# Technoeconomic Assessment of Various Configurations Photovoltaic Systems for Energy and Hydrogen Production

Authors: Rahul Rajeevkumar Urs; Muhammad Sadiq; Ahmad Mayyas; Ameena Al Sumaiti
Journal: International Journal of Energy Research
 (2023)

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## Abstract

Photovoltaic (PV) system grid integration is becoming more global to minimize carbon emissions from traditional power systems. However, alternative solution investigation for maximum technical and economic benefits is often neglected when integrating PV systems. This study utilizes a methodology for evaluating the lifecycle energy generation and levelized cost of energy (LCOE) of PV systems with various configurations using a holistic approach that considers PV system expenditures from installation to the end-of-life PV system operation. In addition, this work focuses on finding a better configuration with different PV modules (monofacial or bifacial) and structure types (mounted or single-axis) for three different utility scale PV sizes (300 kW, 500 kW, and 1000 kW) in Abu Dhabi, UAE, with the maximum power generation and minimal energy losses. Furthermore, the best suitable configuration was identified to be the configuration with a single-axis tracking structure and bifacial PV modules based on their technical and economic performance for the location with two different surface albedo, 0.2 and 0.8. We also study the PV system’s connection in a standalone off-grid solar-electrolyzer combination to produce green hydrogen. Levelized cost of electricity (LCOE) and levelized cost of hydrogen production (LCOH) are calculated, and results show that such PV systems can be used to generate electricity and produce hydrogen at competitive costs that can reach as low as 2.1 cent/kWh and $2.53/kg-H2 for LCOE and LCOH, respectively. Such a low cost is very competitive and can be used to attract new investments in green hydrogen technology in the United Arab Emirates.


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## Body

## 1. Introduction

Annual photovoltaic (PV) system integration will reach 162 GW by 2022, almost 50% more than the prepandemic level of 2019 [1]. This tremendous increase in PV system integration is mainly due to reduced system component costs, attractive incentives, subsidies, and feed-in-tariff (FiT) rates offered by governments worldwide. However, although historical trends are not always indicative of future situations, the analysis in [2] shows that the cost of PV must fall fast enough, which is the case of this technology, to compensate and ensure continued profitability.Increased international cooperation, financial mobilization, strengthened institutions, and comprehensive policy convergence is required to avoid climate disasters. Clean energy investments can protect against shortsighted decisions and the build-up of stranded assets, even though the demand for emission reductions remains unchanged [3]. The method to enhance hosting capacity depends on various factors, including current penetration levels, desired hosting for expansion, capital or regulatory constraints for a particular utility, and available communication infrastructure [4, 5]. In [6, 7], Trovato and Kantharaj and Zurita et al. proposed a novel strategy that maximized the benefits of renewable energy storage systems for load and demand mismatch situations and identified an appealing internal rate of return. Furthermore, sensitivity tests have shown that the profitability of such an investment can increase when other design aspects of the storage asset are considered [7].The loss of power supply probability as a system reliability criterion, the life cycle cost (LCC), and the levelized cost of energy (LCOE) as economic indicators are incorporated as multiobjective optimization functions to maximize the PV system sizing economically [8]. According to Linssen et al. [9], for Germany, the demand side load profiles significantly impact the modeling results in terms of total costs, cost-effectiveness, and cost-optimal system configuration in terms of PV and battery capacity of battery-supported photovoltaic systems. However, the models in references [10, 11] do not address the impact of FiT and energy consumption tariff implications in their modeling, even though the net present value (NPV), internal rate of return (IRR), and payback duration are significant financial indicators used to evaluate an investment.According to Deltenre and Nousdilis [12, 13], zero incentives for PV system installation maintain a positive internal rate of return (IRR). At the same time, a low IRR would not attract many investors. In [14], Yilmaz et al. considered 3 kWp PV plants with different PV technologies. They concluded that to find the optimal PV panel for the selected region, the essential parameters for investors is the breakeven point of the systems. The financial study determined that all the sites studied in [15, 16] had a good profit margin based on IRR, NPV (net present value), and LCOE (levelized cost of electricity). Fuster-Palop et al. in [17] showed that the shadow losses had the most significant impact on economic feasibility for urban areas, and Ameur et al. in [18] estimated the LCOE for different PV module cell types for the Morocco region.As a result, it is a usual practice to examine the technical performance of a PV system before it is installed and operational [19]. Several studies have examined alternative PV system configurations based on mounting structures, PV cell types, string connections, and different inverter topologies, in addition to performing technical evaluations. In [20], Chauhan and Singh have discussed the response of the PV system based on solar irradiance levels for rural areas in India. Moreover, the stochastic nature of its power generation was assessed in [21]. In [22], Tekumalla et al. conducted a comprehensive technoeconomic performance analysis of four solar PV system configurations based on the inverter and mounting structure for India’s location. This study concluded that the energy yield and performance ratio were higher in the case of the dual-axis mounted microinverter system configuration.In a study published in [23] for the UAE location, Said et al. investigated three different module technologies (monocrystalline, polycrystalline, and thin-film) for a central and off-grid system. They found that the central grid PV configuration is more technoeconomically viable, with a higher capacity factor, lower initial investment, higher net present values, and thus shorter payback periods. In [24] for Ghana location, PV systems were compared with and without storage and single and dual-axis tracking system. According to this sensitivity study, two-axis tracking was the optimum technology for the site condition. However, research in the UAE revealed that a standalone PV system supported by a single-axis tracking system is more technologically and economically viable than double-axis systems [25].In the last few years, the hydrogen economy has gained wider adoption by many countries in the Middle East, including Saudi Arabia and the United Arab Emirates. Saudi Arabia, for example, is planning to be one of the world’s biggest exporters of green hydrogen by 2030 [26]. Air products and chemicals, U.S.-based hydrogen production, and hydrogen infrastructure developer, recently signed an agreement in July 2020 with Saudi Arabia’s utilities developer Acwa Power and Neom to develop a $5-billion hydrogen-based ammonia production facility powered by renewable energy in NEOM (the newly developed futuristic megacity in the north of Saudi Arabia where innovation and entrepreneurship in clean energy technologies go together) [27]. Hydrogen has become an exciting and dynamic industry in the UAE, with the construction of green hydrogen facilities already planned and underway. Siemens Energy has collaborated with DEWA (Dubai electricity and water authority) to build the first solar-driven electrolysis hydrogen facility in the Middle East. The facility showcased the UAE’s hydrogen potential at EXPO 2020 in Dubai, UAE. In addition, Masdar (the renewable energy arm of the Abu Dhabi government-owned investing company, Mubadala) has partnered with Siemens Energy, Abu Dhabi Department of Energy, Etihad Airways, Lufthansa, Marubeni Corporation, and Khalifa University to develop an electrolysis facility to produce green hydrogen for forthcoming fuel cell electric vehicle fleets [28].A study by Yates et al. [29] discusses the design parameters of the PV solar system and the economics of hydrogen production using a PV solar-water electrolysis system. In this study, the authors used the Monte Carlo simulation approach to study a wide range of input assumptions to identify the key cost drivers, cost targets, and localized conditions that play a role in determining the cost of energy production for a competitive hydrogen electrolysis system powered by a standalone PV solar system. The analysis covered several states in Spain, the United States, Chili, Japan, and Australia. They found that capital and electricity cost (LCOE) are the key factors shaping the final levelized cost of storage (LCOS) [29]. Similar studies were done for some countries in the Middle East; however, huge discrepancies were reported in the literature on the estimated cost of hydrogen production using solar energy in the Arabian Peninsula (the Arabian Peninsula includes Kuwait, Saudi Arabia, Oman, Qatar, United Arab Emirates (UAE), and Yemen. Saudi Arabia occupies more than two-third of this Peninsula). For example, Al-Sharafi et al. [30] reported cost of hydrogen exceeded $43.2 per kg-H2 in Saudi Arabian cities. The drivers of this high cost were the small size of the renewable systems (integrated system with 2 kW PV array, 3 wind turbines at 2 kW, converter, and 7 batteries storage bank), low wind speeds in the inner cities in the Saudi Arabia, and perhaps the most critical factor is the presence of multiple renewable energy systems in the same site that derived the capital cost up. A recent study by Khan et al. [31], a concentrator photovoltaic-electrolysis (CPV-E) setup that has an efficiency of 28% at 41 suns, 1 sun illumination is equivalent to 1,000 W/m2 at 25°C (without the use of Fresnel lenses) showed that the levelized cost of hydrogen (LCOH) could reach as low as $5.90 per kg and that corresponding cost using high-efficiency crystalline-Si panels can reach $4.90 per kg. These results demonstrate the potential of the large-scale green hydrogen production technology in the Arabian Peninsula. The location not only influences the amount of solar irradiation received by the solar modules but it also influences the expenses of the operation of the PV systems. At the same time, the literature provides research on the performance of PV systems with different mounting/tracking structures and solar cell types (monocrystalline, polycrystalline, etc.).Most of the recent articles on the applications of solar energy in the Middle East have approached this from the energy production perspective [32, 33]. In the current paper, we try to examine the economics and feasibility of installing a hybrid solar-hydrogen system in areas with abundant sunshine. This paper also extends the discussion to study several PV configurations with a PV solar system dedicated to power generation, and another system for hydrogen production. This work analyses the technoeconomic performance of PV systems with two module technology (monofacial and bifacial) and two mounting structures (mounted and single-axis). The objectives of this paper are as follows:
(i)
This study contributes to the literature by comparing different configurations of PV systems’ performance for a given location. Additionally, the significant contribution of this study is to analyze the levelized cost of hydrogen based on the levelized cost of energy of different configurations(ii)
The developed cost model for utility-scale PV solar systems installation in Abu Dhabi, UAE, is used to study the economics of installing these systems in two configurations: (1) a PV system tied to the grid to optimize the cost of ownership and maximize the benefits from the system given the expected feed-in tariff structure in the UAE, and (2) an off-grid standalone system to produce green hydrogenAdditionally, the possible configuration of each PV system considered in this study is identified based on maximum generation with reduced levelized cost of energy (LCOE) and reduced energy losses in the system.This paper is organized as follows: Section2 describes the system in the study and its specification, Section 3 explains the methodology adopted for analyzing the system in the study, and Section 4 discusses the technical and economic analysis of this system and its various configurations. Finally, Section 5 concludes the possible configuration for the considered location.


