Techno-Economic Assessment of Linear Fresnel-Based Hydrogen Production in the MENA Region: Toward Affordable, Locally Driven Deployment for Enhanced Profitability and Reduced Costs

Abstract

The MENA region, with its high solar potential and increasing investments in renewable energy, is transitioning away from fossil fuels toward more sustainable energy systems. To fully benefit from this transition and address issues such as intermittency and energy storage, “green” hydrogen is emerging as a key parameter. When produced using simple and cost-effective technologies like linear Fresnel reflector (LFR), it offers a practical solution. Therefore, assessing the potential of hydrogen production from LFR technology is essential to support the development of the energy sector and promote local industrial growth. This study investigates “green” hydrogen production using a 50 MW concentrated solar power (CSP) system based on LFR technology, where the CSP system generates electricity to power a proton exchange membrane electrolyzer for hydrogen production for three locations, including Ain Beni Mathar in Morocco, Assiout in Egypt, and Tabuk in Saudi Arabia. The results show that Tabuk achieved the highest annual hydrogen production (45.02 kg/kWe), followed by Assiout (38.72 kg/kWe) and Ain Beni Mathar (32.42 kg/kWe), with corresponding levelized costs of hydrogen (LCOH₂) of 6.47 USD/kg, 6.84 USD/kg, and 7.35 USD/kg, respectively. In addition, several sensitivity analyses were conducted addressing the impact of thermal energy storage (TES) on the hydrogen production and costs, the effect of reduced investment costs resulting from the local manufacturing of LFR components, and the futuristic assumption of the electrolyzer cost drop. The integration of TES enhanced hydrogen output and reduced LCOH₂ by up to 9%. Additionally, a future PEM electrolyzer costs projected for 2030 showed that LCOH₂ could decrease by up to 1.3 USD/kg depending on site conditions. These findings demonstrate that combining TES with cost optimization strategies can significantly improve both technical performance and economic feasibility in the MENA region.

1. Introduction

Due to increasing concerns about climate change and limited fossil fuel resources, the global energy sector is shifting toward more sustainable and efficient energy solutions. Renewable Energy (RE) has emerged as one of the most promising alternatives to conventional sources, offering vast potential to meet the world’s growing energy demands. The share of RE nearly doubled from 8.5% in 2004 to 16.7% in 2015 [1], highlighting its escalating significance in the global energy mix. Between 2012 and 2021, RE capacity rose from 480 GW to an impressive 1945 GW, reflecting an average annual growth rate of 17.5% [2]. Despite this rapid expansion, RE sources still face challenges related to their intermittent nature [3]. Addressing this issue requires efficient energy storage methods, like power-to-X technologies, which convert excess intermittent electricity into chemical fuels [4]. Among these, hydrogen (H₂) stands out as a key energy carrier, offering immense potential for future sustainable energy systems [5]. The rise of “green” hydrogen offers a hopeful remedy to the hurdles linked to storing renewable electricity and mitigating problems related to intermittency and fluctuations [6,7]. Furthermore, hydrogen possesses a gravimetric energy density of 140 MJ/kg, which is double the value found for fossil fuels [8].

In recent years, there have been several major power outages that revealed challenges in keeping electrical grids flexible and reliable during the transition to renewable energy. One important case was the recent blackout in Spain and Portugal in April 2025, which was attributed to a photovoltaic plant in the South of Spain. This situation shows the need to use stable and controllable technologies like concentrated solar power (CSP) combined with “green” hydrogen and energy storage to improve grid reliability and handle fluctuations in future energy systems [9].

Beyond its industrial applications, “green” hydrogen holds great promise for reducing global CO₂ emissions, with the potential to cut up to 1.1 Gt annually. It can significantly decrease emissions in key sectors, including transportation and heating, by 95 Mt and 3.1 Gt, respectively. Additionally, it plays an essential role in addressing seasonal electricity storage needs, with the capacity to cover around 15% of the demand [10].

To harness the full potential of “green” hydrogen in reducing global emissions and meeting energy storage needs, the availability of abundant renewable resources is essential. In this regard, the Middle East and North Africa (MENA) region emerges as a strategic area with exceptional renewable energy potential, positioning it as a key contributor to future “green” hydrogen production. Despite the fact that the MENA region holds around one-third of the world’s natural gas reserves and half of the world’s wealth of oil [11], it is increasingly investing in renewable energy. This shift is driven by several pressing factors, including the region’s rapidly growing energy demand, which averages around 10% annually, and fast-paced population growth [12,13]. Additionally, environmental concerns play a significant role, as by 2020, six MENA countries ranked among the top ten nations with the highest per capita CO₂ emissions, while the region as a whole contributes 8% of global CO₂ emissions [14]. To address these challenges, many countries in the MENA region have begun to invest extensively in renewable energy, leveraging the region’s vast potential for solar and wind power. Currently, the region’s installed renewable energy capacity is dominated by hydropower at 81%, followed by wind power at 12%, solar energy at 5%, biomass at 1%, and geothermal energy at 1%. However, the region has set ambitious targets for 2032, aiming for a more balanced energy mix: 40% solar energy, 28% wind power, 28% hydropower, 3% biomass, and 1% geothermal energy [11]. This transition is driven by national objectives that reflect a strong commitment to sustainable energy development. For instance, Morocco aims to achieve 52% renewable energy by 2030, Egypt targets 42% by 2035, Saudi Arabia plans for 30% by 2030, and the United Arab Emirates aims for 44% by 2050 [15]. These ambitious goals underscore the region’s determination to diversify its energy sources, reduce carbon emissions, and ensure long-term energy security.

Building on its significant investments in renewable energy, the MENA region is well positioned to emerge as a global leader in “green” hydrogen production. Its ambitious energy targets lay a solid foundation for large-scale hydrogen development. Recent declarations have demonstrated the region’s commitment to advancing “green” hydrogen initiatives, suggesting a future of energy interdependence. Although many of these projects are still in the feasibility study phase, some have reached advanced stages, indicating concrete progress toward building a sustainable hydrogen economy [16].

The generation of hydrogen encompasses various methodologies, including hydrocarbon reforming methods like steam reforming and partial oxidation [17]. Moreover, “green” hydrogen production utilizes renewable sources, paving the way for eco-friendly pathways. Among these, biomass plays a significant role, categorized into thermochemical and biological processes. Additionally, water splitting emerges as a pivotal avenue, employing techniques such as electrolysis, thermolysis, and photo-electrolysis to produce hydrogen sustainably [17,18]. Electrolysis stands as the predominant method for deriving hydrogen from water, representing a well-established technology. This process involves the application of direct electric current to water, leading to the dissociation of water molecules into hydrogen and oxygen gases [19]. Various types of electrolysis technologies contribute to this process, including alkaline electrolyzers (ALK), proton exchange membrane (PEM) electrolyzers, and solid oxide electrolyzers (SOE), each offering distinct advantages and applications [19,20,21]. In our study, we chose PEM electrolysis for its efficiency in converting liquefied water into pure hydrogen and oxygen gases at temperatures ranging from 20 to near 100 °C. Operating at pressures up to 40 MPa, it reduces energy demand during compression, ensuring high-purity output [21]. Additionally, PEM electrolysis showcases adaptability to the variable electricity output produced by renewable energy sources [22].

