Introduction

The environmental and economic advantages of transitioning to hybrid systems-especially PV/grid configurations-are increasingly acknowledged. Hybrid systems can reduce carbon footprints, enhance energy security, and stabilize grid performance. Grid-connected consumers also benefit financially through surplus electricity sales to the utility, thereby optimizing both load management and renewable energy penetration1. This shift aligns with global trends emphasizing decentralized and distributed energy generation as part of smart grid evolution.

Saudi Arabia is the leading provider of electricity in the Middle East and Northern Africa (MENA) region. In 2018 alone, the country generated 76.9 gigawatts (GW) of electricity, with a peak load demand reaching 61.7 GW that same year2. Notably, over 90% of electricity in Saudi Arabia is generated from fossil fuels, particularly diesel and natural gas1. Diesel remains the preferred energy source due to its low cost, approximately USD 0.096 per liter, placing it among the cheapest globally1. However, this reliance on fossil fuels has raised significant concerns over sustainability, carbon emissions, and economic vulnerability, particularly in a global energy landscape moving towards decarbonization. Despite the Kingdom’s vast solar resources, renewable energy contributes only a minor share to the total energy mix. Saudi Arabia receives approximately 2,200 kWh/m² of solar radiation annually, making it one of the most solar-rich regions in the world1. This solar potential is sufficient to meet domestic energy demands and reduce dependence on diesel generation, yet the full-scale deployment of solar photovoltaic (PV) systems remains suboptimal. Factors such as outdated energy policies, limited grid integration mechanisms, and insufficient investment in renewable technologies have hindered rapid solar adoption. Moreover, the cost disparity between fossil-based and renewable energy further exacerbates this lag. While solar panel prices have declined significantly worldwide, the absence of comprehensive policy incentives and feed-in tariffs in Saudi Arabia results in higher relative costs for solar electricity compared to diesel-based generation3. This is despite evidence from neighboring regions, such as Palestine, where studies have shown that grid-connected PV systems are not only environmentally cleaner but also economically advantageous in the long term1.

Effective PV integration, however, hinges on optimized system design—particularly in inverter sizing, PV array configuration, and load matching. In grid-connected setups, inverters are typically undersized compared to PV arrays due to economic and operational considerations1. These configurations must be carefully tailored to local climatic conditions, irradiance patterns, and load demand profiles to maximize energy yield and system longevity. Given Saudi Arabia’s geographic advantage and year-round clear skies, the country records a daily average horizontal solar irradiance of 6.2 kWh/m², presenting ideal conditions for high-performing solar energy systems2. According to King Abdullah City for Atomic and Renewable Energy (KACARE) and international studies, the cumulative installed PV capacity in Saudi Arabia was estimated at 480 GW by 2018—a dramatic increase from just 40.3 GW in 20102. This upward trajectory is expected to accelerate, particularly under the framework of Vision 2030, which aims to diversify the Kingdom’s energy portfolio. Forecasts suggest that by 2030, solar PV generation could reach 3,278 TWh, a sixfold increase from 2018 figures2. Within this national strategy, PV-grid hybrid systems are viewed as critical enablers of energy transition. They offer the dual advantage of reducing emissions and enhancing system resilience. Real-time pricing enabled through supervisory control and predictive models ensures efficient load balancing, better inverter maintenance schedules, and reduced system downtime1. Moreover, these systems support distributed generation paradigms and serve as a foundation for future smart microgrids—particularly in high-load infrastructures such as airports, universities, and railway stations.

Despite the Kingdom’s immense solar potential and clear environmental imperatives, the uptake of renewable energy remains modest. However, the evolving policy landscape, coupled with technical advancements in hybrid systems and modeling tools such as HOMER, positions Saudi Arabia on the brink of a significant energy transformation.

The main objective of this paper is to investigate the optimal system configuration—PV/grid, PV/battery/grid, battery/grid, or grid-only—that can efficiently meet the high demand of the Makkah railway station in Saudi Arabia while minimizing both cost and environmental impact. Net Present Cost, Levelized Cost of Energy, and CO₂ emissions are the key factors analyzed using HOMER software.

Literature review

Renewable-based microgrids featuring distributed photovoltaic (PV) systems have revolutionized traditional electricity generation paradigms. Unlike centralized generation that relies on distant fossil-fueled power plants, distributed PV systems are decentralized and located close to the point of consumption, allowing end-users to generate electricity directly from solar resources3. This proximity significantly enhances energy efficiency, reduces transmission losses, and increases energy resilience, particularly in remote and high-demand areas4. Distributed PV systems (DPVS) harness the sun’s inexhaustible energy and convert it into usable electricity through rooftop or localized installations, which can function either off-grid or in grid-tied hybrid configurations. These systems are increasingly recognized as essential components of sustainable energy transitions worldwide due to their environmental benefits and modular scalability. As highlighted by Zhang et al.3, consumer perception of household PV systems reveals a growing willingness to invest in distributed generation, especially when economic incentives and supportive policies are present.

