The Impact of Different Solar Tracker Systems on Reliability of Photovoltaic Farms

Document Type : Original Article

Authors

1 Department of Electrical Engineering, Dariun Branch, Islamic Azad University, DariUn, Iran

2 Faculty of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

3 Department of Electrical Engineering, Beyza Branch, Islamic Azad University, Beyza, Iran.

10.22109/jemt.2023.385194.1432

Abstract

Among different renewable resources technologies associated with solar energies, photovoltaic (PV) systems have grown significantly. The produced power of a PV system is proportional to the solar radiation and so due to the variation in the solar radiation during the day and a year, the generated power of the PV farms changes too. Due to the rotational motion of the earth around itself during a day and also the transitional motion of the earth around the sun during the year, the amount and angle of sunlight on PV panels’ surface change. For enhanced amount of solar radiation on PV panels, the solar tracker can be used to place the solar panels perpendicular to the sun's rays. Two kinds of solar tracker systems including single-axis and double-axis trackers can be used in PV farms. In this paper impact of solar trackers on PV farms’ reliability is evaluated. For this purpose, a multi-state reliability model is constructed for PV farms where failure of composed elements and change in solar radiation intensity are considered. For determining the optimal states number and associated probabilities of this model, the XB index and fuzzy c-means clustering method are implemented. The suggested reliability model is applied for the adequacy assessment of power networks containing large-capacity PV farms. Besides, the impact of solar trackers on reliability indices of power networks integrated with PV farms is investigated.

