An Analysis of Hybrid Renewable Energy-Based Hydrogen Production and Power Supply for Off-Grid Systems

Abstract

Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system.

1. Introduction

Using renewable energy sources (RESs) like solar, wind, hydro, tidal, and geothermal power offers several environmental benefits [1,2]. These sources help mitigate climate change by reducing greenhouse gas emissions and other harmful pollutants associated with traditional fossil fuel-based energy generation [3,4,5]. Renewable energy technologies have lower environmental impacts compared to fossil fuels, contributing to environmental sustainability [6]. Renewable energy systems can also lessen the total environmental effect of energy production, increase biodiversity, and cut greenhouse gas emissions [7,8]. The integration of renewable energy with multi-generation systems can further increase efficiency, reduce pollution emissions, and promote sustainability. Overall, the environmental benefits of utilizing renewable energy include lower emissions, reduced pollution, enhanced biodiversity, and a more sustainable approach to meeting energy needs.

Hybrid renewable energy systems play a crucial role in enhancing the efficiency and reliability of off-grid systems. By combining sources like solar, wind, batteries, and diesel generators [9], these systems optimize power generation, minimize fuel consumption, and reduce operational costs [10]. Utilizing diverse renewable sources over extended simulation periods significantly boosts system reliability and resilience, making configurations up to 94% more robust than those based on single-year data [11]. Moreover, experimental analyses demonstrate that hybrid systems, incorporating wind and photovoltaic sources, can cover a substantial portion of energy consumption, with added storage capacity ensuring continuous power supply [12]. However, challenges persist due to the variable nature of solar and wind energies, necessitating careful planning to ensure reliable power supply in off-grid settings [13].

More renewable energy capacity is predicted to be added globally in the next five years than has been the case since the first commercial renewable energy power plant was built more than a century ago. A number of milestones related to renewable energy are anticipated to be reached within the next five years: First, solar PV and wind power will together produce more electricity in 2024 than hydropower. Second, by 2025, renewable energy sources will have surpassed coal as the main source of electricity production. Also, in 2025 and 2026, respectively, nuclear electricity generation will be surpassed by wind and solar PV. By 2028, power generated worldwide will come from renewable energy sources, including wind and solar PV contributing a doubling of the total to 42% as illustrated in Figure 1.

Although there are many signs that technologies have improved renewable energy (RE) off-grid systems, it is crucial to look into the factors that could have an impact on how these technologies are used in the long run or over the course of any renewable project. It is crucial to consider power system operation and system flexibility for renewable energy integration because the majority of electric power systems are installed and built to respond to any sudden changes in power demand requested by different electric loads [15]. Given that both solar and wind energy are seen as potential renewable energy sources, it is worthwhile to look into how well these sources function for hydrogen production and power supply.

Hydrogen and power production can be efficiently integrated through various technologies to meet energy demands sustainably [16]. Processes like tubular reforming, autothermal reforming, and water electrolysis play crucial roles in producing hydrogen with high purity and low environmental impact [17,18,19]. The flexibility of power-to-hydrogen plants allows for profitable operation across different energy sectors, enhancing energy security and reducing greenhouse gas emissions. Additionally, coupling hydrogen production with renewable energy sources can reduce costs and promote the adoption of clean energy technologies. By exploring innovative systems like the iodine–sulfur cycle coupled with high-temperature reactors, both hydrogen and electricity can be efficiently co-produced, improving overall system efficiency.

Combining various energy-consuming businesses with widely-used, well-established electrolysis technology for hydrogen production can efficiently achieve decarbonization [20]. Additionally, countries with surplus renewable energy can utilize it to produce hydrogen, which they can export or transport to other regions of the globe as renewable energy in the form of compressed or liquefied hydrogen gas, or in other forms such as ammonia or methane [21]. To realize the objective, there are two main obstacles to overcome: creating a market for hydrogen and decarbonizing the methods used to produce it in order to limit emissions from various industries. The creation of hydrogen for a cleaner climate and energy supply faces two major obstacles, but these are not mutually exclusive. Several factors contribute to the production of hydrogen, including feedstock selection, government and investor support, growth in production capacity and market size, and the choice of economically viable technologies for production, decarbonization, storage, transmission, and applications. It is important to note that hydrogen generation from different feedstocks, other than the most widely used technologies such as nuclear, wind, and solar energies, will not be considered a clean fuel unless economically advantageous and effective decarbonization techniques are employed [22,23].

