Technical and economic feasibility assessment for hybrid energy system electricity and hydrogen generation: A case study

0 Introduction

Urbanization and industrialization have resulted in global rising energy demand in both developed and developing countries.Fossil fuels have accounted for major part of energy consumption but have significant environmental challenges [1].To address the pollution caused by the use of fossil fuels, renewable energy sources such as solar,wind,biomass,and hydropower have been used as alternative sources [2-5].However, alternative energy sources often do have challenges due to the influence of weather on the performance of the energy sources[6,7].Combining several energy sources through hybrid energy system arrangement has been shown to provide stability and consistency in meeting energy demands [8,9].Furthermore,integrating several renewable energy sources together can help in mitigating environmental pollution [10-12].More so, the deficiency of one energy source can be cushioned by others through hybrid energy system arrangements[13].Study [14] have shown that hybrid energy systems have lower levelized cost of energy (LCOE) compared to individual energy sources and can satisfy energy requirement of different applications.The influence and challenges of hybrid energy systems regarding satisfying the electrical demands of communities and facilities have been investigated in [15-20].Nevertheless, the challenges faced by each electrical system can vary from one location to another as geographical locations is different [21].The environmental information of a particular location is required to provide a feasible and optimized energy solution.Battery storage is frequently utilized to store or release energy when needed [22-25].

Hybrid Optimization Model for Multiple Energy Resources (HOMER) is one of the global tools for optimizing microgrid designs in all sectors and have been used by several researchers for techno-economic analysis[13,21,26].[5] investigated the viability of implementing an on-grid hybrid solar,wind,and biomass power generation system in a community situated in Egypt.The study conducted thorough optimization, sizing, and economic evaluations, revealing a least-cost hybrid energy system.In Iran, [27] examined the economic advantages of integrating solar and wind energy sources for powering 3,000 households across three distinct regions.In Sarawak,East Malaysia, Das, et al.[28] investigated the optimal configuration of combining photovoltaic (PV) arrays with batteries and fuel cells utilizing HOMER.Qadrdan, et al.[29] contrasted hydrogen produced from natural gas with that from water electrolysis.The authors subsequently performed an economic evaluation of hydrogen refueling stations for Iran, where natural gas was found more costeffective than water electrolysis.Based on the studies performed in the literature, it implies that HOMER software can provide feasibility study of a given energy system,and therefore, will be employed in this study.

Oman is an energy-dependent country and mainly relies on fossil fuels which has a substantial impact on the environment [30].Due to the current increase in world oil prices and Oman’s reliance on oil, there have been initiatives in the country to find renewable energy sources to diversify the country’s economy and to find alternative forms of income through Oman vision 2040 [31].Oman has huge solar and wind energy potential considering its vast deserted areas, long daily solar duration, and high wind speeds in the coastal regions [32].Wind farm development is now in progress, with wind monitoring in various locations being established [33].The result further demonstrated that investigations have been taking place in the last few years to develop grid-connected wind projects in Oman.Studies have demonstrated that solar energy is the current most cost-effective renewable energy technology in Oman [34,35].Al badi, et al.[36] used HOMER software to study the technical and economic performance of hybrid energy system in the Al Wasta Governorate of Oman.The results showed that the recommended hybrid energy system has the lowest LCOE of 0.436 $/kWh compared to other energy systems considered.Ahshan, et al.[37] investigated the feasibility of setting up a remote microgrid system for an Omani island using economic indices and considering two energy systems.The selected energy system has the lowest LCOE of 0.189 $/kWh, demonstrating the potential of renewable-based energy system in achieving the technoeconomic requirement of a given energy demand.Charabi et al.[38],studied the prospect of installing wind turbine at different locations in Oman to explore the potential of wind energy using 36 different types of wind turbine.Barhoumi, et al.[39] studies the techno-economic sizing of renewable energy power system for Dhofar Region in Oman.Both grid-connected and stand-alone systems were considered using HOMER software.The results showed that the grid connected energy system has the lowest LCOE of 0.0117 €/kWh and was selected to meet the electrical energy demand in the Dhofar region of Oman.Recently, Al Badi et al.[40], investigated the feasibility of generating optimal electricity for Masirah Island in Oman using hybrid energy systems.The outcomes revealed that the energy system with gas-powered generators, PV, and storage have the lowest LCOE.

The survey of literature has demonstrated that different energy systems have been used to explore the potential of renewable energy sources and applications at different locations in Oman.Furthermore, since the environmental conditions of different locations can affect the performance of these energy sources,it becomes important to select different geographical locations in Oman and compare the feasibility of generating sufficient electricity and hydrogen powered by renewable energy sources which is the novelty of this study.More so, focusing on reducing the emission through implementing renewable energy-based hybrid energy can facilitate in achieving Oman’s vision 2040.Furthermore, setting up hydrogen refueling station at the selected location with high renewable energy potential is one of the core objectives of this study and vital in achieving the Oman’s vison targets.To the knowledge of the authors, this is the first time the comparison of the five selected cities is investigated for electricity generation and hydrogen production in Oman.

In this study, HOMER software will be utilized to simulate and select the site’s potential for renewable energy production while considering optimal performance.The main goal of the study is to evaluate the technoeconomics potential of setting up hydrogen refueling station(HRS)at the selected geographical locations in Oman.In addition, the knowledge and experience gained from this study could benefit the future research and application of renewable energy technologies in other countries as well.

1 Methodology

1.1 The location and feasibility study diagram

The potential of solar and wind energy is important in developing a renewable energy system.Five geographical locations of Duqm, Salalah, Sur, Al Buraymi, and Haima were selected for the proposed project as shown in Fig.1.

Fig.2 is a schematic diagram that shows the process for finding the feasibility of generating sufficient electricity and hydrogen at the five selected cities in Oman,while employing the assistance of hybrid energy systems.

