Techno -economic analysis of solar photovoltaic

Vehicle’s

1. Techno-Economic Analysis of Solar Photovoltaic Systems for Electric Vehicle Charging Stations AMAN SHARMA A41105221074

2. 1 INTRODUCTION Contents 2 METHODOLOGY 3 RESULTS AND DISCUSSION 4 IV. CONCLUSION 5 REFERENCES

3. INTRODUCTION The 21st century has seen a significant shift towards sustainable energy, particularly solar PV systems, driven by the need to address climate change and reduce reliance on fossil fuels. This shift coincides with the growing adoption of electric vehicles (EVs) in the transportation sector. Integrating solar PV systems into EV charging stations offers a promising solution to power sustainable mobility with clean electricity, emphasizing the importance of transitioning to renewable energy sources for EV charging.

4. This research paper conducts a thorough techno-economic analysis of solar PV systems for EV charging stations, aiming to evaluate their feasibility, efficiency, and economic viability. The study focuses on various system configurations, considering geographical and climatic factors. It examines energy production capabilities, reliability, and system efficiency, incorporating lifecycle costs and government incentives. Despite challenges like high capital costs and variability in solar energy production, integrating solar PV systems into EV charging infrastructure offers a promising pathway to reduce the transportation sector's carbon footprint. The paper aims to bridge the gap in comprehensive techno-economic analyses, providing insights for policymakers and industry stakeholders to support the adoption of clean energy technologies. Geographic Distribution of Solar Irradiance

5. METHODOLOGY The methodology employed in this research paper utilizes a dual-pronged approach, combining technical performance evaluation with economic analysis to assess the viability of solar PV systems for EV charging stations. It includes simulation tools for energy production forecasting, reliability assessment, and financial analysis criteria such as Net Present Value (NPV) and Levelized Cost of Electricity (LCOE). The integration with EV charging infrastructure is considered for both grid-tied and off-grid configurations, and sensitivity analysis is conducted to account for uncertainties and variability in key parameters. Overall, the methodology aims to provide valuable insights into the potential of solar energy in supporting sustainable transportation infrastructure.

6. RESULTS AND DISCSSION   In the Results and Discussion section, outcomes from the simulation of solar PV systems for EV charging stations are explored. The simulations, conducted using PVsyst and MATLAB, incorporated variables like solar irradiance, panel efficiency, inverter efficiency, and financial parameters. Both grid-tied and off-grid systems were modeled under various geographic and climatic conditions. Key parameters included solar panel efficiency (18%), inverter efficiency (95%), system losses (14%), annual solar irradiance (ranging from 1,500 to 2,200 kWh/m²), discount rate (5%), and project lifespan (25 years).

7. Table showcases the projected annual energy production from solar PV systems of different sizes across various locations, highlighting the direct correlation between solar irradiance and energy output and Figure illustrates the relationship between the size of the solar PV system (in kWp) and the annual energy production (in MWh), across different locations with varying solar irradiance levels. Table presents the reliability of solar PV systems in meeting EV charging demand, demonstrating the effectiveness of battery storage in reducing unmet load percentages, especially in off-grid configurations. Figure illustrates the system reliability metrics, such as unmet load percentage and excess energy percentage, for off-grid solar PV systems with varying sizes of battery storage. The LCOE decreases with increasing system size, illustrating economies of scale. This metric is crucial for evaluating the economic competitiveness of solar PV systems against traditional electricity sources Location Solar Irradiance (kWh/m²) System Size (kWp) Annual Energy Production (MWh) A 1,500 50 70.5 B 1,800 50 84.6 C 2,200 50 103.4 D 1,500 100 141.0 E 1,800 100 169.2 System Configuration Battery Capacity (kWh) Unmet Load (%) Excess Energy (%) Grid-tied, No Storage N/A 12 Off-grid, Small Storage 50 5 3 Off-grid, Medium Storage 100 2 1 Off-grid, Large Storage 200 0.5 0.2 System Size (kWp) LCOE (USD/kWh) 50 0.08 100 0.075 150 0.07 200 0.065

8. Table and Figure illustrates the financial viability of solar PV systems for EV charging stations across different locations, with NPV values indicating the net benefits over the system's lifespan. Higher solar irradiance directly influences the NPV positively. The results highlight the significant potential of solar PV systems for powering EV charging stations, with energy production and financial viability influenced by geographic location, system size, and storage capacity. Higher solar irradiance levels correlate with greater energy output and financial returns, emphasizing the importance of site selection. Including battery storage improves system reliability, particularly for off-grid setups, despite higher initial costs. The decreasing Levelized Cost of Electricity (LCOE) with larger system sizes supports scaling up solar PV installations for cost-efficiency and sustainability. However, financial attractiveness depends on factors like capital costs, operational expenses, and government incentives, as indicated by Net Present Value (NPV) calculations. Overall, integrating solar PV systems into EV charging infrastructure offers a promising pathway to sustainable transportation, with insights from the study aiding in optimizing system design and maximizing economic returns for broader adoption of renewable energy technologies in the sector. Location System Size (kWp) NPV (USD) A 50 50,000 B 50 75,000 C 50 100,000 D 100 120,000 E 100 150,000

9. CONCLUSION The research on the techno-economic analysis of solar PV systems for EV charging stations concludes that integrating renewable energy sources into transportation infrastructure offers significant potential. Solar PV systems can meet energy demands sustainably, with geographic location and solar irradiance crucial for energy output and financial viability. Battery storage enhances reliability, especially for off-grid setups. Larger installations show cost efficiencies, highlighting the need for strategic planning and investment. The study emphasizes careful site selection, system sizing, and energy storage to maximize benefits. It provides valuable insights for policy-making, investment decisions, and promoting sustainable practices in EV charging solutions,aligning with global efforts to combat climate change and reduce reliance on fossil fuels.

10. REFERENCES Cheruvu et al. (2017): The paper discusses using normal distribution to retrieve temporal associations by Euclidean distance. Trinko et al. (2023): This review paper explores the integration of transportation and electricity systems via electric vehicle charging-as-a-service, focusing on techno-economic and societal benefits. Spandana et al. (2018): The study presents an Internet of Things (loT) based smart water quality monitoring system. Ramu (2018): The paper proposes a secure cloud framework for sharing Electronic Health Records (EHRs) using modified CP-ABE and the attribute bloom filter. Radhakrishna et al. (2017): The research focuses on estimating prevalence bounds of temporal association patterns to discover temporally similar patterns. Nagarjuna et al. (2017): The study presents a smart sensor network-based high-quality air pollution monitoring system using LabVIEW. Jaidass et al. (2018): This paper investigates the luminescence properties of Dy3+ doped lithium zinc borosilicate glasses for photonic applications. Kalyani et al. (2023): The study proposes a method for detecting and classifying diabetic retinopathy using capsule networks. Suresh Goud et al. (2022): The paper discusses the role of ternary hybrid nanofluid in the thermal distribution of a dovetail fin with internal generation of heat. Indira et al. (2022): The research focuses on improving artificial neural network with state order dataset estimation for brain cancer cell diagnosis.