When discussing “green” hydrogen, we are referring to hydrogen generated from renewable energy sources. Among various renewable energy resources, CSP stands out for its advantages over alternatives such as wind turbines and solar photovoltaic (PV) systems. CSP plants utilize mirrors to concentrate sunlight, heating a fluid to generate steam for electricity production. With the ability to store thermal energy for extended periods, CSP facilities offer dependable energy generation and can simultaneously produce electricity and heat for industrial applications [23]. In addition to the advantages mentioned, one of the most mature and economically viable technologies determining a technical option is the linear Fresnel reflector (LFR) technology. This system offers advantages such as lower cost of production, easier maintenance, and scalability, making it a preferred choice for CSP plants [24].

However, as far as the authors are aware, there are fewer studies on the performance of CSP plants in producing hydrogen through water electrolysis compared to PV or wind systems. Amrani et al. [25] conducted a study evaluating Morocco’s potential for large-scale solar-based hydrogen production, utilizing PV and CSP technologies. Employing the analytical hierarchy process (AHP) and geographic information system (GIS), the research reveals that PV units can produce “green” hydrogen at 4.972 USD/kg, while CSP systems yield varying costs: approximately 9.816 USD/kg for Stirling dish technology (CSP-SD) and 5.86 USD/kg for parabolic trough technology (CSP-PTCS). The study underscores the significant role of storage, showing that a 1 MWe CSP-PTCS setup can generate 35.7% more hydrogen annually compared to the same installed capacity of PV [25]. Boudries et al. [26] undertook a techno-economic analysis concerning solar electrolysis for hydrogen generation, specifically investigating a hybrid system combining a parabolic trough solar field, a gas power plant and electrolysis unit in Algeria. Their study delved into various factors such as direct normal irradiance and solar fraction, focusing on sites in both Northern and Southern Algeria to gauge climatic implications. The findings highlighted the significant impact of energy production costs, mainly dictated by solar fraction and solar insolation, on hydrogen production expenses [26]. In another study, Yang et al. [27] proposed a low-carbon operational mode for an Integrated Energy System (IES) using high-temperature electrolytic hydrogen production (HEHP) with CSP. They developed an optimal operational model and assessed its impact on the economy, renewable energy utilization, and carbon emissions. Through a case study, they found that integrating CSP with HEHP increased energy efficiency by 10% and revenue by 14.25%. Moreover, it reduced total carbon emissions by 24.55% compared to CSP alone [27]. Joubi et al. [28] analyzed the economics of solar hydrogen production for a supply chain, comparing CSP and PV technologies with an SOE. They found coupling SOE cells with CSP plants to be the most competitive, achieving the shortest payback period and a cheaper levelized cost of hydrogen (LCOH₂) of 7.85 USD/kg. While the PV plant had a lower levelized cost of electricity (LCOE) at 0.0508 USD/kWh compared to CSP at 0.086 USD/kWh, the SOEC coupled with CSP offered higher revenues [28]. Tang et al. [29] proposed an integrated system that merges a hybrid CSP/PV with an SOE for electricity and hydrogen production. They found that a solar multiple of 2 and 14 h of thermal storage are essential for a continuous power supply. Operating at 275 W, the SOE cell stack demonstrated superior techno-economic performance, resulting in a competitive LCOE of 0.219 USD/kWh and an LCOH of 7.5 USD/kg [29].

Moreover, while extensive research has been conducted in “green” hydrogen production using renewable energy sources, studies investigating hydrogen production through CSP systems remain relatively scarce. Among these, the application of LFR technology for hydrogen generation has received limited attention. This study aims to bridge this gap by evaluating the techno-economic potential of LFR-based CSP systems for hydrogen production in the MENA region. It focuses on three representative countries: Morocco, Egypt, and Saudi Arabia, with specific sites selected for analysis: Ain Beni Mathar, Assiut, and Tabuk, respectively. To ensure accurate simulations, high-resolution ground meteorological data were employed to validate satellite-derived direct normal irradiance (DNI) measurements, focusing initially on the Ain Beni Mathar site. The validated data facilitated performance modeling of LFR systems across these diverse climatic conditions. The electricity produced by these systems was utilized to power PEM electrolyzers for hydrogen generation, providing valuable insights to inform strategic energy planning and support the transition to more sustainable energy solutions.

To better evaluate hydrogen production potential in the MENA region, this study addresses the influence of climatic conditions by analyzing the impact of DNI and ambient temperature on hydrogen output at each selected location. In addition, the role of thermal energy storage (TES) was investigated through an assessment of various storage capacities to identify the optimal configuration that maximizes hydrogen production while minimizing costs. Another novel aspect of this study involves a sensitivity analysis on the effect of reduced investment costs, resulting from the local manufacturing of LFR components within the MENA region. Furthermore, to estimate future trends in the levelized cost of hydrogen (LCOH₂), the study incorporates projected costs of PEM electrolyzers for 2030, along with other key economic parameters relevant to project profitability. These insights aim to support decision-makers in evaluating the feasibility of large-scale LFR-based hydrogen production facilities in the region. Finally, the environmental dimension was considered by analyzing the associated CO₂ emissions across the investigated sites.

2. Methodology

This study aims to evaluate solar hydrogen production in three different MENA locations using LFR technology, chosen for its simplicity in assembly and low-cost implications. However, before the assessment of hydrogen, it is of high importance to evaluate the solar irradiance data and its accuracy. For CSP systems, DNI is the key parameter that determines operational efficiency. Accurate DNI measurements are critical and can be obtained from ground-based instrumentation or satellite sources. While ground-based methods provide high-resolution data within limited sites, satellite data offer broader coverage with compromised accuracy. Besides, DNI ground measurements are scarce in the MENA region, which makes it necessary to use satellite data for the resource assessment projects [30]. To address this issue, our study employed a comparative analysis/statistical comparison of ground-measured and satellite-derived DNI data, with a focus on the Ain Bani Mathar location, which is equipped with a high-precision meteorological station that measures the three components of solar irradiance simultaneously and separately. For the direct component, the pyrheliometer CHP1 from Kipp & Zonen mounted on a solar tracker was used. According to the ISO 9060:2018 standards [31], this sensor is a spectrally flat Class A sensor with a measurement error lower than 2%.