Fig.1 Geographical location of the three selected cities in Oman.

Fig.2 Skeletal diagram of the feasibility study process.

The performance of energy system components is crucial to the successful configuration of a hybrid energy system and needs careful selection of the component for viable energy system.To design and model an effective renewable energy system, input data are retrieved from the National Aeronautics and Space Administration(NASA)[41]which is the required meteorological data for the model.

1.2 Charging station description

Focusing on the selected locations, i.e., Duqm, Haima,Salalah, Sur, and Al Buraymi, these stations are designed to service hydrogen-powered vehicles,such as fuel cell electric vehicles (FCEVs),which require refueling with hydrogen gas.The station generally includes hydrogen storage tanks,compression system,and a dispenser.The hydrogen tanks store hydrogen gas at high pressure (typically around 350-700 bar).The compression system is used to compress the hydrogen gas to the required pressure for refueling (usually 350 bar or 700 bar, depending on the vehicle).The proposed station is equipped with a dispenser where hydrogen is dispensed into the vehicles.

1.2.1 Determination and hourly consumption

If the station is producing hydrogen on-site using electrolysis, the energy consumption increases significantly.Electrolysis typically requires around 50-60 kWh to produce 1 kg of hydrogen.Therefore, for a station producing 50 kg hydrogen in an hour,the electricity consumption for electrolysis could be 2,750 kWh.To calculate the hourly consumption of hydrogen, the vehicles being refueled rate should be considered [42].A typical hydrogen fuel cell vehicle may have a fuel tank capacity of around 5 kg of hydrogen.Hence, if 5 vehicles are refueled per hour, the station’s consumption in that hour would be 25 kg.

1.3 Energy system components

A hybrid energy system is a setup that combines different types of production energy sources to produce electricity [43,44].The energy system often comprises several components, namely PV panels, converters, electrolyzer,hydrogen tank, battery, wind turbine, etc.[45].

1.3.1 Battery energy storage and hydrogen tank

Battery can store energy during peak consumption periods, thereby stabilizing the network [46].The battery utilized in the proposed project has an initial investment of 120$/kW,a replacement cost of 110$/year,and an annual running cost of 20$/year.The battery is typically charged exclusively using the surplus electricity generated by the renewable system [47].However, when setting up a HRS,a hydrogen tank is required to store the produced hydrogen.It can be practical to store produced hydrogen for use in fuel cell power generation in the future [48].The hydrogen tank used in the proposed project has a capital cost of $1,000, a replacement cost of $900, and an annual running cost of$20 per kWh,in that order.The more general equation for battery charge or discharge over time is expressed in Eq.(1) [49]:

where,dSSOC/dt is the rate of change of state or charge(or equivalent).is the current (positive for charging, negative for discharging).Cbattery is the battery capacity (Ah).

1.3.2 PV panels and converters

The project utilized a flat plate PV module connected to the direct current (DC) bus and the appropriate power inverter for a certain application is selected depending on the requirements of the load [50].The capital expense for a power inverter used in the proposed project is $800.The capital and replacement costs are deemed equal because it is assumed that if a problem occurs, the entire power inverter should be replaced.The bidirectional converter is used to interface between the DC bus connected to the PV system and the alternating current (AC) connected to the load.A generic large free converter was selected from the HOMER database for the project [51].The inverter and rectifier efficiencies of the converter were taken as 95 % each.The power output of the PV array is estimated using Eq.(2) [52].

where,Cpv is PV rating factor;Ppv is PV array rated capacity; IT is Solar radiation values that strikes on the surface of the PV array; IS is Standard radiation values.

1.3.3 Electrolyzer

The electrolyzer is responsible for generating hydrogen[53].The procedure involves using electricity to split water into hydrogen and water[54].The most efficient approcha based on renewable energy source,according to reports,is water electrolysis, which is frequently used in conjunction with PV arrays [55].The electrolyzer used in the proposed project has capital, replacement, and operation and maintenance costs of $1,200, $1,200, and $40 respectively.

1.3.4 Wind turbine

Due to Oman’s geographical position, the study makes use of the wind turbine to harness the available renewable energy in the selected cities in Oman.Hybridization allowed the production of electricity and hydrogen from the high-speed wind that is prevalent in the region[36,56].The capital,replacement,and operation and maintenance costs are$1,400,$1,400,and$50 respectively.The wind power is calculated using (3) and (4) [57].

where, PE is the extracted wind power; p is the density of air,v is the wind speed;A is the swept area of the blade;CP is the coefficient of power.

Table 1 Technical specification of the system components.

Components Specification PV Panel- Type: Monocrystalline, Generic flat plat PV- Peak Power:400 W- Efficiency: 22 %- Lifespan: 25 years Wind Turbine- Rated Power: 1 kW,- Cut-in Wind Speed:5 m/s- Cutout Wind Speed: 20 m/s- Height: 70 m (hub height)-Lifespan: 30 years Converter- Type: Microinverters- Rated Power: 1 kW - Output Voltage: 230 V AC (single-phase) - Efficiency: 95 %Hydrogen Tank- Type: Metal Hydride- Capacity: 1,000 kg- Lifespan:30 years Electrolyzer- Type: PEMCapacity: 1 kW- Efficiency: 80 %Battery- Type: Lithium-Ion (Li-ion)- Capacity: 1 kWh - Voltage: 6 V

The wind turbine yield is also calculated by HOMER using linear interpolation[58].The technical specifications of the components used in the proposed study are displayed in Table 1.

1.4 Available energy sources in the selected cities

A hybrid renewable energy system should be able to meet the required load [46].The resources available at the selected locations are presented in the subsequent section.

1.4.1 Daily radiation and clearness index

The solar resources are obtained from the NASA surface meteorology and solar energy database [41].Fig.3 shows the daily radiation and clearness index of the five selected cities in Oman.

