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Artificial Bee Colony-Based GMPPT for Non-homogeneous Operating Conditions in a Bifacial CPVT System

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Part of the book series: Green Energy and Technology ((GREEN))

Abstract

Photovoltaic (PV) modules directly convert the solar energy into electricity with electrical efficiency in 10–20%. The rest of the incident solar radiation reflects from front surface and converts into thermal energy which leads to an increase in the PV module temperature. Thus, PV module efficiency decreases. PV power production can be increased with utilize of thermal energy or cooling of PV module. Photovoltaic–thermal (PV/T) technologies provide both electricity and thermal energy. PV/T absorbs thermal energy from PV module which may lead to decrease the PV module temperature. Thus, its electrical efficiency is higher with respect to PV systems. However, PV/T collectors suffer for high capital costs. To improve their profitability, many concentrating PV (CPV) have been developed to increase the incident solar radiation on the PV surface, simultaneously reducing PV material for unit receiver area. Both electricity and thermal energy from the sun more effectively is used with this mechanism called concentrating photovoltaic–thermal (CPVT) technology. This chapter focuses on artificial bee colony (ABC)-based global maximum power point tracking (GMPPT) for PV string structures in a bifacial CPVT system. This power conditioning unit is applied to bifacial CPVT system for efficient utilization of solar energy under four different non-homogeneous solar radiation and module temperature operating conditions.

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References

  1. Rezk H, Eltamaly AM (2015) A comprehensive comparison of different MPPT techniques for photovoltaic systems. Sol Energy 112:1–11. https://doi.org/10.1016/j.solener.2014.11.010

    Article  Google Scholar 

  2. Eltamaly AM, Farh HMH, Othman MF (2018) A novel evaluation index for the photovoltaic maximum power point tracker techniques. Sol Energy 174:940–956. https://doi.org/10.1016/j.solener.2018.09.060

    Article  Google Scholar 

  3. Algarín CR, Giraldo JT, Álvarez OR (2017) Fuzzy Logic Based MPPT Controller for a PV System. Energies 10:2036. https://doi.org/10.3390/en10122036

    Article  Google Scholar 

  4. Tang S, Sun Y, Chen Y, Zhao Y, Yang Y, Szeto W (2017) An enhanced MPPT method combining fractional-order and fuzzy logic control. IEEE J Photovoltaics 7:640–650. https://doi.org/10.1109/JPHOTOV.2017.2649600

    Article  Google Scholar 

  5. Boukenoui R, Ghanes M, Barbot JP, Bradai R, Mellit A, Salhi H (2017) Experimental assessment of maximum power point tracking methods for photovoltaic systems. Energy 132:324–340. https://doi.org/10.1016/j.energy.2017.05.087

    Article  Google Scholar 

  6. Ounnas D, Ramdani M, Chenikher S, Bouktir T (2017) An efficient maximum power point tracking controller for photovoltaic systems Using Takagi-Sugeno fuzzy models. Arabian J Sci Eng 42:4971–4982. https://doi.org/10.1007/s13369-017-2532-0

    Article  Google Scholar 

  7. Yilmaz U, Kircay A, Borekci S (2018) PV system fuzzy logic MPPT method and PI control as a charge controller. Renew Sustain Energy Rev 81:994–1001. https://doi.org/10.1016/j.rser.2017.08.048

    Article  Google Scholar 

  8. Zainal NA, Yusoff AR, Apen A (2019) Integrated cooling systems and maximum power point tracking of fuzzy logic controller for improving photovoltaic performances. Measurement 131:100–108. https://doi.org/10.1016/j.measurement.2018.08.056

    Article  Google Scholar 

  9. Eke R, Demircan C (2015) Shading effect on the energy rating of two identical PV systems on a building façade. Sol Energy 122:48–57. https://doi.org/10.1016/j.solener.2015.08.022

    Article  Google Scholar 

  10. Koutroulis E, Blaabjerg F (2017) Overview of maximum power point techniques for photovoltaic energy production systems. In: Blaabjerg F, Ionel DM (eds) Renewable energy devices and systems with simulations in MATLAB and ANSYS. CRC Press, Taylor and Francis, pp 91–130

    Google Scholar 

  11. Ji Y-H, Jung D-Y, Won C-Y, Lee B-K, Kim J-W (2009) Maximum power point tracking method for PV array under partially shaded condition. In: Proceedings of IEEE energy conversion congress and exposition. pp 307–312

    Google Scholar 

  12. Ramli MAM, Twaha S, Ishaque K, Al-Turki YA (2017) A review on maximum power point tracking for photovoltaic systems with and without shading conditions. Renew Sustain Energy Rev 67:144–159. https://doi.org/10.1016/j.rser.2016.09.013

