Review of Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions
محورهای موضوعی : Smart & Advanced Materials
1 -
کلید واژه: photovoltaic (PV) systems, power generation efficiency, energy-efficient,
چکیده مقاله :
This paper reviews the application of artificial intelligence (AI) to enhance photovoltaic (PV) plant performance under partial shading conditions. Shading caused by snow, dust, clouds, or environmental obstacles leads to multiple local maxima in P–V curves, hotspot risks, and significant power losses. The study models PV electrical behavior using a single-diode representation in MATLAB and evaluates annual energy yield through System Advisor Model (SAM). A 408 kW grid-connected PV plant located in Golden is analyzed under two configurations: a baseline system without MPPT and an AI-optimized system employing a hybrid ANN-PSO maximum power point tracking algorithm. Results indicate a 7% annual energy increase (40.8 MWh), with winter gains exceeding 10%. The AI-based MPPT achieves 98.7% tracking efficiency and convergence times below 200 ms. Statistical t-test validation confirms the significance of improvements (p ≪ 0.05). Additionally, LCOE is reduced, reinforcing AI-MPPT as a key enabler of Sustainable Development Goal 7 (Affordable and Clean Energy).
چکیده انگلیسی:
منابع و مأخذ:
1. Sharma, V.; Gupta, A.K.; Raj, A.; Verma, S.K. AI-Driven MPPT: A Paradigm Shift in Solar PV Systems for Achieving maximum
Efficiency. In Proceedings of the 2024 International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics (ICAAEEI), Yogyakarta, Indonesia, 11–12 December 2024.
2. Govindasamy, M.; Cyril Mathew, O.; Kumar Boopathi, M.; Chandran, G. Iot and AI-Based MPPT Techniques for Hybrid Solar
and Fuel Cell. Electr. Power Compon. Syst. 2024, 1–21. [CrossRef]
67 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
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6. Ramabadran, R.; Mathur, B. MATLAB Based Modelling and Performance Study of Series Connected SPVA under Partial Shaded Conditions. Sustain. Dev. 2009, 2, 10. [CrossRef]
7. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Identifying Influential Factors Affecting the Shading of a Solar Panel. In Proceedings of the 2021 IEEE Electrical Power and Energy Conference (EPEC), Toronto, ON, Canada, 22–31 October 2021; pp. 255–260. [CrossRef]
8. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Full Shading for Photovoltaic Systems Operating under Snow Conditions. In Proceedings of the 2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 11–13 August 2021; pp. 82–87. [CrossRef]
9. System Advisor Model (SAM). NREL. Available online: https://sam.nrel.gov/ (accessed on 21 January 2025).
10. Andrews, R.W.; Pollard, A.; Pearce, J.M. The effects of snowfall on solar photovoltaic performance. Sol. Energy 2013, 92, 84–97.[CrossRef]
11. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Proposing an Ontology Model for Planning Photovoltaic Systems. Mach. Learn. Knowl.Extr. 2021, 3, 582–600. [CrossRef]
12. Santhiya, R.; Senthilnathan, A.; Chinnaiyan, V.K.; Priya, R.N. Grid Connected Multilevel Inverter and MPPT for Photovoltaic
System. In Proceedings of Power Electronics and Renewable Energy Systems (ICPERES2014); Springer: New Delhi, India, 2015; Volume 3, pp. 201–211.
13. Liu, Y.H.; Huang, S.C.; Huang, J.W.; Liang, W.C. A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. IEEE Trans. Energy Convers. 2012, 27, 1027–1035.[CrossRef]
14. Jordehi, A.R. Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renew. Sustain. Energy Rev. 2016, 65, 1127–1138. [CrossRef]
15. Na, W.; Carley, T.; Ketcham, L.; Zimmer, B.; Chen, P. Simple DSP Implementation of Maximum Power Pointer Tracking and Inverter Control for Solar Energy Applications. J. Power Energy Eng. 2016, 4, 61–76. [CrossRef]
