Sustainable Hybrid Solar-Biogas Systems for Electric Vehicle Charging in Tropical Regions: A Techno-Economic Assessment

Global energy needs increase as population growth, urbanization, and the electrification of various sectors, including transportation, increase. Dependence on non-renewable fossil fuels has had a variety of negative impacts, such as increased greenhouse gas emissions, environmental damage, and energy price volatility [1-3]. To answer this challenge, the use of renewable energy sources such as solar power and biomass is getting more attention to create a clean and sustainable energy system [4, 5]. Integrating the two energy sources in the system, hybrid energy is considered to increase the reliability of energy supply, with solar panels as the primary source, primarily during the day, and biogas generators as a complement at night or in bad weather conditions. This combination provides greater flexibility and efficiency than using a single technology [6-8].

Various previous studies have explored the system's potential, hybrid PV-biogas, in the context of remote and urban electricity. Table 1 summarizes a simulation approach using the HOMER Pro software, which is widely used to examine the technical and economic aspects of renewable energy combinations, both off-grid and on-grid scenarios. These studies show that Hybcan lowers the Levelized Cost of Energy (LCOE), significantly reduces carbon emissions, and offers financially viable, sustainable energy solutions. However, most previous studies have been limited to the general energy supply aspect and have not been specifically directed to applications in electric vehicle charging stations (EV charging stations). The electric transportation sector is one of the main focuses in global energy decarbonization.

Electric vehicle charging stations (EVCS) have relatively large power requirements and are volatile, depending on the time and intensity of use. For this reason, the energy generation system used must not only be able to meet energy needs sustainably, but also be stable in anticipating load spikes [9, 10]. About Us, hybrid PV-biogas has the potential to answer this challenge because it can take advantage of the advantages of photovoltaic technology and biogas plants synergistically [11-13]. In addition, integrating renewable energy systems in EVCS also encourages the transformation of transportation infrastructure towards zero emissions (net-zero emissions with national and global clean energy policies. However, the practical implementation of this system needs to be studied more deeply through a modeling and simulation approach based on actual location data and real operational conditions [14-16].

This research focused on Malang City as a case study location with high solar energy potential and a relatively abundant source of biomass. Using annual weather data and technical parameters of system components, system performance simulations are carried out for a hybrid PV-biogas system using Vsyst and HOMER [17, 18]. This analysis includes technical evaluation in energy generation performance and performance ratio photovoltaic systems, and economic assessments that include net present cost (NPC) calculations and LCOE [19]. In addition, this study also considers environmental aspects in the form of estimating exhaust emissions and system capacity in supporting electric vehicle charging stations with fast charging technology (quick charging). With this approach, it is hoped that a comprehensive picture of the technical and financial feasibility of the system will be obtained, in the context of a real application [20, 21].

Table 1. Research related to PV-Biogas

Recent studies have investigated the integration of renewable sources for EV charging infrastructure with various configurations and application contexts. Himabindu et al. [24] conducted a techno-economic assessment of PV-powered EV charging stations across four Indian cities with different solar irradiance profiles, emphasizing the importance of regional solar potential and local grid conditions in determining feasibility. Their study also recognized the utility of microgrids in supporting rooftop PV-based EV charging systems, similar to those of Irfan et al. [25] simulated hybrid systems using HOMER Pro for decentralized EV loads demonstrated that combinations such as PV–wind–biomass–battery can reduce emissions and improve reliability in peri-urban and rural zones. In another contribution, Zhang et al. [26] focused on PV-biogas hybrid systems applied to agricultural and transport sectors, presenting the benefits of dual-use systems that produce biofertilizer while supplying energy for EV charging stations.

These studies highlight the potential of hybrid renewable energy systems, but tend to focus on rural applications, generalized mini-grids, or isolated system performance. Few have assessed the performance of hybrid PV-biogas systems tailored to EV charging applications in tropical urban contexts with actual charging station load profiles. Moreover, comparative evaluations between off-grid and on-grid network scenarios under such conditions remain scarce. Therefore, this study addresses those gaps by integrating renewable energy modeling focusing on the dynamic energy needs of EV charging infrastructures under tropical climate conditions.

The novelty of this research lies in integrating the hybrid PV-biogas and electric vehicle charging applications studied simultaneously in two network scenarios: off-grid and on-grid. In addition, the modeling approach is carried out in detail by taking into account the technical data of the components, the typical load of electric vehicles, and the operational fluctuations of the charging station [27, 28]. This differs from previous studies on the generation system, which did not consider specific aspects of EV load or adaptive biogas utilization. Thus, this study makes a new contribution to the development of relevant renewable-based integrated energy system designs to support the adoption of electric vehicles, particularly in tropical regions such as Indonesia. These findings are expected to serve as a reference for policymakers, investors, and energy industry players in developing more efficient and sustainable low-emission transportation infrastructure.