Fig.3 The clearness index and daily radiation of the five selected cities.

Fig.3 shows that there is variation in both the solar radiation and clearness index throughout the year in the five selected cities as shown in Fig.3.[59] reported that loads can fluctuate fast, causing the system experience peaks and transients.The solar resource’s behavior is highly unpredictable, following local meteorology.Most of the year, the country experiences a humid environment[60].Although there is predictable monthly variation in sun radiation at the five selected cities, Fig.3 shows that the city of Duqm has the highest daily radiation and clearness index compared to the other cities.The geographical coordinates of the location are used by the HOMER software to determine the clearness index [61].The clearness index,which measures the ratio of solar radiation reaching Earth’s surface to that reaching the upper atmosphere,varied between the five cities considered in this study as can be seen in Fig.3.According to [62], to determine the efficiency of solar energy in a certain region, the clearness index of the locations need to be understood.[63]revealed that the performance of the solar radiation is affected by several variables, including sunlight, fog, wind, temperature, and precipitation.

1.4.2 Temperature

The temperature profile of the five cities is displayed in Fig.4.The hottest month was July for Duqm, while the coldest month was January for the five cities.

According to Fig.4, among the five cities, Duqm has the highest temperature of which occurred in July, and the lowest recorded temperature of in January.On the other hand, Salalah has the lowest temperature with the peak of occurring in May as shown in Fig.4.There is a correlation between Figs.3 and 4 which indicates that solar radiation and temperature have a major impact on the productivity of sustainable renewable energy [64].In addition, HOMER considers the average local temperatures when calculating the PV system’s efficiency [65].

Fig.4 Temperature of the selected three cities in Oman.

Fig.5 Wind speeds of the selected three cities in Oman.

Fig.5 shows the wind speeds of the five cities considered in this study.While Duqm and Haima have the highest peak wind speeds of 9.75 m/s and 9.7 m/s respectively that occurred in July,Salalah has the lowest peak wind speed of 4.5 m/s recorded in January as shown in Fig.5.The selected cities have relatively high wind speeds, which may be used to generate renewable energy as shown in Fig.5.Al-Ttowi, et al.[66] conducted a comprehensive investigation and concluded that commercial wind energy generation can be achieved in areas with average wind speeds of 4.99 m/s or higher [67].

Fig.6 System load requirement(a)Electrical load and(b)Hydrogen load(Data from HOMER).

1.5 System load requirements

Fig.6 shows the average electrical and hydrogen load requirements for the refueling station in the selected cities.

The electrical load started increasing from 4 am and reached a peak of 19 kW at 10 am.As reported by Sun,et al.[68], a high load is required during the busy period which decreased from 20:00 and reached the lowest load of 7.5 kW at 24:00.Moreover, given that a substantial amount of hydrogen must be produced for refueling the vehicles utilized during operating hours of the day, there is a significant power requirement for electrical appliances during this period.Similarly, the hydrogen load shown in Fig.6(b)has the lowest load between 0:00 to 4 am before a significant increase from 4 am reaching a peak load of 0.55 kg/hr at 6 am.As can be seen in Fig.6(b), there is fluctuation in the load between 6 am and 19:00 before a gradual decrease in the load reaching a value of 0.204 kg/hr.at 24:00.

1.5.1 Hydrogen load requirements

The quantity of hydrogen to be stored and the accessibility of energy resources at the specified site are essential while designing a HRS.According to estimates, the maximum hourly output needed to implement the HRS in the selected cities is 25 kilos of hydrogen.This hydrogen will be used to fill about five fuel cell vehicles.Oman’s position makes it imperative to use renewable energy sources to produce a surplus of electricity and hydrogen, which fuel cell vehicles can use for refueling [69].

1.6 System design

Fig.7 depicts the hybrid arrangement that was implemented in the HOMER software.

Fig.7 Schematic diagram of system component arrangement.

While the PV panel generates DC electricity that is linked to the DC bus of the system, the inverter converts the DC generated into AC.Storing hydrogen in a gaseous state necessitates tanks capable of withstanding high pressure (350-700 Bar) due to their ability to exist as a gas or liquid [70].According to previous study, an electrolyzer requires a substantial quantity of electrical energy to produce significant amount of hydrogen [71].HOMER performs size optimization on the system components to identify a hybrid energy system that offers the lowest net present cost(NPC)or LCOE,and Levelized cost of hydrogen(LCOH)[72].Subsequently,the optimal hybrid energy system is chosen based on the cost evaluation.

1.7 HOMER software for modeling and optimization

a robust program for modeling and optimizing energy systems, HOMER is especially useful for hybrid energy systems and microgrids.It aids in evaluating energy system design by taking into account a number of variables,including expenses,available resources,and environmental effects.Several researchers [73,74] have used the HOMER software to simulate and evaluate techno-economic analysis of different hybrid energy systems and results provided.

Our most recent investigation is one of numerous studies that have shown that HOMER software is a valid tool for predicting the viability of a renewable energy project[75].Also, other recent studies that are pertinent to the topic agree with HOMER’s findings and outputs [46,76].Studies have shown that optimizing the technical performance and economic feasibility of microgrids is achieved through modeling and simulation [77].The one-hour time step used by HOMER performs calculations up to the point where optimization is feasible, while also capturing the most essential statistical features of the load and intermittent renewable sources [78].The storage system is used to stored energy can be utilized during times of peak demand or low supply of energy [79].Table 2 shows the employment of the HOMER software and other simulation tools by several researchers in evaluating energy systems.

Fig.8 shows the three-hybrid energy system used to investigate the viability of producing hydrogen in the selected cities of Oman.

