A Techno-Economic Assessment of Solar-Powered EV Charging Infrastructure in Sub-Saharan Africa: A Case Study of Tanzania

A Techno-Economic Assessment of Solar-Powered EV Charging Infrastructure in Sub-Saharan Africa: A Case Study of Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Techno-Economic Assessment of Solar-Powered EV Charging Infrastructure in Sub-Saharan Africa: A Case Study of Tanzania Fred Peter, Juliana Machuve This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8531346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global push for electric vehicle (EV) adoption is reshaping transportation, but its integration into power grids, particularly in developing nations, poses significant challenges and opportunities. In Tanzania, the transport sector's rapid growth strains a national grid characterized by its heavy reliance on hydropower and vulnerability to climate-induced outages. This paper performs a techno-economic assessment of solar-powered EV charging infrastructure, enhanced with Vehicle-to-Grid (V2G) and Battery Swap Station (BSS) models, to bolster mobility and improve grid resilience. The methodology combines simulation using tools like HOMER Pro and Dig SILENT Power Factory, GIS-based mapping, and a detailed economic analysis across multiple scenarios. Key findings suggest that decentralized solar EV hubs offer a significantly more cost-effective and affordable solution for local drivers than the grid-only option, with the BSS model demonstrating the lowest Levelized Cost of Energy (LCOE) at $ 0.095/kWh a 39% reduction compared to the grid-only baseline of $ 0.155/kWh. The BSS model also shows a rapid payback period of just 6.5 years, highlighting its commercial viability. Furthermore, the analysis reveals that while uncoordinated charging can cause significant voltage drops of up to 10% during peak hours, the implementation of smart charging and V2G services can mitigate these effects and provide crucial grid services like peak shaving. The research contributes a vital roadmap for a sustainable e-mobility transition in Tanzania, highlighting the critical role of solar and battery technologies in complementing the country's energy strategy. Electric vehicle techno-economic EV hubs Solar powered Vehicle to Grid Battery Swapping Station Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The global transition towards electric mobility has become a central pillar of climate change mitigation and sustainable energy strategies. Rapid advancements in electric vehicle (EV) technologies, coupled with declining battery costs and supportive policy frameworks, have accelerated EV adoption worldwide, with global sales exceeding 10 million units in 2023 (IEA, 2024). While this transition offers substantial environmental and energy security benefits, it simultaneously introduces critical infrastructure challenges, particularly the need for reliable, scalable, and economically viable EV charging systems that can be effectively integrated into existing power networks. Recent studies emphasize that without coordinated charging strategies and renewable integration, large-scale EV deployment may impose significant stress on power systems, especially in regions with limited grid resilience (Al Wahedi and Bicer, 2022 ; Güven et al., 2025 ). In Sub-Saharan Africa (SSA), the EV transition is unfolding within a markedly different socio-technical context. Although EV penetration remains at an early stage, rapid urbanization and increasing demand for personal and commercial mobility are placing growing pressure on transport and energy infrastructure (World Bank, 2023). Tanzania exemplifies this challenge, where the transport sector is heavily dominated by two- and three-wheelers, which account for more than half of all registered vehicles. These vehicles represent an immediately addressable segment for electrification due to their predictable travel patterns and comparatively lower energy demand (LATRA, 2024; AutoMag.tz, 2025). At the same time, Tanzania’s strong dependence on imported fossil fuels—costing approximately USD 4.08 billion in 2024—underscores the urgent need for locally sourced and renewable-based transport energy solutions (TICGL, 2025). The integration of EV charging infrastructure in Tanzania is, however, constrained by the structural characteristics of the national power grid operated by TANESCO. The grid relies heavily on hydropower, contributing over 45% of installed capacity, making electricity supply highly vulnerable to climate variability and hydrological uncertainty (EWURA-CCC, 2025). Previous research has shown that unplanned EV charging loads can exacerbate peak demand and compromise grid stability in weak or climate-sensitive power systems (Ekren et al., 2021 ; Ihm et al., 2023 ). Consequently, decentralized and renewable-based charging solutions—particularly solar-integrated systems—are increasingly recognized as viable alternatives to mitigate grid stress while enhancing energy access and system reliability (Miao et al., 2020 ; Riayatsyah et al., 2022 ). Techno-economic assessments have emerged as a critical decision-support tool for evaluating such hybrid renewable energy systems. Studies using optimization platforms such as HOMER have demonstrated the technical feasibility and cost-effectiveness of hybrid solar-based systems for EV charging and off-grid or grid-connected applications across diverse geographical contexts (Okedu and Uhunmwangho, 2014 ; Ahammed, 2021 ; Kumar et al., 2024 ). More recent investigations highlight the importance of system configuration, energy storage sizing, and demand management strategies in achieving economically optimal and operationally resilient charging infrastructures (Nallolla and Vijayapriya, 2022 ; Said et al. , 2024). Nevertheless, most existing studies predominantly focus on passenger vehicles in developed or high-income settings, with limited attention given to SSA contexts and motorcycle-dominated transport systems. To address this gap, the present study conducts a comprehensive techno-economic assessment of a decentralized, solar-powered EV charging ecosystem tailored to Tanzania’s unique transport and energy landscape. Specifically, this research evaluates the viability of advanced charging concepts, including Battery Swap Stations (BSS) and Vehicle-to-Grid (V2G) integration, for two- and three-wheelers. Unlike conventional charging-only models, these systems enable EVs to function as distributed energy resources capable of supporting grid stability and load balancing (Mohammed et al., 2023 ; Güven et al., 2025 ). By integrating solar photovoltaics, battery storage, and smart energy management, the study demonstrates how EV infrastructure can simultaneously reduce fossil fuel dependence, enhance grid resilience, and support Tanzania’s national objectives for energy diversification and sustainable development (Tanzania Ministry of Energy, 2024; EWURA, 2025). 2. Literature Review 2.1 Charging Technologies and Grid Integration The rapid adoption of electric vehicles (EVs) has brought charging technologies to the forefront of e-mobility research. These technologies are generally categorized by their power output and corresponding charging speed. Level 1 charging uses a standard 120V AC outlet, providing slow overnight charging. Level 2 utilizes a 240V AC source, offering a much faster charge suitable for homes and workplaces. Level 3, or DC fast charging, bypasses the vehicle's onboard charger to directly charge the battery at a high-power rating, enabling a full charge in under an hour. While DC fast charging is critical for minimizing range anxiety and supporting long-distance travel, its high instantaneous power demand can place significant stress on an aging grid, especially during peak energy consumption periods (Ekren et al., 2021 ; Ihm et al., 2023 ). This challenge has led to the development of intelligent charging strategies. V1G (unidirectional managed charging) allows a grid operator or smart charging platform to control the timing and rate of an EV’s charging session, shifting load to off-peak hours to reduce grid congestion (Al Wahedi and Bicer, 2022 ). A more advanced concept, V2G (Vehicle-to-Grid), is revolutionary in its bidirectional energy flow capability. This technology enables EVs to not only draw power from the grid but also to discharge power back into it. As illustrated in Fig. 1 , this bidirectional flow allows EVs to provide crucial ancillary services, such as frequency regulation and peak shaving, which are particularly valuable in grids with high penetration of intermittent renewable energy sources (Mohammed et al., 2023 ; Güven et al., 2025 ). The ability of V2G to help stabilize a fragile grid makes it a compelling solution for developing nations. Similarly, Battery Swap Stations (BSS) offer a compelling alternative to traditional charging, allowing drivers to quickly exchange a depleted battery for a fully charged one. This model is particularly suited for high-utilization vehicle fleets like two- and three-wheelers, where minimizing downtime is critical, and has been shown to be techno-economically viable when integrated with hybrid renewable energy systems (Kumar et al., 2024 ; Güven et al., 2025 ). 2.2 Solar Energy-Enabled Charging Systems The global push towards decarbonization has positioned the integration of solar energy with EV charging infrastructure as a key strategy for reducing reliance on fossil fuels and lowering electricity costs. These systems typically consist of three primary components: a solar photovoltaic (PV) array to generate power, a Battery Energy Storage System (BESS) to store excess solar energy for use during non-sunlight hours, and a grid connection for backup power and flexibility. A block diagram of this integrated system is shown in Fig. 2 . The techno-economic feasibility of such systems is heavily dependent on local factors such as solar irradiance, electricity tariffs, and land costs. Several studies have demonstrated that declining solar PV costs and advances in battery technologies have significantly enhanced the economic competitiveness of solar-powered EV charging systems (Miao et al., 2020 ; Nallolla and Vijayapriya, 2022 ). In comparable developing-country and island contexts, hybrid solar-based systems optimized using HOMER have been shown to reduce lifecycle costs and improve system reliability (Kalamaras et al., 2019 ; Said et al., 2024). 