16. Energy Harvesting: Solar. Texas Instruments. 2017. Available online: www.ti.com/solar (accessed on 21 January 2025).
68 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
17. Sridhar, R.; Jeevananthan, S.; Dash, S.S.; Selvan, N.T. Unified MPPT Controller for Partially Shaded Panels in a Photovoltaic Array. Int. J. Autom. Comput. 2015, 11, 536–542. [CrossRef]
18. Guaita-Pradas, I.; Marques-Perez, I.; Gallego, A.; Segura, B. Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases. Environ. Monit. Assess 2019, 191, 764. [CrossRef]
19. Pierdicca, R.; Paolanti, M.A.; Piccinini, F.; Zingaretti, P. Automatic faults detection of photovoltaic farms: SolAIr, a deep learning-based system for thermal images. Energies 2020, 13, 6496. [CrossRef]
20. Cordeiro-Costas, M.; Villanueva, D.; Eguía-Oller, P.; Granada-Álvarez, E. Machine learning and deep learning models applied to photovoltaic production forecasting. Appl. Sci. 2022, 12, 8769. [CrossRef]
21. Akpahou, R.; Mensah, L.D.; Quansah, D.A.; Kemausuor, F. Energy planning and modeling tools for sustainable development: A systematic literature review. Energy Rep. 2024, 11, 830–845. [CrossRef]
22. Hannan, M.A.; Al-Shetwi, A.Q.; Ker, P.J.; Begum, R.A.; Mansor, M.; Rahman, S.A.; Dong, Z.Y.; Tiong, S.K.; Mahlia, T.M.I.; Muttaqi, K.M. Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals. Energy Rep.2021, 7, 5359–5373. [CrossRef]
23. Khan, M.; Raza, M.A.; Faheem, M.; Sarang, S.A.; Panhwar, M.; Jumani, T.A. Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation. Eng. Rep. 2024, 6, e12963. [CrossRef]
24. Joshi, P.; Arora, S. Maximum power point tracking methodologies for solar PV systems—A review. Renew. Sustain. Energy Rev. 2017, 70, 1154–1177. [CrossRef]
25. Gupta, A.; Chauhan, Y.K.; Pachauri, R.K. A comparative investigation of maximum power point tracking methods for solar PV system. Sol. Energy 2016, 136, 236–253. [CrossRef]
26. Mirjalili, S.; Mirjalili, S.M. Grey Wolf Optimizer. Adv. Eng. Softw. 2014, 69, 46–61. [CrossRef]
27. Tripathi, A.K.; Aruna, M.; Elumalai, P.V.; Karthik, K.; Khan, S.A.; Asif, M.; Rao, K.S. Advancing solar PV panel power prediction:
A comparative machine learning approach in fluctuating environmental conditions. Case Stud. Therm. Eng. 2024, 59, 104459.[CrossRef]
28. Elbaset, A.A.; Hussein, A.E.; Brisha, A.; Mostafa, R.E. Implementation of a PIC-based, Photovoltaic Maximum Power Point Tracking Control System. Int. J. Emerg. Technol. Adv. Eng. 2014, 4, 10.
29. Shenoy, P.S.; Kim, K.A. Differential power processing for increased energy production and reliability of photovoltaic systems. IEEE Trans. Power Electron. 2013, 68, 2968–2979. [CrossRef]
30. UN. The Sustainable Development Goals Report 2020; Department Goals Report; UN: New York, NY, USA, 2020; ISBN 9789210049603.
31. Singh, A.; Kanaujia, A.; Singh, V.K.; Vinuesa, R. Artificial intelligence for SUSTAINABLE DEVELOPMENT GOALS: Bibliometric patterns and concept evolution trajectories. Sustain. Dev. 2023, 32, 724–754. [CrossRef]
69 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
32. Brunet, C.; Savadogo, O.; Baptiste, P.; Bouchard, M.A.; Rakotoary, J.C.; Ravoninjatovo, A.; Cholez, C.; Gendron, C.; Merveille, N. Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar. Sustainability 2020, 12, 7471. [CrossRef]
33. Izam, N.S.M.N.; Itam, Z.; Sing, W.L.; Syamsir, A. Sustainable Development Perspectives of Solar Energy Technologies with Focus on Solar Photovoltaic—A Review. Energies 2022, 15, 2790. [CrossRef]
34. Khosrojerdi, F.; Akhigbe, O.; Gagnon, S.; Ramirez, A.; Richards, G. Integrating artificial intelligence and analytics in smart grids: A systematic literature review. Int. J. Energy Sect. Manag. 2022, 16, 318–338.