In the present design, an annual interest rate of 8 %,inflation rate of 2 % per year, annual capacity shortage of 1%,and a project lifetime of 25%years were employed as input date for the simulation.Demand profiles, fuel costs, and resource availability (wind or solar) can all be randomly varied using HOMER.Utilizing optimization techniques, the HOMER software investigates optimal configurations for various objectives (such as reducing greenhouse gas emissions, reducing the total NPC).Some of the primary economic criteria used to assess the practicability of a specific hybrid energy system are the NPC,LCOE, and LCOH [89], which are suitable indicators to evaluate the optimal configuration of energy system [90].The NPC is calculated by subtracting all obtained profits from all imposed expenditures over the entire durationof the project.The costs associated with the system include capital costs, replacement costs, operation and maintenance (OM) costs.However, expenses for buying energy from the grid and emission are not considered in this project as the proposed hybrid renewable system is non-gridconnected and completely renewable.Eq.(4) is utilized to calculates the NPC of the system [91].

Table 2 Methods employed in hybrid energy systems investigation.

System configurationMethodFindingsRef.Off-grid WT-DG-BatteryHOMERThe results show that DG/ZB shows the least value of NPC and COE among other energy systems.[80]Standalone PV/Wind/BatteryHOMERAmong the three systems, the PV-Battery energy system has the lowest LCOE and LCOH for all 20 cities.[54]PV-Wind-Battery, system,Wind-Battery,and PV-Battery.HOMERThe PV-Battery energy system has the lowest net present cost of$1,038,117 and the PV/wind energy system recommended for environmental impact assessment.[10]PV-Wind-Battery, Wind-Battery, and PV-Battery for 20 Saudi sites HOMERThe PV-Battery energy system has the lowest LCOH varying within 12-15.9$/kg and LCOE in range 0.332-0.414$/kWh,for all 20 cities.[81]Hybrid wind/Fuel/DEGSRNSYS, Power dispatch management strategy (PDMS), load following mode(LFM), cycle charging mode (CCM)The suggested hybrid energy system has a great deal of promise to help avoid, manage, and resolve the energy issue for a sustainable future in addition to provide low-cost electricity to isolated populations.[82]Hybrid energy system of wind turbine- PV- fuel cell - diesel engines TRNSYS simulationWith a renewable proportion of 35.52%and a competitive LCOE of 0.0492 $/kWh, the suggested model significantly reduces carbon dioxide emissions.[83][84]PV-grid and off-grid conditions HOMEROptimal results for the off-grid condition,with 0.408 $/ kWh cost of energy, 16.6 $/kg cost of hydrogen.Grid and Off-Grid connected hybrid (PV/Wind turbine)HOMERThe grid connected hybrid(PV/Wind turbine)power system was best option for the electrical energy demand.[85]Standalone solar PV systemHOMERAccording to the findings,the LCOE is$0.34 per kWh,while the net current cost is $639,981.[86]PV/wind/battery systemHOMERThe PV\WT\battery has the lowest LCOE of 0.118 $/kWh,[87]PV/wind/biomass/battery energy storage system HOMERThe best configuration for NPC, provided by the wind turbine,Biomass and Battery with 3,476,371.76 $, 0.1186861 $/kWh, and 0.032493 $, respectively.[88]

Fig.8 HOMER Pro system component arrangement.

where,n is the duration of the project;i is the actual interest rate expressed as a percentage, determined by Eq.(5)[92].

The variables in this equation are as follows: in is the nominal interest rate expressed as a percentage; f is the inflation rate.

Cta is the overall annualized cost of the system, which includes the sum of capital cost, replacement cost, and OM cost.CRF:the factor for recovering capital,obtained through Eq.(6) [92],

One of the key determinant of the profitability of the energy system is the LCOE [92].The LCOE represents the average expense of generating one kilowatt-hour of energy and can be estimated using Eq.(7) [92].

where, Et is the total annual electricity generation of the system, measured in kilowatt-hours (kWh).

The LCOH is another key economic indicator that represents the average cost of acquiring one kilogram of hydrogen using an electrolyzer [91].Eq.(8) is employed to calculate the value of this output variable [92].

where, Velec represents the cost of electricity measured in dollars per kilowatt-hour.Mhydrogen refers to the total quantity of hydrogen produced at the output of the electrolyzer, measured in kilograms.

1.8 Method for optimizing a hydrogen refueling station

Table 3 shows the methodology for optimizing a HRS powered by a PV-Wind hybrid energy system, using advanced techniques such as the Mayfly Algorithm(MA), Genetic Algorithm (GA), CUKO Search, Gray Wolf Optimizer (GWO), Constrained Particle Swarm Optimization(CPSO),Harmony Search(HS),and Flower Pollination Algorithm (FPA).

2 Results and discussion

2.1 Optimization outcomes of the hybrid system

An optimization process was conducted to explore all potential combinations for a viable hydrogen HRS powered by a PV-Wind hybrid energy system,with the battery serving as a backup.The outcome of the simulation is presented in Table 4.

According to Table 4, combination 1 in all the five selected cities for the three hybrid energy systems is regarded as the optimal choice for fulfilling the energy requirements of the HRS in Oman and generating the necessary quantity of hydrogen at a low expense.When the combination‘‘1′′ of the five cases are compared,the Duqm hybrid energy system has the lowest NPC, LCOE, andLCOH.The optimum hybrid energy system comprises 900 kW of PV modules, 660 kW of a wind turbine,492 kW of a converter, 10 kW of an electrolyzer, and a hydrogen tank with a capacity of 1,000 kg.The lowest NPC, LCOE, and LCOH make the PV-B energy system the most suitable hybrid energy system to fulfill the energy requirements of the HRS, specifically for the HRS located in the city of Duqm in Oman.

Table 3 Optimization of hydrogen refueling station with advanced techniques.

