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In the contemporary global discourse on environmental and developmental issues, the dual challenges of sustainable green energy and water supply stand paramount. These elements are vitally intertwined with the socio-economic vitality of the world. Notably, regions plagued by freshwater scarcity are increasingly turning to desalination which consists of a reliable and unconventional water source. The intersection of renewable energy with desalination and water purification processes presents a compelling synergy. This approach is particularly pertinent in areas where freshwater shortages coexist with abundant solar energy availability. Additionally, these technologies boast the advantage of low operational and maintenance costs. This paper delves into the design, optimization and financial analysis of a novel, standalone hybrid energy system, integrating photovoltaic and fuel cell technologies, for an agriculture farm situated in Kairouan, Tunisia. Unlike conventional systems, this model foregoes battery storage in favour of hydrogen storage, generated through water electrolysis powered by solar energy. This system harnesses solar energy for direct electrical power generation and hydrogen gas production. A segment of the generated electricity is allocated to electrolysis for green hydrogen production. In times of solar unavailability, the stored hydrogen is reconverted to electricity via a fuel cell. An intriguing aspect of this system is the utilization of saline well water, which, due to its high salt content, necessitates purification through desalination to prevent damage to the electrolysis unit and to render it suitable for agricultural irrigation. [Display omitted] • The production of green hydrogen, water and electricity for agriculture farm in Tunisia is analyzed. • The Levelized cost of green hydrogen produced in the PV farm is 2.25 €/kg. • A PV hydrogen Farm station with a capacity of 140 kW produces 1991 kg of green hydrogen and 235 MW h of electricity yearly. [ABSTRACT FROM AUTHOR]

The present study examines the potential for hydrogen production using the hybrid energy system at the Shagaya renewable power plant. Techno-economic and optimization analyses are used to identify the optimum configurations that reduce costs while increasing the renewable fraction and lowering greenhouse gas emissions. Three configurations were considered, exploring on- and off-grid combinations of photovoltaic solar (PV), wind turbine (WT), fuel cells and batteries. Integrating PV solar with wind power connected to the power grid was found to achieve the lowest levelized cost of energy of 0. 539 $ / kWh and a hydrogen production cost of 6. 85 $ / kg. However, for the stand-alone system where battery storage banks or fuel cells are used, the cost of hydrogen increases to more than 8. 0 $ / kg due to the larger capital cost of the system. The optimized system achieves annual green hydrogen production of 111 877 kg along with an annual carbon dioxide emission reduction of 14 819 kg. A sensitivity analysis proves that COE is more sensitive to PV price than wind turbines and electrolyzers. LCOE falls by 32.3% to 0.365 kWh when the PV unit price drops to 50%. The LCOE falls by 4% to 0.517 kWh when WT costs decrease by 50%. [Display omitted] • PV and wind integration connected to the power grid achieves low LCOE ($0.539/kWh) and hydrogen cost ($6.85/kg). • Battery and fuel cell systems increase hydrogen cost to more than $8.0/kg due to high capital expenses. • Optimized system produces 111,877 kg of green hydrogen annually, reducing CO 2 emissions by 14,819 kg. • LCOE is highly sensitive to PV cost. A 50% PV price drop reduces LCOE by 32.3% to $0.365/kWh. [ABSTRACT FROM AUTHOR]

Green hydrogen reduces carbon dioxide emission, advances the dependency on fossil fuels and improves the economy of the energy sector, especially in developing countries. Hydrogen is required for the green transportation sector and many other industrial applications. However, the high cost of green hydrogen production reduces the fast development of renewable energy projects based on hydrogen production. So, sizing by optimization is required to determine the optimum solutions for green hydrogen production. In this context, this paper aims to analyze three methods that can be developed and implemented for the production of green hydrogen for refueling stations using photovoltaic (PV) systems. Techno-economic models are adopted to calculate the Levelized Hydrogen Cost (LHC) for the PV grid-connected system, stand-alone PV system with batteries, and stand-alone PV system with fuel cells. The photovoltaic systems based green hydrogen refueling stations are optimized using Homer software. The optimization results of the Net Profit Cost (NPC), and the LHC permit the comparison of the three cases and the selection of the optimal solution. The analysis has shown that a 3 MWp grid-connected PV system represents a promising green hydrogen production at an LHC of 5.5 €/kg. The system produces 58 615 kg of green hydrogen per year reducing carbon dioxide emission by 8209 kg per year. The LHC in the stand-alone PV system with batteries, and stand-alone PV system with fuel cells are 5.74 €/kg and 7.38 €/kg, respectively. • Levelized costs of hydrogen produced in photovoltaic refueling stations are analyzed. • Grid-connected photovoltaic system is an optimal solution providing green hydrogen. • Surplus of electricity produced in hydrogen stations is sold back to the power grid. • A 3 MWp photovoltaic system produces 58 615 kg of hydrogen at a levelized cost of 5.5 €/kg. [ABSTRACT FROM AUTHOR]

This paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs. [ABSTRACT FROM AUTHOR]