Performance Assessment of a PV-Powered EV Charging Station Using Conventional and Enhanced MPPT Algorithms with Economic Feasibility Analysis

Performance Assessment of a PV-Powered EV Charging Station Using Conventional and Enhanced MPPT Algorithms with Economic Feasibility Analysis | 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 Article Performance Assessment of a PV-Powered EV Charging Station Using Conventional and Enhanced MPPT Algorithms with Economic Feasibility Analysis Traibiz Omar, Rachad Oulad Ben Zarouala, Aboubakr EL Hammoumi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7434102/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract As the world moves toward cleaner transportation solutions, photovoltaic (PV)-powered electric vehicle (EV) charging stations present a promising solution to reducing greenhouse gas emissions and relieving pressure on conventional power grids. However, the intermittent nature of solar energy poses significant challenges to maintaining a consistent and efficient power supply. To address this, Maximum Power Point Tracking (MPPT) controllers are crucial for continuously extracting the maximum available power from PV systems under dynamic environmental conditions. This study presents a comprehensive evaluation of six well-known MPPT algorithms: Perturb and Observe (P&O), Incremental Conductance (IC), Variable Step-Size P&O (VSS-P&O), Variable Step-Size IC (VSS-IC), and their modified variants, aimed at enhancing the performance of PV-powered EV charging stations. The feasibility and effectiveness of these methods are validated through MATLAB/SIMULINK simulations. Results carried out under both stable and rapidly changing irradiance conditions demonstrate that the modified variable step-size algorithms provide better tracking accuracy, faster convergence, and enhanced power stability, making them well-suited for dynamic scenarios. These improvements contribute to more reliable and energy-efficient solar EV charging infrastructure. Additionally, the study also evaluates the economic viability of an on-grid energy storage system, based on projected energy needs and system design assumptions, including five Dacia Spring EVs and 140 PV panels sized to meet expected consumption. The return on investment analysis reveals a favorable payback period, with the initial investment anticipated to be recovered by the end of the fourth year. Overall, the findings support the development of reliable, economically viable, and environmentally sustainable PV-integrated EV charging solutions. Physical sciences/Energy science and technology Physical sciences/Engineering Photovoltaic systems MPPT Electric Vehicle Charging Economic Feasibility MATLAB Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 1. Introduction The accelerating shift towards Electric Vehicles (EVs) is driving a growing demand for eco-friendly infrastructure to support the increasing number of EVs on the road. Solar-powered EV charging stations have emerged as a sustainable solution to this demand, leveraging photovoltaic (PV) systems to reduce carbon emissions and power the expanding EV market. As the global energy transition gains momentum, the integration of renewable energy sources into the EV charging network has become critical to achieving sustainability goals. In 2022 alone, investments in energy transition technologies reached a record $ 1.3 trillion [ 1 ], a 19% increase from the previous year. Solar PV have played a significant role in this shift, contributing two-thirds of the growth in renewable power capacity, which surpassed 440 GW in 2023 [ 2 ]. The International Energy Agency (IEA) projects that solar PV production capacity could exceed its Net Zero Emissions by 2050 scenario by 2030 if current projects proceed as planned [ 3 ]. Simultaneously, the EV market has experienced exponential growth, accounting for 14% of global auto sales, with numbers doubling every 1.2 years [ 4 ]. Between 2015 and 2022, solar capacity expanded by over 400%, while EV sales surged by 2,000%, as shown in Fig. 1 . Despite these advancements, solar-powered EV charging stations face a significant operational challenge: efficiently tracking the maximum power point (MPP) of PV systems under varying irradiance and weather conditions. Traditional MPPT techniques, such as Perturb and Observe (P&O) and Incremental Conductance (IC), are widely used to optimize power output, but they exhibit limitations in dynamic environments. These include slow response times, power losses, and fluctuations, which can compromise the overall efficiency of charging stations. To better understand and address these limitations, it is essential to examine prior research on grid integration, advanced MPPT strategies, and the economic viability of PV-powered EV charging systems. Recent studies have explored several dimensions of this evolving field. A major focus has been the integration of PV-based charging stations with existing electrical grids to ensure system reliability and stability. Studies such as [ 6 ] propose models that manage power flow and improve voltage stability through grid