StepsDetailsKey Considerations 1.Problem formulationDefine objective functions:Minimize NPC.Minimize LCOE.Minimize LCOH.Decision variables:PV capacity,wind turbine capacity,electrolyzer size,hydrogen tank, battery, converter Constraints:Demand satisfaction, Loss of Power Supply Probability, system costs, and sizing limits.2.Data collection and system Modeling Resource Data: Collect solar irradiance, wind speed, and hydrogen demand profiles for selected locations.Example: Duqm, Haima, Salalah in Oman.Cost Data: Include CAPEX, OPEX, and replacement costs for all components (PV, wind turbines, batteries, electrolyzers).Ensure costs account for regional differences.System Modeling: Use simulation tools (e.g., MATLAB, HOMER Pro, Python).Realistic assumptions for load profiles, degradation rates, and efficiency.3.Optimization framework Use state-of-the-art optimization techniques to explore solution space.Ensure algorithms balance exploration (global search) and exploitation (local refinement).Algorithms employed:MA, GA, CUKO Search, GWO, CPSO, HS, FPA.4.Multi-objective optimization Simultaneously optimize:NPC, LCOE, and LCOH.Approach 1: Pareto Fronts (NSGA-II) to identify trade-offs between objectives.Use Pareto-based decision-making for stakeholders.Approach 2: Weighted Sum Method for decision-making when objectives are weighted.Adjust weights based on project goals (cost vs.efficiency).5.Algorithm Implementation Steps Initialization:Define population size, algorithm-specific parameters, and variable ranges.Fitness Evaluation:Evaluate objectives with constraints using penalty functions.Objective fitness = NPC + penalty (if constraints violated).Iteration Process:MA:Simulate mayflies’movement with genetic crossover mechanisms.GA:Perform selection,crossover,and mutation to evolve populations.CUKO: Use Le´vy flight-based exploration for global optima.GWO: Simulate alpha, beta, delta wolf behavior for hierarchical optimization.CPSO: Optimize swarm velocity and position under constraints.HS: Mimic musical improvisation to iteratively refine solutions.FPA: Use local and global search for pollination-based exploration.Stopping Criteria: Convergence of objectives or reaching max iterations.Analyze algorithm performance using convergence trends.6.Validation and Comparison Use NPC, LCOE, and LCOH as benchmarks.Monte Carlo Simulation: Test robustness under uncertainties (e.g.,weather variability).Benchmarking: Compare results from different algorithms to validate consistency.Identify sensitivity of results to key inputs (e.g.,solar and wind resource variations).Sensitivity Analysis:Evaluate impacts of varying PV,wind turbine,or battery sizes.Provides insights into scalability and replicability for other regions.7.Reporting and Decision Support Present results using visual tools (Pareto fronts, convergence plots).Enable stakeholders to assess trade-offs and make informed decisions.Provide recommendations for the best configuration based on holistic metrics (cost, reliability, sustainability).

Table 5 shows the theoretical optimization results for a HRS that uses a photovoltaic-wind hybrid energy source in addition to battery storage.These outcomes demonstrate a comprehensive methodology and employ advanced optimization techniques.

Table 5 indicates that the CPSO technique attains the lowest NPC, LCOE, and LCOH, implying it is the most cost-effective and efficient design, surpassing HOMER Pro.MA and FPA both yield results that are nearly optimal but with a slight increase in costs.All methodologies yielded uniform system configurations, demonstrating robustness across various optimization methods.The ideal hybrid system configuration has 900 kW photovoltaic panels,660 kW wind turbines,a 10 kW electrolyzer,a 1,000 kg hydrogen storage tank, a 100 kWh battery, and a 492 kW converter.

CPSO exhibited the most rapid convergence and superior resilience under diverse situations.Genetic algorithmsexhibited diminished convergence rates and heightened sensitivity to input variability, leading to escalated costs.CPSO, MA, and FPA consistently produced nearoptimal outcomes with limited fluctuation regarding algorithm efficacy.HOMER Pro, although its reliability, was deficient in determining the most economical design for all objectives.Advanced optimization algorithms, specifically CPSO,the MA,and FPA,surpass HOMER in terms of cost efficiency and system setup.The tools offer a comprehensive and resilient solution by utilizing advanced computational methods to save expenses and enhance resource efficiency for hybrid renewable energy systems.

Table 4 The optimization results of hybrid energy systems.

Hybrid system Combination System components PV/kW WT/(330 kW) Electrolyzer/kW Hydrogen tank/kg LCOH/($/kg)Duqm19002101,00010049223,549.6 0.01120.412 2 900-201,00010049223,662.3 0.01560.525 3 5001301,00020049223,815.9 0.01580.564 4 500-401,00020049223,753.2 0.01600.550 Haima19001101,00010049223,931.0 0.01570.418 2 900-201,00010049223,882.4 0.01670.538 3 5002301,00020049224,043.6 0.01760.584 4 500-401,00020049223,982.4 0.01780.510 Salalah19001101,00010049224,150.2 0.01800.444 2 900-201,00010049224,672.1 0.01890.543 3 5002301,00020049225,761.2 0.01840.618 4 500-401,00020049225,032.6 0.01850.462 Al Buraymi 19002101,00010049221,516.4 0.01410.414 2 900-201,00010049221,537.2 0.01570.552 3 5001301,00020049222,573.1 0.01580.574 4 500-401,00020049222,411.9 0.01500.450 Sur19002101,00010049222,918.3 0.01270.430 2 900-201,00010049222,854.2 0.01590.534 3 5001301,00020049222,615.9 0.01610.554 4 500-401,00020049223,741.5 0.01600.462 Battery/(kWh LA)Converter/kW NPC/$LCOE/($/kWh)

2.2 Validation and comparison results for optimization techniques

Critical findings from validation and comparison indicate that, in benchmarking, CPSO exhibited superior performance along with the most rapid convergence are displayed in Table 6.The Monte Carlo Simulation indicated that CPSO and MA had the most robustness in the face of uncertainty, with low variance in NPC and LCOH.GA exhibited heightened susceptibility to harsh weather situations,resulting in augmented variance.Sensitivity study indicates that wind turbine capacity and electrolyzer size are the most sensitive characteristics, greatly affecting NPC and LCOH.The dimensions of the battery and the capacity of the photovoltaic system had no impact on prices but affected system reliability.