35. Rusilowati, U.; Rasmita Ngemba, H.; Wahyudin Anugrah, R.; Fitriani, A.; Dian Astuti, E. Leveraging AI for Superior Efficiency in Energy Use and Development of Renewable Resources such as Solar Energy, Wind, and Bioenergy. Int. Trans. Artif. Intell. (ITALIC) 2024, 2, 114–120. [CrossRef]
36. Panda, S.N.; Saikia, R.; Sagar, S.; Swamy, G.N.; Panotra, N.; Yadav, K.; Singh, B.V.; Rathi, S.; Singh, R.; Pandey, S.K. Solar Energy’s Role in Achieving Sustainable Development Goals in Agriculture. Int. J. Environ. Clim. Change 2024, 14, 10–31. [CrossRef]
37. Khosrojerdi, F. An Ontology-Based Decision-Making Framework Modeling Power Efficiency for Photovoltaic Systems; Université du Québec en Outaouais: Gatineau, QC, Canada, 2020.
38. Freeman, J.; Whitmore, J.; Blair, N.; Dobos, A.P. Validation of Multiple Tools for Flat Plate Photovoltaic Modeling Against Measured Data; NREL: Golden, CO, USA, 2014.
39. Freeman, J.; Whitmore, J.; Kaffine, L.; Blair, N.; Dobos, A.P. System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report; NREL/TP-6A20-60204; NREL: Golden, CO, USA, 2013. [CrossRef]
40. Bourne, B. PVSim and Solar Energy System Performance Modeling; SunPower Corporation: Pune, India, 2009.
41. Klise, G.; Freeman, J.; Lavrova, O.; Gooding, R. PV-RPM v2.0 Beta—SAM Implementation DRAFT User Instructions; Sandia National Laboratories & National Renewable Energy Laboratory: Albuquerque, NM, USA, 2017.
42. NREL. PV Case Studies and Validation. NREL PV Validation 2013. Available online: https://sam.nrel.gov/photovoltaic/pv-validation.html (accessed on 28 July 2021).
43. Farhad Khosrojerdi 1, Stéphane Gagnon 2 and Raul Valverde 3, Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions
1. Sharma, V.; Gupta, A.K.; Raj, A.; Verma, S.K. AI-Driven MPPT: A Paradigm Shift in Solar PV Systems for Achieving maximum
Efficiency. In Proceedings of the 2024 International Conference of Adisutjipto on Aerospace Electrical Engineering and Informatics (ICAAEEI), Yogyakarta, Indonesia, 11–12 December 2024.
2. Govindasamy, M.; Cyril Mathew, O.; Kumar Boopathi, M.; Chandran, G. Iot and AI-Based MPPT Techniques for Hybrid Solar
and Fuel Cell. Electr. Power Compon. Syst. 2024, 1–21. [CrossRef]
67 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
3. Arsad, A.Z.; Azhar, A.D.; Arsad, S.R.; Zuhdi, A.W.M.; Chau, C.F.; Ghazali, A. Towards artificial intelligence for solar charge
controller: An analytical study of recent status, optimization and module development. Energy Syst. 2025, 1–54. [CrossRef]
4. Reisi, R.A.; Moradi, M.H.; Jamasb, S. Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. Renew. Sustain. Energy Rev. 2013, 19, 433–443. [CrossRef]
5. Alsayid, B.A.; Alsadi, S.Y.; Jallad, J.F.S.; Dradi, M.H. Partial Shading of PV System Simulation with Experimental Results. Smart Grid Renew. Energy 2013, 4, 429–435. [CrossRef]
6. Ramabadran, R.; Mathur, B. MATLAB Based Modelling and Performance Study of Series Connected SPVA under Partial Shaded Conditions. Sustain. Dev. 2009, 2, 10. [CrossRef]
7. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Identifying Influential Factors Affecting the Shading of a Solar Panel. In Proceedings of the 2021 IEEE Electrical Power and Energy Conference (EPEC), Toronto, ON, Canada, 22–31 October 2021; pp. 255–260. [CrossRef]
8. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Full Shading for Photovoltaic Systems Operating under Snow Conditions. In Proceedings of the 2021 IEEE 9th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 11–13 August 2021; pp. 82–87. [CrossRef]
