Review on applications of particle swarm optimization in solar energy systems

  • A. H. Elsheikh
  • M. Abd ElazizEmail author


Solar energy is one of the most important factors used in the development of the countries. Since it is a renewable energy source, it reduces the demand on the non-renewable energy sources such as fossil fuels, oil, natural gas, nuclear, and other sources. Therefore, many researchers have sought to improve the performance of solar energy systems via applying several metaheuristic methods such as particle swarm optimization (PSO) which simulates the behavior of the fish schools or bird flocks. PSO has been used in different applications including engineering, manufacturing, and medicine. The main process of the PSO is to determine the optimal position for each particle inside the population. This is performed through updating the position using the velocity of each particle and the shared information between the particles. The aim of this paper is to provide a review on the PSO’s applications to improve the performance of solar energy systems and to identify the research gap for future work. The literature review used in this study indicates that the PSO is a very promising method to enhance the performance of solar energy systems.


Solar energy Metaheuristic methods Particle swarm optimization Solar collectors Solar cells Photovoltaic/thermal systems Solar stills 


Compliance with ethical standards

Conflict of interest

The authors declared that there is no conflict of interest.


  1. Abdallah S, Badran O, Abu-Khader MM (2008) Performance evaluation of a modified design of a single slope solar still. Desalination 219:222–230CrossRefGoogle Scholar
  2. Abed FM, Kassim MS, Rahi MR (2017) Performance improvement of a passive solar still in a water desalination. Int J Environ Sci Technol 14:1277–1284CrossRefGoogle Scholar
  3. Al-Geelani NA, Piah MA, Bashir N (2015) A review on hybrid wavelet regrouping particle swarm optimization neural networks for characterization of partial discharge acoustic signals. Renew Sustain Energy Rev 45:20–35CrossRefGoogle Scholar
  4. Alhmoud L, Wang B (2018) A review of the state-of-the-art in wind-energy reliability analysis. Renew Sustain Energy Rev 81:1643–1651CrossRefGoogle Scholar
  5. Al-Sulttani AO, Ahsan A, Hanoon AN, Rahman A, Daud NNN, Idrus S (2017a) Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique. Appl Energy 203:280–303CrossRefGoogle Scholar
  6. Al-Sulttani AO, Ahsan A, Rahman A, Nik Daud NN, Idrus S (2017b) Heat transfer coefficients and yield analysis of a double-slope solar still hybrid with rubber scrapers: an experimental and theoretical study. Desalination 407:61–74CrossRefGoogle Scholar
  7. Al-Waeli AHA, Sopian K, Kazem HA, Chaichan MT (2017) Photovoltaic/thermal (PV/T) systems: status and future prospects. Renew Sustain Energy Rev 77:109–130CrossRefGoogle Scholar
  8. Assadi MK, Bakhoda S, Saidur R, Hanaei H (2018) Recent progress in perovskite solar cells. Renew Sustain Energy Rev 81:2812–2822CrossRefGoogle Scholar
  9. Awan AB, Khan ZA (2014) Recent progress in renewable energy—remedy of energy crisis in Pakistan. Renew Sustain Energy Rev 33:236–253CrossRefGoogle Scholar
  10. Banerjee A, Mukherjee V, Ghoshal SP (2014) An opposition-based harmony search algorithm for engineering optimization problems. Ain Shams Eng J 5:85–101CrossRefGoogle Scholar
  11. Barnes DI (2015) Understanding pulverised coal, biomass and waste combustion—a brief overview. Appl Therm Eng 74:89–95CrossRefGoogle Scholar
  12. Biglarian H, Saidi MH, Abbaspour M (2018) Economic and environmental assessment of a solar-assisted ground source heat pump system in a heating-dominated climate. Int J Environ Sci Technol 1–8Google Scholar
  13. Bornatico R, Pfeiffer M, Witzig A, Guzzella L (2012) Optimal sizing of a solar thermal building installation using particle swarm optimization. Energy 41:31–37CrossRefGoogle Scholar