Keywords

Main Subjects

References

1. https://www.irena.org/solar.
2. http://www.pvresources.com/.
3. S. Su, Y. Hu, L. He, K. Yamashita, and S. Wang, “An assessment procedure of distribution network reliability considering photovoltaic power integration,” IEEE Access, vol. 7, pp. 60171–60185, 2019.
4. S.-V. Oprea, A. Bâra, D. Preo¸tescu, and L. Elefterescu, “Photovoltaic power plants (pv-pp) reliability indicators for improving operation and maintenance activities. a case study of pv-pp agigea located in romania,” IEEE Access, vol. 7, pp. 39142–39157, 2019.
5. X. Song, Y. Zhao, J. Zhou, and Z. Weng, “Reliability varying characteristics of pv-ess-based standalone microgrid,” IEEE Access, vol. 7, pp. 120872–120883, 2019.
6. H. Li, J. Ding, J. Huang, Y. Dong, and X. Li, “Reliability evaluation of pv power systems with consideration of time-varying factors,” The Journal of Engineering, vol. 2017, no. 13, pp. 1783–1787, 2017.
7. H. Abunima and J. Teh, “Reliability modeling of pv systems based on time-varying failure rates,” IEEE Access, vol. 8, pp. 14367–14376, 2020.
8. M. Mosadeghy, R. Yan, and T. K. Saha, “A time-dependent approach to evaluate capacity value of wind and solar pv generation,” IEEE transactions on sustainable energy, vol. 7, no. 1, pp. 129–138, 2015.
9. F. Chan and H. Calleja, “Design strategy to optimize the reliability of grid-connected pv systems,” IEEE Transactions on Industrial Electronics, vol. 56, no. 11, pp. 4465–4472, 2008.
10. P. Zhang, Y. Wang, W. Xiao, and W. Li, “Reliability evaluation of grid-connected photovoltaic power systems,” IEEE transactions on sustainable energy, vol. 3, no. 3, pp. 379–389, 2012.
11. S. Ghaemi, S. M. Mosavi Badjani, and J. Salehi, “Stochastic reliability evaluation of the stand-alone photovoltaic systems,” Journal of Energy Management and Technology, vol. 4, no. 4, pp. 84–93, 2020.
12. S. Hosseinalipour, M. Rashidinejad, A. Abdollahi, and P. Afzali, “Optimal risk-constrained peer-to-peer energy trading strategy for a smart microgrid,” Journal of Energy Management and Technology, vol. 7, no. 4, pp. 227–236, 2023.
13. H. Shayeghi and M. Alilou, “Technical-economic management of smart home energy system in the presence of stochastic and seasonal behavior of pv and ev,” Journal of Energy Management and Technology, vol. 6, no. 4, pp. 270–281, 2022.
14. A. A. E. Tawfiq, M. O. A. El-Raouf, M. I. Mosaad, A. F. A. Gawad, and M. A. E. Farahat, “Optimal reliability study of grid-connected pv systems using evolutionary computing techniques,” IEEE Access, vol. 9, pp. 42125–42139, 2021.
15. D. Pandit, N. Nguyen, S. Elsaiah, and J. Mitra, “Analytical assessment of time-varying reliability and penetration limit of pv-integrated systems,” IEEE Transactions on Industry Applications, vol. 58, no. 6, pp. 6886–6898, 2022.
16. A. Ghaedi and H. Gorginpour, “Spinning reserve scheduling in power systems containing wind and solar generations,” Electrical Engineering, pp. 1–20, 2021.
17. A. Nargeszar, A. Ghaedi, M. Nafar, and M. Simab, “Reliability evaluation of the renewable energy-based microgrids considering resource variation,” IET Renewable Power Generation, vol. 17, no. 3, pp. 507–527, 2023.
18. A. Ghaedi, A. Abbaspour, M. Fotuhi-Firuzabad, M. Moeini-Aghtaie, and M. Othman, “Reliability evaluation of a composite power system containing wind and solar generation,” in 2013 IEEE 7th International Power Engineering and Optimization Conference (PEOCO), pp. 483–488, IEEE, 2013.
19. A. Awasthi, A. K. Shukla, M. M. SR, C. Dondariya, K. Shukla, D. Porwal, and G. Richhariya, “Review on sun tracking technology in solar pv system,” Energy Reports, vol. 6, pp. 392–405, 2020.
20. S. Bazyari et al., “A study on the effects of solar tracking systems on the performance of photovoltaic power plants,” Journal of Power and Energy Engineering, vol. 2, no. 04, p. 718, 2014.
21. R. N. Allan et al., Reliability evaluation of power systems. Springer Science & Business Media, 2013.
22. A. Ghaedi, A. Abbaspour, M. Fotuhi-Friuzabad, and M. Parvania, “Incorporating large photovoltaic farms in power generation system adequacy assessment,” Scientia Iranica, vol. 21, no. 3, pp. 924–934, 2014.
23. A. Singla, K. Singh, and V. K. Yadav, “Optimization of distributed solar photovoltaic power generation in day-ahead electricity market incorporating irradiance uncertainty,” Journal of Modern Power Systems and Clean Energy, vol. 9, no. 3, pp. 545–560, 2020.
24. M. Chen and S. A. Ludwig, “Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters,” Journal of Artificial Intelligence and Soft Computing Research, vol. 4, no. 1, pp. 43–56, 2014.
25. R. L. Cannon, J. V. Dave, and J. C. Bezdek, “Efficient implementation of the fuzzy c-means clustering algorithms,” IEEE transactions on pattern analysis and machine intelligence, no. 2, pp. 248–255, 1986.
26. R. Billinton, S. Kumar, N. Chowdhury, K. Chu, K. Debnath, L. Goel, E. Khan, P. Kos, G. Nourbakhsh, and J. Oteng-Adjei, “A reliability test system for educational purposes-basic data,” IEEE Transactions on Power Systems, vol. 4, no. 3, pp. 1238–1244, 1989.
27. W. Li et al., Reliability assessment of electric power systems using Monte Carlo methods. Springer Science & Business Media, 2013.
Journal of Energy Management and Technology
Volume 8, Issue 1
March 2024
Pages 68-77

Files

History

  • Receive Date: 08 February 2023
  • Revise Date: 26 May 2023
  • Accept Date: 30 June 2023

Share

How to cite

Statistics