9. System Advisor Model (SAM). NREL. Available online: https://sam.nrel.gov/ (accessed on 21 January 2025).
10. Andrews, R.W.; Pollard, A.; Pearce, J.M. The effects of snowfall on solar photovoltaic performance. Sol. Energy 2013, 92, 84–97.[CrossRef]
11. Khosrojerdi, F.; Gagnon, S.; Valverde, R. Proposing an Ontology Model for Planning Photovoltaic Systems. Mach. Learn. Knowl.Extr. 2021, 3, 582–600. [CrossRef]
12. Santhiya, R.; Senthilnathan, A.; Chinnaiyan, V.K.; Priya, R.N. Grid Connected Multilevel Inverter and MPPT for Photovoltaic
System. In Proceedings of Power Electronics and Renewable Energy Systems (ICPERES2014); Springer: New Delhi, India, 2015; Volume 3, pp. 201–211.
13. Liu, Y.H.; Huang, S.C.; Huang, J.W.; Liang, W.C. A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions. IEEE Trans. Energy Convers. 2012, 27, 1027–1035.[CrossRef]
14. Jordehi, A.R. Maximum power point tracking in photovoltaic (PV) systems: A review of different approaches. Renew. Sustain. Energy Rev. 2016, 65, 1127–1138. [CrossRef]
15. Na, W.; Carley, T.; Ketcham, L.; Zimmer, B.; Chen, P. Simple DSP Implementation of Maximum Power Pointer Tracking and Inverter Control for Solar Energy Applications. J. Power Energy Eng. 2016, 4, 61–76. [CrossRef]
16. Energy Harvesting: Solar. Texas Instruments. 2017. Available online: www.ti.com/solar (accessed on 21 January 2025).
68 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
17. Sridhar, R.; Jeevananthan, S.; Dash, S.S.; Selvan, N.T. Unified MPPT Controller for Partially Shaded Panels in a Photovoltaic Array. Int. J. Autom. Comput. 2015, 11, 536–542. [CrossRef]
18. Guaita-Pradas, I.; Marques-Perez, I.; Gallego, A.; Segura, B. Analyzing territory for the sustainable development of solar photovoltaic power using GIS databases. Environ. Monit. Assess 2019, 191, 764. [CrossRef]
19. Pierdicca, R.; Paolanti, M.A.; Piccinini, F.; Zingaretti, P. Automatic faults detection of photovoltaic farms: SolAIr, a deep learning-based system for thermal images. Energies 2020, 13, 6496. [CrossRef]
20. Cordeiro-Costas, M.; Villanueva, D.; Eguía-Oller, P.; Granada-Álvarez, E. Machine learning and deep learning models applied to photovoltaic production forecasting. Appl. Sci. 2022, 12, 8769. [CrossRef]
21. Akpahou, R.; Mensah, L.D.; Quansah, D.A.; Kemausuor, F. Energy planning and modeling tools for sustainable development: A systematic literature review. Energy Rep. 2024, 11, 830–845. [CrossRef]
22. Hannan, M.A.; Al-Shetwi, A.Q.; Ker, P.J.; Begum, R.A.; Mansor, M.; Rahman, S.A.; Dong, Z.Y.; Tiong, S.K.; Mahlia, T.M.I.; Muttaqi, K.M. Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals. Energy Rep.2021, 7, 5359–5373. [CrossRef]
23. Khan, M.; Raza, M.A.; Faheem, M.; Sarang, S.A.; Panhwar, M.; Jumani, T.A. Conventional and artificial intelligence based maximum power point tracking techniques for efficient solar power generation. Eng. Rep. 2024, 6, e12963. [CrossRef]
24. Joshi, P.; Arora, S. Maximum power point tracking methodologies for solar PV systems—A review. Renew. Sustain. Energy Rev. 2017, 70, 1154–1177. [CrossRef]
25. Gupta, A.; Chauhan, Y.K.; Pachauri, R.K. A comparative investigation of maximum power point tracking methods for solar PV system. Sol. Energy 2016, 136, 236–253. [CrossRef]
26. Mirjalili, S.; Mirjalili, S.M. Grey Wolf Optimizer. Adv. Eng. Softw. 2014, 69, 46–61. [CrossRef]
27. Tripathi, A.K.; Aruna, M.; Elumalai, P.V.; Karthik, K.; Khan, S.A.; Asif, M.; Rao, K.S. Advancing solar PV panel power prediction:
A comparative machine learning approach in fluctuating environmental conditions. Case Stud. Therm. Eng. 2024, 59, 104459.[CrossRef]
28. Elbaset, A.A.; Hussein, A.E.; Brisha, A.; Mostafa, R.E. Implementation of a PIC-based, Photovoltaic Maximum Power Point Tracking Control System. Int. J. Emerg. Technol. Adv. Eng. 2014, 4, 10.