3.1 Performance ratio performance in solar power generation systems

Evaluating the performance of solar power generation systems is an essential indicator in assessing the effectiveness of converting solar energy into electricity. One of the main parameters used is the Performance Ratio (PR), which represents the efficiency of the photovoltaic system against its ideal conditions, considering factors such as temperature, radiation, and system losses. This study conducted a PR analysis to determine the variation in the monthly performance of solar panel systems operated in Malang City during 2024. The simulation results are shown in Figure 9, which shows fluctuations in the performance ratio from January to December. The highest PR value was recorded in January at 0.840, while the lowest was in September at 0.819. These fluctuations indicate the influence of seasonal climatic conditions on the solar system's output, which is the basis for designing system optimization and maintenance strategies to keep annual performance optimal.

The annual average PR of 0.83 is notably higher than many comparable systems in humid tropical settings, which typically range from 0.75 to 0.82 according to recent studies [45, 46]. This suggests that the selected PV modules and system configuration are well-suited for the local climate of Malang, benefiting from stable solar irradiation and efficient component integration. Seasonal variation, while present, remains within manageable limits and indicates consistent year-round performance. This strengthens the case for using similar hybrid systems in other tropical urban contexts.

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Figure 9. Solar panel system performance ratio

3.2 System performance evaluation through HOMER simulation

Simulations using HOMER software were used in this study to evaluate the performance of PV-biogas hybrid systems in two operational scenarios, namely off-grid and on-grid. This analysis aims to determine the contribution of each component to the total annual electrical energy production and the distribution of its consumption at renewable energy-based electric vehicle charging stations. Table 5 and Figure 10 present the results of the energy output simulation for both configurations. In the off-grid scenario, the contribution of biogas generators (CAT-C13 Prime) reached 57.1% of the total production of 1,054,693 kWh/year, while solar panels (LONGi Solar LR6-72) accounted for 42.9%. In contrast, in on-grid systems, the proportion of production from solar panels increased slightly to 45.9%, with biogas generators at 30.7%, and the remaining 23.4% coming from grid purchases, indicating a lower reliance on fossil fuels in this scheme.

In terms of energy consumption, the off-grid system recorded a total usage of 1,094,321 kWh/year, with the primary load (AC primary load) of 76.4% and the remaining 23.6% used for electric vehicle charging (EV charger served), as shown in Table 6. In the on-grid configuration, the distribution of energy consumption is relatively similar to the total consumption of 1,097,661 kWh/year. However, a small surplus of 0.3% is sold back to the grid (grid sales). The efficiency of electric vehicle charging station services is analyzed through operational indicators summarized in Table 7, showing that the system can serve 8,562 charging sessions per year with a total energy of 209.469 kWh. The average energy consumption per session reached 28.6 kWh with a daily frequency of around 20.9 sessions, indicating that the system has sufficient capacity to support the operation of medium-scale electric vehicle charging stations.

From an environmental perspective, this simulation also evaluates the potential impact of annual exhaust emissions generated by hybrid systems during 2024, as detailed in Table 8. Total carbon dioxide (CO₂) emissions reached 443,869 kg/year, the dominant component contributing to greenhouse gases. In addition, other emissions such as carbon monoxide (35.7 kg/year), non-flammable hydrocarbons (1.02 kg/year), particulate matter (33.36 kg/year), sulfur dioxide (1,813 kg/year), and nitrogen oxides (1,384 kg/year) are also of concern in environmental impact evaluation. This data shows the importance of emission reduction strategies and optimization of renewable resource-based energy systems. Thus, the integration of PV-biogas systems addresses energy needs and offers significant emission reduction potential compared to fossil-based systems in full.

In terms of environmental impact, the annual CO₂ emission of 443,869 kg/year is considerably lower than emissions from conventional diesel-based charging stations, which can exceed 750,000 kg/year for similar energy output [47, 48]. Additionally, reductions in other pollutants such as sulfur dioxide and nitrogen oxides further emphasize the ecological advantage of hybrid renewable energy systems. These findings underscore the emissions reduction potential of integrating biogas and PV in EVCS infrastructure, aligning with national and global net-zero emission targets. Such integration contributes meaningfully to decarbonizing the transportation sector.

Table 5. Electrical energy production results system

Table 6. Energy consumption distribution

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(a) Off-Grid

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(b) On-Grid

Figure 10. Annual distribution of electrical energy output

Table 7. Performance recapitulation of level 3 electric vehicle (EV) charging stations

Table 8. Annual estimated exhaust gas emissions in 2024

3.3 System economic analysis

Evaluation of economic aspects is a key component in assessing the feasibility of implementing renewable energy systems, especially for hybrid PV-biogas configurations used in electric vehicle charging stations. This study conducted economic analysis for off-grid and on-grid scenarios, considering several main cost components such as start-up capital, operational costs, reprocurement costs, residual value, and energy supply costs. Details of the full cost estimate are shown in Table 9, which includes various system units such as the CAT-C13 Prime biogas generator, EnerStore 50 Agile Flow battery, Leonics MTP-4117H inverter, LONGi Solar LR6-72 solar panel, and Highway Charger Level 3. The results show that the system's total cost reaches Rp 25.29 billion in the off-grid scenario. In contrast, in the on-grid scenario, the total is lower, namely Rp 10.24 billion, due to the support of the power grid, which reduces dependence on generators.