  14. Bravo R, Friedrich D (2018) Two-stage optimisation of hybrid solar power plants. Sol Energy 164:187–199CrossRefGoogle Scholar
  15. Buttinger F, Beikircher T, Pröll M, Schölkopf W (2010) Development of a new flat stationary evacuated CPC-collector for process heat applications. Sol Energy 84:1166–1174CrossRefGoogle Scholar
  16. Cheng Z-D, He Y-L, Wang K, Du B-C, Cui FQ (2014) A detailed parameter study on the comprehensive characteristics and performance of a parabolic trough solar collector system. Appl Therm Eng 63:278–289CrossRefGoogle Scholar
  17. Cheng Z-D, He Y-L, Du B-C, Wang K, Liang Q (2015) Geometric optimization on optical performance of parabolic trough solar collector systems using particle swarm optimization algorithm. Appl Energy 148:282–293CrossRefGoogle Scholar
  18. Colangelo G, Favale E, Miglietta P, De Risi A (2016) Innovation in flat solar thermal collectors: a review of the last ten years experimental results. Renew Sustain Energy Rev 57:1141–1159CrossRefGoogle Scholar
  19. Collado FJ (2008) Quick evaluation of the annual heliostat field efficiency. Sol Energy 82:379–384CrossRefGoogle Scholar
  20. Collado FJ (2009) Preliminary design of surrounding heliostat fields. Renew Energy 34:1359–1363CrossRefGoogle Scholar
  21. Collado FJ, Guallar J (2012) Campo: generation of regular heliostat fields. Renew Energy 46:49–59CrossRefGoogle Scholar
  22. Dsilva Winfred Rufuss D, Iniyan S, Suganthi L, Davies PA (2016) Solar stills: a comprehensive review of designs, performance and material advances. Renew Sustain Energy Rev 63:464–496CrossRefGoogle Scholar
  23. Durão B, Joyce A, Mendes JF (2014) Optimization of a seasonal storage solar system using genetic algorithms. Sol Energy 101:160–166CrossRefGoogle Scholar
  24. Elsheikh AH, Sharshir SW, Mostafa ME, Essa FA, Ahmed Ali MK (2018) Applications of nanofluids in solar energy: a review of recent advances. Renew Sustain Energy Rev 82:3483–3502CrossRefGoogle Scholar
  25. Esmin AAA, Coelho RA, Matwin S (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44:23–45CrossRefGoogle Scholar
  26. Farges O, Bézian J-J, El-Hafi M (2018) Global optimization of solar power tower systems using a Monte Carlo algorithm: application to a redesign of the PS10 solar thermal power plant. Renew Energy 119:345–353CrossRefGoogle Scholar
  27. Hadidian-Moghaddam MJ, Arabi-Nowdeh S, Bigdeli M, Azizian D (2017) A multi-objective optimal sizing and siting of distributed generation using ant lion optimization technique. Ain Shams Eng J.
  28. Hajihassani M, Jahed Armaghani D, Kalatehjari R (2018) Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotech Geol Eng 36:705–722CrossRefGoogle Scholar
  29. Hamid NFA, Rahim NA, Selvaraj J (2013) Solar cell parameters extraction using particle swarm optimization algorithm. In: 2013 IEEE conference on clean energy and technology (CEAT), 18–20 Nov. 2013, pp 461–465Google Scholar
  30. Humada AM, Hojabri M, Mekhilef S, Hamada HM (2016) Solar cell parameters extraction based on single and double-diode models: a review. Renew Sustain Energy Rev 56:494–509CrossRefGoogle Scholar
  31. Jain S, Jain NK, Vaughn WJ (2018) Challenges in meeting all of India’s electricity from solar: an energetic approach. Renew Sustain Energy Rev 82:1006–1013CrossRefGoogle Scholar
  32. Kannan N, Vakeesan D (2016) Solar energy for future world: a review. Renew Sustain Energy Rev 62:1092–1105CrossRefGoogle Scholar
  33. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE international conference on neural networks, 1995, vol 4, Nov/Dec 1995, pp 1942–1948Google Scholar
  34. Khalifa AJN (2011) On the effect of cover tilt angle of the simple solar still on its productivity in different seasons and latitudes. Energy Convers Manag 52:431–436CrossRefGoogle Scholar