29. Shenoy, P.S.; Kim, K.A. Differential power processing for increased energy production and reliability of photovoltaic systems. IEEE Trans. Power Electron. 2013, 68, 2968–2979. [CrossRef]
30. UN. The Sustainable Development Goals Report 2020; Department Goals Report; UN: New York, NY, USA, 2020; ISBN 9789210049603.
31. Singh, A.; Kanaujia, A.; Singh, V.K.; Vinuesa, R. Artificial intelligence for SUSTAINABLE DEVELOPMENT GOALS: Bibliometric patterns and concept evolution trajectories. Sustain. Dev. 2023, 32, 724–754. [CrossRef]
69 Journal of Mechanical Research and Application (JMRA), Vol. 15 No.3, 1404(2025), 42-69
32. Brunet, C.; Savadogo, O.; Baptiste, P.; Bouchard, M.A.; Rakotoary, J.C.; Ravoninjatovo, A.; Cholez, C.; Gendron, C.; Merveille, N. Impacts Generated by a Large-Scale Solar Photovoltaic Power Plant Can Lead to Conflicts between Sustainable Development Goals: A Review of Key Lessons Learned in Madagascar. Sustainability 2020, 12, 7471. [CrossRef]
33. Izam, N.S.M.N.; Itam, Z.; Sing, W.L.; Syamsir, A. Sustainable Development Perspectives of Solar Energy Technologies with Focus on Solar Photovoltaic—A Review. Energies 2022, 15, 2790. [CrossRef]
34. Khosrojerdi, F.; Akhigbe, O.; Gagnon, S.; Ramirez, A.; Richards, G. Integrating artificial intelligence and analytics in smart grids: A systematic literature review. Int. J. Energy Sect. Manag. 2022, 16, 318–338.
35. Rusilowati, U.; Rasmita Ngemba, H.; Wahyudin Anugrah, R.; Fitriani, A.; Dian Astuti, E. Leveraging AI for Superior Efficiency in Energy Use and Development of Renewable Resources such as Solar Energy, Wind, and Bioenergy. Int. Trans. Artif. Intell. (ITALIC) 2024, 2, 114–120. [CrossRef]
36. Panda, S.N.; Saikia, R.; Sagar, S.; Swamy, G.N.; Panotra, N.; Yadav, K.; Singh, B.V.; Rathi, S.; Singh, R.; Pandey, S.K. Solar Energy’s Role in Achieving Sustainable Development Goals in Agriculture. Int. J. Environ. Clim. Change 2024, 14, 10–31. [CrossRef]
37. Khosrojerdi, F. An Ontology-Based Decision-Making Framework Modeling Power Efficiency for Photovoltaic Systems; Université du Québec en Outaouais: Gatineau, QC, Canada, 2020.
38. Freeman, J.; Whitmore, J.; Blair, N.; Dobos, A.P. Validation of Multiple Tools for Flat Plate Photovoltaic Modeling Against Measured Data; NREL: Golden, CO, USA, 2014.
39. Freeman, J.; Whitmore, J.; Kaffine, L.; Blair, N.; Dobos, A.P. System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report; NREL/TP-6A20-60204; NREL: Golden, CO, USA, 2013. [CrossRef]
40. Bourne, B. PVSim and Solar Energy System Performance Modeling; SunPower Corporation: Pune, India, 2009.
41. Klise, G.; Freeman, J.; Lavrova, O.; Gooding, R. PV-RPM v2.0 Beta—SAM Implementation DRAFT User Instructions; Sandia National Laboratories & National Renewable Energy Laboratory: Albuquerque, NM, USA, 2017.
42. NREL. PV Case Studies and Validation. NREL PV Validation 2013. Available online: https://sam.nrel.gov/photovoltaic/pv-validation.html (accessed on 28 July 2021).
43. Farhad Khosrojerdi 1, Stéphane Gagnon 2 and Raul Valverde 3, Leveraging AI for Sustainable Energy Development in Solar Power Plants Operating Under Shading Conditions