2.3 Frameworks and Models Beyond the technical components, the successful deployment of a nationwide e-mobility ecosystem requires comprehensive frameworks that guide implementation and policy. Regulatory Frameworks: Governments and regulatory bodies play a critical role in shaping the EV market. Key regulatory mechanisms include: Time-of-Use (ToU) Tariffs: These tariffs incentivize EV owners to charge during off-peak hours when electricity is cheaper, helping to manage grid demand and reduce the need for costly grid upgrades (Al Wahedi and Bicer, 2022 ). Interconnection Standards: Establishing clear technical standards for connecting charging stations and EVs to the grid is essential for ensuring safety, reliability, and interoperability (Ihm et al., 2023 ). Incentive Programs: Policies such as tax credits, grants for charging infrastructure, and reduced import duties on EVs can accelerate adoption and lower the financial barrier for consumers and businesses (Riayatsyah et al., 2022 ). Implementation Models: Several operational and business models have emerged to deploy EV charging infrastructure. These include: Public-Private Partnerships (PPPs): Governments can collaborate with private companies to share the financial and operational risks of building large-scale charging networks. This model is particularly effective for bridging funding gaps and leveraging private sector expertise (Nallolla and Vijayapriya, 2022 ). Service-Oriented Models: Business models such as "Charging-as-a-Service" or the BSS model monetize the charging service itself rather than just the electricity. As discussed in Section 2.1 , the BSS model is highly effective for high-utilization fleets, as it minimizes vehicle downtime and can be integrated with decentralized energy systems, providing a robust solution for areas with unreliable grid access (Kumar et al., 2024 ; Güven et al., 2025 ). 2.4 Research Gaps Despite extensive research on EV–grid integration and hybrid renewable energy systems, a significant research gap remains concerning developing nations. Existing studies are largely concentrated in developed or high-income regions and primarily focus on passenger vehicles and conventional charging paradigms (Ekren et al., 2021 ; Ihm et al., 2023 ). The specific context of Sub-Saharan Africa—characterized by grid vulnerabilities, climate-sensitive power generation, and the dominance of two- and three-wheeler transport—remains insufficiently addressed in the literature. Furthermore, limited attention has been given to advanced concepts such as V2G and battery swapping as grid-supporting mechanisms within decentralized solar-powered charging ecosystems (Mohammed et al., 2023 ; Güven et al., 2025 ).This study directly addresses this gap by providing a comprehensive, context-specific techno-economic analysis that integrates both technical performance and economic feasibility for solar-enabled EV charging systems tailored to developing-country conditions. 3. Methodology This study employs a rigorous, multi-faceted methodology to conduct a techno-economic assessment of solar-powered EV charging infrastructure in Tanzania. The approach integrates empirical data with advanced simulation tools and scenario-based analysis to provide a comprehensive and reproducible evaluation. 3.1 Study Area and Data Collection The case study focuses on Dar es Salaam, Tanzania’s largest metropolitan area and primary economic hub, selected due to its high population density, rapid urban expansion, and concentrated traffic flow dominated by two- and three-wheelers. These characteristics make the city particularly suitable for evaluating decentralized electric vehicle (EV) charging solutions targeting high-utilization fleets. In addition to the urban core, a representative peri-urban location was included to assess the scalability, robustness, and financial viability of the proposed charging configurations under lower load density and weaker grid conditions. This dual spatial focus is consistent with prior hybrid energy system studies conducted in rapidly urbanizing and infrastructure-constrained regions (Kalamaras et al., 2019 ; Said et al., 2024). To ensure robustness and consistency with established techno-economic assessment practices, the study employed a multi-source dataset structured in line with HOMER-based modeling approaches reported in the literature. Solar resource data were represented using long-term typical meteorological conditions commonly adopted in hybrid renewable energy feasibility studies. Such datasets provide hourly profiles of global horizontal irradiance and ambient temperature, which are critical inputs for accurately modeling photovoltaic (PV) system performance and storage behavior (Okedu and Uhunmwangho, 2014 ; Ahammed, 2021 ). The use of typical-year meteorological data is widely accepted for evaluating system performance under representative climatic conditions rather than short-term variability (Riayatsyah et al., 2022 ). Electric load demand was modeled using representative commercial load profiles adapted to reflect the operational characteristics of EV charging and battery swapping stations. This approach aligns with prior studies where conventional demand profiles were adjusted to capture the temporal and power requirements of EV charging infrastructure (Ekren et al., 2021 ; Ihm et al., 2023 ). Particular attention was given to peak demand periods and load variability, which are critical determinants of storage sizing and system economics in hybrid renewable configurations (Nallolla and Vijayapriya, 2022 ). Transport demand for the charging and Battery Swap Station (BSS) scenarios was synthesized based on documented operational patterns of high-utilization EV fleets. Battery swapping has been shown to be especially effective for two- and three-wheeler applications, where frequent use and minimal downtime are essential for economic viability (Kumar et al., 2024 ; Güven et al., 2025 ). Parameters such as daily energy demand, charging frequency, and temporal distribution were therefore incorporated to generate a realistic and granular load profile suitable for system simulation and optimization. This synthesis-based approach is consistent with prior techno-economic studies where empirical fleet data are limited but operational behavior is well understood (Al Wahedi and Bicer, 2022 ; Mohammed et al., 2023 ). 3.2 System Modeling and Simulation Tools The study utilized two industry-standard simulation tools to perform the techno-economic and power systems analyses. The selection of these tools was based on their proven reliability, accuracy, and suitability for complex energy systems modeling. HOMER Pro This software was employed for the techno-economic optimization of the hybrid power system. It is specifically designed to model and optimize decentralized energy systems, making it an ideal tool for assessing the viability of solar PV and battery storage-integrated EV charging hubs. The HOMER model included a solar PV array, a battery energy storage system (BESS), an EV charging load profile, and a grid connection for backup power. The software determined the optimal size of each component to minimize the Levelized Cost of Energy (LCOE) and the Net Present Cost (NPC) over a defined project lifetime, considering all capital and operational expenditures. DIgSILENT Power Factory This tool was used to conduct a detailed load flow and stability analysis of the local power distribution network. DIgSILENT's powerful simulation capabilities allowed for a precise assessment of the impact of EV charging and V2G services on the grid. Specifically, it was used to analyze voltage stability, frequency regulation, and the potential for EVs to provide peak shaving services to the grid during periods of high demand, thereby providing a quantitative measure of their ability to act as distributed energy resources. 3.3 Key Assumptions To ensure the reproducibility of the study, the following key technical and economic assumptions were made for the HOMER Pro model: Project Lifetime A 20-year project horizon was used for the financial analysis, a standard practice for renewable energy infrastructure projects. Discount Rate A real discount rate of 8% was applied to all financial calculations, reflecting the economic conditions and project risk in the region. Component Costs Component costs were based on current market trends and regional data (IRENA, 2024; Empower New Energy, 2024), including Solar PV modules: $ 450− $ 600 per kWp (kilowatt-peak), including mounting and installation. Lithium-ion batteries: $ 250− $ 350 per kWh. EV Chargers: $ 500− $ 1,500 for Level 2 AC chargers and $ 15,000− $ 30,000 for DC fast chargers. Grid Electricity Tariff The national grid tariff for commercial use, approximately $ 0.094/kWh, was sourced from TANESCO’s public records (GlobalPetrolPrices.com, 2024). Battery Degradation Battery degradation was modeled at an annual rate of 2 − 3%, with a replacement cost considered at the end of the battery’s useful life. 3.4 Scenario Definition and Analysis The assessment was based on four distinct scenarios, each representing a plausible pathway for the deployment of EV charging infrastructure in Tanzania. These scenarios were chosen to cover a range of technological and business models and to provide a comprehensive comparison. Grid-only Charging This baseline scenario represents the conventional approach, where charging stations are powered solely by the national grid. It serves as a benchmark for comparing the cost, emissions, and reliability of the renewable energy-based systems. This is particularly relevant in a country where grid reliability can be a challenge. Solar-powered Charging (Off-grid) This scenario models a fully off-grid system powered by solar PV and a BESS. It is particularly relevant for remote or underserved areas with limited or no grid access, providing a sustainable solution for the expansion of e-mobility into peri-urban and rural areas. Hybrid Solar + Grid + V2G This is the core scenario of the study. It represents a grid-connected charging hub that utilizes a solar PV array, a BESS, and V2G-enabled EVs to provide ancillary grid services. This model is critical for demonstrating how EVs can support grid stability and reduce reliance on fossil fuel-based generation during peak hours, which is a significant challenge for the national grid. Battery Swap Station (BSS) This scenario focuses specifically on a BSS model for two- and three-wheelers. The BSS is powered by a hybrid solar-grid system, and the analysis assesses its financial viability based on the high turnover rate of battery swaps. This model addresses a unique and vital segment of the Tanzanian transport sector and offers a direct solution to the long charging times that are a major barrier to the adoption of e-boda-bodas and e-tuk-tuks (AutoMag.tz, 2025; TYCORUN, 2025). 4. Results This section presents the factual findings from the HOMER Pro, DIgSILENT PowerFactory, and GIS analyses, organized to follow the logical flow of the research questions. 