Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems
Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duratio...
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| author | Mohamed Aatabe Wissam Jenkal Mohamed I. Mosaad Shimaa A. Hussien |
| author_facet | Mohamed Aatabe Wissam Jenkal Mohamed I. Mosaad Shimaa A. Hussien |
| author_sort | Mohamed Aatabe |
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| description | Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. |
| format | Article |
| id | doaj-art-609db1c4e0bd49a0ac77206cfa55b7ed |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-609db1c4e0bd49a0ac77206cfa55b7ed2025-08-20T04:00:50ZengMDPI AGEnergies1996-10732025-07-011815389910.3390/en18153899Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer SystemsMohamed Aatabe0Wissam Jenkal1Mohamed I. Mosaad2Shimaa A. Hussien3LISTI, National School of Applied Sciences, Ibn Zohr University, Agadir B.P. 1136, MoroccoLISTI, National School of Applied Sciences, Ibn Zohr University, Agadir B.P. 1136, MoroccoRoyal, Commission Yanbu Colleges Institutes, Yanbu Industrial College, Yanbu 46452, Saudi ArabiaElectrical Department, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi ArabiaStandalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments.https://www.mdpi.com/1996-1073/18/15/3899standalone PV microgridPEM hydrogen productionstochastic energy managementMarkov decision process |
| spellingShingle | Mohamed Aatabe Wissam Jenkal Mohamed I. Mosaad Shimaa A. Hussien Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems Energies standalone PV microgrid PEM hydrogen production stochastic energy management Markov decision process |
| title | Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems |
| title_full | Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems |
| title_fullStr | Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems |
| title_full_unstemmed | Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems |
| title_short | Stochastic Control for Sustainable Hydrogen Generation in Standalone PV–Battery–PEM Electrolyzer Systems |
| title_sort | stochastic control for sustainable hydrogen generation in standalone pv battery pem electrolyzer systems |
| topic | standalone PV microgrid PEM hydrogen production stochastic energy management Markov decision process |
| url | https://www.mdpi.com/1996-1073/18/15/3899 |
| work_keys_str_mv | AT mohamedaatabe stochasticcontrolforsustainablehydrogengenerationinstandalonepvbatterypemelectrolyzersystems AT wissamjenkal stochasticcontrolforsustainablehydrogengenerationinstandalonepvbatterypemelectrolyzersystems AT mohamedimosaad stochasticcontrolforsustainablehydrogengenerationinstandalonepvbatterypemelectrolyzersystems AT shimaaahussien stochasticcontrolforsustainablehydrogengenerationinstandalonepvbatterypemelectrolyzersystems |