Satellite data used are extracted from the Copernicus Atmosphere Monitoring Service (CAMS) and specifically provide clear-sky meteorological data. CAMS is a European program delivering reliable satellite-based information to support environmental monitoring and climate research [32].

After validating the satellite data’s reliability, the next step is to model the CSP system employing LFR technology. This model was applied to various geographically diverse regions—namely, Morocco, Egypt, and Saudi Arabia—to simulate the system’s performance in distinct climates. The electricity harvested from the CSP system was used to feed a PEM electrolyzer to estimate the generated hydrogen. Subsequently, a comprehensive technical and economic analysis was conducted to evaluate the technology’s performance in terms of hydrogen production.

2.1. Validation of the DNI Satellite Measurements

To assess the accuracy of solar irradiance, the use of statistical indicators is crucial. Among the most commonly used metrics are mean bias error (MBE), root-mean-square error (RMSE), normalized root-mean-square error (NRMSE), test statistic (TS), and standard deviation (SD) [33].

The MBE is a calculation of the average error, indicating the model’s bias. A positive MBE suggests the model overestimates actual conditions, whereas a negative MBE indicates an underestimation. An MBE of zero does not imply the absence of error within the model. It can be found from:

$$MBE={\displaystyle \frac{1}{N}}{\sum }_{i=1}^N\left(M_i^s-M_i^g\right).$$

(1)

In this formula, $M_i^g$ represents the measurements taken on the ground, while $M_i^s$ denotes the estimates obtained via satellite.

The RMSE metric provides insight into the satellite dataset’s immediate efficacy by comparing DNI data against satellite-derived values. The closer the RMSE is to zero, the more accurate the model is deemed to be. It can be calculated by:

$$RMSE={\displaystyle \frac{1}{N}}\sqrt{{\sum }_{i=1}^N{\left(M_i^s-M_i^g\right)}^2}.$$

(2)

Moreover, the NRMSE, expressed in the equation below, adjusts the RMSE by the average of the measured data, providing a gauge of the model’s relative error and long-term performance:

$$NRMSE={\displaystyle \frac{\frac{1}{N}\sqrt{\sum _{i=1}^N{\left(M_i^s-M_i^g\right)}^2}}{\frac{1}{N}\sum _{i=1}^N\overline{M_i^g}}}.$$

(3)

The test statistic (TS) is employed to assess the significance of the divergence between satellite-derived DNI values and those measured at the ground level. Generally, a lower TS value signifies higher model precision:

$$TS=\sqrt{{\displaystyle \frac{\left(N-1\right)MBE2}{\left(RMS{E}^2-MB{E}^2\right)}}}.$$

(4)

Finally, the standard deviation (SD) measures the variability between satellite-measured irradiance data and ground-level observations, as shown here:

$$SD=\sqrt{{\displaystyle \frac{N\times (RMS{E}^2-MB{E}^2)}{N-1}}}.$$

(5)

Figure 1 illustrates the daily deviations throughout the year, calculated using the formula:

$$\epsilon ={\displaystyle \frac{{DNI}_g-{DNI}_s}{{DNI}_g}}.$$

(6)

In this formula ${DNI}_g$ is the DNI daily availability for ground measurements and ${DNI}_s$ is the DNI daily availability for satellite measurements. As depicted, the deviation revealed a predominant clustering around the zero line, indicating a strong correlation for the majority of the days. Nevertheless, there was observable variability, with certain days exhibiting greater deviations. These variances could be attributed to environmental factors affecting satellite readings, such as aerosols’ optical depth (AOD) variation, water vapor, and clouds. The computed mean deviation of −0.064 signifies a minor overestimation by the satellite data. Overall, the satellite data demonstrated an acceptable accuracy, with most daily deviations varying between −10% and 10%, especially during the spring and summer (the most important period for CSP plant production) when the sky is clear. This pattern is in accordance with the statistical indicators, with most metrics being acceptable and relatively low values.

Table 1. Statistical indicators results.

Statistical Indicators	Daily	Monthly
MBE (W/m2)	−19.6	−19.8
RMSE (W/m2)	47.8	32.1
NRMSE (W/m2)	20.3	13.6
TS	8.6	2.6
SD (%)	20.3	14.0

gathers statistical indicators evaluating the performance of DNI records from ground and satellite data, revealing several insights. Both daily and monthly analyses exhibit a consistent underestimation of satellite DNI compared to ground DNI, as indicated by negative MBE values (−19.6 W/m² and −19.8 W/m², respectively). Additionally, RMSE values show the average magnitude of differences between the datasets, with higher values for daily comparisons (47.8 W/m²) than monthly ones (32.1 W/m²), suggesting reduced discrepancies over longer time periods. NRMSE values demonstrate relative errors, with higher values for daily comparisons (20.3%) than monthly ones (13.6%), indicating improved agreement over monthly averages. TS values suggest significant differences between the datasets on a daily basis (8.6), but reduced discrepancies when averaged over a month (2.6). Finally, SD values reflect the variability of differences between the datasets, with higher variability observed for daily comparisons (20.3%) than monthly ones (14.0%). Overall, while there are discrepancies between satellite and ground DNI data, particularly on a daily basis, the agreement improves when data are averaged over longer time periods, such as a month. With these results, we can say that the satellite data offer a good estimation for solar irradiance, and therefore, they can be used to simulate the hydrogen yield with an acceptable daily deviation of around 5.06%.

2.2. Solar Resource Assessment of the Investigated Locations

This section evaluates the solar resources of the three locations: Ain Beni Mathar, Morocco (34°0′34.9″ N 2°1.943′ W), Tabuk, Saudi Arabia (28°23′50″ N, 36°34′44″ E), and Assiout, Egypt (27°10′51.46″ N 31°11′1.25″ E). To evaluate the solar potential, the frequency distribution of hourly DNI data is calculated and plotted in Figure 2. This configuration is very important since it provides the number of hours during which the power plant can maintain efficient operation over the year. Ain Beni Mathar exhibits a broad range of DNI values, peaking at intermediate intensities that indicate a significant number of moderately sunny hours. Notably, approximately 13% of the hours show DNI levels approaching 700 W/m², with a considerable frequency of hours exceeding 500 W/m². In the case of Tabuk, a notable portion of hours approximately 6% of the hours record DNI values above 800 W/m² with the dominant range lying between 590 and 850 W/m². Similarly, at the Assiout site, the majority of hours fall within the 650 to 870 W/m² interval, also at a frequency of about 6%, accompanied by a significant number of hours where the irradiance exceeds 400 W/m². Overall, these results confirm that all three locations provide favorable conditions for effective LFR operation.