4.1 Techno-Economic Feasibility The techno-economic analysis, performed using HOMER Pro, demonstrates a clear financial advantage for scenarios that incorporate solar PV and battery storage. The simulations indicate that the Levelized Cost of Energy (LCOE) is significantly lower for solar-hybrid systems compared to the grid-only baseline. Specifically, scenarios incorporating solar PV (Scenarios B, C, and D) exhibit lower LCOE than the grid-only option (Scenario A), driven by Tanzania’s high solar potential and the regional LCOE of solar generation in East Africa, estimated at $ 0.07– $ 0.16/kWh (Miao et al., 2020 ; Said et al., 2024). The Battery Swap Station (BSS) model (Scenario D) consistently delivers the most attractive economic metrics, with a projected LCOE of 0.095 USD/kWh and a payback period of 6.5 years. Its superior performance is attributable to high utilization rates and optimized load profiles that ensure efficient use of both solar and battery assets. Similarly, the Hybrid + V2G system (Scenario C) achieves a lower LCOE of 0.105 USD/kWh, highlighting the value of integrating smart charging and bidirectional energy flows into the grid (Güven et al., 2025 ; Al Wahedi and Bicer, 2022 ). Figure 1 presents a comparative overview of LCOE and Net Present Cost (NPC) across all four scenarios over the 20-year project lifetime. The figure illustrates that solar-integrated systems (B, C, D) consistently outperform the grid-only baseline in both cost and long-term financial viability. Table 1 provides a detailed breakdown of key financial metrics, including initial capital cost, annual operating cost, LCOE, NPC, and payback period for each scenario. This tabulated summary supports a quantitative comparison, emphasizing the economic feasibility of decentralized and hybrid charging infrastructures in Tanzania. Table 1 Summary of techno-economic metrics for each charging scenario. Scenarios Initial Capital Cost (USD) Operating Cost (USD/yr) LCOE (USD/kWh) NPC (USD) Payback Period (Years) Grid-only (A) 20,000 8,000 0.155 110,000 N/A Solar-only (B) 120,000 500 0.110 165,000 12 Hybrid + V2G (C ) 100,000 1,500 0.105 150,000 9 Battery Swap Station (BSS) (D) 150,000 2,000 0.095 180,000 6.5 The BSS model (Scenario D) consistently exhibited the most attractive economic metrics, with a projected LCOE of 0.095/kWh, making it the most cost-effective solution. This is attributed to its high utilization rate and optimized load profile, which ensures the solar and battery assets are used efficiently. Its relatively short payback period of 6.5 years suggests a strong business case for private investment. 4.2 Grid Stability Analysis The DIgSILENT Power Factory analysis reveals the significant technical challenges associated with large-scale, uncoordinated EV charging. Figure 2 illustrates the voltage profile along a representative distribution feeder under the grid-only charging scenario during the evening peak period. The simulation indicates that during peak charging hours (17:00–20:00), voltage levels at the electrically distant nodes decline by up to 10%, falling below acceptable operational limits. These results confirm that a rapid transition to e-mobility without coordinated charging strategies would impose substantial stress on Dar es Salaam’s existing distribution network, potentially compromising voltage stability, and power quality. Conversely, the analysis of the Hybrid Solar + Grid + V2G model (Scenario 3) demonstrates a substantial improvement in distribution grid stability. The coordinated operation of solar generation, battery storage, and bidirectional vehicle-to-grid services enables the system to absorb excess photovoltaic power during daytime hours and discharge electricity back to the grid during evening peak demand. This coordinated energy exchange effectively mitigates voltage drops along the distribution feeder. As shown in Fig. 3, the resulting voltage profile remains stable across all feeder nodes and well within acceptable operating limits, highlighting the grid-supportive role of V2G-enabled charging infrastructure. Finally, the analysis demonstrated that uncoordinated charging in Scenario 1 can led to significant voltage drops and stress on local transformers during peak hours. Conversely, the Hybrid Solar + Grid + V2G model (Scenario 3) will show that EVs, when integrated with smart charging and V2G services, can stabilize the grid by absorbing excess solar power during the day and providing power back during evening peaks, thus mitigating a major grid challenge (Gbandi and Oyedepo, 2023). In contrast, the results for Scenario 1 (grid-only, uncoordinated charging) indicate pronounced voltage drops and increased stress on local distribution transformers during peak evening hours. This confirms that large-scale EV adoption without smart charging coordination can exacerbate existing grid constraints. The comparative analysis clearly shows that the Hybrid Solar + Grid + V2G configuration transforms EVs from passive loads into active distributed energy resources, capable of enhancing voltage stability and mitigating one of the major technical barriers to e-mobility deployment in developing power systems (Al Wahedi and Bicer, 2022 ; Ihm et al., 2023 ). 4.3 Optimal Siting Locations The GIS analysis used to identify optimal locations for charging hubs in densely populated areas of Dar es Salaam with high traffic flow and commercial activity. For BSS models, the optimal sites will be located along major commuter routes and in areas with a high concentration of boda-boda drivers. The GIS analysis successfully identified optimal locations for both conventional charging hubs and BSSs. Figure 4 displays a heat map of ideal charging hub locations in Dar es Salaam based on a multi-criteria analysis. Figure 4 GIS-based heat map of optimal locations for EV charging hubs in Dar es Salaam. The darker areas indicate the most suitable sites. The optimal sites for conventional charging hubs were found to be concentrated in densely populated commercial zones and near major transport nodes, where load density and accessibility are highest. For the BSS model, the analysis identified optimal locations along major commuter routes and within the central business district, where the concentration of e-boda-boda and e-tuk-tuk drivers is the highest. These locations are strategically positioned to maximize convenience and minimize driver downtime, which are critical factors for the success of the BSS business model. 4.4 Sensitivity Analysis: The sensitivity analysis demonstrates that the financial viability of EV charging infrastructure is highly responsive to key economic parameters, particularly battery costs and electricity tariffs. Reductions in battery prices substantially improve project economics across all scenarios, notably shortening payback periods and lowering the Levelized Cost of Energy (LCOE). For instance, a 20% decrease in battery capital cost could reduce the payback period of the BSS model (Scenario D) from 6.5 years to under 5.5 years, reinforcing its attractiveness for private investment (Kumar et al., 2024 ; Güven et al., 2025 ). Similarly, the implementation of variable electricity tariffs, such as lower rates during off-peak hours, can significantly enhance both economic and technical outcomes. Off-peak incentives encourage smart charging, shifting EV load away from periods of peak grid demand, which not only reduces operating costs but also mitigates voltage drops and stress on distribution transformers. Hybrid scenarios incorporating V2G are particularly sensitive to tariff structures, as bidirectional energy flows enable users to export electricity back to the grid during high-price periods, generating additional revenue streams and improving the overall Net Present Cost (NPC) (Al Wahedi and Bicer, 2022 ; Ihm et al., 2023 ). This analysis confirms that policy and market mechanisms are as critical as technical design for ensuring the financial and operational success of decentralized charging infrastructure in Tanzania. The results emphasize the need for integrated planning that combines cost reduction strategies, tariff optimization, and smart charging management to maximize both economic benefits and grid stability. Figure 4. Sensitivity analysis of the payback period for the Battery Swap Station (BSS) scenario. Tornado diagram showing the impact of variations in key parameters battery cost, electricity tariff, solar capital expenditure, and load utilization on the payback period. Battery cost and electricity tariff exhibit the highest sensitivity, highlighting their dominant role in determining the financial viability of decentralized EV charging systems. The sensitivity analysis results are illustrated using a tornado diagram, shown in Fig. 4, which depicts the influence of key economic parameters on the payback period of the Battery Swap Station (BSS) scenario. Battery cost exhibits the greatest impact on project viability, with a ± 20% variation shifting the payback period between approximately 5 and 7 years. Electricity tariff variations also show a pronounced effect, reflecting the importance of tariff design and off-peak charging incentives. In contrast, changes in solar capital expenditure and load utilization have a comparatively moderate influence. Overall, the results confirm that reductions in battery costs and the implementation of favorable electricity tariff structures are critical drivers for improving the financial performance of EV charging infrastructure. 5. Discussion The findings of this study highlight that the successful deployment of electric mobility in developing-country contexts requires policy frameworks that extend beyond fiscal incentives alone. While tax exemptions and import duty reductions can accelerate early adoption, the results indicate that electricity pricing structures play a more decisive role in ensuring long-term grid stability and economic viability. Previous techno-economic studies demonstrate that time-of-use and managed charging tariffs are effective mechanisms for shifting EV demand away from peak periods, thereby reducing grid congestion and deferring costly infrastructure upgrades (Al Wahedi and Bicer, 2022 ; Ihm et al., 2023 ). In the Tanzanian context, such tariff structures would be particularly beneficial given the evolving nature of the national grid and its sensitivity to demand fluctuations. The analysis further confirms the strategic importance of Battery Swap Stations (BSS) as a charging paradigm tailored to high-utilization two- and three-wheeler fleets. Consistent with recent studies on hybrid EV charging ecosystems, BSS significantly reduce vehicle downtime and enable higher asset utilization compared to conventional plug-in charging, especially for motorcycles and light commercial vehicles (Kumar et al., 2024 ; Güven et al., 2025 ). However, the scalability of the BSS model is contingent upon technical standardization, particularly with respect to battery pack specifications and charging interfaces. Without standardized components, interoperability across stations and manufacturers remains limited, constraining network expansion and increasing system costs—an issue also highlighted in broader assessments of sustainable EV charging infrastructure (Güven et al., 2025 ). From a socio-economic perspective, the decentralized and service-oriented charging models evaluated in this study offer benefits that extend beyond energy system performance. Hybrid solar-based charging infrastructure has been shown to stimulate local employment through installation, operation, and maintenance activities, particularly in regions where centralized grid expansion is slow or capital-intensive (Kalamaras et al., 2019 ; Nallolla and Vijayapriya, 2022 ). Moreover, by reducing reliance on fossil fuels and lowering operating costs, such systems can enhance the economic resilience of transport operators and small-scale service providers. These findings reinforce the argument that e-mobility, when coupled with renewable energy systems, can function as both an energy transition mechanism and a local economic development catalyst (Riayatsyah et al., 2022 ; Mohammed et al., 2023 ). The study also demonstrates that a hybrid, decentralized charging approach is better aligned with regions characterized by constrained or climate-sensitive power grids. Rather than imposing additional stress on centralized infrastructure, solar-integrated charging systems complement existing grid capacity by supplying localized generation and storage, thereby improving overall system resilience (Miao et al., 2020 ; Said et al., 2024). This is particularly relevant for Sub-Saharan African cities, where grid reinforcement often lags behind rapid urban and transport growth. The results therefore support a transition pathway that prioritizes distributed energy resources over immediate large-scale grid overhauls, in line with findings from similar developing and island contexts (Okedu and Uhunmwangho, 2014 ; Kalamaras et al., 2019 ). Despite these insights, the study is subject to several limitations. Data availability remains a key constraint, particularly regarding real-world driving cycles and charging behaviour of electric two- and three-wheelers, as well as detailed grid operational parameters. As a result, the techno-economic assessment relies on synthesized load profiles and assumptions derived from comparable studies and regional analogues. While this approach is widely accepted in early-stage feasibility analyses (Ahammed, 2021 ; Ekren et al., 2021 ), future research would benefit from empirical fleet monitoring and detailed grid interaction studies to improve model accuracy and reduce uncertainty. 