Environmental and operational advantages of distributed PV

One of the most profound advantages of DPVS is their contribution to environmental sustainability. Solar energy is emission-free at the point of use, thereby reducing greenhouse gas emissions, improving air quality, and mitigating climate change impacts5,6. According to Soomar et al.6, optimized deployment of PV systems can play a significant role in achieving national and international climate goals, particularly when integrated into hybrid configurations that increase grid reliability. Another key benefit is the reduction of transmission and distribution (T&D) losses. Traditional power plants transmit electricity over long distances, which results in energy losses and inefficiencies, especially in expanding urban environments or sparsely populated rural areas7. Distributed PV systems eliminate much of this waste by generating electricity closer to demand centers, a solution particularly relevant for countries like Saudi Arabia where infrastructure gaps and high solar irradiance intersect. Świerzewski and Kalina8 add that decentralized energy systems, such as biomass or PV-based microgrids, can also enhance grid flexibility and reduce peak demand.

Hybrid system configurations: global experiences

To maximize the potential of DPVS, hybrid systems have become a focal point of recent research. These configurations typically combine PV systems with batteries, diesel generators (DG), or grid support to balance supply variability and optimize reliability. Among these, PV/grid and PV/battery/grid systems are the most commonly studied models. Seedahmed et al.9 conducted a comprehensive feasibility study in Saudi Arabia and concluded that PV/grid systems offer superior performance in terms of both economic and environmental metrics. Compared to standalone or PV/battery configurations, PV/grid hybrids achieved a lower Levelized Cost of Energy (LCOE) and reduced CO₂ emissions. Notably, the integration of diesel generators into hybrid systems, while beneficial for backup supply, often leads to increased emissions and maintenance costs, undercutting sustainability goals.

Globally, diverse hybrid configurations have been explored. For example, in Canada, a PV-WT-Battery-Biomass system was modeled for urban deployment, showcasing strong environmental performance despite biomass sourcing challenges9. In Tanzania, Cho and Valenzuela10 demonstrated the feasibility of a PV-DG-Battery system in off-grid rural settings, emphasizing that optimized component sizing is critical for cost-effectiveness. Malik et al.11 similarly highlighted the potential of PV-wind-battery hybrids in India, where seasonal variation necessitates multi-source energy input to ensure continuity. In West Africa, Nigeria’s application of PV-DG-WT-Battery systems in six small urban residential clusters has further contributed to the growing knowledge base on hybrid systems. These studies underscore that while PV alone is valuable, combining it with batteries, wind turbines, or biomass can significantly improve system resilience and load matching, especially in settings with irregular grid availability.

Regional relevance and the Saudi Arabian context

The case for hybrid PV systems in Saudi Arabia is particularly compelling given its high solar irradiance, large industrial loads, and government-driven decarbonization agenda under Vision 2030. Al-Hanoot et al.12 conducted a techno-economic evaluation of grid-connected PV-battery systems in Saudi industrial buildings, finding that optimal sizing and pricing policies can make renewables cost-competitive with fossil fuels. Their findings reaffirm the conclusion by Seedahmed et al.9 that PV/grid hybrid models offer a financially viable and environmentally preferable alternative to diesel-heavy power mixes. The policy and market environment in Saudi Arabia, however, remains a barrier to rapid renewable deployment. Despite having among the highest solar radiation levels globally, the lack of feed-in tariffs and grid buyback mechanisms limits the economic appeal of grid-connected PV systems for many consumers13. Meckling et al.14 argue that public misconceptions about the costs of clean energy often delay necessary policy shifts and stress the importance of correcting these myths to mobilize public and private investment in renewables. Additionally, regulatory frameworks that support energy storage, grid interaction, and decentralized generation are underdeveloped in many Gulf countries. Colmenar-Santos et al.15 provide a valuable comparative analysis by evaluating legislative and economic frameworks that support energy storage integration in Spain, offering a useful model for policymakers in Saudi Arabia.