Further investment feasibility analysis was carried out by calculating financial indicators such as NPC, LCOE, simple payback period, discounted payback period, return on investment (ROI), and internal rate of return (IRR), which are summarized in Table 10. In the off-grid scenario, even though the NPC value is higher, which is IDR 25.29 billion, the system shows quite a promising financial performance with an LCOE of IDR 1,825.07/kWh, a simple payback in 4.13 years, and an IRR of 24.6%. Meanwhile, in the on-grid configuration, although the LCOE is higher by IDR 3,610.73/kWh, the main advantage lies in the faster return on capital, which is only 2.64 years in simple terms and 2.85 years in discount. In addition, the ROI on the on-grid system reaches 29%, and the IRR is even higher, at 36.4%.

The difference in LCOE and payback periods between off-grid and on-grid configurations is primarily driven by system dependency on grid electricity and component usage intensity. The off-grid system, while more capital-intensive due to higher storage and generator capacity, results in a lower LCOE over time due to reduced energy purchase costs. On the other hand, the on-grid setup benefits from immediate cost savings by leveraging existing grid infrastructure but incurs higher LCOE because of energy purchase and peak-time rates. These trade-offs highlight the importance of location-specific planning and investment strategy alignment.

Table 9. Estimated investment in PV-Biogas and EV charging integrated energy systems

Table 10. Total system economic evaluation

This analysis shows that both configurations have viable economic potential, depending on the investment objectives and operational context. Off-grid systems are superior in energy production cost efficiency per kWh, making them suitable for remote locations without power grid access. In contrast, on-grid systems offer faster returns on capital and a lighter cost structure overall, making them a rational choice in urban or semi-urban areas with the electrical infrastructure already in place. Therefore, the best configuration should be tailored to the stakeholders' location conditions, long-term goals, and financial readiness.

To strengthen the credibility and robustness of the economic assessment, a sensitivity analysis was performed by altering several key input parameters that significantly affect system feasibility. These parameters include component costs (with a variation of ±20%), biogas fuel prices (±15%), and electric vehicle (EV) penetration rates (±30%). These values were selected based on plausible fluctuations observed in local market trends and national policy adjustments. This analysis aims to evaluate how changes in these parameters influence the financial performance metrics such as LCOE and payback period for both off-grid and on-grid scenarios.

The simulation results show that the LCOE for the off-grid system ranged from IDR 1,550/kWh in the most favourable conditions to IDR 2,130/kWh in more challenging economic scenarios. Meanwhile, the on-grid configuration exhibited LCOE values between IDR 3,120/kWh and IDR 4,090/kWh. The simple payback period for the off-grid system fluctuated between 3.4 and 5.2 years, while it varied from 2.2 to 3.1 years for the on-grid scenario. These findings suggest that the proposed hybrid system retains economic viability within a reasonable range of uncertainty, reinforcing its potential scalability and applicability across different contexts. Moreover, this sensitivity assessment underlines the importance of adaptive planning and policy support in mitigating investment risks.

3.4 Research limitations

This study has several limitations that need to be considered when interpreting the results and implications of the analysis. First, the meteorological data used is sourced from PVsyst software with Meteonorm reference, which is generalized and does not include daily or seasonal microclimate dynamics locally in Malang City. This can potentially affect the accuracy of solar energy production simulation results. Second, the system simulation was carried out using HOMER software, which, although comprehensive in technical and economic calculations, has limitations in modeling component degradation, energy price variability, and electric vehicle demand dynamics in the long term. In addition, this study assumes that the energy consumption pattern of electric vehicle charging stations is fixed and linear. In contrast, in operational reality, energy demand can fluctuate highly depending on the day, season, and growth of electric vehicle users.

Furthermore, the cost and return on investment analysis are based on macroeconomic projections such as inflation, discounts, and unit prices of energy and system components that are assumed to be constant during the study period. Uncertainty over exchange rate fluctuations, energy policies, and government incentives can significantly alter the system's economic viability in real implementation. In addition, the exhaust emissions analyzed in this study only include direct emissions from biogas generators and do not consider indirect emissions from the production process and distribution of system components, so the overall environmental potential has not been fully described.

Considering these limitations, the results of this study should be positioned as a preliminary study that provides an overview of the technical and economic potential of a hybrid PV-biogas system integrated with electric vehicle charging stations. To obtain more accurate and applicable results, follow-up studies are needed with a field data-based approach, life cycle assessment, and national and regional energy policy integration. A deeper evaluation of grid stability, electric vehicle user behavior, and system response to peak load scenarios will also enhance the validity and relevance of the results of this study in the context of real implementation.