## 2. System Description

The three utility-scale PV systems considered in this study have capacities of 300 kW, 500 kW, and 1000 kW each. The system location is Abu Dhabi, UAE. The PV system specification is shown in Table1. The PV modules are either monofacial or bifacial that is installed using either a mounting structure or a single-axis tracking system. As a result, each PV system is assessed in four different configurations (C1, C2, C3, and C4), as tabulated in Table 2. Furthermore, a sensitivity analysis was conducted for all system configurations with varied albedo and feed-in tariffs.Table 1
PV system specification.
ParameterValueSourceGlobal horizontal irradiance2015 kWh/m2/yearPVsystOptimum tilt in degrees30[30]Weather databaseMeteonorm 8.0LocationAbu DhabiLatitude24.24 NLongitude54.65 EType/efficiency of the inverter/replacementString/94%/15 yearsType/efficiency of monofacial moduleMonocrystalline/15.36%PVsystType/efficiency of bifacial moduleMonocrystalline/17.69%PVsystOhmic loss (DC circuit)1.50%/yearModule quality loss0.80%/year[34, 35]Module power loss at MPP2.00%/yearPVsystString power loss at MPP0.10%/yearPVsystTable 2
Configurations of PV systems.
ParameterConfig 1 (C1)Config 2 (C2)Config 3 (C3)Config 4 (C4)Module typeMonoBiMonoBiStructureMountingMountingSingle-axisSingle-axisFor the hydrogen production system, we study the economics of producing hydrogen using a proton exchange membrane (PEM) electrolyzer with different rated sizes in kW depending on the PV system’s size and estimated annual electricity generation. PEM electrolyzer is composed of PEM stack as the core subsystem connected to other subsystems in the balance of plant. The critical functions of these subsystems are to supply the required water and power to complete the stack’s electrochemical reactions, collect and dry produced hydrogen, and manage the temperature and pressure in the stack [36].


## 3. Methodology

In this section, we discuss the system design and methodology adopted in developing the power generation system based on the different configurations of the PV-solar system in Section3.1. In contrast, in Section 3.2, we discuss the PEM water electrolyzer’s functional specification and system design and the method used to calculate the levelized cost of hydrogen production.