Because of the special qualities of renewable resources, changing load demand and a large number of factors and characteristics to take into account, designing a renewable hybrid energy system of the right scale is more difficult than designing one from a single source. By utilizing all of the system’s components, an optimal size approach can help to minimize these challenges and determine the least amount of money needed. To find the best balance between system reliability and cost, a variety of sizing techniques can be used, like graphical construction, iterative, probabilistic, and artificial intelligence approaches.

Many studies have been conducted on the use of hybrid renewable energy systems to generate hydrogen and electricity. These studies, which have focused on a range of parameters evaluating the deployment of hydrogen technologies such as hydrogen storage tanks (HST), fuel cells, and electrolyzers, have examined both grid-connected and off-grid hybrid power systems. Al-Sharafi et al. [24] used HOMER to analyze areas for power generation and hydrogen synthesis from solar and wind sources in Saudi Arabia. They discovered that, in the Yanbu region, a battery bank storage system, PV, and WT combination results in a minimum COE of 0.609 USD/kWh. Also, they discovered that the minimal COE at Abha is 1.208 USD/kWh for battery or hydrogen storage systems. Using HOMER, Qolipour et al. [25] demonstrated the techno-economic viability of producing hydrogen and power at Hendijan, Iran. They concluded that Hendijan could finance a hydrogen, solar, and wind hybrid energy system. Tian Xia et al. [26] conducted a techno-economic evaluation for off-grid hybrid power plant to co-supply hydrogen and electricity to a remote micro-community. According to the sensitivity evaluation results in this study, a simple water velocity fluctuation of +10% resulted in a 20% decrease in net present cost (NPC) and levelized cost of energy (LCOE) among the four variables. Also, Kusakana et al. conducted a study to assess the possibility of hydrokinetic energy for electrifying a distant and isolated area in South Africa [27]. HOMER software was used to fulfill the study’s objectives. When compared to installing a diesel generator, PV system, and wind turbine, the economic and environmental outcomes favored this approach. Mohammadi et al. [28] carried out a study using solid oxide electrolyzers and dish collectors for hydrogen production and discovered that the levelized cost of hydrogen (LCH) was 9.12 USD/kg H₂. In Gökçeada, Turkey, a techno-economic study was conducted for a hydrogen refueling station for two different kinds of hybrid energy systems [29]. Totals of 8.92 USD/kg H₂ for the wind–photovoltaic–battery system and 11.08 USD/kg H₂ for the wind–battery system were found to be the levelized costs of hydrogen.

A number of projects have been undertaken around the world to electrify remote and isolated areas using renewable energy sources, including those in Malaysia [30], Cambodia [31], Sub-Saharan Africa [32], west China [33], India [34,35], Pakistan [36], Mexico [37], Venezuela [38], and Algeria [39]. The cost-effective configuration was demonstrated and identified by the optimization studies and findings. In addition, Nurunnabi et al. [40] investigated the potential of solar and wind energy in different parts of Bangladesh to carry out a sensitivity and feasibility assessment of RE-based off-grid microgrids and grid-connected microgrids. This study illustrated the possibilities for renewable energy at the chosen sites. To find a cost-effective solution, size optimization and sensitivity analysis were conducted based on specific critical performance variables. The National Renewable Energy Laboratory’s (NREL) Hybrid Optimization of Multiple Electric Renewables (HOMER) software was used in the majority of previous research. This program is regarded as one of the best tools for evaluating hybrid renewable energy systems (HRESs), according to a number of studies and earlier works [41].

The review of the literature showed that most previous studies on RE grid-connected systems lacked comprehensive analysis to determine the effects of backup systems on project lifetime design costs and performance. Examining the technical and financial implications of two distinct scenarios, one with and one without an off-grid backup system, is a crucial component that is currently lacking. Throughout the course of the project, these assessments can help with decision-making by providing relevant predictions about potential outcomes.