For a more detailed assessment of the solar resource distribution, in Figure 3, the daily average DNI frequency distributions for (a) Ain Beni Mathar, (b) Tabuk, and (c) Assiout illustrate that each site experiences a wide range of irradiance levels throughout the year. Ain Beni Mathar’s distribution peaks at a daily average DNI of around 300–350 W/m², with a maximum frequency near 8%. Tabuk reaches a higher frequency of roughly 12% at a daily average DNI of 200–300 W/m², indicating a large number of days with moderate irradiance. Meanwhile, Assiout shows its main concentration at a daily average DNI between 250 and 350 W/m², peaking at around 9%.

2.3. Solar System Description

LFR technology is considered a well-established form of CSP plant technology that holds significant promise for economic benefits due to several key advantages. These include the utilization of ground mirrors, which enable lighter structures and mitigate wind resistance, minimize land usage by reducing shading between collector rows, decrease energy consumption for tracking with a fixed receiver, and simplify cleaning procedures for ground mirrors, enhancing overall efficiency [24]. The Fresnel plant used in this study has a total capacity of 50 MWe, composed of IndustrialSolar_Fresnel_PTR70 collectors with DOWTHEM A (The Dow Chemical Company, Midland, TX, USA) as a heat transfer fluid (HTF), which can reach a temperature of 400 °C. The technical specifications for the 50 MWe power plant are delineated in

Table 2. The technical specifications of the power plant.

Property	Specification
Nominal capacity	50 MWe
Collector type	IndustrialSolar_Fresnel_PTR70 2012
Collector length	64 m
Aperture width	7.5 m
Effective mirror area	351 m2
Focal length	4 m
HCE diameter	6.55 cm
Optical efficiency	63.50%
HTF type	DOWTHEM A
Storage capacity	0 h

.

2.4. Simulation Software

To evaluate the electrical performance of the LFR collector, we will employ the Greenius software(version 4.9.0), developed by the German Aerospace Center (DLR) [34]. Greenius facilitates the simulation of various solar systems, incorporating project site weather data, the specific solar energy technology employed, and technical and economic parameters as inputs. The simulation outputs from Greenius yield valuable information, including project energy production, solar system efficiency data, and technical and economic metrics. Notably, the software encompasses multiple technologies such as concentrated solar power, photovoltaic systems, and wind systems. Greenius stands out as a recommended tool for simulating CSP plant systems [35]. Subsequently, the electricity output obtained from the simulation will be utilized through an electrolyzer model to estimate hydrogen production.

To simulate the electricity production, the following mathematical model is used. The total absorbed power is calculated using the following equation [36]:

$${\dot{Q}}_{in}=A_N·DNI·ELF·IAM\left(\theta \right)·{\eta }_{opt}.$$

(7)

In this equation, $A_N$ represents the net aperture area of the LFC collector, $DNI$ is the direct normal irradiance, $IAM\left(\theta \right)$ is the incidence angle modifier, which depends on two incidence angles: the longitudinal angle and the transversal angle [34], $ELF$ represent the “End Losses Factor”, which depends on the collector length $L$ and the focal length $F$, and, finally, ${\eta }_{opt}$ denotes the optical efficiency which relies on the glass cover transmissivity (${\tau }_T$) and the absorptivity (${\alpha }_a$) of the selective coating of the absorber tube, and mirror reflectivity (${\rho }_r$).

$${\eta }_{opt}={\tau }_T·{\alpha }_a·{\rho }_r.$$

(8)

The thermal power transferred to the HTF from the receiver tube can be determined using the following equation:

$${\dot{Q}}_{abs}=\dot{m}{c}_p.\left(T_{out}-T_{in}\right),$$

(9)

where $\dot{m}$ (kg/s) is the mass flow rate of the HTF, representing the amount of fluid passing through the receiver per second, $c_p$ (J/kg·K) denotes the specific heat capacity of the HTF, and $T_{in}$ and $T_{out}$ are the inlet and outlet temperatures of the HTF, respectively.

The thermal losses of the receiver tube, denoted as ${\dot{Q}}_{loss}$, correspond to the difference between the incident radiation power received by the tube and the thermal energy absorbed by the HTF.

$${\dot{Q}}_{loss}={\dot{Q}}_{in}-{\dot{Q}}_{abs}$$

(10)

2.5. Hydrogen Production

In the realm of renewable energy, hydrogen production unfolds through diverse processes, encompassing biomass conversion and water splitting methodologies. Biomass routes are delineated via thermochemical and biological avenues, each offering its array of benefits and challenges. Water splitting, a pivotal facet, unfolds across electrolysis, thermolysis, and photo-electrolysis modalities, each distinct in its operation and implications [17].

Within electrolysis, a spectrum of technologies emerges, including the PEM electrolyzer, the ALK electrolyzer, and solid oxide electrolyzer cells [37]. Our investigation centers on the PEM electrolyzer, chosen for its heightened efficiency, robust operational lifespan, and compatibility with sustainable energy sources [38]. The quantity of hydrogen generated is determined through the formula:

$$M_{H_2}={\displaystyle \frac{E_{el}.{\eta }_{elec}}{{HHV}_{H_2}}},$$

(11)

here $E_{el}$ represent the electricity generated by the renewable source, while ${\eta }_{elec}$ and ${HHV}_{H_2}$ signify the electrolyzer efficiency set at 75% [39], and the higher heating value of hydrogen that stands at 39.4 kWh/kg, respectively. It is noteworthy that water electrolysis necessitates direct current, whereas the electricity produced by the Fresnel collector is in the form of alternating current. Therefore, to bridge this disparity, a converter (AC/DC) is employed. The efficiency of this converter stands at 92% [26].

2.6. Economic Analysis

From an economic perspective, one of the most frequently employed metrics for assessing project viability is the levelized cost. In the context of “green” hydrogen production, the LCOH₂ emerges as a key factor in evaluating projects reliant on renewable resources. Calculated in US dollars per kilogram (USD/kg), the LCOH₂ stands out as a crucial indicator for determining the feasibility of such ventures. This metric is computed using the following formula [40]:

$$LCO{H}_2={\displaystyle \frac{\sum _{i=0}^N\left[(C_{LFR}+C_e)({1+T)}^{-i}\right]}{\sum _{i=0}^N\left[M_{H_2,i}{(1+T)}^{-i}\right]}}.$$

(12)

where $C_{LFR}$ and $C_e$ are the investment costs of the Fresnel collector technology and the electrolyzer, respectively; $T$, $N$ and $i$ signify the discount rate, project lifetime, and the considered year, respectively. The investment cost of the electrolyzer can be outlined as follows [25]:

$$C_{el}=C_{cap}+C_{rep}+C_{OM},$$

(13)

Here, $C_{cap}$ denotes the capital cost of the electrolyzer, while $C_{rep}$ and $C_{OM}$ signify the replacement and maintenance and operation costs, each representing 2% and 25% of the electrolyzer’s capital cost [39], respectively. The capital cost of the electrolyzer can be determined through the following equation [39]:

$$C_{cap}=C_i\times {\displaystyle \frac{M_{H_2}K}{8760{\eta }_{ele}}}.$$

(14)

The unit price of the electrolyzer $C_i$, stands at 368 USD/kWe, while $K$ represents the theoretical energy at 52.5 kWh/kg. Additionally, ${\eta }_{ele}$ denotes the electrolyzer efficiency.