Emerging innovations and optimization challenges

Several recent studies have contributed to the understanding of optimization strategies for hybrid PV systems. Shi et al.16 introduced radiative intensity regulation in solar fuel conversion to enhance efficiency, a concept that could improve the design of high-performance PV systems in sun-rich areas like the Middle East. Meanwhile, Adu-Kankam17 explored the role of collaborative renewable energy communities, proposing a framework where localized energy generation can be shared within community microgrids, thus maximizing collective efficiency. Le et al.13 provided a critical assessment of Vietnam’s feed-in tariff policies and their effect on PV uptake, drawing parallels to the Saudi context where such incentives remain limited. Their findings suggest that strategic economic incentives can accelerate solar penetration, especially when coupled with clear regulatory standards for grid access and technical performance. Furthermore, Jia et al.18 emphasized the importance of heat recovery systems in improving the overall energy efficiency of thermal generation plants, a concept that can be adapted to hybrid solar-thermal systems in Saudi Arabia’s industrial zones. Their study suggests that integration between solar and waste heat systems can yield synergistic benefits in combined heat and power (CHP) setups. Another major concern in hybrid system deployment is the balance between technical efficiency and economic viability. As highlighted by Zhang et al.3, consumer adoption is heavily influenced by upfront costs, perceived reliability, and the availability of maintenance support. These socio-economic factors must be integrated into technical optimization models to ensure successful system deployment and long-term sustainability.

Summary of knowledge gaps and research direction

Despite the wealth of global studies on hybrid renewable systems, significant gaps remain. There is a notable lack of real-time performance monitoring in most published research, limiting the ability to validate simulation-based predictions. Additionally, few studies have focused specifically on high-load public infrastructure, such as transport hubs, hospitals, and universities in the MENA region, an area with unique demand profiles and climate constraints. Moreover, while HOMER software has been widely used in feasibility studies, future research should explore dynamic modeling tools that account for predictive load growth, weather anomalies, and policy shifts. Meckling et al.14 argue that integrating economic modeling with political economy insights is essential to driving policy-relevant research outcomes.

To conclude, the literature clearly shows that hybrid PV/grid systems represent a viable path forward for sustainable energy deployment in Saudi Arabia and globally. However, their success depends on the integration of advanced optimization tools, enabling policy frameworks, and user-centered design. With growing environmental urgency and declining renewable technology costs, it is imperative to close the research and policy gaps that inhibit widespread adoption, particularly in large commercial and public infrastructure settings where impact potential is highest.

Methodology

HOMER optimization process

Hybrid Optimization of Multiple Energy Resources (HOMER) software is used for hybrid power system modeling and assessing various system configurations and the techno-economic feasibility of different options to optimize the design for performance and cost-effectiveness19,20. HOMER combines engineering, economics, and several value streams in a single model, then quickly calculates complex problems to establish the most cost-effective solution. With this powerful tool, an energy professional can perform a feasibility analysis of the microgrid system quickly and accurately. As a result, these professionals can minimize the cost, accurately forecast and optimize the return on investment (ROI), increase resilience, reduce carbon emissions, maximize EV energy savings and charging revenues, and effectively explore the combined heat and power20. The system developers can also reduce uncertainty, and make quick, informed decisions.

Input parameters used in HOMER software

Load demand data

The electrical consumption in Makkah varies monthly due to different reasons like religious occasions, holidays, and climate change. The daily load profile of Makkah railway station is shown in Fig. 1. In this case, the peak load demand for the station, acquired from Haramain High-speed Railway (HHR) authority, is 6.324 MW with an average load of 78.316 MWh per day. By feeding the load demand data into the HOMER software, the system developers can accurately and quickly determine the feasibility of the PV/battery/grid and PV/grid systems to determine the most suitable configuration for the Makkah railway station. Table 1 illustrates the output from the HOMER simulation using the load data for Makkah Railway station.

Fig. 1
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The daily load profile of Makkah railway station obtained using HOMER Software (version 3.18.4, https://www.homerenergy.com).

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Table 1 The load Data.
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Solar irradiance

Solar irradiance is the power per unit area received from the sun in electromagnetic radiation form. It is measured in watts per square meter21. According to King Abdullah City for Atomic and Renewable Energy, the average solar radiation for location, latitude 21.331 degrees North and longitude 39.949 degrees East, is 5.98 kWh/m2 each day (Appendix 1). From the illustration in Fig. 2, it is clear the area’s maximum irradiance occurs between March and September. It peaks in June, while the lowest rate occurs in December.

Fig. 2
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Global solar radiation in Makkah obtained using HOMER Software (version 3.18.4, https://www.homerenergy.com).