### 3.1. PV System Design and Economic Model

The selection of PV modules, inverters, and the balance of system components are all elements of the technoeconomic assessment of PV systems. As illustrated in Figure1, the critical objective of analyzing a PV system before installation is to determine the system’s feasibility by estimating the total energy produced and transmitted to the grid. Furthermore, this assessment allows for determining the system’s levelized cost of energy and payback period throughout its 25-year operational life. Using PVsyst software, the three PV systems described in the system description section were simulated. PVsyst (https://www.pvsyst.com/help/general_descr.htm) is a commercial package that can be used to design and analyze the grid-connected, standalone, pumping, and DC-grid (public transportation) PV systems with its extensive meteorological and PV system components data. Additionally, PVsyst quickly generates comprehensive results while allowing the designer to control most of the influencing parameters of energy generation. For example, Wittmer et al. used PVsyst to optimize the ground covering ratio and tilt angle that maximizes the economic benefits of PV installations in Geneva, Switzerland, to minimize the cost and maximize the benefits of the systems [37]. Furthermore, Mermoud et al. used PVsyst to simulate the energy yield from a bifacial PV module with different tilt angles [38].Figure 1
Proposed methodology for the technoeconomic study of various PV configurations.All four configurations for each system were assessed using generic PV system components from software. As shown in Table2, the PV system was designed in a wide range of configurations. For example, the monofacial PV module was chosen for configuration 1, and the bifacial PV module was chosen for configuration 2 in the first two configurations with mounting structures. Similarly, for the last two configurations with single-axis tracking, the monofacial PV module was chosen for configuration 3 and the bifacial PV module for configuration 4. The generic PVsyst components data and their default values for inverter and module were considered for simulation. The component specifications and system assumptions are presented in Table 1. Additionally, the values for ohmic loss, module quality loss, module, and string power loss in Table 1 are assumed to be the default values set by the PVsyst software. This table lists all design points for the PV system including the global horizontal irradiance, which is the measure of the solar irradiation intensity in any location. A value of GHI that is greater than 1,000 indicates a suitable location for PV system to be installed in [10, 11]. We also considered two monocrystalline PV modules in this study. The first one is a monofacial PV module with an efficiency of 15.36% and the second PV module is a bifacial with efficiency of 17.69%. Major AC and DC losses in the systems are estimated and tabulated in the last four rows of Table 1.PVsyst simulations are run for each PV system to determine the annual energy generated from the four defined configurations. In addition, each configuration was assessed for two albedo surfaces: 0.2 (less reflecting surface) and 0.8 (highly reflective surface). Dry sand/soil surfaces have an albedo of 0.2 a surface with an albedo of 0.8 typically requires snow or the construction of a white concrete surface (artificial surface). As a result, a total of 24 PVsyst simulations were run for three systems with four different configurations and two different albedo surfaces. All of these simulations were run individually using PVsys software and the results of each run were tabulated for further analysis. We focused on certain parameters from the simulation outputs including the metrics discussed in Equations (1)–(5). Among the most important metrics we were looking at are levelized cost of electricity (LCOE), lifetime cost of the system (LCS), and discounted payback period. These economic metrics help policymakers make good comparison between different configurations using neutral unbiased results. Policymakers can use the simulation results to decide on what configuration they want to recommend. For example, they can recommend the system with the lowest LCOE or the system that is associated with the shortest payback period.Detailed PV-system cost breakdown are shown in Tables1 and 2 in the Supplementary information document. Table 2 shows the values for three major systems (300, 500, and 1,000 kW) in $/W basis, while Table 3 summarizes the total system cost for 12 configurations of the systems illustrated in stage 1 and stage 2 in Figure 2.Table 3
PV systems description.
ParameterSystem 1System 2System 3Peak power in kW3005001000PV module power in W (monofacial)250PV module power in W (bifacial)250Bifacial gain of bifacial PV moduleUp to 30%Inverter power in kW3005001000No. of PV modules (monofacial)120020004000No. of PV modules (bifacial)120020004000Figure 2
Various PV configurations with different albedo.Furthermore, the ratio of the lifetime cost of the system (LCS) and lifetime energy generation (LEG) is necessary to calculate the LCOE to determine the optimal configuration [39]. The lifetime of a PV system is assumed to be 25 years in this calculation. As the technical behavior of the system changes between years 1 and 25, the system degradation rate is assumed to be 0.8% per year. As a result, Equation (1) can be used to calculate the LCOE.
(1)LCOE=LCSLEG.The sum of the cost of system components (PV module, inverter, and module structure), system installation, the balance of supply components, operations, and maintenance costs over the system’s lifespan is known as the LCS, which is expressed in the following:(2)LCS=CCAPEX+∑n=024O&Mi1+in,where CCAPEX is the capital costs (cost of system components, system installation, and balance of supply components), O&Mi is the discounted operation and maintenance cost, n is the system lifetime in years, and i is the discount rate.The annual energy generation varies mainly as a result of changes in weather and system degradation. Therefore, the real-time weather values acquired from the Meteonorm data incorporated in PVsyst software for the PV system site were adopted in this study. The system degradation rate (D) is estimated to be 0.8% yearly. As a result, Equation (3) was used to estimate the total energy generated by the system over its lifetime.
(3)LEG=EG1+∑i=124EGi1+Di.Finally, the system’s basic payback period (PBP) can be estimated as its lifetime cost ratio to its annual profit. The feed-in-tariff (FiT) offered by the utility to energy producers for exporting generated power to the grid represents the system’s annual profit.
(4)PBP=LCSEG×FiT.The disadvantage of the basic payback period (PBP) equation is that it only considers energy generated in the first year of system operation. However, the modified Equation (5) can provide a better PBP estimate because energy generation varies over time due to system degradation.
(5)PBp=LCSLEG/25×FiT.