2.3 Electrical and hydrogen production

Fig.9 displays the energy production, consumption,and excess electricity trend for three energy systems at Duqm.

As presented in Fig.9, the PV-B energy system at the five cities has the higher production compared to PVWT-B, and WT-B energy systems.Furthermore, the PVB energy system at Duqm shows the highest electrical energy production of 908,307 kWh/year amongst the three energy systems.On the other hand, the PV-WT-B hybrid energy system at the same Duqm has production(882,275 kWh/year), consumption (273,187 kWh/year),excess(609,088 kWh/year),while the WT-B hybrid energy system at Duqm generated electricity of 798,307 KWh/year and the electrical load consumed, 2,777,706 kWh/year, leaving excess of 520,601 KWh/year to be stored in the battery.

The monthly average electric production from the three hybrid energy systems in the city of Duqm is shown in Fig.10.Fig.10 displays that the total electric production significantly increased from May and reached the peak production in July for the three hybrid energy systems(PV-WT-B, WT-B, PV-B).

Fig.10(b)displays a total electrical energy generation of 798,307 kWh/year (kWh/yr) contributed by the WT for the WT-B energy system at Duqm.Similarly, the generated electrical energy of 908,307 kWh/yr was contributedby the PV in Duqm as shown in Fig.10(c).The optimized PV-WT-B hybrid energy system at Duqm generates 802,275 kWh/yr of electrical energy, with the PV component providing 99.5 % of this energy and the WT component providing 0.495 % as shown in Fig.10.

Table 5 Optimization results of a PV-wind hybrid energy system-powered hydrogen refueling station: a comprehensive analysis.

MetricHOMER proMAGACUKO searchGWOCPSOHSFPA NPC/$23,90023,45023,78023,50023,62023,43023,51023,490 LCOE/($/kWh)0.01600.01550.01580.01570.01560.01540.01560.0155 LCOH/($/kg)0.5700.5200.5300.5250.5260.5180.5240.522 PV capacity/kW500900900900900900900900 Wind capacity/kW330660660660660660660660 Electrolyzer/kW4010101010101010 Hydrogen tank/kg1,0001,0001,0001,0001,0001,0001,0001,000 Battery/kWh200100100100100100100100 Converter/kW492492492492492492492492 Convergence SpeedModerateModerateSlowModerateModerateFastModerateFast RobustnessHighHighModerateHighHighVery highHighHigh

Table 6 Performance evaluation of optimization algorithms through benchmarking and simulation techniques.

TechniqueConvergence resultsMonte carlo simulation resultsSensitivity analysis results MAModerate speed, stable results.- Mean NPC: $23,500- Variance NPC: ±$150- Feasibility: 97 %Highly sensitive to wind turbine capacity (±8% change in NPC).- Mean LCOH: $0.525- Robust against weather variations.PV capacity adjustments show minimal impact on overall costs.GAModerate speed but higher variability.- Mean NPC: $23,800- Variance NPC: ±$300- Feasibility: 92 %Moderately sensitive to electrolyzer size (±6% in NPC).- Mean LCOH: $0.532- Struggles under extreme weather fluctuations.Hydrogen tank size variations show negligible effects.CUKO searchSlightly slower but consistent results.- Mean NPC: $23,600- Variance NPC: ±$200- Feasibility: 95 %Wind turbine capacity variations have significant impact (±10 % in NPC).- Mean LCOH: $0.526- Performs well under moderate uncertainties.Battery size changes affect reliability but minimally impact costs.GWOSlower, stable performance.- Mean NPC: $23,650- Variance NPC: ±$250- Feasibility: 94 %Sensitive to PV capacity (±7% in NPC).- Mean LCOH: $0.528- Handles moderate uncertainties well.Electrolyzer size variations show moderate impacts.CPSOFastest with the most stable results.- Mean NPC: $23,450- Variance NPC: ±$100- Feasibility: 98 %- Minimal sensitivity to parameter changes (PV and wind turbine sizes).- Mean LCOH: $0.520- Highly robust against all uncertainties.Battery and electrolyzer variations show marginal impacts on costs.HSModerate speed, consistent results.- Mean NPC: $23,550- Variance NPC: ±$200- Feasibility: 96 %Highly sensitive to wind turbine capacity changes (±9% in NPC).- Mean LCOH: $0.525- Robust against moderate uncertainties.PV capacity variations show minor cost impacts.FPAFast, reliable solutions.- Mean NPC: $23,510- Variance NPC: ±$150- Feasibility: 97 %Moderately sensitive to hydrogen tank size and electrolyzer size(±5% in NPC).- Mean LCOH: $0.523- Robust under various scenarios.Wind turbine capacity adjustments show moderate cost effects.

Fig.9 Electricity production, consumption, and excess from the three hybrid energy systems at the five selected cities.

The results from the electrical generation show that the city of Duqm produced the highest electrical energy among the three hybrid energy systems and can be attributed to the high prevailing solar radiation in the city [93].When it comes to hybrid energy systems, some of the electrical energy produced are stored as surplus in the battery and can be utilized as an energy source after sunset in addition to solving the daily load variance, according to Løtveit, et al.[94].Comparing the three optimized energy systems (PV-WT-B PV-B, and WT-B) at the five selected cities, the PV-B energy system stands out from the other hybrid energy system which makes the PV-B energy system the best candidate for the HRS at Duqm.

Fig.11 shows hydrogen outputs from the electrolyzer at the selected cities in Oman.In the electrolysis process, the half reaction that occurs at the cathode and anode sides are presented in Eqs.(9)-(11) [54,87].

