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A reinforcement learning approach for MPPT control method of photovoltaic sources

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  • Kofinas, P.
  • Doltsinis, S.
  • Dounis, A.I.
  • Vouros, G.A.

Abstract

Photovoltaic arrays are the means to convert solar power into electricity, and a significant way to generate renewable and clean energy. To be efficient, a photovoltaic must generate constantly the maximum possible power and under different environmental conditions. Finding the maximum generated power has been a known issue in the industry using methods of classic control theory with very good results. However, those solutions are case-specific resulting to increased set-up effort. This work proposes a universal RLMPPT control method based on a reinforcement learning (RL) method that tracks and adjusts the maximum power point of a photovoltaic source without any prior knowledge. A Markov Decision Process (MDP) model for the Maximum Power Point Tracking (MPPT) photovoltaic process is defined and an RL algorithm is proposed and evaluated on a number of photovoltaic sources. The proposed RLMPPT control method has the advantage of being applicable to different PV sources with minimum set-up time. To evaluate the RLMPPT control method performance, a number of simulations run under different environmental and operating conditions and a comparison with the conventional method of Perturb and Observe (P&O) is performed. Results show quick response and close to optimal behavior without requiring any prior knowledge.

Suggested Citation

  • Kofinas, P. & Doltsinis, S. & Dounis, A.I. & Vouros, G.A., 2017. "A reinforcement learning approach for MPPT control method of photovoltaic sources," Renewable Energy, Elsevier, vol. 108(C), pages 461-473.
  • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:461-473
    DOI: 10.1016/j.renene.2017.03.008
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    References listed on IDEAS

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    1. Saravanan, S. & Ramesh Babu, N., 2016. "Maximum power point tracking algorithms for photovoltaic system – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 192-204.
    2. Tafticht, T. & Agbossou, K. & Doumbia, M.L. & Chériti, A., 2008. "An improved maximum power point tracking method for photovoltaic systems," Renewable Energy, Elsevier, vol. 33(7), pages 1508-1516.
    3. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    4. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    5. Bhatnagar, Pallavee & Nema, R.K., 2013. "Maximum power point tracking control techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 224-241.
    6. Houssamo, Issam & Locment, Fabrice & Sechilariu, Manuela, 2010. "Maximum power tracking for photovoltaic power system: Development and experimental comparison of two algorithms," Renewable Energy, Elsevier, vol. 35(10), pages 2381-2387.
    7. Patcharaprakiti, Nopporn & Premrudeepreechacharn, Suttichai & Sriuthaisiriwong, Yosanai, 2005. "Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system," Renewable Energy, Elsevier, vol. 30(11), pages 1771-1788.
    8. Garrigós, Ausias & Blanes, José M. & Carrasco, José A. & Ejea, Juan B., 2007. "Real time estimation of photovoltaic modules characteristics and its application to maximum power point operation," Renewable Energy, Elsevier, vol. 32(6), pages 1059-1076.
    9. Bahgat, A.B.G. & Helwa, N.H. & Ahmad, G.E. & El Shenawy, E.T., 2005. "Maximum power point traking controller for PV systems using neural networks," Renewable Energy, Elsevier, vol. 30(8), pages 1257-1268.
    10. Kumar, Gaurav & Panchal, Ashish K., 2014. "Geometrical prediction of maximum power point for photovoltaics," Applied Energy, Elsevier, vol. 119(C), pages 237-245.
    11. Dounis, Anastasios I. & Kofinas, Panagiotis & Alafodimos, Constantine & Tseles, Dimitrios, 2013. "Adaptive fuzzy gain scheduling PID controller for maximum power point tracking of photovoltaic system," Renewable Energy, Elsevier, vol. 60(C), pages 202-214.
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    2. Kostas Bavarinos & Anastasios Dounis & Panagiotis Kofinas, 2021. "Maximum Power Point Tracking Based on Reinforcement Learning Using Evolutionary Optimization Algorithms," Energies, MDPI, vol. 14(2), pages 1-23, January.
    3. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    4. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    5. Eneko Artetxe & Jokin Uralde & Oscar Barambones & Isidro Calvo & Imanol Martin, 2023. "Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin," Mathematics, MDPI, vol. 11(9), pages 1-21, May.
    6. Emad M. Ahmed & Mokhtar Aly & Ahmed Elmelegi & Abdullah G. Alharbi & Ziad M. Ali, 2019. "Multifunctional Distributed MPPT Controller for 3P4W Grid-Connected PV Systems in Distribution Network with Unbalanced Loads," Energies, MDPI, vol. 12(24), pages 1-19, December.
    7. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    8. Bahrami, Milad & Gavagsaz-Ghoachani, Roghayeh & Zandi, Majid & Phattanasak, Matheepot & Maranzanaa, Gaël & Nahid-Mobarakeh, Babak & Pierfederici, Serge & Meibody-Tabar, Farid, 2019. "Hybrid maximum power point tracking algorithm with improved dynamic performance," Renewable Energy, Elsevier, vol. 130(C), pages 982-991.
    9. Dorotea Dimitrova Angelova & Diego Carmona Fernández & Manuel Calderón Godoy & Juan Antonio Álvarez Moreno & Juan Félix González González, 2024. "A Review on Digital Twins and Its Application in the Modeling of Photovoltaic Installations," Energies, MDPI, vol. 17(5), pages 1-29, March.
    10. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.
    11. Moacyr A. G. de Brito & Victor A. Prado & Edson A. Batista & Marcos G. Alves & Carlos A. Canesin, 2021. "Design Procedure to Convert a Maximum Power Point Tracking Algorithm into a Loop Control System," Energies, MDPI, vol. 14(15), pages 1-17, July.
    12. Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.

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