### 3.2. Levelized Cost of Hydrogen Production

While the PV solar system is designed as a grid-connected system, we also studied its economic performance if used to produce green hydrogen as a fuel for other end uses such as fuel cell electric vehicles or as a feedstock in the petrochemical industries. In this context, we will use the levelized cost of hydrogen (LCOH), an economic metric, to evaluate the cost of producing hydrogen over the specified lifetime of the project or system [28]. The discounted lifetime cost of constructing and operating a hydrogen production facility is known as the cost per energy unit, which covers all relevant costs incurred by the producer, including capital cost, operation and maintenance, feedstock cost, and financing costs [28]. The levelized cost of hydrogen production (LCOH) is the ratio of the total amortized costs of the hydrogen plant to the total amount of hydrogen expected to be produced over the plant’s lifetime. Both cost terms are calculated using the net present value. This means that future costs and outputs are discounted compared to today’s costs and outputs. It is essential to highlight that LCOH is a production cost metric and does not include any costs associated with delivery, storage, or dispensing of the produced hydrogen, nor costs of the end-use adaptation [40, 41].
(6)NPVTotalCost=∑nCCAPEX+COPEX1+in,and(7)NPVH2Production=∑nE1+in.Then, the LCOH can be expressed as(8)NPVTotalCost=∑nCCAPEX+COPEX/1+in∑nChp/1+in,where CAPEX is the capital cost ($), OPEX is the operating cost ($), Chp is the hydrogen production cost in ($), n is the system lifetime in years, and i is the discount rate,The annual hydrogen productionMH2 can be calculated from the input power for the electrolyzer system at the defined electrolyzer size EES multiplied by the efficiency of the electrolyzer system (η), then divided by the lower heating value of the hydrogen LHVH2.
(9)MH2=EES.ηLVHH2.This study calculated the standardized cost of hydrogen production using the H2A model from the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) [42]. The Hydrogen Analysis (H2A) model provides transparent reporting for process design assumptions and a consistent cost analysis methodology for hydrogen production at central facilities and distribution (e.g., for onsite hydrogen refueling stations). A schematic of the PV-PEM electrolysis system is shown in Figure 3. More details about the PEM electrolysis system components and functions are provided in the SI document.Figure 3
Schematic of the hydrogen production system using PV-PEM electrolyzer.Table4 summarizes the critical functional specifications of the electrolysis systems (EC1, EC2, and EC3), including the estimated operating window (current density and voltage), system electrical efficiency, and parasitic losses. The size of the PEM electrolyzer is assumed to be 60% of the nameplate PV size as recommended by the NREL for the standalone PV-electrolyzer configurations [40]. According to this report from NREL, oversizing the PV system or undersizing the electrolysis system may result in more significant curtailed electricity generated by the PV system. Therefore, the standalone PV-PEM electrolysis system’s optimal size should carefully consider the PV and electrolyzer sizes to optimize the PEM electrolyzer capacity factor and, more importantly, optimize the LCOH. The rated system power is assumed to be the power consumption in hydrogen production (excluding the power consumption in the balance of the plant, which shares 9.7% of the gross system power).Table 4
Functional specifications of the unitized PEM-RFC.
ParameterEC1EC2EC3UnitSourceRated system power180300600kWGross system power200330660kWOperating temperature82°C[36]Operating pressureUp to 20Bar[43]Total PGM loading†1.5g/m2[43]Total cell area450cm2[43]Current density2000mA/cm2[36]Reference voltage1.7V[36]Power density3.8W/cm2Single-cell power1.710kWSystem electrical efficiency63%Parasitic losses††9.7%Cells per system117194388†PGM: platinum group metal. ††Percentage of the nominal system power.Since the LEG for each PV size depends on the configuration (C1, C2, C3, or C4), PV will generate different LEG (see Table5). The electrolyzer capacity factor is the ratio between the consumed kWh in the electrolyzer (assuming it is the average annual kWh generated by the PV system) over the maximum power the electrolyzer can consume over one year.
(10)Electrolyzercapacityfactor=AnnualkWhsuppliedbythePVEES∗24∗365,where EES is the electrolyzer size in kW, the annual kWh supplied by the PV can be calculated by dividing the LEG over the project lifetime (=25 years).Table 5
Lifetime energy generation (LEG), levelized cost of energy (LCOE) of PV system configurations alongside the electrolyzer size, levelized cost of hydrogen production (LCOH), and electrolyzer capacity factor.
PV size300 kW500 kW1000 kWElectrolyzer size180 kW300 kW600 kWAlbedoParameterC1C2C3C4C1C2C3C4C1C2C3C40.2LEG in GWh11.16811.71912.48413.12319.38220.33521.63722.74838.76540.67143.2745.497LCOE in $/kWh0.02770.02670.02680.02580.02580.02490.0250.02410.0250.02410.02430.0233LCOH in $/kg4.54.334.184.013.513.383.293.162.832.732.772.66Electrolyzer capacity factor in %29.234.231.837.130.435.633.338.630.435.633.138.40.8LEG in GWh11.50613.48512.53614.60519.96723.35621.85125.33339.93546.80243.4550.492LCOE in $/kWh0.02690.02320.02670.02320.0250.02170.02480.02160.02430.0210.02410.021LCOH in $/kg4.383.84.163.643.422.993.263.042.762.512.762.53Electrolyzer capacity factor in %28.329.731.733.329.531.032.934.629.531.032.934.6


## 3.1. PV System Design and Economic Model

The selection of PV modules, inverters, and the balance of system components are all elements of the technoeconomic assessment of PV systems. As illustrated in Figure1, the critical objective of analyzing a PV system before installation is to determine the system’s feasibility by estimating the total energy produced and transmitted to the grid. Furthermore, this assessment allows for determining the system’s levelized cost of energy and payback period throughout its 25-year operational life. Using PVsyst software, the three PV systems described in the system description section were simulated. PVsyst (https://www.pvsyst.com/help/general_descr.htm) is a commercial package that can be used to design and analyze the grid-connected, standalone, pumping, and DC-grid (public transportation) PV systems with its extensive meteorological and PV system components data. Additionally, PVsyst quickly generates comprehensive results while allowing the designer to control most of the influencing parameters of energy generation. For example, Wittmer et al. used PVsyst to optimize the ground covering ratio and tilt angle that maximizes the economic benefits of PV installations in Geneva, Switzerland, to minimize the cost and maximize the benefits of the systems [37]. Furthermore, Mermoud et al. used PVsyst to simulate the energy yield from a bifacial PV module with different tilt angles [38].Figure 1
Proposed methodology for the technoeconomic study of various PV configurations.All four configurations for each system were assessed using generic PV system components from software. As shown in Table2, the PV system was designed in a wide range of configurations. For example, the monofacial PV module was chosen for configuration 1, and the bifacial PV module was chosen for configuration 2 in the first two configurations with mounting structures. Similarly, for the last two configurations with single-axis tracking, the monofacial PV module was chosen for configuration 3 and the bifacial PV module for configuration 4. The generic PVsyst components data and their default values for inverter and module were considered for simulation. The component specifications and system assumptions are presented in Table 1. Additionally, the values for ohmic loss, module quality loss, module, and string power loss in Table 1 are assumed to be the default values set by the PVsyst software. This table lists all design points for the PV system including the global horizontal irradiance, which is the measure of the solar irradiation intensity in any location. A value of GHI that is greater than 1,000 indicates a suitable location for PV system to be installed in [10, 11]. We also considered two monocrystalline PV modules in this study. The first one is a monofacial PV module with an efficiency of 15.36% and the second PV module is a bifacial with efficiency of 17.69%. Major AC and DC losses in the systems are estimated and tabulated in the last four rows of Table 1.PVsyst simulations are run for each PV system to determine the annual energy generated from the four defined configurations. In addition, each configuration was assessed for two albedo surfaces: 0.2 (less reflecting surface) and 0.8 (highly reflective surface). Dry sand/soil surfaces have an albedo of 0.2 a surface with an albedo of 0.8 typically requires snow or the construction of a white concrete surface (artificial surface). As a result, a total of 24 PVsyst simulations were run for three systems with four different configurations and two different albedo surfaces. All of these simulations were run individually using PVsys software and the results of each run were tabulated for further analysis. We focused on certain parameters from the simulation outputs including the metrics discussed in Equations (1)–(5). Among the most important metrics we were looking at are levelized cost of electricity (LCOE), lifetime cost of the system (LCS), and discounted payback period. These economic metrics help policymakers make good comparison between different configurations using neutral unbiased results. Policymakers can use the simulation results to decide on what configuration they want to recommend. For example, they can recommend the system with the lowest LCOE or the system that is associated with the shortest payback period.Detailed PV-system cost breakdown are shown in Tables1 and 2 in the Supplementary information document. Table 2 shows the values for three major systems (300, 500, and 1,000 kW) in $/W basis, while Table 3 summarizes the total system cost for 12 configurations of the systems illustrated in stage 1 and stage 2 in Figure 2.Table 3
PV systems description.
ParameterSystem 1System 2System 3Peak power in kW3005001000PV module power in W (monofacial)250PV module power in W (bifacial)250Bifacial gain of bifacial PV moduleUp to 30%Inverter power in kW3005001000No. of PV modules (monofacial)120020004000No. of PV modules (bifacial)120020004000Figure 2
Various PV configurations with different albedo.Furthermore, the ratio of the lifetime cost of the system (LCS) and lifetime energy generation (LEG) is necessary to calculate the LCOE to determine the optimal configuration [39]. The lifetime of a PV system is assumed to be 25 years in this calculation. As the technical behavior of the system changes between years 1 and 25, the system degradation rate is assumed to be 0.8% per year. As a result, Equation (1) can be used to calculate the LCOE.
(1)LCOE=LCSLEG.The sum of the cost of system components (PV module, inverter, and module structure), system installation, the balance of supply components, operations, and maintenance costs over the system’s lifespan is known as the LCS, which is expressed in the following:(2)LCS=CCAPEX+∑n=024O&Mi1+in,where CCAPEX is the capital costs (cost of system components, system installation, and balance of supply components), O&Mi is the discounted operation and maintenance cost, n is the system lifetime in years, and i is the discount rate.The annual energy generation varies mainly as a result of changes in weather and system degradation. Therefore, the real-time weather values acquired from the Meteonorm data incorporated in PVsyst software for the PV system site were adopted in this study. The system degradation rate (D) is estimated to be 0.8% yearly. As a result, Equation (3) was used to estimate the total energy generated by the system over its lifetime.
(3)LEG=EG1+∑i=124EGi1+Di.Finally, the system’s basic payback period (PBP) can be estimated as its lifetime cost ratio to its annual profit. The feed-in-tariff (FiT) offered by the utility to energy producers for exporting generated power to the grid represents the system’s annual profit.
(4)PBP=LCSEG×FiT.The disadvantage of the basic payback period (PBP) equation is that it only considers energy generated in the first year of system operation. However, the modified Equation (5) can provide a better PBP estimate because energy generation varies over time due to system degradation.
(5)PBp=LCSLEG/25×FiT.