The contributions of this study concerning hybrid renewable energy-based hydrogen production and power supply for off-grid systems are summarized as follows: (1) The use of technical and economic models that take component replacement and degradation into account, allowing for a more precise technical and economic assessment. (2) Finding the system component combination that can meet electric load demand without causing a loss of energy source and with the lowest net present cost, energy cost, and hydrogen production cost is extremely important. (3) A high-capacity factor may reflect the effectiveness of the equipment used, but it does not always indicate the good economic feasibility of systems. This paper presents two scenarios and designs and evaluates the system’s sensitivity to changes in its variables. The study involves analyzing the interactions between various input parameters to identify modifications that can impact the system’s functionality. The system’s performance is assessed by considering several factors such as hydrogen generation, electrolyzer input power, fuel cell output, LCOE, NPC, operational cost, and renewable fraction. A thorough investigation was conducted to verify the suggested system’s adaptability to the relevant operational factors.

This paper is organized as follows. Section 2 describes more details of the hybrid energy system. Section 3 introduces the sizing of system components and related data. Section 4 describes the simulation-founded results and discussion for all proposed scenarios. Finally, Section 5 presents the research conclusions.

2. The Hybrid Energy System

Figure 2 illustrates the overall renewable hybrid energy system in use. It generates both electricity and hydrogen, which are subsequently stored and reclaimed to generate electricity in response to load demands as demanded by a control system.

2.1. Components of System

2.1.1. Captured Energy

Individual cells are arranged in series and parallel in a PV module according to the output voltage and current that is required. Figure 3 illustrates an example of a PV module’s power–voltage and current–voltage relationships, which are proportionate to those of the individual cells and depend on the internal connection architecture.

The Shockley diode equation is frequently used to determine the current–voltage relationship of an array. This equation assumes that identical modules are arranged in an array of number of strings (Ns) strings in parallel, number of modules (Nm) in series forming each string, and number of cells (Nc) per PV module [42] as:

$$I=N_s{I}_{ph}-N_s{I}_{rs}\left[\mathrm{e}\mathrm{x}\mathrm{p}\left\{\frac{q\left(V+I{R}_S{N}_c{N}_m/N_s\right)}{N_c{N}_m\kappa k_B{T}_c}\right\}-1\right]-\frac{\left(V+I{R}_s{N}_c{N}_m/N_S\right)}{R_{sh}{N}_c{N}_m/N_s}$$

(1)

where V is the load voltage, Rs is the series internal resistance, Rsh is the shunt resistance, κ is the ideality factor, $k_B$ is the Boltzmann constant [1.380 × 10⁻²³ J·K⁻¹], and Iph is the photocurrent owing to radiated flux G.

The mechanical power output (Pm) of the perfect wind turbine is determined by air speed and the well-known Betz’s Law as:

$$P_m=\frac{1}{2}{C}_p\rho A{v}_w^3$$

(2)

where vw is the speed of wind, A is the turbine blade sweep area, and ρ is the air density. The power curve, which is defined as the connection between wind speed mechanical and output power, is theoretically represented by Equation (2). A true wind generator runs between a maximum wind speed (the cut-out speed, vco), which is determined by engineering and safety concerns, and a minimum wind speed (the cut-in speed, vci), which is ultimately determined by friction in the mechanism. Power output is capped at a maximum, or rated value, Pr, which happens at rated wind speed vr < vco in order to protect the turbine. The power curve is non-linear between vci and vr, and the turbine’s operation is regulated to extract the most power possible. In Figure 4, the common power curve is summarized. Regions 1 and 4 are not producing any electricity.

The most practicable method for producing hydrogen on an industrial scale with renewable resources at the moment is water electrolysis. The proton exchange membrane (PEM) electrolyzer and alkaline electrolyzer are the two commercially available low-temperature systems that now dominate the market out of the three main types of modern electrolyzer. The instantaneous energy efficiency of the electrolyzer cell may generally be calculated using the following formula [42]:

$${\eta }_c=\frac{N{V}_{tn}I}{P_{in}}$$

(3)

where I is the input current passing through the cell and Pin is the stack’s total input power, Vtn is the thermo-neutral voltage, and N is the number of cells in the stack of the electrolyzer cell and may be computed using the formula below:

$$q_{H_2}^{req}=\frac{N_0{N}_{S}I}{2FU}$$

(4)

where N₀ is the fuel cell stack number, Ns is the number of series cells for each stack, F is Faraday constant [96,485 C mol⁻¹], U is the use rate, and $q_{H_2}^{req}$ is the needed amount of hydrogen flow to match the load changes.

