archive,” 2012. servs/index e.html
  24. [24] J. A. Duffie and W. A. Beckman, “Solar Engineering of Thermal Processes,” John Wiley and Son publication Inc., 2006.
Cite this article as:
Zakiya Alfughi, Shahryar Rahnamayan, and Bekir Yilbas, “Multi-Objective Solar Farm Design Based on Parabolic Collectors,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 256-270, 2018
Zakiya Alfughi, Shahryar Rahnamayan, and Bekir Yilbas, J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.2, pp. 256-270, 2018

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Last updated on Jun. 22, 2018