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Justification for using HOMER software

The selection of HOMER software for this study is grounded in its widespread credibility, versatility, and proven effectiveness in modeling hybrid renewable energy systems, particularly under uncertain and variable economic and environmental conditions. HOMER (Hybrid Optimization of Multiple Energy Resources), developed by the U.S. National Renewable Energy Laboratory (NREL), is a leading simulation tool designed to evaluate the technical and economic feasibility of hybrid energy systems comprising solar PV, batteries, diesel generators, and grid connections22. HOMER’s key strength lies in its ability to perform comprehensive techno-economic analysis through multi-variable optimization. It enables system designers to model thousands of different system configurations across varying input scenarios such as load profiles, solar radiation, fuel prices, and capital costs23. This provides a ranked list of feasible solutions based on key performance metrics like Net Present Cost (NPC), Levelized Cost of Energy (LCOE), and renewable energy fraction. This makes HOMER particularly suitable for studies seeking cost-optimal system configurations under dynamic conditions, such as those prevalent in high-load environments like the Makkah railway station.

Compared to generic optimization tools or manually constructed financial models, HOMER is tailored specifically for hybrid energy systems and includes pre-built modules for solar irradiance data, component lifetimes, operating reserve margins, and dispatch strategies. It incorporates sensitivity analysis and scenario modeling, which are essential for evaluating system robustness under fluctuating grid prices, inflation rates, and changes in solar irradiance, factors that are especially relevant in Saudi Arabia’s evolving energy market23. Several recent peer-reviewed studies have successfully used HOMER to assess hybrid system feasibility in similar contexts, reinforcing its relevance to this work. For instance, Seedahmed et al.9 and Al-Hanoot et al.12 employed HOMER to model grid-connected PV systems in Saudi Arabia, validating its capability to inform investment decisions and policy recommendations in the region. Additionally, HOMER supports integration with NASA-SSE or KACARE datasets for irradiance and temperature inputs, ensuring that simulations reflect realistic local conditions. Given the complexity of the PV/grid hybrid configuration under consideration (including time-of-use pricing, seasonal load variation, and operational constraints) HOMER offers a reliable, transparent, and repeatable framework for simulation and decision-making22. Its adoption in this study ensures methodological rigor and enhances the comparability of results with global best practices in renewable energy planning.

Comparison of HOMER software with other related software

Compared to other simulation tools like RETScreen, MATLAB-Simulink, or TRNSYS, HOMER is specifically designed for hybrid renewable energy systems and offers greater ease of use for techno-economic analysis. Although RETScreen excels in pre-feasibility assessments and energy savings calculations, it lacks the multi-scenario optimization and dispatch modeling found in HOMER23. MATLAB-Simulink provides detailed dynamic system modeling and control simulations but requires advanced programming skills and is more suitable for component-level or real-time simulations rather than system-wide feasibility22. TRNSYS focuses heavily on transient thermal and building energy modeling, making it ideal for HVAC or solar thermal studies but less efficient for PV-grid-battery systems. HOMER, on the other hand, combines load analysis, economic evaluation, and energy balance in a single user-friendly platform, offering robust sensitivity and optimization features that make it especially effective for assessing distributed PV hybrid systems under variable conditions12,22,23. Its ability to process thousands of system configurations and provide ranked solutions based on cost and performance makes it the most appropriate tool for this study.

Design specifications

The proposed design specification includes a converter, load, battery storage, and solar PV connected to a utility grid, as illustrated in Fig. 3. Electric load receives energy from the grid and the solar PV in an intelligent switch between using solar power, battery storage, and grid power.

Fig. 3
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Design Specifications.

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PV arrays

The energy generated by PV depends on various factors including the amount of electromagnetic radiation received from the sun (solar irradiance) and the load demand. With the illustrated design, Makkah railway station will generate clean, renewable energy using the PV panels during daytime which is from 6 am to 6 pm. The surplus electricity from the PV system is fed to the utility (grid) when the buying rate exceeds the selling rate to reduce unused energy and attain the reasonable returns1,24. This ensures the electric load receives uninterrupted power from the hybrid system to remain functional both on-grid and off-grid. The estimated cost of a PV in the Saudi market is 800 $/kW, with 700 $/kW replacement and 10 $/year O&M costs, as in Table 2.

Table 2 PV Data.
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Converter

The inverter converts the DC power from the PV system to AC to supply the load. The converter is also used as a rectifier to allow battery charging during the night. Center converters are very important due to their high efficiency and easy installation1. In this case, the estimated cost of a converter in the Saudi market is 350 $/kW, with 300 $/kW replacement and 10 $/year O&M costs, as shown in Table 3.

Table 3 Converter Data.
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Battery storage

The battery is needed to provide the necessary energy when the PV output is zero. It is also needed to support the billing management system. While using the battery can help during grid outages, the associated costs are relatively high. Table 4 illustrates the battery storage data.

Table 4 Storage system Data.
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Utility system

The utility grid system satisfies the load power needs when there is little to no power from the PV battery system. When there is no solar irradiance at night, the utility grid energizes the load demand and charges the battery. The Saudi energy market’s buying prices is 30 Halalah/kWh (0.080 $/kWh) for consumption more than 6000 kWh/Month. In this study, the grid sell back price is assumed to be 0.013 $/kWh as Saudi has not yet implemented one25. Table 5 illustrates the utility rates data.