2.1.2. Energy Storage

Without any additional features, it can be expected that a battery will charge and discharge at the needed rate within the constraints set by its depth of discharge (DOD) and state of charge (SOC). At the most basic level, the SOC and battery current are connected, assuming no energy is lost during storage and retrieval of charge, by:

$$SOC(t)=SOC(0)+\frac{1}{C}{\int }_0^t I\left(V_{bat},t^{\prime }\right)d{t}^{\prime }$$

(5)

where C represents the battery’s capacity, I denotes its current, and Vbat denotes its voltage.

Fundamentally, hydrogen storage can be thought of as a perfect battery that does not take pressure, temperature, or dynamics into account when taking in hydrogen (“charging”) and delivering it (“discharging”) as needed, within the bounds of its capacity. At a more sophisticated level, the state of hydrogen (SOH), which is an analogy with the battery SOC, can be used to represent the amount of hydrogen in storage:

$$SOH(t)=SOH(0)+\frac{1}{C_H}{\int }_0^t {\dot{m}}_H\left(p,t^{\prime }\right)d{t}^{\prime }$$

(6)

where p is the hydrogen pressure, ${\dot{m}}_H$ is the hydrogen mass flow rate, and CH is the storage’s gravimetric capacity.

3. Sizing the System Component

From a technical perspective, optimal operation and cost minimization depend on the proper sizing of the component capacities in power and energy terms. The optimal component sizes should be established by simulating the complete system while it follows its specified control method. Global factors, such as annual energy consumption and the amount of time that a component can exist without a main energy input, can be used to determine a component’s initial size. This means that sizing must be performed repeatedly in order to optimize the system in accordance with goals. Because certain component sizes, such as PV array size and hydrogen storage capacity, may be coupled such that raising one compensates for decreasing the other, the configuration arrived at by optimizing merely over technical aspects may not be unique.

The condition of the atmosphere, specifically the temperature, humidity, wind direction, and air pressure, among other elements, causes variations in the meteorological data at different geographical places. It is therefore anticipated that various energy system architectures will satisfy the same load requirement. The goal is to look at the potential for renewable energy and how variations in system component size affect the price of energy. In light of this, this study will evaluate the economic feasibility of producing hydrogen and generating power using renewable hybrid energy systems. Figure 5 shows the two proposed systems in this study.

3.1. Load Demand Estimation

Because renewable energy supplies have an intermittent profile, it is necessary to predict meteorological information for a particular area in order to precisely design the system component. In this study, 100% renewable energy systems were examined. This indicates that the loss of power supply probability (LPSP) for energy systems is around zero. The LPSP is calculated as the ratio of the entire needed load to the sum of all hourly loss of power supply values. When an energy system’s power output equals the total load demand, or LPSP zero, it indicates that the system’s components are sized to use renewable energy sources to supply the full load demand.

Load estimation is a crucial aspect of simulation. In this study, two types of loads were examined to analyze the system. The first hypothetical load is a DC load that consumes around 200 kWh per day. This load is assumed to be connected to the DC bus. Figure 6 displays the estimated AC and DC daily primary load profile for each month.

3.2. The System Cost Estimation

HOMER Pro is an advanced software application that is specifically crafted to analyze and forecast the most efficient system configuration under custom-defined limitations, in order to achieve the lowest NPC. This powerful algorithm is able to determine the optimal sizing for each component within the system, thus reducing the risk of power supply failure while also minimizing energy costs [43,44]. In order to carry out the calculation, a thorough techno-economic analysis is conducted. This analysis takes into consideration the consistent increase in prices of system components. Furthermore, the calculation factors in the nominal interest rate and an annual inflation rate of 2%, both of which are location-specific. Additionally, the project is assumed to have a lifetime of 25 years.