integration. This approach ensures load balancing, reduces peak demand, and optimizes overall system stability. Additionally, works like [ 7 ] present strategies that combine renewable energy sources, such as solar, to reduce grid load and carbon emissions, offering more efficient infrastructure. Moreover, studies [ 8 – 10 ] emphasize integrating PV systems with energy storage, highlighting the role of bidirectional Vehicle-to-Grid (V2G) technology for grid stability and cost-effective charging. These systems improve overall grid performance and reduce dependency on conventional power sources. Alongside grid integration, maximizing energy capture through advanced MPPT control remains a cornerstone of technical development. Several papers, such as [ 14 ] and [ 15 ], explore how AI-based methods, such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), can optimize MPPT performance. These advanced algorithms improve energy output under varying environmental conditions. Furthermore, papers like [ 16 ] propose modified Perturb and Observe (P&O) and Incremental Inductance methods that address real-world conditions, optimizing dynamic and steady-state response. Other studies [ 17 – 19 ] compare these MPPT techniques with traditional algorithms, emphasizing the performance improvement in energy extraction, especially under fluctuating irradiance levels. In parallel, the literature also addresses the environmental and economic benefits of solar-powered EV charging infrastructure. Studies [ 25 ] and [ 26 ] examine how renewable energy sources help reduce overall infrastructure costs, mitigate grid load, and decrease carbon emissions. These papers highlight the financial sustainability of PV charging systems and their contributions to reducing environmental impact. Despite extensive research on the integration of photovoltaic systems into electric vehicle charging infrastructure, there remains a significant gap in optimizing MPPT performance under real-world conditions. Prior studies have predominantly concentrated on aspects such as grid integration, energy storage solutions, and the economic feasibility of PV systems. However, limited attention has been given to the efficiency challenges posed by rapidly fluctuating irradiance, which often results in suboptimal MPPT performance, reduced energy output, and inefficient charging of EVs. This research aims to address this critical gap by evaluating six well-known MPPT algorithms tailored for PV-powered EV charging stations. The selected algorithms include well-established methods -Perturb and Observe (P&O) and Incremental Conductance (IC)- as well as more sophisticated Variable Step-Size (VSS) algorithms and their modified variants. By testing these algorithms under varying irradiance levels and Standard Test Conditions (STCs), the study seeks to assess their effectiveness in enhancing power tracking speed, reducing fluctuations, and improving overall energy output efficiency. The ultimate objective is to identify the most efficient MPPT algorithm for real-world applications, thereby contributing to the optimization of solar energy utilization in EV charging systems and supporting the transition to sustainable energy solutions. Additionally, the research will evaluate the economic feasibility of implementing these optimized MPPT algorithms in real-world scenarios. This includes a cost-benefit analysis to determine the potential savings in energy costs and the overall return on investment for PV-powered EV charging stations. The ultimate objective is to identify the most efficient MPPT algorithm while ensuring that the economic implications support the viability of solar energy utilization in EV charging systems, thus contributing to the transition to sustainable energy solutions. The remainder of this paper is organized as illustrated in Fig. 2 . Section 2 focuses on the materials and methods, incorporating insights from recent studies on the modeling of the PV-powered EV charging stations, additionally it delves into the MPPT algorithms. Section 3 presents the simulation results and discusses the findings comprehensively, emphasizing their relevance to real-world applications. Section 4 evaluates the economic feasibility of the proposed system, while Section 5 concludes the paper with a summary and recommendations for future research. 2. Materials and methods To evaluate the performance of PV-powered EV charging stations under different MPPT algorithms, a comprehensive system model was developed using MATLAB/Simulink. This methodology section outlines the system architecture and component configurations. The model replicates real-world operating conditions and incorporates grid interaction, energy storage, and EV charging dynamics to reflect practical application settings. A key element of the MPPT process is the DC-DC converter connected to the output of the PV array. This converter adjusts its duty cycle based on feedback from the MPPT controller, ensuring efficient power transfer by regulating the array’s voltage and optimizing power generation. The PV-Powered EV charging system integrates grid connectivity along with