Regarding LFR technology, the investment cost is determined through the following formula:

$$C_{LFR}=C_{imp}+C_{field}+C_{HTF}+C_{PB}+C_{conv}+C_{OM}.$$

(15)

The investment cost comprises various components, including $C_{imp}$ for site improvements, $C_{field}$ for the solar field, $C_{HTF}$ for the HTF, $C_{Pb}$ for the power block, and $C_{conv}$ for conversion costs, respectively. Additionally, $C_{OM}$ represents maintenance and operation costs [41]. All economic parameters used in this study are summarized in Table 3.

To assess the project’s profitability, it is essential to calculate key economic indicators and performance metrics.

The net present value (NPV) is calculated in this study using the following equation [42]:

$$NPV={\sum }_{i=1}^N{CF}_i\times {\left(1+T\right)}^{-i}-TIC$$

(16)

where $CF$ is the cash flow, defined as the difference between the cash revenue and the cash outflow. The cash revenue is determined by selling the hydrogen produced at 8.66 USD/kg (8 EUR/kg [43]). The cash outflow refers to the expenses required for project operation, and $T$ is the discount rate. The $TIC$ is the total investment cost, and it is calculated as follows:

$$TIC=C_{LFR}+C_e+C_{conv}$$

(17)

Another parameter is the internal rate of return (IRR) [44], which is the discount rate when the NPV equals zero. Finally, the last parameter to assess profitability is the payback period (PBP), which can be calculated using the following equation [45]:

$$PBP={\displaystyle \frac{TIC}{Netannualprofit}}$$

(18)

Table 3. Cost components for the LFR collector.

Parameter	Value	Reference
LFR collector
Site improvements	20 USD/m2	[39]
Solar/Heliostat field	150 USD/m2	[39]
Heat transfer fluid system	47 USD/m2	[39]
Storage	32 USD/kWht	[39]
Power plant	1300 USD/kWe	[45]
Conversion costs	130 USD/kW	[37]
Fixed maintenance and operation cost	66 USD/kW-year	[39]
Variable maintenance and operation cost	4 USD/MWh	[39]
Electrolyzer costs
Unit price of electrolyzer	368 USD/kWe	[24]
Replacement cost	2% of unit price	[37]
Maintenance and operation cost	25% of unit price	[37]

Table 3. Cost components for the LFR collector.

Parameter	Value	Reference
LFR collector
Site improvements	20 USD/m2	[39]
Solar/Heliostat field	150 USD/m2	[39]
Heat transfer fluid system	47 USD/m2	[39]
Storage	32 USD/kWht	[39]
Power plant	1300 USD/kWe	[45]
Conversion costs	130 USD/kW	[37]
Fixed maintenance and operation cost	66 USD/kW-year	[39]
Variable maintenance and operation cost	4 USD/MWh	[39]
Electrolyzer costs
Unit price of electrolyzer	368 USD/kWe	[24]
Replacement cost	2% of unit price	[37]
Maintenance and operation cost	25% of unit price	[37]

2.7. Environmental and Sustainability Assessment

Assessing the environmental impact, particularly the reduction of CO₂ emissions, is crucial for evaluating the sustainability of the project. Another important technical parameter is the CO₂ emission reduction, which quantifies the carbon mitigation achieved by the H₂-CSP project compared to a conventional fossil fuel-based hydrogen production. The annual CO₂ emission reduction can be calculated using the following equation [46]:

$${{\mathrm{C}\mathrm{O}}_2}_{\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}(\mathrm{k}\mathrm{g})=\mathrm{A}\mathrm{n}\mathrm{n}\mathrm{u}\mathrm{a}\mathrm{l} \mathrm{e}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{t}\mathrm{y} \mathrm{g}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{C}\mathrm{S}\mathrm{P}(\mathrm{k}\mathrm{W}\mathrm{h})\times 0.318\left({\displaystyle \frac{\mathrm{k}\mathrm{g}}{\mathrm{k}\mathrm{W}\mathrm{h}}}\right)$$

(19)

3. Results and Discussion

3.1. Hydrogen Production

The daily hydrogen production is presented in Figure 4, showing variations across the three sites: Ain Beni Mathar, Tabuk, and Assiout. Tabuk records the highest average daily production, including 24 h at approximately 6.2 tons/day, maintaining a stable output with minimal values, generally above 2 tons/day. In contrast, Ain Beni Mathar and Assiout have similar average productions of around 4.4 and 5.3 tons/day, respectively, although Assiout experiences more pronounced fluctuations. This variation in hydrogen production closely follows the pattern of daily DNI, as illustrated in Figure 1, which is expected since hydrogen production is entirely powered by solar energy. The average daily DNI values at each site further highlight this correlation, with 7.35 kWh/m²/day in Tabuk, 7.26 kWh/m²/day in Assiout, and 5.65 kWh/m²/day in Ain Beni Mathar. The alignment between DNI and hydrogen production confirms that fluctuations in solar energy directly impact hydrogen output. Since the system relies on LFR technology coupled with PEM electrolyzers, powered exclusively by renewable energy, any variations in DNI naturally translate into corresponding changes in hydrogen generation.

To have a more global vision regarding hydrogen production, Figure 5 displays the monthly hydrogen yield for the selected locations. It can be seen that for Tabuk, the highest hydrogen production is recorded from May through August, reaching nearly 250 tons/month in July. Similarly, Ain Beni Mathar and Assiout display important hydrogen yield during these months, peaking at around 200 tons/month. Conversely, production is at its lowest in January, with Ain Beni Mathar falling below 50 tons/month, whereas Tabuk’s and Assiout’s outputs show a slightly higher minimum production, suggesting more consistent operations throughout the year. The average production reinforces these observations, with Tabuk leading at 187.5 tons/month, Assiout at 165.3 tons/month, and Ain Beni Mathar at 135.2 tons/month. Moreover, the disparity in production between the sites is obvious during winter and autumn, with the difference between Ain Beni Mathar and Tabuk reaching 65.8% in January and tapering to 12.6% in June. The gap between Ain Beni Mathar and Assiout is also notable, at 61.2% in January, narrowing significantly to 2.38% in June.

For the sake of accuracy, another way to present hydrogen yield for a location is by plotting the production of a typical day over a specific period. This method is important for benchmarking locations and assessing project bankability. Additionally, it clarifies regular production patterns and helps identify operational efficiencies or deficiencies.