Table 5 Utility rates data.
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Key equations and performance metrics

PV array sizing

To support system sizing and economic evaluation, the following core formulas were used in conjunction with HOMER simulation outputs:

$$P_{PV} = \frac{E_{load}}{H \times PR}$$

Where:

  • Ppv = required PV capacity (kW).

  • Eload​ = daily energy demand (kWh/day).

  • H = average daily solar radiation (kWh/m²/day).

  • PR = performance ratio (typically 0.75–0.85 for grid-connected PV).

Net present cost (NPC)

NPC is the total cost of installing and operating the system over its lifetime, accounting for capital, O&M, fuel, replacement, and salvage costs. Its formula is:

$${\rm NPC} = \sum_{t=1}^T \frac{C_t}{(1+r)^t}$$
(1)

Where:

  • Ct = net annual cost in year t.

  • r = discount rate.

  • T = project lifetime (years).

Alternatively, if the annualized cost is known:

$${\rm NPC} = \frac{C_{\rm annual}}{{\rm CRF}(r,T)}$$
(2)

Note

This second form is often used in software like HOMER to convert between NPC and annualized costs.

Where:

  • Cannual = Total annualized cost.

  • CRF (r, T) = Capital Recovery Factor.

    $${\rm CRF} (r,T) = \frac{r(1 + r)^T}{(1 + r)^T - 1}$$

Levelized cost of energy (LCOE)

LCOE represents the average cost per kilowatt-hour (kWh) of electricity produced and is used to compare the economic efficiency of different energy systems. Its formula is:

$$LCOE = \frac{\sum_{t=0}^T \frac{C_t}{(1+r)^t}}{\sum_{t=0}^T\frac{E_t}{(1+r)^t}}$$

Where:

  • Ct = total cost in year t.

  • Et​ = total energy produced in year t.

  • r = discount rate.

  • T = system lifetime (years).

Simulation results and discussions

Comparison of system configurations

System 1: PV/Grid

The HOMER simulation results as detailed in Table 6 present four different system configurations, focusing on key economic and technical metrics. In System 1 (PV/grid) with 4,763 kW of PV, stands out as the best option because of its attractive financial and environmental sustainability. This configuration demonstrated the lowest Net Present Cost (NPC) at 27.9 million USD, the lowest Levelized Cost of Energy (LCOE) at 0.0755 USD/kWh, and a 26.7% renewable fraction. Its Internal Rate of Return (IRR) stood at 9.3%, confirming its strong financial sustainability. This makes it both a cost-effective and environmentally favorable choice. The renewable fraction of 26.7% represents a significant contribution from solar energy, helping reduce dependence on the grid while maintaining manageable costs. Therefore, System 1 emerged as the optimal choice due to its balance between capital cost, reliability, and environmental benefit. It relies on solar PV during daylight hours and the utility grid during night hours or when solar generation is insufficient, ensuring 24/7 power availability without the added cost of battery storage.

Table 6 Results of various system configurations.
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System 2: PV/Battery/Grid

System 2 (PV/Battery/Grid) with 4,846 kW of PV emerges as the second-best option due to a higher Capital Expenditure (CAPEX) and a slightly longer payback period. However, the difference is marginal, and it can still be considered a viable option, especially if there is a goal to slightly increase the renewable fraction. However, systems 3 (Grid only) and system 4 (Battery/Grid) are the least attractive because they are financially unsustainable due to higher LCOE and operating costs, with no renewable contribution. Besides, they offer no return on investment and contribute nothing to Saudi’s sustainability goals. In the long run, they are the most expensive due to continuous grid dependency and rising electricity prices.

The optimal configuration from both an economic and environmental perspective shows that the PV/grid hybrid system with a Net Present Cost (NPC) of 27.9 million USD and a Levelized Cost of Energy (LCOE) of 0.0755 USD/kWh, and CO2 emissions of 13,283 tons is the most suitable design system that suits Makkah railway station. This configuration achieves a notable 26.72% reduction in CO2 emissions compared to the base system (grid only). The PV/grid hybrid model is more suitable from the environmental sustainability perspective because of its minimal carbon footprint characterized by low CO2 emissions. Economically, the PV/grid hybrid system offers long-term economic benefits because solar PVs have a longer lifetime of about 25 years, fair replacement costs, and relatively low maintenance costs. Therefore, the inclusion of batteries provides backup power during grid outages and improves energy self-consumption. However, it increases complexity, O&M cost, and replacement frequency as batteries typically have a 10-year lifespan.