energy storage options to enhance flexibility. Excess energy can be stored for future use or fed back into the grid, supporting both grid stability and resilience. As illustrated in Fig. 3 , the primary goal is to utilize grid connectivity, allowing the PV system to charge both the load and the EV battery. If the EV is disconnected from the grid, an automatic disconnection occurs, enabling the PV system to power the load directly and charge the stationary battery. This approach maximizes the use of solar energy while providing operational adaptability, depending on the presence of the EV. 2.1. PV Modeling The single-diode equivalent circuit, as shown in Fig. 4 , is used to simulate the PV array, accurately representing the nonlinear I-V properties of solar cells. A current source is used to represent the photogenerated current in this model, a diode is used to account for junction behavior, internal losses are represented by a series resistance (Rs), and leakage paths are modeled by a shunt resistance (Rsh). Dynamic simulations and MPPT algorithm testing can benefit from the single-diode model's ability to reconcile computational simplicity and accuracy. The array output is predicted to reach roughly 500 V under standard test conditions (STC) when modules are connected in series, with a maximum power point voltage of about 30 to 31 V per module. Table 1 presents the specifications of the PV module used. Table 1 Specifications of the PV module and total PV array used in the study (under STC*) Parameter Symbol Module Value Array Value (16 in series) Unit Module Model — Soltech 1STH-250-WH — — Parallel Strings — 1 1 — Series Modules per String — 16 16 — Maximum Power Pmax 250.205 4003.28 W Voltage at Maximum Power Point Vmp 30.7 491.2 V Current at Maximum Power Point Imp 8.15 8.15 A Open-Circuit Voltage Voc 37.3 596.8 V Short-Circuit Current Isc 8.66 8.66 A Number of Cells per Module — 60 960 — *STC: Standard Test Conditions (irradiance = 1000 W/m², cell temperature = 25°C, air mass = 1.5). 2.2. DC–DC Converter Modeling An interleaved buck converter topology, as illustrated in Fig. 5 , is used to control the voltage level between the PV array and the DC connection. By using numerous parallel buck converter phases, the interleaving technique enhances transient response, distributes thermal stress, and lowers input and output current ripple. This type optimizes the interaction between the PV system and the storage or inverter stage by stepping down the PV voltage from approximately 500 V to roughly 400 V. Pulse-width modulation (PWM) controls a fast-recovery diode, an inductor, and a high-speed MOSFET in each converter stage. Table 2 shows the Converter Specifications. Table 2 Converter Specifications Parameter Value Internal Resistance, Ron (Ω) 1×10⁻³ Snubber Resistance, Rs (Ω) 1×10⁵ Resistance, Ron (Ω) 0.001 Forward Voltage (V) 0.8 Snubber Resistance, Rs (Ω) 500 Snubber Capacitance, Cs (F) 250×10⁻⁹ 2.3. Inverter Modeling To convert the regulated DC voltage into AC power that would satisfy the grid and EV charging requirements, a three-phase voltage-source inverter (VSI) is modeled. To achieve balanced AC output, the inverter's insulated gate bipolar transistors (IGBTs) are arranged in a bridge configuration, with sinusoidal PWM controlling each leg. To represent realistic inverter performance, the model as shown in Fig. 6 incorporates switching losses and delay characteristics. The output can be fed directly into the EV interface or synchronized with the grid using a nominal 300 V AC supply. Table 3 presents the Inverter Specifications. Table 3 Three-Phase Voltage-Source Inverter (VSI) Specifications Parameter Value / Description Inverter Type Three-phase Voltage-Source Inverter (VSI) Switching Devices Insulated Gate Bipolar Transistors (IGBTs) Control Technique Sinusoidal Pulse Width Modulation (SPWM) Output Voltage (Nominal) 300 V AC Grid Synchronization Supported 2.4. Bi-directional Converter Modeling A bi-directional DC–DC converter is introduced to manage the power exchange between the battery energy storage system (BESS) and the DC link (Fig. 7 ). It operates in buck mode during charging (reducing the DC bus voltage to match battery voltage) and in boost mode during discharging (raising battery voltage to supply the DC bus). The converter includes a bidirectional switch, realized using synchronous MOSFETs, and operates under dual-mode PWM control depending on system demand and battery SOC. This dual functionality enhances energy flow flexibility and ensures efficient charge/discharge cycles. Table 4 shows the bidirectional DC–DC converter specifications. Table 4 Bidirectional DC–DC Converter Specifications Parameter Value Internal Resistance, Ron (Ω) 1×10⁻³ Snubber Resistance, Rs (Ω) 1×10⁵ 2.5. Stationary Battery Modeling To capture dynamic charge/discharge behavior, the stationary battery system is modeled using an equivalent circuit technique that includes an open-circuit voltage source, internal resistance, and a parallel RC branch. Because of its extended lifespan, high energy density, and efficiency, lithium-ion technology was chosen. When PV output is