    Article  Google Scholar 

  13. Rezk H, Fathy A, Abdelaziz AY (2017) A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renew Sustain Energy Rev 74:377–386. https://doi.org/10.1016/j.rser.2017.02.051

    Article  Google Scholar 

  14. Benyoucef AS, Chouder A, Kara K, Silvestre S, Ait Sahed O (2015) Artificial bee colony based algorithm for maximum power pointtracking (MPPT) for PV systems operating under partial shaded conditions. Applied Soft Comput 32:38–48. https://dx.doi.org/10.1016/j.asoc.2015.03.047

    Article  Google Scholar 

  15. Seyedmahmoudian M, Rahmani R, Mekhilef S, Oo AMT, Stojchevski A, Soon TK, Ghandhari AS (2015) Simulation and hardware implementation of new maximum power point tracking technique for partially shaded PV system using hybrid DEPSO method. IEEE Trans Sustain Energy 6:850–862. https://doi.org/10.1109/TSTE.2015.2413359

    Article  Google Scholar 

  16. Sundareswaran K, Sankar P, Nayak PSR, Simon SP, Palani S (2015) Enhanced energy output from a PV system under partial shaded conditions through Artificial Bee Colony. IEEE Trans Sustain Energy 6:198–209. https://doi.org/10.1109/TSTE.2014.2363521

    Article  Google Scholar 

  17. Javed MY, Murtaza AF, Ling Q, Qamar S, Gulzar MM (2016) A novel MPPT design using generalized pattern search for partial shading. Energy Build 133:59–69. https://doi.org/10.1016/j.enbuild.2016.09.054

    Article  Google Scholar 

  18. Kumar CHS, Rao RS (2016) A novel global MPP tracking of photovoltaic system based on whale optimization algorithm. Int J Renew Energy Dev 5:225–232. https://doi.org/10.14710/ijred.5.3.225-232

    Article  Google Scholar 

  19. Kaced K, Larbes C, Ramzan N, Bounabi M, Dahmane ZE (2017) Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions. Sol Energy 158:490–503. https://doi.org/10.1016/j.solener.2017.09.063

    Article  Google Scholar 

  20. Ram JP, Rajasekar N (2017) A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC). Energy 118:512–525. https://doi.org/10.1016/j.energy.2016.10.084

    Article  Google Scholar 

  21. Ram JP, Rajasekar N (2017) A new robust, mutated and fast tracking LPSO method for solar PV maximum power point tracking under partial shaded conditions. Appl Energy 201:45–59. https://doi.org/10.1016/j.apenergy.2017.05.102

    Article  Google Scholar 

  22. Rezk H, Fathy A (2017) Simulation of global MPPT based on teaching–learning-based optimization technique for partially shaded PV system. Electr Eng 99:847–859. https://doi.org/10.1007/s00202-016-0449-3

    Article  Google Scholar 

  23. Mao M, Duan Q, Duan P, Hu B (2018) Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions. Trans Inst Meas Control 40:2178–2199. https://doi.org/10.1177/0142331217697374

    Article  Google Scholar 

  24. Wu Z, Yu D, Kang X (2018) Application of improved chicken swarm optimization for MPPT in photovoltaic system. Optimal Control Appl Methods 39:1029–1042. https://doi.org/10.1002/oca.2394

    Article  MathSciNet  MATH  Google Scholar 

  25. Farh HMH, Eltamaly AM, Othman MF (2018) Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system. PLoS ONE 13(11):e0206171. https://doi.org/10.1371/journal.pone.0206171

    Article  Google Scholar 

  26. Eltamaly AM, Farh HMH (2019) Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC, Solar Energy 177:306–316. https://doi.org/10.1016/j.solener.2018.11.028

    Article  Google Scholar 

  27. Goud JS, Kalpana R, Singh B, Kumar S (2018) Maximum power point tracking technique using artificial bee colony and hill climbing algorithms during mismatch insolation conditions on PV array. IET Renew Power Gener 12:1915–1922. https://doi.org/10.1049/iet-rpg.2018.5116

    Article  Google Scholar 

  28. Bernardo LR, Perers B, Hakansson H, Karlsson B (2011) Performance evaluation of low concentrating photovoltaic/thermal systems: a case study from Sweden. Sol Energy 85:1499–1510. https://doi.org/10.1016/j.solener.2011.04.006

    Article  Google Scholar 

  29. Calise F, Vanoli L (2012) Parabolic trough photovoltaic/thermal collectors: design and simulation model. Energies 5:4186–4208. https://doi.org/10.3390/en5104186