6. Conclusion This techno-economic assessment demonstrates that a strategic approach to solar-powered electric vehicle (EV) charging infrastructure can not only address Tanzania's mobility challenges but also serve as a powerful catalyst for its clean energy transition. The findings show that scenarios integrating solar PV with advanced services like Vehicle-to-Grid (V2G) and Battery Swap Stations (BSS) are technically feasible and more economically viable than conventional grid-only charging. The results confirm that the transition to e-mobility, particularly for the dominant two- and three-wheeler segments, can be a valuable complement to Tanzania's ambitious energy goals. EVs, when integrated with smart charging, can function as distributed energy resources, providing peak shaving services that improve grid resilience and stability. This model, particularly relevant for Dar es Salaam's rapidly growing urban centers, transforms EVs from a potential burden on the grid into a solution for a major energy challenge. The economic analysis highlighted the exceptional potential of the Battery Swap Station (BSS) model. Its high utilization rate and attractive financial metrics, including the lowest LCOE and a rapid payback period, position it as a commercially compelling and scalable solution tailored to the unique needs of local drivers. This model directly addresses the primary barriers of range anxiety and long charging times, making e-mobility a more accessible and practical choice for the country's most vulnerable road users. Ultimately, the success of this transition hinges on supportive policy and strategic planning. The government's role in creating a favorable regulatory environment, including innovative tariff designs, establishing technology standards, and launching pilot projects, is crucial. By embracing these findings, Tanzania can secure a path towards a more sustainable, resilient, and affordable energy and transport future. Declarations Author Contribution F.P conceptualized the study, designed the assessment framework, conducted the literature review, collected and analysed the data, developed and validated the techno-economic and grid analysis models, and prepared the original manuscript. J.M contributed through critical review of the study design, methodology, analysis, and manuscript, providing technical and academic guidance. All authors read and approved the final version of the manuscript References A. F. Güven, N. Ate, S. Alotaibi, T. Alzahrani, A. M. Amsal, and S. K. Elsayed “Sustainable hybrid systems for electric vehicle charging infrastructures in regional applications”, Scientific Reports, Vol. 15, No. 1, art. no. 4199, 2025. https://doi.org/10.1038/s41598-025-87985-7 T. R. Said, B. Kichonge, and T. Kivevele, “Optimal design and analysis of a grid-connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar”, Energy Science and Engineering, Vol. 12, No. 5, pp. 2137–2163, May 2024. https://doi.org/10.1002/ese3.1735 P. H. Kumar, R. R. Gopi, R. Rajarajan, N. B. Vaishali, K. Vasavi, and P. S. Kumar “Prefeasibility techno-economic analysis of hybrid renewable energy system”, e-Prime-Advances in Electrical Engineering, Electronics and Energy, Vol. 7, art. no. 100443, 2024. https://doi.org/10.1016/j.prime.2024.100443 E. Kalamaras, M. Belekoukia, Z. Lin, B. Xu, H. Wang, and J. Xuan, “Techno-economic Assessment of a Hybrid Off-grid DC System for Combined Heat and Power Generation in Remote Islands”, Energy Procedia, Vol. 158, pp. 6315–6320, 2019. https://doi.org/10.1016/j.egypro.2019.01.406 A. Al Wahedi and Y. Bicer, “Techno-economic optimization of novel stand-alone renewables-based electric vehicle charging stations in Qatar”, Energy, vol. 243, art. no. 123008, 2022. https://doi.org/10.1016/j.energy.2021.123008 C. Miao, K. Teng, Y. Wang, and L. Jiang, “Technoeconomic analysis on a hybrid power system for the uk household using renewable energy: A case study”, Energies, Vol. 13, No. 12, art. no. 3231, 2020. https://doi.org/10.3390/en13123231 A. S. Mohammed, S. M. Atnaw, A. O. Salau, and J. N. Eneh, “Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles”, Energy Reports, Vol. 9, pp. 2213–2228, 2023. https://doi.org/10.1016/j.egyr.2023.01.042 O. Ekren, C. Hakan Canbaz, and Ç. B. Güvel, “Sizing of a solar-wind hybrid electric vehicle charging station by using HOMER software”, Journal of Cleaner Production, Vol. 279, art. no. 123615, 2021. https://doi.org/10.1016/j.jclepro.2020.123615 J. Ihm, B. Amghar, S. Chun, and H. Park, “Optimum Design of an Electric Vehicle Charging Station Using a Renewable Power Generation System in South Korea”, Sustainability, Vol. 15, No. 13, art. no. 9931, 2023. https://doi.org/10.3390/su15139931 T. M. I. Riayatsyah, T. A. Geumpana, I. M. R. Fattah, S. Rizal, and T. M. I. Mahlia, “Techno-Economic Analysis and Optimization of Campus Grid Connected Hybrid Renewable Energy System Using HOMER Grid”, Sustainability, Vol. 14, No. 13, art. no. 7735, 2022. https://doi.org/10.3390/su14137735 C. A. Nallolla and P. Vijayapriya, “Optimal Design of a Hybrid Off-Grid Renewable Energy System Using Techno-Economic and Sensitivity Analysis for a Rural Remote Location”, Sustainability, Vol. 14, No. 22, art. no. 15393, 2022. https://doi.org/10.3390/su142215393 K. E. Okedu, R. Uhunmwangho “Optimization of Renewable Energy Efficiency using HOMER”, International Journal of Renewable Energy Research, Vol. 14, No. 2, pp. 421-427, 2014. https://doi.org/10.20508/ijrer.v4i2.1231.g6294 S. Ahammed, “Optimization of Hybrid Renewable Energy System (HRSE) Using Homer Pro”, IRE Journals, Vol. 5, No. 5, pp. 192-201, 2021 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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13:06:26","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73485,"visible":true,"origin":"","legend":"","description":"","filename":"ce486cdf1485462cab072ea4911d24551structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/f153c17da8b04e2ed1c3042f.xml"},{"id":100408560,"identity":"65a64118-6430-4be6-8492-fd6352e49543","added_by":"auto","created_at":"2026-01-16 13:06:21","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80668,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/81ad3bcd8e8aa4b7823765fc.html"},{"id":100408817,"identity":"96743c22-9829-4a02-8468-511c77103e4d","added_by":"auto","created_at":"2026-01-16 13:06:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA conceptual diagram illustrating the power flow in unidirectional (V1G) and bidirectional (V2G) charging.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/54971088cd64812e397c51ad.png"},{"id":100408695,"identity":"4dd085ee-0841-4f49-8fae-6073ba898ab6","added_by":"auto","created_at":"2026-01-16 13:06:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA block diagram illustrating the key components of an off-grid or hybrid solar-enabled EV charging station.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/ce7e5180f92839a834506f8f.png"},{"id":100408686,"identity":"88934899-e116-4241-91f1-19c86c6e607c","added_by":"auto","created_at":"2026-01-16 13:06:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":192301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1:\u003c/strong\u003e Levelized Cost of Energy (LCOE) and Net Present Cost (NPC) for each charging infrastructure scenario. Levelized Cost of Energy (LCOE, USD/kWh) and Net Present Cost (NPC, USD) for four EV charging infrastructure scenarios: Grid-only (A), Solar-only (B), Hybrid + V2G (C), and Battery Swap Station (BSS, D). The BSS model demonstrates the lowest LCOE and a favorable NPC, reflecting high asset utilization and optimized load profiles. Dual-axis representation highlights both operational cost efficiency (LCOE) and long-term financial viability (NPC).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/1e2371989c80d6699f9e7f5a.jpeg"},{"id":100408681,"identity":"a516d3ec-f9e4-4760-9578-c21888e3d67a","added_by":"auto","created_at":"2026-01-16 13:06:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2:\u003c/strong\u003e Voltage profile on the distribution feeder under the grid-only charging scenario during peak evening hours.Voltage profile along a representative distribution feeder simulated in DIgSILENT PowerFactory for the grid-only charging scenario during peak evening demand (17:00–20:00). The results show significant voltage drops at downstream nodes, with deviations reaching up to 10% below nominal levels, exceeding acceptable distribution network operating limits and indicating potential risks to grid stability under uncontrolled EV charging conditions\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/784b83e4e690305d751cd035.png"},{"id":100408716,"identity":"cc29bddd-eb4f-425c-b193-5e3cf93e519c","added_by":"auto","created_at":"2026-01-16 13:06:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3:\u003c/strong\u003e Voltage profile on the distribution feeder with integrated V2G services, showcasing grid stabilization Figure 3 illustrates the stabilizing effect of integrated V2G services on the distribution network, particularly at electrically distant nodes where voltage violations are most likely to occur under conventional charging scenarios. These findings are consistent with previous studies demonstrating that bidirectional EV charging, when combined with renewable energy systems, can provide ancillary services such as voltage regulation and peak load support in weak or evolving grids (Mohammed et al., 2023; Güven et al., 2025).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/6003e3960295aaa53138724d.png"},{"id":100408263,"identity":"8c111fb3-7d65-4ceb-9e32-acb047fb1d5f","added_by":"auto","created_at":"2026-01-16 13:05:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1059793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4:\u003c/strong\u003e GIS-based heat map of optimal locations for EV charging hubs in Dar es Salaam. The darker areas indicate the most suitable sites.