## 3.2. Levelized Cost of Hydrogen Production

While the PV solar system is designed as a grid-connected system, we also studied its economic performance if used to produce green hydrogen as a fuel for other end uses such as fuel cell electric vehicles or as a feedstock in the petrochemical industries. In this context, we will use the levelized cost of hydrogen (LCOH), an economic metric, to evaluate the cost of producing hydrogen over the specified lifetime of the project or system [28]. The discounted lifetime cost of constructing and operating a hydrogen production facility is known as the cost per energy unit, which covers all relevant costs incurred by the producer, including capital cost, operation and maintenance, feedstock cost, and financing costs [28]. The levelized cost of hydrogen production (LCOH) is the ratio of the total amortized costs of the hydrogen plant to the total amount of hydrogen expected to be produced over the plant’s lifetime. Both cost terms are calculated using the net present value. This means that future costs and outputs are discounted compared to today’s costs and outputs. It is essential to highlight that LCOH is a production cost metric and does not include any costs associated with delivery, storage, or dispensing of the produced hydrogen, nor costs of the end-use adaptation [40, 41].
(6)NPVTotalCost=∑nCCAPEX+COPEX1+in,and(7)NPVH2Production=∑nE1+in.Then, the LCOH can be expressed as(8)NPVTotalCost=∑nCCAPEX+COPEX/1+in∑nChp/1+in,where CAPEX is the capital cost ($), OPEX is the operating cost ($), Chp is the hydrogen production cost in ($), n is the system lifetime in years, and i is the discount rate,The annual hydrogen productionMH2 can be calculated from the input power for the electrolyzer system at the defined electrolyzer size EES multiplied by the efficiency of the electrolyzer system (η), then divided by the lower heating value of the hydrogen LHVH2.
(9)MH2=EES.ηLVHH2.This study calculated the standardized cost of hydrogen production using the H2A model from the U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) [42]. The Hydrogen Analysis (H2A) model provides transparent reporting for process design assumptions and a consistent cost analysis methodology for hydrogen production at central facilities and distribution (e.g., for onsite hydrogen refueling stations). A schematic of the PV-PEM electrolysis system is shown in Figure 3. More details about the PEM electrolysis system components and functions are provided in the SI document.Figure 3
Schematic of the hydrogen production system using PV-PEM electrolyzer.Table4 summarizes the critical functional specifications of the electrolysis systems (EC1, EC2, and EC3), including the estimated operating window (current density and voltage), system electrical efficiency, and parasitic losses. The size of the PEM electrolyzer is assumed to be 60% of the nameplate PV size as recommended by the NREL for the standalone PV-electrolyzer configurations [40]. According to this report from NREL, oversizing the PV system or undersizing the electrolysis system may result in more significant curtailed electricity generated by the PV system. Therefore, the standalone PV-PEM electrolysis system’s optimal size should carefully consider the PV and electrolyzer sizes to optimize the PEM electrolyzer capacity factor and, more importantly, optimize the LCOH. The rated system power is assumed to be the power consumption in hydrogen production (excluding the power consumption in the balance of the plant, which shares 9.7% of the gross system power).Table 4
Functional specifications of the unitized PEM-RFC.
ParameterEC1EC2EC3UnitSourceRated system power180300600kWGross system power200330660kWOperating temperature82°C[36]Operating pressureUp to 20Bar[43]Total PGM loading†1.5g/m2[43]Total cell area450cm2[43]Current density2000mA/cm2[36]Reference voltage1.7V[36]Power density3.8W/cm2Single-cell power1.710kWSystem electrical efficiency63%Parasitic losses††9.7%Cells per system117194388†PGM: platinum group metal. ††Percentage of the nominal system power.Since the LEG for each PV size depends on the configuration (C1, C2, C3, or C4), PV will generate different LEG (see Table5). The electrolyzer capacity factor is the ratio between the consumed kWh in the electrolyzer (assuming it is the average annual kWh generated by the PV system) over the maximum power the electrolyzer can consume over one year.
(10)Electrolyzercapacityfactor=AnnualkWhsuppliedbythePVEES∗24∗365,where EES is the electrolyzer size in kW, the annual kWh supplied by the PV can be calculated by dividing the LEG over the project lifetime (=25 years).Table 5
Lifetime energy generation (LEG), levelized cost of energy (LCOE) of PV system configurations alongside the electrolyzer size, levelized cost of hydrogen production (LCOH), and electrolyzer capacity factor.
PV size300 kW500 kW1000 kWElectrolyzer size180 kW300 kW600 kWAlbedoParameterC1C2C3C4C1C2C3C4C1C2C3C40.2LEG in GWh11.16811.71912.48413.12319.38220.33521.63722.74838.76540.67143.2745.497LCOE in $/kWh0.02770.02670.02680.02580.02580.02490.0250.02410.0250.02410.02430.0233LCOH in $/kg4.54.334.184.013.513.383.293.162.832.732.772.66Electrolyzer capacity factor in %29.234.231.837.130.435.633.338.630.435.633.138.40.8LEG in GWh11.50613.48512.53614.60519.96723.35621.85125.33339.93546.80243.4550.492LCOE in $/kWh0.02690.02320.02670.02320.0250.02170.02480.02160.02430.0210.02410.021LCOH in $/kg4.383.84.163.643.422.993.263.042.762.512.762.53Electrolyzer capacity factor in %28.329.731.733.329.531.032.934.629.531.032.934.6


## 4. Results and Discussion

For economic analysis of renewable energy, qualitative and quantitative data are necessary to identify significant metrics [44, 45]. The nexus between renewable energy consumption and economic growth has already been discussed in [46, 47]. Machine learning algorithms can also be used to examine efficient renewable energy investments, which in turn can help researchers develop more accurate technoeconomic analysis [48–50]. Good technoeconomic models can inform good decisions if done in a proper way. For example Bhuiyan et al. [46] and Dincer et al. [47] developed good technoeconomic models to study the effect of renewable energy systems on the economic growth and investments in green energy technology.In this section, we discuss the results obtained from the technoeconomic models of the different configurations of the PV solar systems in Section4.1. At the same time, we present and discuss the results of the hydrogen production model in Section 4.2.