low, the battery powers the charging station, and when irradiance is at its highest, it stores excess energy. SOC-based control, load balancing, and voltage regulation are important features that are modeled to ensure the best possible integration with the power management system. Table 5 presents the stationary battery specifications. Table 5 Stationary Battery Specifications Parameter Value Type Lithium-Ion Nominal Voltage (V) 240 (12 × 20) Rated Capacity (Ah) 48 Initial State of Charge (%) 50 Battery Response Time (s) 0.0001 2.6. MPPT control A comprehensive study [ 24 ] identifies eight categories of MPPT techniques, which are essential for optimizing PV power systems. These techniques continuously adjust the operating point to adapt to changing environmental conditions. Among these, Perturb and Observe (P&O) and Incremental Conductance (IC) are two prominent MPPT methods. While P&O can take longer to reach the MPP due to external disturbances, IC provides accurate and efficient tracking of rapidly changing irradiance conditions. Enhancements are necessary to address the limitations of traditional P&O and IC methods, such as drift issues and constraints in managing various irradiation settings. Modified versions of P&O and IC algorithms have been developed to improve tracking accuracy and peak power extraction in PV systems. These modifications address the shortcomings of conventional methods, resulting in more reliable and efficient MPPT solutions across diverse environmental conditions. Modified P&O Algorithm The Modified P&O technique is an enhancement of the traditional P&O. It adjusts the duty cycle more responsively to changes in the power-voltage characteristics of the PV system. When both power and voltage increase, a positive offset causes a decrease in duty cycle and voltage, leading to a quicker shift towards the new MPP. This modification improves tracking speed and efficiency. Figure 8 (a) illustrates the flowchart form of the modified P&O algorithm. Modified IC Algorithm The Modified IC algorithm enhances the traditional IC method by incorporating adjustments to improve performance under various environmental conditions. These modifications optimize the MPPT process for more efficient power extraction from PV systems under changing solar conditions. Figure 8 (b) illustrates the flowchart form of the Modified IC algorithm. 3. Results and Discussion This section evaluates the performance of various MPPT methods, including traditional approaches like P&O and IC, as well as enhanced methods such as Variable Step Size P&O/IC and Modified Variable Step Size P&O/IC. Using MATLAB/Simulink, simulations are performed under both stable and dynamic conditions, encompassing standard and variable solar irradiation scenarios. The analysis focuses on the efficiency of these algorithms in tracking the maximum power point, handling steady-state power fluctuations, and optimizing converter performance. This section explores the effectiveness of various MPPT methods while an EV is being charged. It evaluates how these approaches manage EV-related factors such as charging load and power variations, and how effectively they extract power from solar panels. This analysis provides insights into the algorithms' ability to optimize power generation and distribution between the EV, DC load, and grid during real-time charging scenarios. The system configuration is detailed in Fig. 9 . 3.1. Test under STC Figure 10 presents the performance of the PV system using six distinct MPPT algorithms under STC of 1000 W/m² irradiance and 25°C temperature. Conventional methods such as P&O and IC successfully reach the MPP but exhibit significant power oscillations. Enhanced algorithms improve efficiency by reducing these oscillations. Among them, the Modified Variable Step Size IC algorithm demonstrates the fastest response in reaching the MPP, while Modified Variable Step Size P&O achieves 100% power efficiency with minimal steady-state oscillations. The analysis of EV performance using six different MPPT algorithms, as shown in sections (a) and (b) of Fig. 11 , reveals that the traditional P&O and IC methods are less efficient compared to other algorithms. These conventional methods also experience more significant oscillations while reaching the MPP. In contrast, the Modified Variable Step Size P&O and Variable Step Size IC algorithms demonstrate superior efficiency in tracking the MPP. The simulations illustrated in sections (c) and (d) of Figure 11 show the EV's state of charge (SOC), starting at 20%. Notably, both the Mod. VSS P&O and VSS IC algorithms exhibit the fastest response in achieving the highest SOC, with their timings being comparable to other evaluated algorithms. In accordance with the established methodology and based on the data presented in Fig. 12 , an additional experiment was conducted under STC to evaluate the effectiveness of the DC load using different MPPT techniques. Previous analyses indicated that the conventional P&O and IC algorithms exhibited significant oscillations while tracing the MPP. In contrast, alternative strategies, specifically the Modified Variable Step Size P&O and Modified Variable Step Size IC algorithms, demonstrated superior efficiency in achieving the MPP with minimal oscillations. 