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/9368770cead73bb8f392dc06.png"},{"id":100408878,"identity":"b7dc6ce9-ec20-4b6c-a725-9a8deefc254f","added_by":"auto","created_at":"2026-01-16 13:06:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":53723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4. Sensitivity analysis of the payback period for the Battery Swap Station (BSS) scenario.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/80a9e1b77920881edec9b88b.png"},{"id":100857950,"identity":"b5c0cc63-cd55-494b-bbf3-bd4a344be137","added_by":"auto","created_at":"2026-01-22 07:23:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8531346/v1/4c764251-5ac4-4fec-90a7-86f8272acda6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Techno-Economic Assessment of Solar-Powered EV Charging Infrastructure in Sub-Saharan Africa: A Case Study of Tanzania","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global transition towards electric mobility has become a central pillar of climate change mitigation and sustainable energy strategies. Rapid advancements in electric vehicle (EV) technologies, coupled with declining battery costs and supportive policy frameworks, have accelerated EV adoption worldwide, with global sales exceeding 10\u0026nbsp;million units in 2023 (IEA, 2024). While this transition offers substantial environmental and energy security benefits, it simultaneously introduces critical infrastructure challenges, particularly the need for reliable, scalable, and economically viable EV charging systems that can be effectively integrated into existing power networks. Recent studies emphasize that without coordinated charging strategies and renewable integration, large-scale EV deployment may impose significant stress on power systems, especially in regions with limited grid resilience (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Sub-Saharan Africa (SSA), the EV transition is unfolding within a markedly different socio-technical context. Although EV penetration remains at an early stage, rapid urbanization and increasing demand for personal and commercial mobility are placing growing pressure on transport and energy infrastructure (World Bank, 2023). Tanzania exemplifies this challenge, where the transport sector is heavily dominated by two- and three-wheelers, which account for more than half of all registered vehicles. These vehicles represent an immediately addressable segment for electrification due to their predictable travel patterns and comparatively lower energy demand (LATRA, 2024; AutoMag.tz, 2025). At the same time, Tanzania\u0026rsquo;s strong dependence on imported fossil fuels\u0026mdash;costing approximately USD 4.08\u0026nbsp;billion in 2024\u0026mdash;underscores the urgent need for locally sourced and renewable-based transport energy solutions (TICGL, 2025).\u003c/p\u003e \u003cp\u003eThe integration of EV charging infrastructure in Tanzania is, however, constrained by the structural characteristics of the national power grid operated by TANESCO. The grid relies heavily on hydropower, contributing over 45% of installed capacity, making electricity supply highly vulnerable to climate variability and hydrological uncertainty (EWURA-CCC, 2025). Previous research has shown that unplanned EV charging loads can exacerbate peak demand and compromise grid stability in weak or climate-sensitive power systems (Ekren et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, decentralized and renewable-based charging solutions\u0026mdash;particularly solar-integrated systems\u0026mdash;are increasingly recognized as viable alternatives to mitigate grid stress while enhancing energy access and system reliability (Miao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Riayatsyah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechno-economic assessments have emerged as a critical decision-support tool for evaluating such hybrid renewable energy systems. Studies using optimization platforms such as HOMER have demonstrated the technical feasibility and cost-effectiveness of hybrid solar-based systems for EV charging and off-grid or grid-connected applications across diverse geographical contexts (Okedu and Uhunmwangho, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ahammed, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). More recent investigations highlight the importance of system configuration, energy storage sizing, and demand management strategies in achieving economically optimal and operationally resilient charging infrastructures (Nallolla and Vijayapriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Said \u003cem\u003eet al.\u003c/em\u003e, 2024). Nevertheless, most existing studies predominantly focus on passenger vehicles in developed or high-income settings, with limited attention given to SSA contexts and motorcycle-dominated transport systems.\u003c/p\u003e \u003cp\u003eTo address this gap, the present study conducts a comprehensive techno-economic assessment of a decentralized, solar-powered EV charging ecosystem tailored to Tanzania\u0026rsquo;s unique transport and energy landscape. Specifically, this research evaluates the viability of advanced charging concepts, including Battery Swap Stations (BSS) and Vehicle-to-Grid (V2G) integration, for two- and three-wheelers. Unlike conventional charging-only models, these systems enable EVs to function as distributed energy resources capable of supporting grid stability and load balancing (Mohammed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). By integrating solar photovoltaics, battery storage, and smart energy management, the study demonstrates how EV infrastructure can simultaneously reduce fossil fuel dependence, enhance grid resilience, and support Tanzania\u0026rsquo;s national objectives for energy diversification and sustainable development (Tanzania Ministry of Energy, 2024; EWURA, 2025).\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Charging Technologies and Grid Integration\u003c/h2\u003e \u003cp\u003eThe rapid adoption of electric vehicles (EVs) has brought charging technologies to the forefront of e-mobility research. These technologies are generally categorized by their power output and corresponding charging speed. Level 1 charging uses a standard 120V AC outlet, providing slow overnight charging. Level 2 utilizes a 240V AC source, offering a much faster charge suitable for homes and workplaces. Level 3, or DC fast charging, bypasses the vehicle's onboard charger to directly charge the battery at a high-power rating, enabling a full charge in under an hour. While DC fast charging is critical for minimizing range anxiety and supporting long-distance travel, its high instantaneous power demand can place significant stress on an aging grid, especially during peak energy consumption periods (Ekren et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis challenge has led to the development of intelligent charging strategies. V1G (unidirectional managed charging) allows a grid operator or smart charging platform to control the timing and rate of an EV\u0026rsquo;s charging session, shifting load to off-peak hours to reduce grid congestion (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A more advanced concept, V2G (Vehicle-to-Grid), is revolutionary in its bidirectional energy flow capability. This technology enables EVs to not only draw power from the grid but also to discharge power back into it. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this bidirectional flow allows EVs to provide crucial ancillary services, such as frequency regulation and peak shaving, which are particularly valuable in grids with high penetration of intermittent renewable energy sources (Mohammed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The ability of V2G to help stabilize a fragile grid makes it a compelling solution for developing nations.\u003c/p\u003e \u003cp\u003eSimilarly, Battery Swap Stations (BSS) offer a compelling alternative to traditional charging, allowing drivers to quickly exchange a depleted battery for a fully charged one. This model is particularly suited for high-utilization vehicle fleets like two- and three-wheelers, where minimizing downtime is critical, and has been shown to be techno-economically viable when integrated with hybrid renewable energy systems (Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Solar Energy-Enabled Charging Systems\u003c/h2\u003e \u003cp\u003eThe global push towards decarbonization has positioned the integration of solar energy with EV charging infrastructure as a key strategy for reducing reliance on fossil fuels and lowering electricity costs. These systems typically consist of three primary components: a solar photovoltaic (PV) array to generate power, a Battery Energy Storage System (BESS) to store excess solar energy for use during non-sunlight hours, and a grid connection for backup power and flexibility. A block diagram of this integrated system is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe techno-economic feasibility of such systems is heavily dependent on local factors such as solar irradiance, electricity tariffs, and land costs. Several studies have demonstrated that declining solar PV costs and advances in battery technologies have significantly enhanced the economic competitiveness of solar-powered EV charging systems (Miao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nallolla and Vijayapriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In comparable developing-country and island contexts, hybrid solar-based systems optimized using HOMER have been shown to reduce lifecycle costs and improve system reliability (Kalamaras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Said et al., 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Frameworks and Models\u003c/h2\u003e \u003cp\u003eBeyond the technical components, the successful deployment of a nationwide e-mobility ecosystem requires comprehensive frameworks that guide implementation and policy. Regulatory Frameworks: Governments and regulatory bodies play a critical role in shaping the EV market. Key regulatory mechanisms include:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTime-of-Use (ToU) Tariffs: These tariffs incentivize EV owners to charge during off-peak hours when electricity is cheaper, helping to manage grid demand and reduce the need for costly grid upgrades (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInterconnection Standards: Establishing clear technical standards for connecting charging stations and EVs to the grid is essential for ensuring safety, reliability, and interoperability (Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIncentive Programs: Policies such as tax credits, grants for charging infrastructure, and reduced import duties on EVs can accelerate adoption and lower the financial barrier for consumers and businesses (Riayatsyah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eImplementation Models: Several operational and business models have emerged to deploy EV charging infrastructure. These include:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type:lower-roman;\"\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePublic-Private Partnerships (PPPs): Governments can collaborate with private companies to share the financial and operational risks of building