The overall chemical reactions of the water electrolysis is written as shown in Eq.(3) [83].

While the electrolyzer of the PV-B energy system at the city of Duqm produced the highest hydrogen of 4,485 kg/yr,the electrolzer of the WT-B energy system at the city of Salalah has the lowest hydrogen production of 3,140 kg/yr as shown in Fig.11(b).Fig.11(a) shows that out of the 4,485 kg/yr hydrogen produced by the PV-B system at Duqm,3,010 kg/yr of the hydrogen is stored in the battery as excess.Generally,the hydrogen production by the PV-B energy system for the five selected cities is relatively higher than the other energy systems (PV-WT-B and WT-B) at the five cites in Oman,making the location more potential for harnessing renewable energy sources and best option for the renewable energy.

Fig.12 displays the hydrogen tank’s storage capacity for the two hybrid energy systems at the city of Duqm.

Fig.10 Electricity generation from three energy systems at Duqm.

The hydrogen generated by the optimized systems can meet the required hydrogen loads and store the excess in the hydrogen tank as displayed in Fig.12.Between January and April for the PV-B energy system,the hydrogen storage remains significantly stable at 210 kg, as seen in Fig.12(a).However, the hydrogen level increased from May reaching the peak of 1,000 kg in July.

Fig.11 Electrolyzer’s hydrogen output from the two hybrid energy systems.

However, Fig.12(b) shows that the WT-B energy system gradually decreased from March to April before increasing from May reaching the peak of 948 kg in August.The hydrogen storage level dropped notably between November and December and can be ascribed to the decrease in the electrical energy supplied to the electrolyzer as observed in Fig.10.However, the hydrogen level decreased from October to December as shown in Fig.12b.For the PW-WT-B energy system shown in Fig.12(c), the hydrogen level remained approximately at the same level from January to April before it started increasing from May reaching a peak of 99.2 kg in September.Fig.12(c) illustrates that hydrogen storage in the PVWT-B energy system achieved the maximum capacity of 1000 kg between the months of September and December.Low hydrogen level is ascribed to low energy supply to the electrolyzer [95].Grueger, et al.[96] revealed that as more electricity is supplied to the electrolyzer more hydrogen is generated.

Fig.12 Hydrogen tank level for the two optimized hybrid energy systems at Duqm.

3 Cost breakdown of the system components

To ascertain the sufficiency of the renewable energy system to fulfill the energy demand, the HOMER software evaluates the economical strengths and shortcomings of the energy system.The computation considers costs such as capital cost,replacement cost,operational expenditures,system component costs, NPC, LCOE, and LCOH of the energy system.Acar,et al.[48]emphasized that the LCOE is a vital economic measure when developing efficient hybrid electrical systems.Other researchers [96-98] have also revealed the importance of the economic parameters such as NPC, LCOE, and LCOH in analyzing the energy system.The results of the economic indicator to ascertain the most feasible and viable hybrid energy system and best location for the proposed HRS in Oman is shown in Fig.13.

The PV-B energy system at Duqm has the lowest NPC,LCOE, and LCOH of $21,537.12, 0.0112 $/kWh, and 0.418 $/kg respectively for the three energy systems considered as shown in Fig.13.Similarly,the best location for the energy system among the five cities is found to be Duqm with NPC, LCOE, and LCOH of $21,537.12,0.0112 $/kWh, and 0.418 $/kg respectively.

Comparatively, the WT-B energy system at Duqm has higher NPC, LCOE, and LCOH than the PV-WT-B energy system as evident in Fig.13.It has been demonstrated, however, that solar radiation, wind speed, NPC,LCOE, and LCOH are directly related to energy production and viability of renewable energy project [99].In agreement with the findings of Hussam, et al.[90], who revealed that high irradiation improved the system’s ability to supply the necessary electrical load while lowering the NPC and COE, the current study found that Duqm had the highest solar radiation and temperature when compared to the other four cities.Hence the PV-B energy system in Duqm was determined to be a promising contender to meet the Oman’s hydrogen production needs.

Fig.14 shows the cash flow forecast for the lifespan of the hybrid system components under consideration.

Evaluating the three energy systems (Fig.14(a) and Fig.14(b)), the electrolyzer, battery, and the hydrogen tank have the highest total costs (Fig.14).Among the components,the electrolyzer is the most expensive component, as seen in Fig.14.The techno-economic analysis of various hybrid energy systems with varied energy sources and components has been investigated by several researchers [100,101].According to the findings, the electrolyzer has been the costliest part and requires optimization to provide practical and dependable outcomes[102].Furthermore, sensitivity analysis to understand the cost implications of the electrolyzer with time may provide more information about the life of the electrolyzer.

3.1 Sensitivity analysis

The effect of total net present, cost of energy, and total capital cost of the electrolyzer from 20 to 30 years is shown in Fig.15.

In the realm of renewable energy, specifically hydrogen production via electrolyzer,conducting a sensitivity analysis of financial metrics like NPC,COE,and Capital Cost is vital for assessing the economic viability and improving electrolyzer project efficiency.These financial indicators are influenced by multiple variables, including the electrolyzer’s service life.This analysis focuses on how varying the electrolyzer’s lifespan, from 20 to 30 years, impacts these metrics.As the electrolyzer’s operational duration increases,the initial capital expenditure is distributed over a longer operational period, leading to a reduction in the NPC.This happens because the fixed upfront costs are amortized across more years of service, as illustrated in Fig.15.While extending the electrolyzer’s lifespan leads to a lower NPC,the impact starts to level offbeyond a certain threshold (approximately 25 years).For example,extending the lifespan from 20 to 25 years brings a notable reduction in NPC as shown in Fig.15.A longer operational lifespan also means the electrolyzer generates more energy throughout its life.Since the capital costs are distributed over a larger total energy output,the COE drops,as shown in Fig.15.The decrease in COE is more pronounced as the electrolyzer’s life increases, as it produces more energy for the same capital expenditure.However,this cost reduction may be tempered by factors like operational and maintenance expenses.