3.2. Irradiance Variation Test at Constant STC Temperature Expanding the investigation of the six MPPT techniques under STC, we conducted a study addressing a sudden change in irradiance, as shown in Fig. 13. Initially, power output was 3800 W, but at t = 0.6 s, it sharply dropped to 2000 W. Another shift occurred at t = 1.2 s, reducing the power to 0 W. Power stayed at 0 W until t = 1.7 s, then rose back to 2000 W, ultimately reaching 3800 W by the end of the simulation at t = 3 s. Simulation tests confirmed the efficiency of the algorithms, especially the enhanced versions designed to improve MPP tracking. In Fig. 13, the six MPPT techniques were evaluated under STC. While traditional algorithms could detect the MPP during the sudden drop to 2000 W, they exhibited considerable oscillations. In contrast, enhanced algorithms significantly reduced steady-state oscillations near the MPP. The modified variable step-size P&O and IC methods demonstrated superior performance, effectively handling abrupt irradiance changes with quick responses and minimal oscillations near the MPP. Examining the transition to EV performance, we observe in Fig. 14 that the EV's power initially began at 2800 W. At t = 0.6 s, the power unexpectedly dropped to approximately 1000 W, remaining there until t = 2.4 s, before rising again to 2800 W, where it remained until the simulation ended at t = 3 s. In Figures 14-a and 14-b, the six MPPT methods were tested under STC. During a sudden drop in irradiance, traditional algorithms were able to detect the MPP but exhibited considerable oscillations, especially around 1000 W. In contrast, enhanced algorithms significantly reduced steady-state oscillations near the MPP. In Figures 14-c and 14-d, the EV's SOC is depicted, starting at 20%. Notably, the Modified Variable Step-Size P&O and IC methods demonstrated fast response times in achieving peak SOC levels, performing comparably with other algorithms. Furthermore, the performance of the DC load is evaluated using the previously mentioned MPPT techniques, as shown in Figure 15. Traditional methods, P&O and IC, exhibit noticeable oscillations during MPP tracking. In contrast, enhanced approaches, including the Modified Variable Step-Size P&O and Modified Variable Step-Size IC, prove significantly more effective, achieving the MPP with minimal oscillations and improved stability. During periods of zero solar radiation, the grid is essential for maintaining a stable power supply. It acts as a reliable backup, ensuring continuous electricity to both the EV and the DC load when solar irradiance drops to zero. However, grid support is constrained to a brief window, only occurring between 1.2 s and 1.7 s, as shown in Fig. 16 . This highlights the critical importance of grid reliability and fast response, as it must swiftly compensate for the loss of solar energy to meet the power demands. This study examines six MPPT methods: P&O, VSS P&O, Modified VSS P&O, INC, VSS INC, and Modified VSS INC. These techniques are compared across several criteria, including oscillation levels, tracking effectiveness, response time to sudden irradiance changes, implementation complexity, and cost, as summarized in Table 6 . The comparison highlights the strengths and limitations of each method, providing a comprehensive understanding of their performance under varying operating conditions. In addition, Table 7 provides a summary of the comparison results, highlighting the overall performance of the evaluated MPPT algorithms. Table 6 Comparative Analysis of MPPT Algorithms Based on Key Performance Criteria. Criterion P&O and INC VSS P&O and VSS INC M VSS P&O and M VSS INC Oscillation Level Higher oscillations near MPP under changing conditions Reduced oscillations via adaptive step size Minimal oscillations by dynamically adjusting step size based on power change rate Effectiveness Effective in stable conditions; poor under rapid changes Improved performance in variable irradiance and temperature Superior tracking performance across all weather conditions Response Time (Irradiance Changes) Slower due to fixed step size Faster response via dynamic step size Fastest response due to advanced dynamic adjustment mechanisms Complexity Low complexity; easy to implement Moderate complexity from variable step size logic High complexity due to advanced calculations for dynamic control Cost Low cost; minimal hardware and processing needs Moderate cost due to added control features Higher cost from sophisticated logic and increased computational demands Table 7 Comparison of MPPT Algorithms Based on Performance, Complexity, and Cost. Algorithm Oscillation Level Overall Efficiency Reaction Time in the Event of Abrupt Irradiance Changes Complexity Cost P&O Very High 92.09% Low Simple Medium VSS P&O High 96.87% Fast Medium Medium M VSS P&O Neglected 99.37% Very fast Complex Expensive INC Very High 87.79% Low Simple Medium VSS INC High 96.19% Fast Medium Medium M VSS INC Neglected 100% Very