large-scale charging networks. This model is particularly effective for bridging funding gaps and leveraging private sector expertise (Nallolla and Vijayapriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eService-Oriented Models: Business models such as \"Charging-as-a-Service\" or the BSS model monetize the charging service itself rather than just the electricity. As discussed in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e, the BSS model is highly effective for high-utilization fleets, as it minimizes vehicle downtime and can be integrated with decentralized energy systems, providing a robust solution for areas with unreliable grid access (Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research Gaps\u003c/h2\u003e \u003cp\u003eDespite extensive research on EV\u0026ndash;grid integration and hybrid renewable energy systems, a significant research gap remains concerning developing nations. Existing studies are largely concentrated in developed or high-income regions and primarily focus on passenger vehicles and conventional charging paradigms (Ekren et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The specific context of Sub-Saharan Africa\u0026mdash;characterized by grid vulnerabilities, climate-sensitive power generation, and the dominance of two- and three-wheeler transport\u0026mdash;remains insufficiently addressed in the literature. Furthermore, limited attention has been given to advanced concepts such as V2G and battery swapping as grid-supporting mechanisms within decentralized solar-powered charging ecosystems (Mohammed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).This study directly addresses this gap by providing a comprehensive, context-specific techno-economic analysis that integrates both technical performance and economic feasibility for solar-enabled EV charging systems tailored to developing-country conditions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThis study employs a rigorous, multi-faceted methodology to conduct a techno-economic assessment of solar-powered EV charging infrastructure in Tanzania. The approach integrates empirical data with advanced simulation tools and scenario-based analysis to provide a comprehensive and reproducible evaluation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Area and Data Collection\u003c/h2\u003e \u003cp\u003eThe case study focuses on Dar es Salaam, Tanzania\u0026rsquo;s largest metropolitan area and primary economic hub, selected due to its high population density, rapid urban expansion, and concentrated traffic flow dominated by two- and three-wheelers. These characteristics make the city particularly suitable for evaluating decentralized electric vehicle (EV) charging solutions targeting high-utilization fleets. In addition to the urban core, a representative peri-urban location was included to assess the scalability, robustness, and financial viability of the proposed charging configurations under lower load density and weaker grid conditions. This dual spatial focus is consistent with prior hybrid energy system studies conducted in rapidly urbanizing and infrastructure-constrained regions (Kalamaras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Said et al., 2024).\u003c/p\u003e \u003cp\u003eTo ensure robustness and consistency with established techno-economic assessment practices, the study employed a multi-source dataset structured in line with HOMER-based modeling approaches reported in the literature. Solar resource data were represented using long-term typical meteorological conditions commonly adopted in hybrid renewable energy feasibility studies. Such datasets provide hourly profiles of global horizontal irradiance and ambient temperature, which are critical inputs for accurately modeling photovoltaic (PV) system performance and storage behavior (Okedu and Uhunmwangho, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ahammed, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The use of typical-year meteorological data is widely accepted for evaluating system performance under representative climatic conditions rather than short-term variability (Riayatsyah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElectric load demand was modeled using representative commercial load profiles adapted to reflect the operational characteristics of EV charging and battery swapping stations. This approach aligns with prior studies where conventional demand profiles were adjusted to capture the temporal and power requirements of EV charging infrastructure (Ekren et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particular attention was given to peak demand periods and load variability, which are critical determinants of storage sizing and system economics in hybrid renewable configurations (Nallolla and Vijayapriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTransport demand for the charging and Battery Swap Station (BSS) scenarios was synthesized based on documented operational patterns of high-utilization EV fleets. Battery swapping has been shown to be especially effective for two- and three-wheeler applications, where frequent use and minimal downtime are essential for economic viability (Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Parameters such as daily energy demand, charging frequency, and temporal distribution were therefore incorporated to generate a realistic and granular load profile suitable for system simulation and optimization. This synthesis-based approach is consistent with prior techno-economic studies where empirical fleet data are limited but operational behavior is well understood (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 System Modeling and Simulation Tools\u003c/h2\u003e \u003cp\u003eThe study utilized two industry-standard simulation tools to perform the techno-economic and power systems analyses. The selection of these tools was based on their proven reliability, accuracy, and suitability for complex energy systems modeling.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHOMER Pro\u003c/strong\u003e \u003cp\u003eThis software was employed for the techno-economic optimization of the hybrid power system. It is specifically designed to model and optimize decentralized energy systems, making it an ideal tool for assessing the viability of solar PV and battery storage-integrated EV charging hubs. The HOMER model included a solar PV array, a battery energy storage system (BESS), an EV charging load profile, and a grid connection for backup power. The software determined the optimal size of each component to minimize the Levelized Cost of Energy (LCOE) and the Net Present Cost (NPC) over a defined project lifetime, considering all capital and operational expenditures.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDIgSILENT Power Factory\u003c/strong\u003e \u003cp\u003eThis tool was used to conduct a detailed load flow and stability analysis of the local power distribution network. DIgSILENT's powerful simulation capabilities allowed for a precise assessment of the impact of EV charging and V2G services on the grid. Specifically, it was used to analyze voltage stability, frequency regulation, and the potential for EVs to provide peak shaving services to the grid during periods of high demand, thereby providing a quantitative measure of their ability to act as distributed energy resources.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Key Assumptions\u003c/h2\u003e \u003cp\u003eTo ensure the reproducibility of the study, the following key technical and economic assumptions were made for the HOMER Pro model:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProject Lifetime\u003c/strong\u003e \u003cp\u003eA 20-year project horizon was used for the financial analysis, a standard practice for renewable energy infrastructure projects.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiscount Rate\u003c/strong\u003e \u003cp\u003eA real discount rate of 8% was applied to all financial calculations, reflecting the economic conditions and project risk in the region.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComponent Costs\u003c/strong\u003e \u003cp\u003eComponent costs were based on current market trends and regional data (IRENA, 2024; Empower New Energy, 2024), including\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSolar PV modules: \u003cspan\u003e$\u003c/span\u003e450\u0026minus;\u003cspan\u003e$\u003c/span\u003e600 per kWp (kilowatt-peak), including mounting and installation.\u003c/p\u003e \u003cp\u003eLithium-ion batteries: \u003cspan\u003e$\u003c/span\u003e250\u0026minus;\u003cspan\u003e$\u003c/span\u003e350 per kWh.\u003c/p\u003e \u003cp\u003eEV Chargers: \u003cspan\u003e$\u003c/span\u003e500\u0026minus;\u003cspan\u003e$\u003c/span\u003e1,500 for Level 2 AC chargers and \u003cspan\u003e$\u003c/span\u003e15,000\u0026minus;\u003cspan\u003e$\u003c/span\u003e30,000 for DC fast chargers.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGrid Electricity Tariff\u003c/strong\u003e \u003cp\u003eThe national grid tariff for commercial use, approximately \u003cspan\u003e$\u003c/span\u003e0.094/kWh, was sourced from TANESCO\u0026rsquo;s public records (GlobalPetrolPrices.com, 2024).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBattery Degradation\u003c/strong\u003e \u003cp\u003eBattery degradation was modeled at an annual rate of 2\u0026thinsp;\u0026minus;\u0026thinsp;3%, with a replacement cost considered at the end of the battery\u0026rsquo;s useful life.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Scenario Definition and Analysis\u003c/h2\u003e \u003cp\u003eThe assessment was based on four distinct scenarios, each representing a plausible pathway for the deployment of EV charging infrastructure in Tanzania. These scenarios were chosen to cover a range of technological and business models and to provide a comprehensive comparison.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGrid-only Charging\u003c/strong\u003e \u003cp\u003eThis baseline scenario represents the conventional approach, where charging stations are powered solely by the national grid. It serves as a benchmark for comparing the cost, emissions, and reliability of the renewable energy-based systems. This is particularly relevant in a country where grid reliability can be a challenge.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSolar-powered Charging (Off-grid)\u003c/strong\u003e \u003cp\u003eThis scenario models a fully off-grid system powered by solar PV and a BESS. It is particularly relevant for remote or underserved areas with limited or no grid access, providing a sustainable solution for the expansion of e-mobility into peri-urban and rural areas.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHybrid Solar\u0026thinsp;+\u0026thinsp;Grid\u0026thinsp;+\u0026thinsp;V2G\u003c/strong\u003e \u003cp\u003eThis is the core scenario of the study. It represents a grid-connected charging hub that utilizes a solar PV array, a BESS, and V2G-enabled EVs to provide ancillary grid services. This model is critical for demonstrating how EVs can support grid stability and reduce reliance on fossil fuel-based generation during peak hours, which is a significant challenge for the national grid.