The average component cost was calculated using input data gathered from manufacturers and literature sources. This approach was necessary due to the presence of numerous manufacturers in the market producing system components for energy systems [24,45,46,47]. A PV array has a 25-year lifespan, with capital and replacement costs set at 3000 USD/kW and operations and maintenance at 10 USD/kW annually. Wind turbines are assumed to have a 2000 USD/kW capital and replacement cost, as well as an O and M cost of 20 USD/kW year; their lifetime is 25 years. The initial and replacement costs of the electrolyzer are assumed to be 2000 dollars per kW, with an efficiency of 85% and a 15-year lifespan. Fuel cells are assumed to have a 50% efficiency, a 3000 USD/kW initial and replacement cost, and a 40,000 h lifespan. The estimated cost of capital and replacement for a 1 kWh Li-ion battery is 450 USD/kWh, with a 15-year lifespan. The estimated capital and replacement costs for a hydrogen tank are 1500 USD/kg H₂ and a its lifespan is 25 years [48].

4. Results and Discussion

Since the technical requirements of the components of a hybrid energy system are already included in HOMER Pro’s algorithm in a generic form, no technical specifications are needed during the optimization process. The software requires the lifespan, efficiency, and capital and replacement costs of the system components. It is also necessary to specify an interest rate and annual inflation rate in order to compute the economic analysis discount. Furthermore, the software offers the option to obtain necessary weather data by connecting to the NASA atmospheric science data center using the longitude and latitude of the selected location. Figure 7 illustrates the monthly average global solar radiation and wind speed at the mentioned locations. It should be noted that the results will demonstrate a more accurate evaluation that takes into consideration realistic characteristics that could have a significant impact on the system performance by using data from a specific installation site for solar radiation and wind seed. Additionally, the outcomes will be susceptible to modifications in the parameters and outside variables such as weather-related delays, equipment prices, and backup system types.

4.1. Optimization Process

Based on load flow control, two optimized distinct renewable energy systems were investigated in this work, as seen in Figure 5. Once the user-defined limitations converge at the lowest NPC, HOMER’s optimal system design was discovered. In this scenario, HOMER minimizes the LPSP and COE to determine the ideal component sizing. For instance, HOMER simulated several PV array sizes and wind turbine counts to scale the energy system’s storage component until there was insufficient power to meet the load.

In this optimization, HOMER Pro forms 8760 values for each of the input datasets. Typically, HOMER Pro is unable to represent transitory changes for longer than an hour. In order to perform an economic analysis and rank the system configurations, we set optimization constraints and input load, weather, and economic data to calculate the NPC and COE using HOMER Pro. The system configurations were then ranked based on their NPC. The simplified HOMER Pro optimization flowchart is shown in Figure 8.

4.2. Power and Hydrogen Production

This model examines an independent photovoltaic–hydrogen system. The electrolyzer uses extra power to produce hydrogen, which is then stored in the hydrogen tank. Using the hydrogen that has been stored as fuel, the fuel cell produces energy. In System A, the microgrid requires around 321 kWh per day and has a peak power demand of 64 kW. The proposed system uses the sources explained in Figure 9 to generate electricity to meet the load requirements. Similarly, in System B, the microgrid requires 452 kWh per day and has a peak power demand of 64 kW, as can be seen from the same Figure.

Table 1. Production Details for Systems A and B.

System Production	System A	System B	Unit
PV	135,630	181,278	kWh/year
Fuel Cell	14,372	31,741
WT	3613	7226
Total	153,615	220,245
Electrolyzer	868	1905	kg/year
FC Operation Hours	1496	5454	h/year
FC NO of starts	617	378	starts/year
FC Operational Lifetime	26.7	7.33	year
FC Capacity Factor	8.20	18.1	%
Total Fuel Consumed	868	1905	kg

illustrates the production details for both systems. The absence of a battery bank in System B requires the system to increase the amount of electricity production. The PV production increased by almost 25% and the energy produced by the wind turbines and FC almost doubled. This is because the battery bank annual throughput in System A represented around 21,275 kWh/year.