### 4.1. Economics and Performance of the PV Solar System

Three PV systems with peak power of 300 kW, 500 kW, and 1000 kW were simulated, each having four configurations. The identical inverters were chosen for all three systems but with different numbers based on system peak power. In addition, the PV system description and specification in Tables1 and 3 were inserted into the PVsyst software for simulation.The system energy generation (E_sys) pattern for each configuration differed throughout the simulation. The difference in energy yield between monofacial and bifacial PV modules and changes in mounting systems induced this variation (fixed and single-axis tracking structures). In addition, the fluctuation in energy generation was influenced by the surface’s albedo. However, the variation pattern for all configurations in all three systems remains the same because all three systems were simulated for the exact location with the same system components and assumptions.PV modules were installed on fixed mounting frames in the first two configurations. Even though the PV module in the second configuration (C2) was bifacial, there was around a 5% increase in PV generation compared to the first configuration (C1). In addition, compared to C1, C2’s specific energy generation (E_spe in kWh/kWp/year) and normalized energy generation (Yf in kWh/kWp/day) improved. As a result, C2’s performance ratio (PR) increased due to the slight change in energy generation than C1. While C1 and C2 had the same system losses (Ls), C2 had lower PV array losses (Lc) than C1. This variation was observed in both 0.2 and 0.8 albedo surfaces; these values are tabulated in Table2 in the Supplementary information (SI) document.PV modules were installed on single-axis tracking mechanisms in the last two configurations. With a surface albedo of 0.2 and surface albedo of 0.8, the fourth configuration with a bifacial module generated 5% and 16% more than the third configuration. This significant gain over C4 was attributed to the increased energy yield and the single-axis tracking structure of the bifacial module. In addition, when compared to C3, C4’s specific energy generation and normalized energy generation increased. Therefore, C4’s performance ratio (PR) increased slightly due to the shift in energy generation compared to C3.Furthermore, while system losses in C4 were higher than in C3, PV array losses were lower in C4 than in C3. As a result, with a surface albedo of 0.8, C3 had the lowest performance ratio (PR), whereas C4 had the best. Finally, with both 0.2 and 0.8 surface albedo, the fourth configuration of each system performed best, whereas the first configuration performed poorly.The feasibility of the PV system can be determined by an economic study performed before installation. The system components, installation costs, and long-term operation and maintenance costs are considered when performing economic analysis. In addition, financial incentives provided by the government or utilities in the form of system installation cost subsidies or feed-in tariffs (FiT) for electricity imported to the grid would raise the number of installed grid-connected PV systems in any region or country that aim to boost its transition into the renewable energy technologies [37, 38]. It is also crucial to determine whether bifacial modules are more cost-effective [34]. The approximate prices for system installation were taken from the solar power costs report by IRENA in [51]. These numbers are shown in Table 1 in the SI document. For the three systems, the sales tax of 5% and the discount rate of 4% [52–54] were considered. From Equation (2), it was discovered that the overall system cost for all three systems was highest for C4 and lowest for C1. The bifacial module and single-axis tracking structure are more expensive than the monofacial module and standard mounting structure. Even though they are additional costs, it is vital to assess their feasibility.From Equation (1), the LCOE was computed for each configuration with the system cost and tabulated in Table 5. The LCOE of C4 was the lowest for each system, with a surface albedo of 0.2 and 0.8, whereas the LCOE of C1 was the highest. Although the cost of system components was higher for C4 and lower for C1, the difference in LCOE was due to the difference in lifetime energy generation. In addition, as shown in Table 5 and Figure 4, C2 with bifacial modules and mounting structures did not observe a significant increase in lifetime energy generation compared to C1, and the LCOE was just slightly different. In contrast, C3 generated more energy than C2 and had a lower LCOE because of its monofacial modules and single-axis tracking structure.Figure 4
Lifetime energy and LCOE of PV system configurations.The payback period (PBP) for PV systems determines the system’s additional technoeconomic feasibility. The feed-in-tariff rates (0.04 $/kWh, 0.08 $/kWh, and 0.12 $/kWh) were assumed in this study to understand their impact on the payback period of PV systems which would attract more investors. As a result, the PBP for each configuration is calculated based on the average energy generation from PV systems (including system degradation during 25 years of operation), tabulated in Table 6. The PBP of the C4 configuration of the PV system was found to be earlier than the C1 configuration for both the surface 0.2 and 0.8. Furthermore, the PBP difference between C1 and C2 was much smaller than that between C3 and C4.Table 6
Payback period of PV system configurations for varying feed-in tariff.
PBP in yearsFeed-in tariff $/kWh300 kW500 kW1000 kWC1C2C3C4C1C2C3C4C1C2C3C4Albedo=0.20.0421.4719.8720.6119.0318.3417.1317.816.5817.2116.1216.7515.640.086.7226.456.976.4016.125.886.115.855.885.655.885.630.124.033.894.043.883.713.583.723.583.583.453.593.45Albedo=0.80.0420.0314.9620.4215.4917.2413.2417.4613.6416.2212.5316.6213.020.086.465.386.665.585.894.936.045.115.664.745.854.940.123.893.34.023.433.5863.053.683.1653.452.933.583.066