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBattery Swap Station (BSS)\u003c/strong\u003e \u003cp\u003eThis scenario focuses specifically on a BSS model for two- and three-wheelers. The BSS is powered by a hybrid solar-grid system, and the analysis assesses its financial viability based on the high turnover rate of battery swaps. This model addresses a unique and vital segment of the Tanzanian transport sector and offers a direct solution to the long charging times that are a major barrier to the adoption of e-boda-bodas and e-tuk-tuks (AutoMag.tz, 2025; TYCORUN, 2025).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThis section presents the factual findings from the HOMER Pro, DIgSILENT PowerFactory, and GIS analyses, organized to follow the logical flow of the research questions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Techno-Economic Feasibility\u003c/h2\u003e \u003cp\u003eThe techno-economic analysis, performed using HOMER Pro, demonstrates a clear financial advantage for scenarios that incorporate solar PV and battery storage. The simulations indicate that the Levelized Cost of Energy (LCOE) is significantly lower for solar-hybrid systems compared to the grid-only baseline. Specifically, scenarios incorporating solar PV (Scenarios B, C, and D) exhibit lower LCOE than the grid-only option (Scenario A), driven by Tanzania\u0026rsquo;s high solar potential and the regional LCOE of solar generation in East Africa, estimated at \u003cspan\u003e$\u003c/span\u003e0.07\u0026ndash;\u003cspan\u003e$\u003c/span\u003e0.16/kWh (Miao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Said et al., 2024).\u003c/p\u003e \u003cp\u003eThe Battery Swap Station (BSS) model (Scenario D) consistently delivers the most attractive economic metrics, with a projected LCOE of 0.095 USD/kWh and a payback period of 6.5 years. Its superior performance is attributable to high utilization rates and optimized load profiles that ensure efficient use of both solar and battery assets. Similarly, the Hybrid\u0026thinsp;+\u0026thinsp;V2G system (Scenario C) achieves a lower LCOE of 0.105 USD/kWh, highlighting the value of integrating smart charging and bidirectional energy flows into the grid (G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comparative overview of LCOE and Net Present Cost (NPC) across all four scenarios over the 20-year project lifetime. The figure illustrates that solar-integrated systems (B, C, D) consistently outperform the grid-only baseline in both cost and long-term financial viability.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed breakdown of key financial metrics, including initial capital cost, annual operating cost, LCOE, NPC, and payback period for each scenario. This tabulated summary supports a quantitative comparison, emphasizing the economic feasibility of decentralized and hybrid charging infrastructures in Tanzania.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of techno-economic metrics for each charging scenario.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial Capital Cost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperating Cost (USD/yr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCOE (USD/kWh)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPC (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePayback Period (Years)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrid-only (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar-only (B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e165,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid\u0026thinsp;+\u0026thinsp;V2G (C )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBattery Swap Station (BSS) (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe BSS model (Scenario D) consistently exhibited the most attractive economic metrics, with a projected LCOE of 0.095/kWh, making it the most cost-effective solution. This is attributed to its high utilization rate and optimized load profile, which ensures the solar and battery assets are used efficiently. Its relatively short payback period of 6.5 years suggests a strong business case for private investment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Grid Stability Analysis\u003c/h2\u003e \u003cp\u003eThe DIgSILENT Power Factory analysis reveals the significant technical challenges associated with large-scale, uncoordinated EV charging. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the voltage profile along a representative distribution feeder under the grid-only charging scenario during the evening peak period. The simulation indicates that during peak charging hours (17:00\u0026ndash;20:00), voltage levels at the electrically distant nodes decline by up to 10%, falling below acceptable operational limits. These results confirm that a rapid transition to e-mobility without coordinated charging strategies would impose substantial stress on Dar es Salaam\u0026rsquo;s existing distribution network, potentially compromising voltage stability, and power quality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConversely, the analysis of the Hybrid Solar\u0026thinsp;+\u0026thinsp;Grid\u0026thinsp;+\u0026thinsp;V2G model (Scenario 3) demonstrates a substantial improvement in distribution grid stability. The coordinated operation of solar generation, battery storage, and bidirectional vehicle-to-grid services enables the system to absorb excess photovoltaic power during daytime hours and discharge electricity back to the grid during evening peak demand. This coordinated energy exchange effectively mitigates voltage drops along the distribution feeder. As shown in Fig.\u0026nbsp;3, the resulting voltage profile remains stable across all feeder nodes and well within acceptable operating limits, highlighting the grid-supportive role of V2G-enabled charging infrastructure. Finally, the analysis demonstrated that uncoordinated charging in Scenario 1 can led to significant voltage drops and stress on local transformers during\u003c/p\u003e \u003cp\u003e peak hours. Conversely, the Hybrid Solar\u0026thinsp;+\u0026thinsp;Grid\u0026thinsp;+\u0026thinsp;V2G model (Scenario 3) will show that EVs, when integrated with smart charging and V2G services, can stabilize the grid by absorbing excess solar power during the day and providing power back during evening peaks, thus mitigating a major grid challenge (Gbandi and Oyedepo, 2023).\u003c/p\u003e \u003cp\u003eIn contrast, the results for Scenario 1 (grid-only, uncoordinated charging) indicate pronounced voltage drops and increased stress on local distribution transformers during peak evening hours. This confirms that large-scale EV adoption without smart charging coordination can exacerbate existing grid constraints. The comparative analysis clearly shows that the Hybrid Solar\u0026thinsp;+\u0026thinsp;Grid\u0026thinsp;+\u0026thinsp;V2G configuration transforms EVs from passive loads into active distributed energy resources, capable of enhancing voltage stability and mitigating one of the major technical barriers to e-mobility deployment in developing power systems (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Optimal Siting Locations\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GIS analysis used to identify optimal locations for charging hubs in densely populated areas of Dar es Salaam with high traffic flow and commercial activity. For BSS models, the optimal sites will be located along major commuter routes and in areas with a high concentration of boda-boda drivers. The GIS analysis successfully identified optimal locations for both conventional charging hubs and BSSs. Figure\u0026nbsp;4 displays a heat map of ideal charging hub locations in Dar es Salaam based on a multi-criteria analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 4\u003c/strong\u003e \u003cp\u003eGIS-based heat map of optimal locations for EV charging hubs in Dar es Salaam. The darker areas indicate the most suitable sites.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe optimal sites for conventional charging hubs were found to be concentrated in densely populated commercial zones and near major transport nodes, where load density and accessibility are highest. For the BSS model, the analysis identified optimal locations along major commuter routes and within the central business district, where the concentration of e-boda-boda and e-tuk-tuk drivers is the highest. These locations are strategically positioned to maximize convenience and minimize driver downtime, which are critical factors for the success of the BSS business model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Sensitivity Analysis:\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis demonstrates that the financial viability of EV charging infrastructure is highly responsive to key economic parameters, particularly battery costs and electricity tariffs. Reductions in battery prices substantially improve project economics across all scenarios, notably shortening payback periods and lowering the Levelized Cost of Energy (LCOE). For instance, a 20% decrease in battery capital cost could reduce the payback period of the BSS model (Scenario D) from 6.5 years to under 5.5 years, reinforcing its attractiveness for private investment (Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the implementation of variable electricity tariffs, such as lower rates during off-peak hours, can significantly enhance both economic and technical outcomes. Off-peak incentives encourage smart charging, shifting EV load away from periods of peak grid demand, which not only reduces operating costs but also mitigates voltage drops and stress on distribution transformers. Hybrid scenarios incorporating V2G are particularly sensitive to tariff structures, as bidirectional energy flows enable users to export electricity back to the grid during high-price periods, generating additional revenue streams and improving the overall Net Present Cost (NPC) (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e This analysis confirms that policy and market mechanisms are as critical as technical design for ensuring the financial and operational success of decentralized charging infrastructure in Tanzania. The results emphasize the need for integrated planning that combines cost reduction strategies, tariff optimization, and smart charging management to maximize both economic benefits and grid stability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4. Sensitivity analysis of the payback period for the Battery Swap Station (BSS) scenario.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTornado diagram showing the impact of variations in key parameters battery cost, electricity tariff, solar capital expenditure, and load utilization on the payback period. Battery cost and electricity tariff exhibit the highest sensitivity, highlighting their dominant role in determining the financial viability of decentralized EV charging systems.\u003c/p\u003e \u003cp\u003eThe sensitivity analysis results are illustrated using a tornado diagram, shown in Fig.