Systems 3 and 4: grid only and battery/grid

The configurations of systems 3 and 4 resulted in the highest NPCs (29.6 million USD) and LCOEs (0.080–0.0801 USD/kWh). Furthermore, they had 0% renewable contribution, making them the least sustainable and cost-effective options. Depending solely on grid power subjects the system to fuel price volatility and fails to leverage the region’s abundant solar resources. These models were therefore excluded from optimal scenarios.

Seasonal performance and load matching

As shown in Table 7, the proposed PV/grid hybrid system is projected to generate approximately 29.71 GWh of electricity annually. This substantial production is primarily dedicated to meeting the station’s energy demands. In fact, 96% of the produced energy is utilized directly to supply the station’s operations, and 26.7% of the total energy produced is covered by renewable solar power. The renewable energy share peaked during the summer months when solar irradiance was highest, and decreased during winter (November–February), leading to greater grid reliance.

Table 7 Electrical energy statistics for the winning configuration.
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Figure 4 reveals a steady pattern in the contribution of grid power throughout the year, even in months where PV production is at its peak. This consistent dependence on grid energy suggests that while the PV system is a valuable contributor to the station’s overall energy mix, it is not entirely sufficient to meet the full demand at any point during the year. This underscores the role of the hybrid system in ensuring uninterrupted power supply, balancing the intermittency of solar energy with the stability of grid electricity.

Fig. 4
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Annual energy production of PV/grid hybrid system obtained using HOMER Software (version 3.18.4, https://www.homerenergy.com).

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The railway station’s daily load profile shows peak consumption during morning and evening rush hours, consistent with public transit demand cycles. The PV system aligns well with the mid-day peak load, reducing grid dependency during expensive peak tariff periods. Figure 1 confirms a peak load of 6.324 MW with an average demand of 3.263 MW. This continuous base load makes the case for hybrid generation highly compelling.

One notable aspect is the significant increase in energy production during the summer months, particularly from May to August. These months exhibit the highest levels of solar irradiance, leading to increased PV generation. However, despite the boost in solar power during this period, the station still draws a portion of its energy from the grid. This indicates that the station’s energy demands are high enough that even during optimal solar conditions, supplementary grid energy remains essential. During the winter months, such as November through February, the reliance on the grid increases as PV output decreases.

Several patterns in the PV system’s behavior can be observed as illustrated in Fig. 5. First, the PV system operates exclusively during daylight hours, which is expected for a solar energy system. The most significant generation occurs between 6 AM and 6 PM, with peak production usually taking place around noon, which is when solar irradiance is at its highest. This is reflected by the dense concentration of yellow and bright orange segments in the middle of the day, indicating strong solar power generation. The PV power output fluctuates seasonally, as shown by the changing intensity of colors along the x-axis (days of the year). During the summer months, there is a notable increase in PV power output due to longer daylight hours and higher levels of solar irradiance. The warmer months, particularly around the middle of the year (days 180 to 240), show the highest levels of power output, as indicated by the frequent appearance of yellow and orange hues on the heat map. This period of higher production coincides with longer days and clearer weather, making summer the most productive season for solar power generation. In contrast, the winter months, particularly at the beginning and end of the year (days 1–90 and 270–365), show less intense PV output. This is evident from the darker colors dominating the map during these periods. Shorter daylight hours and reduced solar irradiance in the winter months lead to lower overall energy generation. However, even during these months, the system still generates a significant amount of energy during the middle of the day, as reflected by the patches of yellow and orange that appear around noon.

Fig. 5
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Daily and hourly performance of the PV system obtained using HOMER Software (version 3.18.4, https://www.homerenergy.com).

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Figure 6 represents the projected cash flow over 25 years for the Makkah Railway Station project. The cash flow is categorized into different cost components, including capital expenditure, operating costs, replacement costs, and salvage value. The y-axis shows the financial values, ranging from negative (costs) to positive (returns), while the x-axis represents each year in the 25-year project timeline. At the start of the project, we see a significant negative cash flow, represented by the deep green bar in year 0. This corresponds to the capital costs required to initiate the project, including the purchase and installation of the PV system and grid connection infrastructure. Capital expenditures typically dominate the early stages of renewable energy projects where the initial investment is substantial.

Fig. 6
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The projected cash flow over 25 years of the winning configuration obtained using HOMER Software (version 3.18.4, https://www.homerenergy.com).

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Financial, cash flow analysis and environmental impact

As highlighted in Fig. 6, the project’s cash flow over its 25-year lifetime was as follows:

  • Year 0: High capital expenditure (CAPEX) related to PV system installation and grid interconnection.

  • Year 12: Spike in replacement costs, primarily due to inverter and component lifespan expiration.

