2024 : 4 : 24
Aliasghar Ghadimi

Aliasghar Ghadimi

Academic rank: Associate Professor
ORCID: https://orcid.org/0000-0001-7276-2221
Education: PhD.
ScopusId: 56678490500
Faculty: Engineering
Address: Arak University
Phone: 08632625620


Uncertainty-aware energy management strategies for PV-assisted refuelling stations with onsite hydrogen generation
Fuel cell electric vehicles Photovoltaic Uncertainty Robust optimization
Journal Journal of Cleaner Production
Researchers Marcos Tostado Veliz ، Aliasghar Ghadimi ، Mohammad Reza Miveh ، Mohammad Bayat ، Francisco Jurado


One of the main barriers for the wide penetration of fuel cell electric vehicles is the lack of proper infrastructures for hydrogen transportation that hinders the implantation of refuelling stations. This barrier could be overcome by deploying onsite hydrogen generators based on mature electrolysis and hydrogen storage technologies. This way, the necessity of hydrogen transportation is avoided. In addition, electrolysers can be onsite supplied by means of renewable generators like photovoltaic panels, while the produced hydrogen can also be destined to generate electricity through fuel cells thus obtaining a monetary revenue. Thereby, the economy of the system may be improved in order to make viable this kind of infrastructures. However, the optimal coordination of the different assets is challenging and requires the use of energy management tools to pursue the optimal performance of the installation. In this kind of infrastructures, the energy management problem is performed under substantial uncertainties; moreover, these unknown parameters have a very different character. Thus, while energy pricing and renewable generation can be forecasted using conventional techniques, refuelling demand is highly unpredictable. To this end, this paper proposes a novel stochastic-interval model for the optimal scheduling of photovoltaic-assisted refuelling stations. The new proposal uses interval notation to model the inherent uncertainty of renewable generation and energy pricing, while the vehicle demand is modelled using a more suitable approach based on scenarios. In this regard, a comprehensive stochastic model for fuel cell electric vehicles is developed, which is based on reported driving behaviour and common characteristics of commercial vehicles. To solve the problem subjected to uncertainties, an iterative solution methodology is developed which allows adopting risk-seeker and risk-averse operational strategies. A case study is analysed to validate the new proposal an