\u0026nbsp;4, which depicts the influence of key economic parameters on the payback period of the Battery Swap Station (BSS) scenario. Battery cost exhibits the greatest impact on project viability, with a\u0026thinsp;\u0026plusmn;\u0026thinsp;20% variation shifting the payback period between approximately 5 and 7 years. Electricity tariff variations also show a pronounced effect, reflecting the importance of tariff design and off-peak charging incentives. In contrast, changes in solar capital expenditure and load utilization have a comparatively moderate influence. Overall, the results confirm that reductions in battery costs and the implementation of favorable electricity tariff structures are critical drivers for improving the financial performance of EV charging infrastructure.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study highlight that the successful deployment of electric mobility in developing-country contexts requires policy frameworks that extend beyond fiscal incentives alone. While tax exemptions and import duty reductions can accelerate early adoption, the results indicate that electricity pricing structures play a more decisive role in ensuring long-term grid stability and economic viability. Previous techno-economic studies demonstrate that time-of-use and managed charging tariffs are effective mechanisms for shifting EV demand away from peak periods, thereby reducing grid congestion and deferring costly infrastructure upgrades (Al Wahedi and Bicer, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ihm et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the Tanzanian context, such tariff structures would be particularly beneficial given the evolving nature of the national grid and its sensitivity to demand fluctuations.\u003c/p\u003e \u003cp\u003eThe analysis further confirms the strategic importance of Battery Swap Stations (BSS) as a charging paradigm tailored to high-utilization two- and three-wheeler fleets. Consistent with recent studies on hybrid EV charging ecosystems, BSS significantly reduce vehicle downtime and enable higher asset utilization compared to conventional plug-in charging, especially for motorcycles and light commercial vehicles (Kumar et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, the scalability of the BSS model is contingent upon technical standardization, particularly with respect to battery pack specifications and charging interfaces. Without standardized components, interoperability across stations and manufacturers remains limited, constraining network expansion and increasing system costs\u0026mdash;an issue also highlighted in broader assessments of sustainable EV charging infrastructure (G\u0026uuml;ven et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a socio-economic perspective, the decentralized and service-oriented charging models evaluated in this study offer benefits that extend beyond energy system performance. Hybrid solar-based charging infrastructure has been shown to stimulate local employment through installation, operation, and maintenance activities, particularly in regions where centralized grid expansion is slow or capital-intensive (Kalamaras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nallolla and Vijayapriya, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, by reducing reliance on fossil fuels and lowering operating costs, such systems can enhance the economic resilience of transport operators and small-scale service providers. These findings reinforce the argument that e-mobility, when coupled with renewable energy systems, can function as both an energy transition mechanism and a local economic development catalyst (Riayatsyah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study also demonstrates that a hybrid, decentralized charging approach is better aligned with regions characterized by constrained or climate-sensitive power grids. Rather than imposing additional stress on centralized infrastructure, solar-integrated charging systems complement existing grid capacity by supplying localized generation and storage, thereby improving overall system resilience (Miao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Said et al., 2024). This is particularly relevant for Sub-Saharan African cities, where grid reinforcement often lags behind rapid urban and transport growth. The results therefore support a transition pathway that prioritizes distributed energy resources over immediate large-scale grid overhauls, in line with findings from similar developing and island contexts (Okedu and Uhunmwangho, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kalamaras et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these insights, the study is subject to several limitations. Data availability remains a key constraint, particularly regarding real-world driving cycles and charging behaviour of electric two- and three-wheelers, as well as detailed grid operational parameters. As a result, the techno-economic assessment relies on synthesized load profiles and assumptions derived from comparable studies and regional analogues. While this approach is widely accepted in early-stage feasibility analyses (Ahammed, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ekren et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), future research would benefit from empirical fleet monitoring and detailed grid interaction studies to improve model accuracy and reduce uncertainty.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis techno-economic assessment demonstrates that a strategic approach to solar-powered electric vehicle (EV) charging infrastructure can not only address Tanzania's mobility challenges but also serve as a powerful catalyst for its clean energy transition. The findings show that scenarios integrating solar PV with advanced services like Vehicle-to-Grid (V2G) and Battery Swap Stations (BSS) are technically feasible and more economically viable than conventional grid-only charging. The results confirm that the transition to e-mobility, particularly for the dominant two- and three-wheeler segments, can be a valuable complement to Tanzania's ambitious energy goals. EVs, when integrated with smart charging, can function as distributed energy resources, providing peak shaving services that improve grid resilience and stability. This model, particularly relevant for Dar es Salaam's rapidly growing urban centers, transforms EVs from a potential burden on the grid into a solution for a major energy challenge.\u003c/p\u003e \u003cp\u003eThe economic analysis highlighted the exceptional potential of the Battery Swap Station (BSS) model. Its high utilization rate and attractive financial metrics, including the lowest LCOE and a rapid payback period, position it as a commercially compelling and scalable solution tailored to the unique needs of local drivers. This model directly addresses the primary barriers of range anxiety and long charging times, making e-mobility a more accessible and practical choice for the country's most vulnerable road users. Ultimately, the success of this transition hinges on supportive policy and strategic planning. The government's role in creating a favorable regulatory environment, including innovative tariff designs, establishing technology standards, and launching pilot projects, is crucial. By embracing these findings, Tanzania can secure a path towards a more sustainable, resilient, and affordable energy and transport future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.P conceptualized the study, designed the assessment framework, conducted the literature review, collected and analysed the data, developed and validated the techno-economic and grid analysis models, and prepared the original manuscript. J.M contributed through critical review of the study design, methodology, analysis, and manuscript, providing technical and academic guidance. All authors read and approved the final version of the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eA. F. Güven, N. Ate, S. Alotaibi, T. Alzahrani, A. M. Amsal, and S. K. Elsayed “Sustainable hybrid systems for electric vehicle charging infrastructures in regional applications”, Scientific Reports, Vol. 15, No. 1, art. no. 4199, 2025.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.1038/s41598-025-87985-7\u003c/p\u003e\n\u003cp\u003eT. R. Said, B. Kichonge, and T. Kivevele, “Optimal design and analysis of a grid-connected hybrid renewable energy system using HOMER Pro: A case study of Tumbatu Island, Zanzibar”, Energy Science and Engineering, Vol. 12, No. 5, pp. 2137–2163, May 2024.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.1002/ese3.1735\u003c/p\u003e\n\u003cp\u003eP. H. Kumar, R. R. Gopi, R. Rajarajan, N. B. 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Ahammed, “Optimization of Hybrid Renewable Energy System (HRSE) Using Homer Pro”, IRE Journals, Vol. 5, No. 5, pp. 192-201, 2021\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electric vehicle, techno-economic, EV hubs, Solar powered, Vehicle to Grid, Battery Swapping Station","lastPublishedDoi":"10.21203/rs.3.rs-8531346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8531346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global push for electric vehicle (EV) adoption is reshaping transportation, but its integration into power grids, particularly in developing nations, poses significant challenges and opportunities. In Tanzania, the transport sector's rapid growth strains a national grid characterized by its heavy reliance on hydropower and vulnerability to climate-induced outages. This paper performs a techno-economic assessment of solar-powered EV charging infrastructure, enhanced with Vehicle-to-Grid (V2G) and Battery Swap Station (BSS) models, to bolster mobility and improve grid resilience. The methodology combines simulation using tools like HOMER Pro and Dig SILENT Power Factory, GIS-based mapping, and a detailed economic analysis across multiple scenarios. Key findings suggest that decentralized solar EV hubs offer a significantly more cost-effective and affordable solution for local drivers than the grid-only option, with the BSS model demonstrating the lowest Levelized Cost of Energy (LCOE) at \u003cspan\u003e$\u003c/span\u003e0.095/kWh a 39% reduction compared to the grid-only baseline of \u003cspan\u003e$\u003c/span\u003e0.155/kWh. The BSS model also shows a rapid payback period of just 6.5 years, highlighting its commercial viability. Furthermore, the analysis reveals that while uncoordinated charging can cause significant voltage drops of up to 10% during peak hours, the implementation of smart charging and V2G services can mitigate these effects and provide crucial grid services like peak shaving. The research contributes a vital roadmap for a sustainable e-mobility transition in Tanzania, highlighting the critical role of solar and battery technologies in complementing the country's energy strategy.\u003c/p\u003e","manuscriptTitle":"A Techno-Economic Assessment of Solar-Powered EV Charging Infrastructure in Sub-Saharan Africa: A Case Study of Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:04:05","doi":"10.21203/rs.3.rs-8531346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70d91039-78cd-4b33-af63-27885a5e8ed4","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T18:51:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:04:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8531346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8531346","identity":"rs-8531346","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}