  • Year 25: A modest salvage value is returned, representing the residual worth of remaining equipment.

Annual operating costs remain relatively stable throughout the project’s lifespan. The payback period is approximately 9 years, after which the system generates net financial returns. Following the initial capital outlay, the operating costs for the system, represented by the yellowish-brown bars, appear annually across the project timeline. These costs remain relatively stable year by year, reflecting the ongoing maintenance, operational management, and other associated costs of running the PV/grid hybrid system. However, during the 12th year, we observe an additional cost, represented by a deeper orange bar. This likely corresponds to a replacement cost for certain system components, such as inverters or batteries, which typically require replacement or significant maintenance after a decade of operation. Toward the end of the project lifecycle, particularly in year 25, we see a positive bar indicating a salvage value, which is the residual value of the system components that can still be utilized or sold after the 25-year period. This salvage return helps to offset the costs incurred over the years, slightly improving the overall financial performance of the project.

In conclusion, the cash flow chart highlights the typical financial dynamics of a large- scale renewable energy project. High initial capital costs are followed by consistent operating expenses, periodic replacement costs, and eventual salvage returns, offering insights into the long-term economic sustainability of the PV/grid hybrid system. The optimal PV/Grid system reduced CO₂ emissions by 26.72%, contributing to Saudi Arabia’s Vision 2030 sustainability targets. The renewable fraction of 26.7% represents the proportion of energy supplied directly by solar PV to the station’s operations. While this fraction might appear modest, it significantly reduces fossil fuel dependency and grid strain during high-demand periods. System 2, although offering a slightly higher renewable share, did not justify the additional financial burden.

Sensitivity analysis

This analysis is typically used to investigate the system performance when dealing with uncertain parameters that may arise due to various reasons such as fluctuating grid prices and PV capital cost. In this section, variations of different parameter (± 15%) will be investigated to assess their effects on the NPC, renewable fraction, and CO2 emissions of the system.

The impact of grid power price variation on NPC and renewable fraction

As indicated in Fig. 7(a), a variation in the price of the grid power is directly proportional to both NPC and renewable fraction. As the grid prices increase, people and businesses turn to solar PV and other renewable sources as cost-effective alter- natives. This increases the demand for renewable energy, subsequently pushing renewable energy prices high. The variation in grid and solar PV energy prices triggers discounted benefits and cost changes1. As the grid power price changes, so does the NPC in the same direction. If the change is a reduction, it occurs until a base price is reached. An increase in the grid prices results in an increase in the renewable fraction and vice versa. As the grid prices increase, grid-connected consumers consider switching to or supplementing their power needs with cost-efficient solar energy1. In-grid consumers integrate solar PV into their systems and feed the surplus solar energy to the grid to reduce their energy costs. This leads to increased solar energy generation and a subsequent rise in the renewable fraction.

Fig. 7
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Sensitivity analysis of various system parameters: (a) grid power price (b) PV capital cost, (c) annual solar radiation, (d) project lifetime.

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The impact of PV capital cost variation on NPC and renewable fraction

Based on Fig. 7(b), the variation in PV capital cost is directly proportional to the net present cost (NPC). An increase in the PV capital cost results in a steady increase in the NPC, while a decrease in the PV capital cost results in a steady decline in the NPC until the floor value is reached. The PV capital cost variation is inversely proportional to the renewable fraction. Renewable fraction is the total fraction of energy delivered to the load from renewable power sources like PV21. An increase in the cost of the solar plants means a higher capital demand for the company. To balance economic performance, the firm may reduce its overall purchase and installation of solar panels, resulting in reduced solar PV energy. This leads to reduced solar energy being generated in the system, thus decreasing the renewable fraction. Initially, when the PV capital cost increases, the renewable fraction first declines steeply until it reaches a constant point. Further, the increase in capital cost variation reduces the renewable fraction but at a relatively slower rate than the first point. This is because the firms have gotten adequate time to plan and increase resource allocation for investing in renewable energy. As firms increase funds for renewable energy, a further increase in the PV capital cost results in a slowly reducing renewable fraction until a constant floor is reached.

The impact of annual solar radiation variation on NPC and renewable fraction

The variation in annual solar radiation is inversely proportional and directly proportional to NPC and renewable fraction, respectively. Saudi has an annual solar radiation of 2200 kWh/m2. An increase in the annual solar radiation leads to an increase in renewable fraction because the solar PV connected to the grid harnesses optimum solar energy, and the surplus is fed into the main grid1. The increased solar energy supply into the utility grid increases the total renewable fraction. Similarly, a reduction in annual solar radiation means a reduced amount of solar energy harnessed. Most in-grid consumers are forced to depend on the grid for adequate power supply because the reduced solar radiation leads to reduced solar power harnessed and fed into the grid26. This results in a reduced renewable fraction in the main grid. As evident from Fig. 7(c), an increase in annual solar radiation first triggers an immediate renewable fraction decline, followed by a steep increase for an extended period before saturation is reached.