Electrolysis is a viable method for generating carbon-free hydrogen using RE and nuclear sources. Electrolysis can be defined as the use of electricity to separate water into oxygen and hydrogen. This reaction occurs in a machine known as an electrolyzer. Hydrogen generation equipment can vary in size, ranging from small, appliance-sized units suitable for decentralized usage to larger, centralized facilities that can be integrated with renewable or non-greenhouse-gas-emitting power generation methods. Figure 10 displays the amount of power supplied to the electrolyzer’s input in both systems. The total input energy and capacity factor of the electrolyzer were 40,287 kWh/year and 9.20%, respectively, for System A and 88,379 kWh/year with a 20.2% capacity factor for System B. Throughout the year, the maximum input power for both systems was measured between 7 a.m. and 5 p.m. Indeed, the electrolyzer generates hydrogen by a chemical process capable of separating the oxygen and hydrogen molecules from which water is made using electricity. In System B, it is noticeable that the hours of operation are higher than in System A, with approximately 4897 h/year compared to 3087 h/year in System A.

Two technologies that are essential to the shift to a low-carbon, sustainable energy future are fuel cells and hydrogen electrolysis. In this study, fuel cells utilize an electrochemical process to convert chemical energy into electrical energy, while hydrogen electrolysis involves using electricity to break down water into hydrogen and oxygen. One essential technology for producing hydrogen for fuel cell systems is hydrogen electrolysis. The procedure is eco-friendly since it generates green hydrogen using electricity from renewable energy sources which are, in this research, solar and wind power. Furthermore, hydrogen generated via electrolysis has the ability to be transported and stored for use in fuel cells as well as other uses like cooling and heating. Figure 11 shows the output power generation produced by FC for both systems A and B throughout the year. In this research, we examined applications for off-grid systems and proposed the use of fuel cells as a backup solution when RE sources are insufficient. The results in Figure 11 illustrate that the fuel cell in System A produced around 14,372 kWh/year, while in System B the amount of energy produced was close to 31,741 kWh/year. This indicates that the FC hours of operation in System B are much higher than in System A, as illustrated in Table 1. This also shows the significate impact of the FC on operational life in System B, even though the system showed a higher capacity factor for the FC.

4.3. Economic Analysis

The system’s nominal cash flow (NCF) discounted to the first year is known as the discounted cash flow (DCF). The actual discount rate taken into account in this approach is 6%. The attractiveness and investment opportunity of the system were estimated with the aid of this method. The annual total discounted cash flows for the project over a 25-year period were added up to determine the NPC for this system. As can be seen in Figure 12, the results indicated that the system NPC was around USD 746,499.58 for System A and almost USD 879,218.95 for System B. Also, the LCOE in System B was higher due to the higher capital cost. This indicates that System A is more profitable than System B and the existence of a battery bank has a good impact economically. Also, the results show that System A is more appropriate since it has the potential to turn a profit during the project’s estimated lifetime.

A comprehensive summary of the net present values of the various project costs over the course of the project’s 25-year lifespan is provided in Figure 13. The battery needs to be changed after 15 years in System A. However, there is no replacement in System B as there is no battery system. FC replacement does not happen in System A within the project’s 25 years because the generator only runs for roughly 1496 h a year, or 37,400 h in total during the project’s lifetime, which is below the generator’s lifetime operating hours of 40,000 h.

5. Conclusions

This study aims to explore the potential of an off-grid renewable hybrid energy system that can generate both electricity and hydrogen. The study analyzes the impact of battery storage system availability on the sizing of system components, performance, NPC, and COE. Using HOMER Pro, two different types of renewable energy systems were simulated to identify the optimal setup that can reduce the system cost and meet load demand. The results show that the electrolyzer in System A generated 40,287 kWh/year with a capacity factor of 9.20%, while System B generated 88,379 kWh/year with a capacity factor of 20.2%. The maximum input power for both systems was measured between 7 a.m. and 5 p.m. throughout the year. Also, it has been found that the electrolyzer in System B had more operating hours than that in System A, with approximately 4897 h/year compared to 3087 h/year for System A. In terms of output power generation produced by the FC for both systems, System A’s FC produced around 14,372 kWh/year, while system B’s FC produced around 31,741 kWh/year. This indicates that the FC in System B had more operating hours than that in System A. Although System B had a higher capacity factor for the FC, this resulted in a significant impact on the FC’s operational lifetime. Future research can evaluate the life-cycle environmental effects of global H₂ production while taking the decarbonization of power, local feedstock availability, and technological advancements into account.