According to Figure 6, the number of production hours varies monthly across the three sites. In December, Tabuk and Assiout operate for 7 h per day, while Ain Beni Mathar operates for 6 h per day; in contrast, during June characterized by high DNI values, all sites achieve 10 production hours per day, with the peak hydrogen production consistently observed between 11:00 a.m. and 2:00 p.m. Notably, December records the minimum peak production values, with maximum hydrogen outputs of 709 kg/h for Tabuk, 615.15 kg/h for Assiout, and 379.18 kg/h for Ain Beni Mathar, whereas June exhibits the highest values, reaching 883.55 kg/h for Tabuk, 775.66 kg/h for Assiout, and 811.9 kg/h for Ain Beni Mathar. Moreover, production variability among the sites is more pronounced during colder months; for instance, in January, the production difference between Ain Beni Mathar and Tabuk is 60.90% and between Tabuk and Assiout is 36.09%, while in July these differences are markedly reduced to 4.28% and 12.74%, respectively.

For a broader vision, the typical day by season is presented in Figure 7. The seasonal production profiles reveal distinct hydrogen production durations and maximum output values across the three sites. In winter, hydrogen production occurs for 6–7 h, with maximum outputs of 392.39 kg/h at Ain Beni Mathar, 603.11 kg/h at Assiout, and 769.12 kg/h at Tabuk. In the fall, production extends to 8 h, yielding maximum values of 603.86 kg/h at Ain Beni Mathar, 690.02 kg/h at Assiout, and 812.15 kg/h at Tabuk. During spring, the production period lasts 9 h, with maximum outputs of 782.63 kg/h at Ain Beni Mathar, 731.67 kg/h at Assiout, and 838.38 kg/h at Tabuk, while in summer, hydrogen production occurs for 10 h, achieving maximum productions of 807.41 kg/h at Ain Beni Mathar, 737.18 kg/h at Assiout, and 870.67 kg/h at Tabuk.

Notably, the production differences between Tabuk and the other sites vary seasonally, with Tabuk exceeding Ain Beni Mathar by approximately 49.0% in the winter, 25.6% in the fall, 6.7% in the spring, and 7.3% in the summer, while the differences between Tabuk and Assiout are approximately 21.6%, 15.0%, 12.7%, and 15.3% for winter, fall, spring, and summer, respectively. This pattern is attributed to the fact that all hydrogen is produced using renewable electricity generated by LFR collectors powering a PEM electrolyzer, making the seasonal variations fully compatible with the nature of solar radiation (as indicated by DNI values in the case of CSP LFR) throughout the year.

3.2. Impact of Climate Variables on Hydrogen Production

This section examines the influence of two key climate factors, DNI and ambient temperature, on daily hydrogen production. To this end, correlation analyses were performed to assess the relationship between these variables and hydrogen output, aiming to highlight the potential impact of environmental conditions on the system performance. Figure 8 and Figure 9 illustrate the correlation between daily hydrogen production and the two examined parameters.

As shown in Figure 8, DNI exhibits a strong influence on hydrogen production, particularly in Tabuk, where the majority of data points closely align with the fitted trend line. This can be attributed to the low variability in DNI measurements at this location. In contrast, Ain Beni Mathar displays a more scattered distribution of data points, reflecting slightly higher fluctuations in DNI as compared with Tabuk. This observation clearly demonstrates that both the magnitude and consistency of DNI significantly affect hydrogen production performance.

Regarding ambient temperature, Figure 9 shows that days with higher temperature records correspond to higher hydrogen production. This observation is consistent with the fact that hotter days seem to correspond to days with higher DNI values, the main driver for electricity production in CSP technologies.

3.3. Techno-Economic Comparison

After assessing hydrogen capacity production at the three locations, this section aims to evaluate the economic aspect of hydrogen production. To this end, the LCOH₂ is calculated and presented in Figure 10 together with the annual hydrogen production. It can be observed that Tabuk is the first-ranked location, with an annual yield of 2251 tons/year, significantly outstripping Ain Beni Mathar at 1621 tons/year and Assiout at 1936 tons/year. Obviously, Tabuk is also delineated as the best location for hydrogen production, with an LCOH₂ of 6.47 USD/kg, underscoring a favorable cost structure relative to Ain Beni Mathar’s 7.35 USD/kg and Assiout’s 6.84 USD/kg.

This economic leverage is primarily attributed to the use of LFR systems, which are not only simple but also a mature CSP technology, presenting a significant cost advantage due to their established presence in the market.

According to

Table 4. A comparison of hydrogen production costs and system configurations for different solar technologies and electrolyzer types.

Reference	Location	Solar System	Chemical Process	LCOH2 (USD/kg)
This study	Morocco	CSP (Fresnel collector)	PEM	6.47–7.35
[47]	Chile	CSP + storage (parabolic trough collector)	PEM &ALK	PEM: 5.46 ALK: 4.23
[25]	Morocco	CSP + storage (parabolic trough collector and Stirling dish)	PEM	PTC: 5.86 SD: 9.81
[48]	N/A	CSP+ storage (Fresnel collector)	PEM	5.67
[49]	Australia	CSP + storage	SOE	6.1–8.9
[51]	N/A	CSP + storage	SOE	6.96
[50]	USA	CSP (Stirling dish)	SOE	5.9–9.1
IRENA (2020) [52]	Global (CSP)	CSP + electrolysis	PEM/ALK	6–10 (2020)
IEA (2021) [53]	Global (CSP)	CSP + electrolysis	PEM	6–8 (2020)

, the LFR-based CSP system presented in this study achieves a levelized cost of hydrogen (LCOH₂) between 6.84 USD/kg and 7.35 USD/kg without thermal energy storage, positioning it as a competitive option compared to other CSP technologies. For instance, CSP systems using CSP-PTCS with storage report lower costs, such as 5.46 USD/kg (PEM) and 4.23 USD/kg (ALK) [47], while hybrid configurations using CSP-PTCS and CSP-SD show more varied results, ranging from 5.86 USD/kg to 9.81 USD/kg [25]. LFR systems with integrated storage also show improvements, with LCOH₂ values near 5.67 USD/kg [48]. In terms of SOE, CSP systems report costs between 5.9 USD/kg and 9.1 USD/kg [49,50].

When compared to global projections, the results of this study align with estimates from the International Renewable Energy Agency (IRENA) (6–10 USD/kg) [52] and the International Energy Agency (IEA) (6–8 USD/kg) [53] for CSP-powered hydrogen production. These findings confirm that LFR technology, despite slightly higher costs in the base case, remains economically relevant within international ranges and could become even more competitive through storage integration and investment cost reductions.