The rising solar radiation encourages consumers to invest in solar energy harnessing to minimize cost, reduce carbon emissions, and promote sustainability. The renewable fraction increases as more consumers feed surplus solar energy to the grid1. In the case of reduced annual solar radiation, an inverse process occurs, resulting in a reduced renewable fraction.

An increase in annual solar radiation results to a decline in NPC while a decrease in annual solar radiation results to an increase in the NPC. As annual solar radiation increases and renewable energy supply to the grid increases, many consumers switch to solar energy resource to reduce their energy expenses. Since the renewable energy is cost-effective, the NPC significantly reduces. In contrast, a decline in annual solar radiation leads to a reduced amount of solar energy supplied to the grid by in-grid consumers. As a result, consumers heavily rely on the main grid to satisfy their energy needs. The increased demand for grid power causes the grid prices to peak, eventually resulting to reducing NPC.

The impact of project lifetime variation on NPC and renewable fraction

A variation in project lifetime is directly proportional to NPC. As depicted in Fig. 7(d), an increase in the project lifetime results in a steady exponential increase of NPC from the base value. NPC of an element is the sum of the present values of all costs of installing and operating it over the project lifetime, less the present value of all the revenues it earns over the project lifetime1. In this case, the converter’s capital cost was $350, a replacement was $300, O&M was $10 per year, and a lifetime was 12 years. The storage battery’s capital was $1,000; maintenance was $900, $12 O&M per year, and 10 years project lifetime. Increasing the project lifetime significantly lowers future costs, thus promoting the net price cost and vice versa.

Variation in project lifetime results in irregular variation in renewable fraction. Increasing the project lifetime first reduces the renewable fraction slowly; then, the reduction plunges steeply. After ten years, the replacement and maintenance of the system components increase the effectiveness of solar PV in harnessing solar energy24. This increases the renewable fraction in the hybrid system, justifying the sudden spike in renewable fraction until it reaches about 26.7% of the total energy in the hybrid grid. After another 15 years, the solar panels reach their lifetime and must be replaced. During this period, the amount of solar energy harnessed by the less effective solar panels results in a reduced percentage of renewable fraction in the hybrid system1. The solar panels remain functional, although less efficient. This means they still harness and produce solar energy, attributing to a constant rate of 25.9% until the replacement is done.

Conclusion

This study significantly assessed the technical and economic viability of distributed PV-based hybrid systems for the high-load Makkah Railway Station using the HOMER software. Four system configurations were simulated and analyzed: PV/Grid, PV/Battery/Grid, Grid Only, and Battery/Grid. The results confirmed that the PV/Grid hybrid system offers the most balanced solution in terms of cost-effectiveness, environmental sustainability, and operational reliability. The optimal configuration achieved a Net Present Cost (NPC) of 27.9 million USD, a Levelized Cost of Energy (LCOE) of 0.0755 USD/kWh, and a renewable energy fraction of 26.7%, reducing CO₂ emissions by 26.72%. Compared to other configurations, it also offered the most favorable payback period and Internal Rate of Return (IRR), making it the most suitable model for deployment in large-scale infrastructure with high and consistent energy demands.

Practical implications

The study’s findings provide strong evidence to support Saudi Arabia’s Vision 2030 goals, particularly those related to energy diversification, sustainability, and economic optimization. The study reinforces the importance of prioritizing PV/Grid hybrid systems in high-demand public facilities such as railway stations, airports, hospitals, and industrial zones. The PV/Grid system’s cost-efficiency and environmental benefits also make it a compelling choice for replication in other high-irradiance regions within the MENA region and globally. Additionally, the modular design and scalability of PV systems provide a strategic advantage for policymakers and utility planners aiming to phase in renewable energy without massive upfront infrastructure overhaul. The results further suggest that integrating such systems with real-time pricing mechanisms and grid interaction frameworks could significantly improve energy efficiency at the macro level.

Future research

While this study provides a robust techno-economic analysis based on simulated conditions, several areas remain open for further investigation. Therefore, future studies should include field-based deployment and operational monitoring to validate the simulation outputs under actual load profiles and weather variability. Data logging and system diagnostics would offer valuable insights into long-term performance, component degradation, and system downtime. With ongoing adoption of smart technologies, future studies should also assess whether the integration of artificial intelligence (AI) and machine learning algorithms to predict load fluctuations and adjust energy dispatch in real time would further optimize system performance and cost savings.