### 4.2. Levelized Cost of Hydrogen Production

The levelized cost of hydrogen production via electrolysis will ensure 100% production of green hydrogen. Unfortunately, producing green hydrogen using intermittent renewable energy sources such as PV solar or wind will lower the electrolyzer’s capacity. Lowering the capacity factor means the electrolyzer will be used for a shorter year (Assuming that the electrolyzer can be utilized theoretically in 365 days per year, and 24 hours per day). On the other hand, the PV system supplies electricity at competitive prices below the purchased electricity from the grid. In many cases, purchasing electricity at lower prices is not competitive, especially when there are different pricing rates for weekdays vs. weekends or at different times in the day. For example, Abu Dhabi Distribution Company (ADCC), the leading utility company in Abu Dhabi, sells electricity for the industrial sector at 27 fils/kWh (7.4 cent/kWh) between 10:00 am and 10:00 pm in the hot summer months (1 June–30 September) and at 36.6 fils/kWh (10 cents/kWh) in the other months and off-peak hours [55]. A quick comparison with the LCOE produced from the PV systems shows how the well-designed PV system can be installed and operated at highly competitive costs. The low LCOE also indicates a competitive opportunity to produce green hydrogen in the United Arab Emirates.Similar to the LCOE, we found that the LCOH of C4 atalbedo=0.8 with a 600 kW PEM electrolyzer was the lowest among all studied configurations with LCOH=$2.53/kg-H2, an albedo of 0.2 results in a little higher LCOH=$2.66/kg-H2. In contrast, the LCOE of C1 at albedo=0.2 was the highest (see Table 5 for all other LCOH values). This confirms that C1 generates lower LEG, thus higher LCOE compared to the C4 configuration. Further, the overall LCOH results show that we can produce hydrogen at a highly competitive cost that can reach as low as $2.53/kg-H2 using an integrated PV solar-electrolyzer system (1000 kW PV/600 kW electrolyzer system) at LCOE=$0.021/kWh. Most of the cost comes from the system capital cost, which contributes about 45% of the LCOH; followed by the fixed O&M cost, which shares 34.7%; and electricity cost, which shares 20% of the LCOH (Figure 5). Another crucial functional parameter of the PEM electrolyzer is the electrolyzer capacity factor, calculated using Equation (10). The LEG determines the electrolyzer capacity factor. Higher performance PV systems (i.e., PV with the bifacial modules) yield higher LEG values and higher average annual generated electricity. Thus, these configurations will yield better capacity factors since the PEM electrolyzer will benefit more from the larger values of power it can utilize to produce hydrogen. A particle method to improve the electrolyzer operating conditions, including lowering the LCOH and increasing the capacity factor, can be made by using a hybrid system with energy storage using rechargeable batteries or other storage. However, this hybrid PV-battery-electrolyzer system may lead to a higher hydrogen production cost, so careful technoeconomic analysis can guide such design before jumping to conclusions.Figure 5
Tornado chart showing the most critical parameters that shape the LCOH.The waterfall chart in Figure6 shows the effects of changing the critical factors by ±10% of their nominal values. The LCOH can be reduced by 14.2% from $2.53/kg-H2 to $2.17/kg-H2 if the R&D can achieve a 10% increase in the electrolyzer capacity factor, 10% decrease in the energy consumption (expressed as utility consumption in the figure), 10% decrease in the capital cost, and 10% decrease in the fixed O&M. Here we can see that the improvements in the electrolyzer capacity factor will have the most significant effect on the LCOH, and this is because the electrolyzer will be operating 34.6% of the time around the year and will be sitting idle for more than 65.4% of the time. Thus any improvements or the addition of a low-cost battery storage system to store generated electricity during the day and then supply it to the electrolyzer during the night can lower the overall LCOH. However, this needs careful consideration and proper design analysis to avoid adding extra cost to the integrated PV-electrolyzer system.Figure 6
Waterfall chart showing the effect of R&D in improving the PEM electrolyzer performance, which can reflect in lowering the LCOH.


## 4.1. Economics and Performance of the PV Solar System

Three PV systems with peak power of 300 kW, 500 kW, and 1000 kW were simulated, each having four configurations. The identical inverters were chosen for all three systems but with different numbers based on system peak power. In addition, the PV system description and specification in Tables1 and 3 were inserted into the PVsyst software for simulation.The system energy generation (E_sys) pattern for each configuration differed throughout the simulation. The difference in energy yield between monofacial and bifacial PV modules and changes in mounting systems induced this variation (fixed and single-axis tracking structures). In addition, the fluctuation in energy generation was influenced by the surface’s albedo. However, the variation pattern for all configurations in all three systems remains the same because all three systems were simulated for the exact location with the same system components and assumptions.PV modules were installed on fixed mounting frames in the first two configurations. Even though the PV module in the second configuration (C2) was bifacial, there was around a 5% increase in PV generation compared to the first configuration (C1). In addition, compared to C1, C2’s specific energy generation (E_spe in kWh/kWp/year) and normalized energy generation (Yf in kWh/kWp/day) improved. As a result, C2’s performance ratio (PR) increased due to the slight change in energy generation than C1. While C1 and C2 had the same system losses (Ls), C2 had lower PV array losses (Lc) than C1. This variation was observed in both 0.2 and 0.8 albedo surfaces; these values are tabulated in Table2 in the Supplementary information (SI) document.PV modules were installed on single-axis tracking mechanisms in the last two configurations. With a surface albedo of 0.2 and surface albedo of 0.8, the fourth configuration with a bifacial module generated 5% and 16% more than the third configuration. This significant gain over C4 was attributed to the increased energy yield and the single-axis tracking structure of the bifacial module. In addition, when compared to C3, C4’s specific energy generation and normalized energy generation increased. Therefore, C4’s performance ratio (PR) increased slightly due to the shift in energy generation compared to C3.Furthermore, while system losses in C4 were higher than in C3, PV array losses were lower in C4 than in C3. As a result, with a surface albedo of 0.8, C3 had the lowest performance ratio (PR), whereas C4 had the best. Finally, with both 0.2 and 0.8 surface albedo, the fourth configuration of each system performed best, whereas the first configuration performed poorly.The feasibility of the PV system can be determined by an economic study performed before installation. The system components, installation costs, and long-term operation and maintenance costs are considered when performing economic analysis. In addition, financial incentives provided by the government or utilities in the form of system installation cost subsidies or feed-in tariffs (FiT) for electricity imported to the grid would raise the number of installed grid-connected PV systems in any region or country that aim to boost its transition into the renewable energy technologies [37, 38]. It is also crucial to determine whether bifacial modules are more cost-effective [34]. The approximate prices for system installation were taken from the solar power costs report by IRENA in [51]. These numbers are shown in Table 1 in the SI document. For the three systems, the sales tax of 5% and the discount rate of 4% [52–54] were considered. From Equation (2), it was discovered that the overall system cost for all three systems was highest for C4 and lowest for C1. The bifacial module and single-axis tracking structure are more expensive than the monofacial module and standard mounting structure. Even though they are additional costs, it is vital to assess their feasibility.From Equation (1), the LCOE was computed for each configuration with the system cost and tabulated in Table 5. The LCOE of C4 was the lowest for each system, with a surface albedo of 0.2 and 0.8, whereas the LCOE of C1 was the highest. Although the cost of system components was higher for C4 and lower for C1, the difference in LCOE was due to the difference in lifetime energy generation. In addition, as shown in Table 5 and Figure 4, C2 with bifacial modules and mounting structures did not observe a significant increase in lifetime energy generation compared to C1, and the LCOE was just slightly different. In contrast, C3 generated more energy than C2 and had a lower LCOE because of its monofacial modules and single-axis tracking structure.Figure 4
Lifetime energy and LCOE of PV system configurations.The payback period (PBP) for PV systems determines the system’s additional technoeconomic feasibility. The feed-in-tariff rates (0.04 $/kWh, 0.08 $/kWh, and 0.12 $/kWh) were assumed in this study to understand their impact on the payback period of PV systems which would attract more investors. As a result, the PBP for each configuration is calculated based on the average energy generation from PV systems (including system degradation during 25 years of operation), tabulated in Table 6. The PBP of the C4 configuration of the PV system was found to be earlier than the C1 configuration for both the surface 0.2 and 0.8. Furthermore, the PBP difference between C1 and C2 was much smaller than that between C3 and C4.Table 6
Payback period of PV system configurations for varying feed-in tariff.
PBP in yearsFeed-in tariff $/kWh300 kW500 kW1000 kWC1C2C3C4C1C2C3C4C1C2C3C4Albedo=0.20.0421.4719.8720.6119.0318.3417.1317.816.5817.2116.1216.7515.640.086.7226.456.976.4016.125.886.115.855.885.655.885.630.124.033.894.043.883.713.583.723.583.583.453.593.45Albedo=0.80.0420.0314.9620.4215.4917.2413.2417.4613.6416.2212.5316.6213.020.086.465.386.665.585.894.936.045.115.664.745.854.940.123.893.34.023.433.5863.053.683.1653.452.933.583.066