4. Optimization and Sensitivity Analysis

4.1. Thermal Energy Storage Influence on Hydrogen Yield and Levelized Cost

This section investigates the impact of integrating thermal energy storage (TES) into the LFR system to evaluate its effects on hydrogen production and cost. The goal is to optimize TES duration to enhance system performance and achieve a more cost-effective “green” hydrogen production.

According to Figure 11a, hydrogen production increases with longer TES duration and then stabilizes once high storage capacity is reached. This demonstrates the importance of selecting the best TES capacity for optimal production and costs. As illustrated in Figure 11b, the levelized cost of hydrogen (LCOH₂) decreases as TES time increases, reaching its lowest values around 3 to 4 h, approximately 7.21 USD/kg for Ain Beni Mathar, 6.35 USD/kg for Tabuk, and 6.6 USD/kg for Assiout before rising again for longer storage periods.

This trend reflects the typical behavior of thermal storage in CSP systems, where there is a limit to how much heat can be stored effectively. Beyond this limit, additional TES does not improve hydrogen production but results in higher costs. Therefore, optimizing TES duration is key to balancing hydrogen yield and production cost.

To further investigate the TES impact on the project feasibility, a comparison of the various economic and technical parameters used in this study is performed, and the results are summarized in

Table 5. Hydrogen production and economic parameters by site.

Parameter	Ain Beni Mathar		Assiout		Tabuk
	Without TES	With TES	Without TES	With TES	Without TES	With TES
MH2 (tons)	1621.85	1779.81	1936.33	2658.39	2251.20	2666.74
LCOH2 (USD/Kg)	7.35	7.21	6.84	6.26	6.47	6.36
NPV (millions USD)	23.56	29.25	41.36	77.37	59.18	74.06
IRR (%)	10	11	12	16	14	15
CO2 mitigation (kt)	29.45	32.32	35.16	48.27	40.88	48.42
PBP (YEAR)	9.18	8.78	7.65	6.09	6.61	6.39

.

As can be observed, the integration of thermal energy storage (TES) leads to higher hydrogen production and better economic indicators. For example, at Assiout, the NPV increases from USD 41.36 to 77.37 million, mainly due to a reduction in LCOH₂, which decreases from 6.84 USD/kg to 6.26 USD/kg. At Ain Beni Mathar and Tabuk, the LCOH₂ also decreases, from 7.35 USD/kg to 7.21 USD/kg and from 6.47 USD/kg to 6.36 USD/kg, respectively. This is explained by the improved system operation with TES. Among the different storage configurations studied, the case with the lowest LCOH₂ was selected and considered as the optimal case.

In terms of the environmental impact, all sites show significant CO₂ mitigation, both with and without storage. For instance, at Tabuk, avoided CO₂ emissions reach 40.88 kilotons without storage and increase to 48.42 kilotons with storage.

Similarly, Ain Beni Mathar achieves 29.45 kilotons without storage and 32.32 kilotons with storage, while Assiout shows reductions of 35.16 kilotons without storage and 48.27 kilotons with storage. These results confirm the positive contribution of the system to reducing emissions.

4.2. Sensitivity Analysis of Investment Cost Reduction

Given the unique characteristics of LFR systems and the regional context of MENA countries, a sensitivity analysis was conducted to assess the impact of reducing capital investment costs on the LCOH₂.

LFR systems are structurally simpler than other CSP technologies and primarily rely on flat mirror arrays, which are easier to manufacture and install. This technical simplicity opens the door for local manufacturing and assembly, significantly lowering costs compared to imported components. Several MENA countries have already initiated the local production of materials and components used in CSP plants, including glass, steel structures, and electrical systems.

According to the World Bank report (2011), countries like Egypt and Morocco have demonstrated the ability to locally supply a significant share of CSP-related infrastructure and services, with some projects already achieving over 50–60% local content in solar field construction [54]. In this study, to assess the impact of local manufacturing of LFR structures, a sensitivity analysis was conducted by considering investment cost reductions ranging from 5% to 40%, with a 5% step for each simulation run. This range reflects a realistic outlook on the potential for future local industrial development and enhanced cost competitiveness within the MENA region.

As illustrated in Figure 12a, the LCOH₂ consistently decreases with increasing investment cost reductions for all three sites, both without and with TES. For instance, at Ain Beni Mathar, LCOH₂ decreases from 7.35 USD/kg down to 6.77 USD/kg without TES, and from 7.21 USD/kg down to 6.68 USD/kg with TES. At Assiout, the reduction is from 6.84 USD/kg to 6.35 USD/kg without TES, and from 6.26 USD/kg to 5.91 USD/kg with TES. Similarly, at Tabuk, LCOH₂ decreases from 6.47 USD/kg to 6.05 USD/kg without TES, and from 6.36 USD/kg to 6.01 USD/kg with TES. This clear descending trend demonstrates significant economic benefits as capital costs are lowered by up to 40%. This decrease in LCOH₂ is primarily attributed to the potential for local manufacturing and assembly of key LFR system components in the MENA region, which reduces dependency on imported, high-cost materials and components.

Concurrently, the net present value (NPV) of these projects shows a strong increasing trend with reduced investment costs, signaling improved project profitability (see Figure 12b). For example, without TES, NPV increases from around USD 23.6 million to USD 54.7 million at Ain Beni Mathar, USD 41.4 million to USD 73.2 million at Assiout, and USD 59.2 million to USD 91.8 million at Tabuk. When TES is included, these values further improve, reaching maximum NPVs of approximately USD 62.0 million (Ain Beni Mathar), USD 112.9 million (Assiout), and USD 111.1 million (Tabuk), respectively.

The comprehensive data summarized in

Table 6. The impact of investment cost reduction on LCOH₂ and key economic indicators for three sites (without TES).

	Ain Beni Mathar				Assiout				Tabuk
Cost Reduction	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBP (Year)	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBP (Year)	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBP (Year)
Ref (without TES)	7.35	23.56	9.85%	9.18	6.84	41.36	12.35	7.65	6.47	59.18	14.63	6.61
5%	7.28	27.45	10.66	8.63	6.78	45.34	13.25	7.21	6.42	63.25	15.62	6.23
10%	7.21	31.34	11.55	8.09	6.72	49.32	14.24	6.77	6.37	67.33	16.70	5.86
15%	7.14	35.23	12.53	7.57	6.66	53.30	15.33	6.34	6.32	71.40	17.90	5.49
20%	7.06	39.12	13.60	7.05	6.60	57.29	16.53	5.92	6.27	75.48	19.24	5.13
25%	6.99	43.01	14.80	6.54	6.54	61.27	17.88	5.50	6.21	79.56	20.74	4.78
30%	6.92	46.90	16.15	6.04	6.48	65.25	19.41	5.09	6.16	83.63	22.44	4.43
35%	6.84	50.79	17.69	5.56	6.42	69.24	21.15	4.69	6.11	87.71	24.38	4.08
40%	6.77	54.68	19.45	5.08	6.35	73.22	23.16	4.29	6.05	91.78	26.64	3.74

and

Table 7. The impact of investment cost reduction on LCOH₂ and key economic indicators for three sites (with TES).

