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AI-driven hybrid control for hydrogen-integrated microgrids: Probabilistic energy management with vehicle-to-grid
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Technology and Design.ORCID iD: 0000-0002-8118-8329
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Chemical Engineering, Process Technology. KTH, School of Industrial Engineering and Management (ITM), Centres, KTH Climate Action Centre, CAC.ORCID iD: 0000-0001-5886-415X
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Building Technology and Design. School of Business, Society and Engineering, Mälardalen University, Västerås, Sweden.ORCID iD: 0000-0002-9361-1796
2025 (English)In: International journal of hydrogen energy, ISSN 0360-3199, E-ISSN 1879-3487, Vol. 146, article id 149994Article in journal (Refereed) Published
Abstract [en]

Despite the exciting potential of microgrids in future smart energy systems, they encounter significant challenges, including fluctuations in energy demand and output, as well as the unpredictable behavior of electric vehicles. This article examines the ability of microgrids to enhance the integration of renewable energy sources to achieve Zero-Energy Buildings (ZEBs) and facilitate the deployment of Vehicle-to-Grid (V2G) technologies. The designed microgrid comprises vehicles utilizing V2G technology for daily energy storage and a hydrogen cycle featuring electrolyzers and fuel cells for seasonal storage. Probability functions based on uncertainty for distance, arrival, and departure periods from charging stations are formulated to mitigate uncertainties associated with electric vehicles (EVs). A genetic algorithm is employed to optimally regulate EVs' charging and discharging range and the hydrogen cycle's dynamic configuration. The system's feasibility is evaluated for a district in Tehran, characterized by a hot semi-arid climate per the Köppen climate classification, comprising 600 EVs and 3000 residential and 55 commercial buildings. The performance of the suggested smart system is compared with traditional scenarios from techno-ecological, economic, and environmental perspectives. The findings indicate that 62.6 % of the overall energy demand is met by renewable sources (wind and solar), and the microgrid can independently fulfill the need for over 50 % of the year, owing to the implemented hybrid optimum controllers. The findings indicate that 41 % and 16 % of total renewable electricity generation are stored in hydrogen systems and electric vehicles, respectively, highlighting their significant potential for both short-term and long-term storage. Compared to the same traditional scenarios, the suggested system, with an annual energy gain of 8.9 GWh, exhibits superior performance due to its little reliance on the grid while simultaneously ensuring the happiness of electric vehicle owners and the stability of energy storage systems. The intelligent microgrid demonstrates significant efficiency, conserving over 12,600 MWh of energy and decreasing more than 8800 tons of CO<inf>2</inf> emissions. Furthermore, this system generates a substantial financial benefit of approximately USD 468,000, highlighting its notable environmental and economic merits.

Place, publisher, year, edition, pages
Elsevier BV , 2025. Vol. 146, article id 149994
Keywords [en]
Hydrogen storage, Microgrid, Optimal energy management, Probability function, Vehicle-to-grid technology, Zero-energy building
National Category
Energy Systems Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-368537DOI: 10.1016/j.ijhydene.2025.06.184ISI: 001540424900010Scopus ID: 2-s2.0-105008087398OAI: oai:DiVA.org:kth-368537DiVA, id: diva2:1990419
Note

QC 20250820

Available from: 2025-08-20 Created: 2025-08-20 Last updated: 2025-08-20Bibliographically approved

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Behzadi, AmirmohammadDuwig, ChristopheSadrizadeh, Sasan

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