  35. Khare A, Rangnekar S (2013) A review of particle swarm optimization and its applications in solar photovoltaic system. Appl Soft Comput 13:2997–3006CrossRefGoogle Scholar
  36. Kumar A, Prakash O, Dube A (2017a) A review on progress of concentrated solar power in India. Renew Sustain Energy Rev 79:304–307CrossRefGoogle Scholar
  37. Kumar S, Singh A, Dhar A (2017b) Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters. AIP Adv 7:085117CrossRefGoogle Scholar
  38. Kuok KK, Harun S, Shamsuddin SM (2010) Particle swarm optimization feedforward neural network for modeling runoff. Int J Environ Sci Technol 7:67–78CrossRefGoogle Scholar
  39. Li C, Zhai R, Yang Y (2017) Optimization of a heliostat field layout on annual basis using a hybrid algorithm combining particle swarm optimization algorithm and genetic algorithm. Energies 10:1924CrossRefGoogle Scholar
  40. Li C, Zhai R, Liu H, Yang Y, Wu H (2018) Optimization of a heliostat field layout using hybrid PSO-GA algorithm. Appl Therm Eng 128:33–41CrossRefGoogle Scholar
  41. Lu S-M (2018) A global review of enhanced geothermal system (EGS). Renew Sustain Energy Rev 81:2902–2921CrossRefGoogle Scholar
  42. Mahor A, Prasad V, Rangnekar S (2009) Economic dispatch using particle swarm optimization: a review. Renew Sustain Energy Rev 13:2134–2141CrossRefGoogle Scholar
  43. Mansour FA, Nizam M, Anwar M (2017) Prediction of the optimum surface orientation angles to achieve maximum solar radiation using Particle Swarm Optimization in Sabha City Libya. In: IOP conference series: materials science and engineering, vol 176, p 012029Google Scholar
  44. Mao M, Zhang L, Duan Q, Oghorada OJK, Duan P, Hu B (2017) A two-stage particle swarm optimization algorithm for MPPT of partially shaded PV arrays. Int J Green Energy 14:694–702CrossRefGoogle Scholar
  45. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemometr Intell Lab Syst 149:153–165CrossRefGoogle Scholar
  46. Michael JJ, Iniyan S, Goic R (2015) Flat plate solar photovoltaic–thermal (PV/T) systems: a reference guide. Renew Sustain Energy Rev 51:62–88CrossRefGoogle Scholar
  47. Mohamed AF, Elarini MM, Othman AM (2014) A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system. J Adv Res 5:397–408CrossRefGoogle Scholar
  48. Mousavi SM, Mostafavi ES, Jiao P (2017) Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method. Energy Convers Manag 153:671–682CrossRefGoogle Scholar
  49. Mughal MA, Ma Q, Xiao C (2017) Photovoltaic cell parameter estimation using hybrid particle swarm optimization and simulated annealing. Energies 10:1213CrossRefGoogle Scholar
  50. Oliva D, Abd El Aziz M, Ella Hassanien A (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154CrossRefGoogle Scholar
  51. Panchal HN, Patel S (2017) An extensive review on different design and climatic parameters to increase distillate output of solar still. Renew Sustain Energy Rev 69:750–758CrossRefGoogle Scholar
  52. Piroozmand P, Boroushaki M (2016) A computational method for optimal design of the multi-tower heliostat field considering heliostats interactions. Energy 106:240–252CrossRefGoogle Scholar
  53. Rea JE, Oshman CJ, Olsen ML, Hardin CL, Glatzmaier GC, Siegel NP, Parilla PA, Ginley DS, Toberer ES (2018) Performance modeling and techno-economic analysis of a modular concentrated solar power tower with latent heat storage. Appl Energy 217:143–152CrossRefGoogle Scholar
  54. Rey A, Zmeureanu R (2018) Multi-objective optimization framework for the selection of configuration and equipment sizing of solar thermal combisystems. Energy 145:182–194CrossRefGoogle Scholar
  55. Rezaee Jordehi A (2018) Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Sol Energy 159:78–87CrossRefGoogle Scholar
  56. Salgado Conrado L, Rodriguez-Pulido A, Calderón G (2017) Thermal performance of parabolic trough solar collectors. Renew Sustain Energy Rev 67:1345–1359CrossRefGoogle Scholar