	Ain Beni Mathar				Assiout				Tabuk
Cost Reduction	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBT (Year)	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBT (Year)	LCOH2 (USD/kg)	NPV (Millions USD)	IRR %	PBT (Year)
Ref (with TES)	7.21	29.25	10.44%	8.78	6.26	77.37	16.03%	6.09	6.36	74.06	15.18%	6.39
5%	7.15	33.34	11.26%	8.26	6.22	81.80	17.07%	5.74	6.32	78.69	16.18%	6.04
10%	7.08	37.44	12.16%	7.75	6.17	86.24	18.21%	5.41	6.27	83.32	17.27%	5.68
15%	7.01	41.54	13.15%	7.26	6.13	90.68	19.47%	5.08	6.23	87.95	18.47%	5.34
20%	6.95	45.64	14.25%	6.77	6.08	95.12	20.87%	4.75	6.18	92.58	19.82%	4.99
25%	6.88	49.74	15.47%	6.29	6.04	99.56	22.45%	4.43	6.14	97.21	21.33%	4.65
30%	6.81	53.84	16.85%	5.81	6.00	104.00	24.25%	4.11	6.09	101.84	23.05%	4.31
35%	6.74	57.94	18.42%	5.35	5.95	108.44	26.31%	3.79	6.05	106.47	25.02%	3.98
40%	6.68	62.04	20.23%	4.89	5.91	112.88	28.71%	3.48	6.01	111.10	27.30%	3.65

capture the key economic and technical indicators across all scenarios. These include LCOH₂, NPV, IRR, and PBT for each site under varying investment cost reductions, both with and without TES. This detailed tabulation facilitates a holistic understanding of the project performance, illustrating how reduced capital costs consistently improve economic feasibility and accelerate returns while also optimizing hydrogen production costs.

We need to highlight that these simulations of the economic parameters for the cases with the TES system were conducted only for the optimal cases/references identified by the lowest LCOH₂ values, as described in the previous section.

4.3. Electrolyzer Cost Reduction and 2030 Scenario Analysis

To explore the potential for future hydrogen cost reductions, a sensitivity analysis was conducted based on the expected decrease in PEM electrolyzer capital costs by 2030. According to the IRENA [55], the capital cost of PEM electrolyzers was approximately 371 USD/kW in 2020 and is projected to decline by about 34.5%, reaching around 243 USD/kW by 2030. This projected reduction was used in the current study to assess its impact on the LCOH₂, particularly in the context of the MENA region.

As shown in Figure 13a,b, this future scenario leads to lower LCOH₂ values across all sites. Without TES, the hydrogen cost drops to 7.14 USD/kg in Ain Beni Mathar, 6.63 USD/kg in Assiout, and 6.26 USD/kg in Tabuk. With TES integration, the cost is further reduced, reaching 6.99 USD/kg, 6.04 USD/kg, and 6.14 USD/kg, respectively. These results highlight that both cost reduction and TES integration can significantly enhance hydrogen competitiveness in the MENA region.

4.4. Limitations of This Study

This study has some limitations. The validation of satellite-derived DNI data was performed only at the Ain Beni Mathar site, and no ground validation data were available for Assiout and Tabuk, which may introduce uncertainty in the results for these locations. Additionally, the electrolyzer efficiency was assumed static throughout the simulations, which may not fully capture operational variations. Finally, the modeling framework relies on assumptions inherent to simulation software and does not account for all real-world operational constraints, which could influence the accuracy of performance predictions. These limitations suggest avenues for future work to refine the analysis and improve robustness.

5. Conclusions

This study focused on evaluating the potential of “green” hydrogen production in the MENA region using CSP systems based on LFR technology. Given the importance of accurate data in such projects, DNI measurements from the Ain Beni Mathar (Morocco) site were utilized to validate satellite-derived DNI data. This validation, performed using statistical indicators, confirmed a strong correlation between satellite and ground-based measurements, enabling the reliable use of satellite data for simulations across different locations.

The study covered three sites in the MENA region—Ain Beni Mathar (Morocco), Assiout (Egypt), and Tabuk (Saudi Arabia)—known for their high solar potential. Simulations were conducted with a 50 MWe CSP system featuring LFR collectors, and the electricity generated was used to power a PEM electrolyzer for hydrogen production. Results revealed that Tabuk achieved the highest annual hydrogen output of 2251 tons/year, followed by Assiout with 1936 tons/year and Ain Beni Mathar with 1621 tons/year. Correspondingly, LCOH₂ was 6.47 USD/kg, 6.84 USD/kg, and 7.35 USD/kg, respectively. Monthly production peaked during the summer months, with Tabuk reaching nearly 250 tons/month in July, Assiout reaching approximately 200 tons/month, and Ain Beni Mathar slightly below 200 tons/month. In terms of typical daily production per season, Tabuk consistently outperformed the other sites during spring and summer, with daily averages reaching around 860.33 kg/day compared to 826 kg/day at Ain Beni Mathar and 780.66 kg/day at Assiout.

During fall and winter, production levels were closer across the three sites, reflecting reduced solar availability, but maintaining consistent operation.

Correlation analyses confirmed a strong positive relationship between hydrogen production and key climate variables, DNI and ambient temperature, highlighting the critical influence of environmental conditions on the system’s performance.

The integration of TES was shown to further improve hydrogen yield and reduce costs. Optimal TES durations of around 3 to 4 h decreased the LCOH₂ to approximately 6.26 USD/kg in Assiout, 6.36 USD/kg in Tabuk, and 7.21 USD/kg in Ain Beni Mathar, representing notable cost improvements compared to configurations without TES. Additionally, TES enhanced economic indicators such as NPV, demonstrating improved project viability.

Sensitivity analyses on investment cost reductions from 5% to 40%, reflecting the potential for local manufacturing in the MENA region, revealed substantial decreases in LCOH₂, with values falling to as low as 5.91 USD/kg in Assiout (with TES) and improved profitability across all sites. Additionally, projected reductions in PEM electrolyzer capital costs by 2030 further reduced hydrogen production costs, with LCOH2 potentially dropping to around 6.04 USD/kg in Assiout and 6.14 USD/kg in Tabuk when combined with TES integration.

These findings highlight the significant potential of the MENA region to become a global leader in “green” hydrogen production due to its abundant solar resources and favorable climatic conditions. The competitive LCOH₂ values observed for the LFR-based system reinforce the economic viability of this technology for large-scale hydrogen production, positioning the region as a strong candidate for cost-effective and sustainable hydrogen development.