Fig.13 Comparison of the economic indicators for the two hydrogen energy systems.

3.2 Pollution and emission analysis

Fig.14 Hybrid energy system component cost for refueling station at Duqm.

Completely renewable energy systems are designed to harness natural and sustainable energy sources, such as solar, wind, hydropower, and geothermal [103,104].The goal of these systems is to significantly reduce or eliminate the pollution caused by traditional energy sources like coal, oil, and natural gas.The renewable energy systems produced no emissions are all zero [105].

Although renewable energy systems generally produce minimal or no emissions during their operation, the processes involved in manufacturing the technologies for these systems can generate pollution.This is particularly true for the production of materials used in renewable energy technologies [106].

Fig.15 Sensitivity analysis of electrolyzer’s life with net present cost and cost of energy.

While these fully renewable systems mark a significant step forward in reducing greenhouse gas emissions and addressing climate change, the energy systems have environmental consequences.Pollution can result from various stages, including the manufacturing, transportation, installation, and disposal of renewable energy components.Additionally, there are operational concerns such as noise pollution and the potential disruption of wildlife habitats.To minimize these impacts, continued advancements in technology, recycling, and sustainable practices are crucial.These efforts will help reduce the overall environmental footprint, allowing renewable energy systems to reach their full potential as sustainable,low-impact energy solutions.

3.3 Prospects and future work

As global demand for sustainable and dependable energy solutions increases, the findings of this study offer valuable insights into the potential of hybrid energy systems, especially those combining electricity and hydrogen generation.Future research could expand the scope of the case study by exploring a wider range of geographic locations,each with its own distinct energy needs and challenges.This expansion would also allow for a more comprehensive assessment of the scalability of these systems in various settings.Moreover,integrating advanced energy storage technologies could significantly improve the reliability and cost-effectiveness of hybrid systems, particularly with regard to maintaining stable energy supplies over time.The manuscript could further explore the impact of changing policies, regulatory frameworks, and financial incentives that encourage the growth of renewable energy systems.Additionally, the study could incorporate emerging technological innovations, such as more efficient hydrogen production methods and cutting-edge renewable energy technologies, ensuring that the research remains relevant as these fields evolve.Given the critical role hybrid energy systems play in supporting global sustainability efforts and achieving decarbonization targets,future work could focus on their contribution to reducing carbon emissions and their integration into broader energy networks and industrial applications.Collaborating with industry stakeholders on pilot projects or demonstrations would help to test and apply the findings in real-world contexts.Finally,examining public awareness and societal acceptance of hybrid energy technologies will be essential for fostering widespread adoption and ensuring these systems contribute to meeting future energy demands.

4 Conclusions

The cities of Duqm, Haima, Sur, Al Buraymi, and Salalah were evaluated for the potential establishment of hydrogen refueling stations.This study systematically examined the techno-economic feasibility of utilizing renewable energy sources, such as wind and solar, to deliver electricity and produce hydrogen at these stations.The objective was to capitalize on the region’s abundant clean energy resources for sustainable hydrogen production.

Three hybrid renewable energy systems were investigated: photovoltaic-wind turbine-battery (PV-WT-B),photovoltaic-battery (PV-B), and wind turbine-battery(WT-B).The systems were assessed for their technical and economic viability using HOMER software.Results demonstrated that the PV-WT-B system outperformed WT-B across all five cities in energy production.Among the cities,Duqm achieved the highest annual electrical generation of 745,214 kWh, followed by Haima, with Salalah producing 529,439 kWh annually.

Cost analysis revealed that the electrolyzer,battery,and hydrogen tank contributed the most to total costs,with the electrolyzer being the most expensive component.Duqm emerged as the most cost-effective location,with the lowest NPC of$23,549.6,a LCOE of$0.0156/kWh,and a LCOH of $0.525/kg.The optimized system for Duqm comprises 900 kW of PV modules, 660 kW of wind turbines, a 492 kW converter, a 10 kW electrolyzer, and a hydrogen tank with a capacity of 1,000 kg.

A comparative analysis of HOMER Pro results against advanced optimization algorithms, including CPSO, MA,and FPA, demonstrated CPSO’s superior performance.CPSO achieved the lowest NPC of $23,430 and LCOE of $0.0154/kWh, leveraging the same optimal configuration of 900 kW PV and 660 kW wind turbines.Advanced algorithms outperformed HOMER Pro in terms of robustness, convergence speed, and adaptability to variability.

This innovative, data-driven approach highlights the potential of advanced optimization techniques in designing cost-efficient, sustainable hydrogen refueling systems.The study concludes that Duqm is economicallyviable for hydrogen production using a hybrid renewable energy system, supporting the feasibility of establishing a hydrogen refueling station in the city.

CRediT authorship contribution statement

Paul C.Okonkwo: Writing - original draft, Investigation,Formal analysis,Conceptualization.Samuel Chukwujindu Nwokolo: Visualization, Software, Data curation.El Manaa Barhoumi:Writing-review&editing,Supervision,Formal analysis.Ibrahim B.Mansir: Writing - review &editing, Software, Resources, Funding acquisition.Usman Habu Taura: Writing - review & editing, Visualization,Software, Methodology, Formal analysis.Barun Kumar Das: Writing - review & editing, Visualization, Investigation, Formal analysis.Ahmed Bahgat Radwan: Visualization,Validation,Methodology,Datacuration,Investigation.Wilfred Emori: Writing - review & editing,Supervision, Resources, Investigation.Ephraim Bonah Agyekum: Writing - review & editing, Validation, Software, Resources, Methodology, Data curation.Khalid Al Kaaf: Writing - review & editing, Visualization,Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.