  57. Sathyamurthy R, El-Agouz SA, Dharmaraj V (2015) Experimental analysis of a portable solar still with evaporation and condensation chambers. Desalination 367:180–185CrossRefGoogle Scholar
  58. Sawant PT, Bhattar CL (2016) Optimization of PV system using particle swarm algorithm under dynamic weather conditions. In: 2016 IEEE 6th international conference on advanced computing (IACC), 27–28 Feb. 2016, pp 208–213Google Scholar
  59. Sera D, Mathe L, Kerekes T, Spataru SV, Teodorescu R (2013) On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE J Photovolt 3:1070–1078CrossRefGoogle Scholar
  60. Sharma AK, Sharma C, Mullick SC, Kandpal TC (2017) Solar industrial process heating: a review. Renew Sustain Energy Rev 78:124–137CrossRefGoogle Scholar
  61. Sharshir SW, Yang N, Peng G, Kabeel AE (2016) Factors affecting solar stills productivity and improvement techniques: a detailed review. Appl Therm Eng 100:267–284CrossRefGoogle Scholar
  62. Sharshir SW, Elsheikh AH, Peng G, Yang N, El-Samadony MOA, Kabeel AE (2017a) Thermal performance and exergy analysis of solar stills—a review. Renew Sustain Energy Rev 73:521–544CrossRefGoogle Scholar
  63. Sharshir SW, Peng G, Wu L, Essa FA, Kabeel AE, Yang N (2017b) The effects of flake graphite nanoparticles, phase change material, and film cooling on the solar still performance. Appl Energy 191:358–366CrossRefGoogle Scholar
  64. Sharshir SW, Peng G, Wu L, Yang N, Essa FA, Elsheikh AH, Mohamed SIT, Kabeel AE (2017c) Enhancing the solar still performance using nanofluids and glass cover cooling: experimental study. Appl Therm Eng 113:684–693CrossRefGoogle Scholar
  65. Shi J, Zhang W, Zhang Y, Xue F, Yang T (2015) MPPT for PV systems based on a dormant PSO algorithm. Electr Power Syst Res 123:100–107CrossRefGoogle Scholar
  66. Siddhartha V, Sharma N, Varun G (2012) A particle swarm optimization algorithm for optimization of thermal performance of a smooth flat plate solar air heater. Energy 38:406–413CrossRefGoogle Scholar
  67. Tabet I, Touafek K, Bellel N, Bouarroudj N, Khelifa A, Adouane M (2014) Optimization of angle of inclination of the hybrid photovoltaic-thermal solar collector using particle swarm optimization algorithm. J Renew Sustain Energy 6:053116CrossRefGoogle Scholar
  68. Tiwari GN, Shukla SK, Singh IP (2003) Computer modeling of passive/active solar stills by using inner glass temperature. Desalination 154:171–185CrossRefGoogle Scholar
  69. Tripathi R, Tiwari GN (2006) Thermal modeling of passive and active solar stills for different depths of water by using the concept of solar fraction. Sol Energy 80:956–967CrossRefGoogle Scholar
  70. Tsilingiris PT (2009) Analysis of the heat and mass transfer processes in solar stills—the validation of a model. Sol Energy 83:420–431CrossRefGoogle Scholar
  71. Verma SK, Tiwari AK, Chauhan DS (2017) Experimental evaluation of flat plate solar collector using nanofluids. Energy Convers Manag 134:103–115CrossRefGoogle Scholar
  72. Wolf M, Noel GT, Stirn RJ (1977) Investigation of the double exponential in the current—voltage characteristics of silicon solar cells. IEEE Trans Electron Dev 24:419–428CrossRefGoogle Scholar
  73. Xu R (2015) Light scattering: a review of particle characterization applications. Particuology 18:11–21CrossRefGoogle Scholar
  74. Ye M, Wang X, Xu Y (2009) Parameter extraction of solar cells using particle swarm optimization. J Appl Phys 105:094502CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2018

Authors and Affiliations

  1. 1.Department of Production Engineering and Mechanical DesignTanta UniversityTantaEgypt
  2. 2.School of Computer Science and TechnologyWuhan University of TechnologyWuhanChina
  3. 3.Department of Mathematics, Faculty of ScienceZagazig UniversityZagazigEgypt

Personalised recommendations