    Article  Google Scholar 

  30. Calise F, Palombo A, Vanoli L (2012) A finite-volume model of a parabolic trough photovoltaic/thermal collector: energetic and exergetic analyses. Energy 46:283–294. https://doi.org/10.1016/j.energy.2012.08.021

    Article  Google Scholar 

  31. Calise F, Dentice d’Accadia M, Roselli C, Sasso M, Tarielli F (2014) Desiccant-based AHU interacting with a CPVT collector: simulation of energy and environmental performance. Sol Energy 103:574–594. https://doi.org/10.1016/j.solener.2013.11.001

    Article  Google Scholar 

  32. Manokar AM, Winston DP, Vimala M (2016) Performance analysis of parabolic trough concentrating photovoltaic thermal system. Procedia Technol 24:485–491. https://doi.org/10.1016/j.protcy.2016.05.083

    Article  Google Scholar 

  33. De Soto W, Klein SA, Beckman WA (2006) Improvement and validation of a model for photovoltaic array performance. Sol Energy 80:78–88. https://doi.org/10.1016/j.solener.2005.06.010

    Article  Google Scholar 

  34. Villalva MG, Gazoli JR, Filho ER (2009) Comprehensive approach to modelling and simulation of photovoltaic arrays. IEEE Trans Power Electron 24:1198–1208. https://doi.org/10.1109/TPEL.2009.2013862

    Article  Google Scholar 

  35. Lo Brano V, Ciulla G (2013) An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data. Appl Energy 111:894–903. https://doi.org/10.1016/j.apenergy.2013.06.046

    Article  Google Scholar 

  36. Shongwe S, Hanif M (2015) Comparative analysis of different single-diode PV modelling methods. IEEE J Photovoltaics 5:938–946. https://doi.org/10.1109/JPHOTOV.2015.2395137

    Article  Google Scholar 

  37. Yıldıran N, Tacer E (2016) Identification of photovoltaic cell single diode discrete model parameters based on datasheet values. Sol Energy 127:175–183. https://doi.org/10.1016/j.solener.2016.01.024

    Article  Google Scholar 

  38. Senturk A, Eke R (2017) A new method to simulate photovoltaic performance of crystalline silicon photovoltaic modules based on based on datasheet values. Renew Energy 103:58–69. https://doi.org/10.1016/j.renene.2016.11.025

    Article  Google Scholar 

  39. Barth N, Jovanovic R, Ahzi S, Khaleel MA (2016) PV panel single and double diode models: optimization of the parameters and temperature dependence. Sol Energy Mater Sol Cells 148:87–98. https://doi.org/10.1016/j.solmat.2015.09.003

    Article  Google Scholar 

  40. Elbaset AA, Ali H, El Sattar MA (2016) New seven parameters model for amorphous silicon and thin film PV modules based on solar irradiance. Sol Energy 138:26–35. https://doi.org/10.1016/j.solener.2016.08.056

    Article  Google Scholar 

  41. Zegaoui A, Aillerie M, Petit P, Charles JP (2016) Universal transistor-based hardware simulator for real time simulation of photovoltaic generators. Sol Energy 134:193–201. https://doi.org/10.1016/j.solener.2016.05.005

    Article  Google Scholar 

  42. Mathwork, Matlab https://www.mathworks.com/. (Access: 11.08.2018)

  43. Topray Solar www.topraysolar.com. (Access: 11.08.2018)

  44. Karaboga D (2005) An ideal based on honey bee swarm for numerical optimization. Technical Report—TR06 Erciyes University, Engineering Faculty, Computer Engineering Department, Kayseri, Turkey

    Google Scholar 

  45. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132. https://doi.org/10.1016/j.amc.2009.03.090

    Article  MathSciNet  MATH  Google Scholar 

  46. Özkaraca O, Keçebaş P, Demircan C, Keçebaş A (2017) Thermodynamic optimization of a geothermal-based organic rankine cycle system using an artificial bee colony algorithm. Energies 10:1691. https://doi.org/10.3390/en10111691

    Article  Google Scholar 

  47. Özkaraca O, Keçebaş A, Demircan C (2018) Comparative thermodynamic evaluation of a geothermal power plant by using the advanced exergy and artificial bee colony methods. Energy 156:169–180. https://doi.org/10.1016/j.energy.2018.05.095

    Article  Google Scholar 

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Demircan, C., Keçebaş, A., Bayrakçı, H.C. (2020). Artificial Bee Colony-Based GMPPT for Non-homogeneous Operating Conditions in a Bifacial CPVT System. In: Eltamaly, A., Abdelaziz, A. (eds) Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-05578-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-05578-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05577-6

  • Online ISBN: 978-3-030-05578-3

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