## 4.2. Levelized Cost of Hydrogen Production

The levelized cost of hydrogen production via electrolysis will ensure 100% production of green hydrogen. Unfortunately, producing green hydrogen using intermittent renewable energy sources such as PV solar or wind will lower the electrolyzer’s capacity. Lowering the capacity factor means the electrolyzer will be used for a shorter year (Assuming that the electrolyzer can be utilized theoretically in 365 days per year, and 24 hours per day). On the other hand, the PV system supplies electricity at competitive prices below the purchased electricity from the grid. In many cases, purchasing electricity at lower prices is not competitive, especially when there are different pricing rates for weekdays vs. weekends or at different times in the day. For example, Abu Dhabi Distribution Company (ADCC), the leading utility company in Abu Dhabi, sells electricity for the industrial sector at 27 fils/kWh (7.4 cent/kWh) between 10:00 am and 10:00 pm in the hot summer months (1 June–30 September) and at 36.6 fils/kWh (10 cents/kWh) in the other months and off-peak hours [55]. A quick comparison with the LCOE produced from the PV systems shows how the well-designed PV system can be installed and operated at highly competitive costs. The low LCOE also indicates a competitive opportunity to produce green hydrogen in the United Arab Emirates.Similar to the LCOE, we found that the LCOH of C4 atalbedo=0.8 with a 600 kW PEM electrolyzer was the lowest among all studied configurations with LCOH=$2.53/kg-H2, an albedo of 0.2 results in a little higher LCOH=$2.66/kg-H2. In contrast, the LCOE of C1 at albedo=0.2 was the highest (see Table 5 for all other LCOH values). This confirms that C1 generates lower LEG, thus higher LCOE compared to the C4 configuration. Further, the overall LCOH results show that we can produce hydrogen at a highly competitive cost that can reach as low as $2.53/kg-H2 using an integrated PV solar-electrolyzer system (1000 kW PV/600 kW electrolyzer system) at LCOE=$0.021/kWh. Most of the cost comes from the system capital cost, which contributes about 45% of the LCOH; followed by the fixed O&M cost, which shares 34.7%; and electricity cost, which shares 20% of the LCOH (Figure 5). Another crucial functional parameter of the PEM electrolyzer is the electrolyzer capacity factor, calculated using Equation (10). The LEG determines the electrolyzer capacity factor. Higher performance PV systems (i.e., PV with the bifacial modules) yield higher LEG values and higher average annual generated electricity. Thus, these configurations will yield better capacity factors since the PEM electrolyzer will benefit more from the larger values of power it can utilize to produce hydrogen. A particle method to improve the electrolyzer operating conditions, including lowering the LCOH and increasing the capacity factor, can be made by using a hybrid system with energy storage using rechargeable batteries or other storage. However, this hybrid PV-battery-electrolyzer system may lead to a higher hydrogen production cost, so careful technoeconomic analysis can guide such design before jumping to conclusions.Figure 5
Tornado chart showing the most critical parameters that shape the LCOH.The waterfall chart in Figure6 shows the effects of changing the critical factors by ±10% of their nominal values. The LCOH can be reduced by 14.2% from $2.53/kg-H2 to $2.17/kg-H2 if the R&D can achieve a 10% increase in the electrolyzer capacity factor, 10% decrease in the energy consumption (expressed as utility consumption in the figure), 10% decrease in the capital cost, and 10% decrease in the fixed O&M. Here we can see that the improvements in the electrolyzer capacity factor will have the most significant effect on the LCOH, and this is because the electrolyzer will be operating 34.6% of the time around the year and will be sitting idle for more than 65.4% of the time. Thus any improvements or the addition of a low-cost battery storage system to store generated electricity during the day and then supply it to the electrolyzer during the night can lower the overall LCOH. However, this needs careful consideration and proper design analysis to avoid adding extra cost to the integrated PV-electrolyzer system.Figure 6
Waterfall chart showing the effect of R&D in improving the PEM electrolyzer performance, which can reflect in lowering the LCOH.


## 5. Conclusion

Three systems with 300 kW, 500 kW, and 1000 kW were subjected to a technoeconomic analysis. Each system had four configurations based on the module and mounting structure. PVsyst software was used to model these configurations for surface albedo 0.2 and 0.8 for Abu Dhabi, UAE. Model results showed that installing a hybrid solar-hydrogen system for the production of green hydrogen in the UAE is economic and can play a key role in the transitioning toward more sustainable energy and fuels in the UAE. From the technical assessment, the fourth configuration of each system, which included a combination of bifacial modules and a single-axis tracking structure, generated more energy than any other configuration, despite having higher array losses. However, compared to the first configuration, the second configuration with a bifacial module and mounting structure did not observe a substantial increase in energy generation. Specifically, configurations with bifacial modules performed well with a surface albedo of 0.8, boosting the performance ratio of PV systems.Furthermore, with albedo 0.2 and 0.8, the fourth configuration generated more energy than the first configuration, generating more than 17.3% and 26.4%, respectively. We also found that the fourth configuration in each system with the maximum lifetime energy generation can reduce the levelized energy cost, despite the system’s higher lifecycle cost. This reduction is due to the prevalence of increased energy generation over additional costs. For example, for albedo 0.2 and 0.8, the levelized energy cost of the fourth configuration was 7.3% and 15.7% lower, respectively, than the first configuration. In addition, the payback period of the fourth configuration is shorter than the first configuration, with an albedo of 0.2 and 0.8, respectively, for all three systems. Results of the cost model where the PV system was used to produce hydrogen using proton exchange membrane fuel cell favor configuration C4 (bifacial cell with a single-axis tracking system), which can produce hydrogen at a competitive cost that reaches as low as $2.53/kg-H2. Other configurations can also produce hydrogen at highly competitive costs, which indicates the competitiveness of producing green hydrogen in the UAE.Based on these findings, we can say that a PV system with a bifacial module and single-axis tracking structure is more feasible than a PV system with a monofacial module combined with the mounting structure for the UAE targeted location for both objectives, electricity generation and hydrogen production.



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*Source: 1612600-2023-02-06.xml*