fast Complex Expensive 4. Economic Evaluation of the On-Grid Energy Storage System In this paper, two complementary yet distinct studies are conducted. The first one (In sections 2 and 3 ) focuses on the technical modeling and simulation of a PV-powered EV charging station configured with a single 16-module series string, aimed at evaluating system performance under various MPPT control strategies. The second current study (In section 4 ) evaluates the economic viability of an on-grid energy storage system. By comparing the initial costs, operating expenses, savings generated, and return on investment (ROI) with those of a system without storage, we demonstrate that while the initial costs are higher, the economic benefits of optimized energy management justify the investment. Figure 17 illustrates a conceptual design of the proposed PV-powered electric vehicle charging station. The system integrates a rooftop PV array into a modern and streamlined canopy structure, designed to simultaneously accommodate and charge five Dacia Spring electric vehicles. Behind the charging station, a bi-directional connection is established with a stationary Battery Energy Storage System, ensuring energy buffering and stability. Additionally, the station maintains grid connectivity for reliability during low solar irradiance or high demand periods. While visually demonstrating a futuristic architectural approach, this image serves as a preliminary design representation and not a real-world implementation. The assumptions made for selecting the number of components is based on projected energy needs and operational requirements. The choice of five Dacia Spring cars is rooted in the anticipated transportation demands of the study, considering average daily travel distances and the efficiency of EVs. Similarly, the installation of 140 PV panels is determined by the estimated energy consumption of the study and the available roof space for solar installation, ensuring sufficient energy production to meet both direct usage and charging needs for the vehicles. These assumptions are integral to the feasibility analysis and provide a framework for optimizing the system’s design. 4.1. Initial Costs The evaluation of initial costs for the on-grid energy storage system includes the addition of batteries and modifications to components. Table 8 presents a detailed estimate of the necessary investments. This initial cost estimate, totaling 290,560 USD, highlights the significant impact of batteries on the overall cost. It serves as a basis for evaluating the project's financial viability. Table 8 Estimation of the necessary investments (in USD) Component Quantity Unit Cost (USD) Total Cost (USD) Dacia Spring EVs 5 21,800 109,000 Charging Stations 5 1,420 7,100 PV Panels (320W) 140 125 17,500 Inverters 5 2,000 10,000 Smart Logger 1 600 600 Smart Meter 1 160 160 Storage Batteries 15 8,800 132,000 Wiring, Grounding, Breakers, Enclosures — — 8,000 Infrastructure + Labor — — 5,500 Permits, Insurance — — 700 Total — — 290,560 4.2. Operating Costs Analyzing operating costs is crucial for understanding recurring expenses. With proper battery sizing, the system will not require electricity purchases. As indicated in Table 9 , annual maintenance costs total 3,000 USD, indicating a significant reduction in operating expenses due to the absence of electricity costs from the grid. Table 9 Annual maintenance costs (in USD) Component Annual Cost (USD) Maintenance (Panels, Inverters, etc.) 2,200 Monitoring & Repairs 800 Total OPEX 3,000 4.3. Savings Generated Savings come primarily from using surplus energy to power secondary loads, thereby reducing energy billing costs. Annual savings generated by the on-grid energy storage system total 87,400 USD. Although slightly lower than a system without storage, these savings reflect effective energy management. Table 10 presents the annual savings. Table 10 Annual savings (in USD) Source Annual Saving (USD) Surplus Energy Offset 13,400 EV Usage Cost Avoidance 74,000 Total Annual Benefit 87,400 4.4. Return on Investment (ROI) Figure 18 and Table 11 present the cash flow analysis over an eight-year period. Table 11 Annual cash flow over an eight-year period (in USD) Year Annual Cash Flow (USD) Cumulative Cash Flow (USD) Start −290,560 −290,560 Year 1 85,000 −205,560 Year 2 85,000 −120,560 Year 3 85,000 −35,560 Year 4 85,000 49,440 Year 5 85,000 134,440 Year 6 85,000 219,440 Year 7 85,000 304,440 Year 8 85,000 389,440 The economic analysis of the on-grid energy storage system shows an initial investment of 290,560 USD, annual operating costs of 3,000 USD, and annual savings of 87,400 USD. The ROI is projected to be achieved by the end of the fourth year, confirming the financial viability of the project. This system represents a cost-effective solution for adopting renewable energy, enhancing energy efficiency, and contributing to sustainability. 5. Conclusion This study presented a dual analysis involving both technical and economic evaluations of a PV-powered EV charging station integrated with an on-grid energy storage system. Six MPPT algorithms—P&O, IC, VSS-P&O, VSS-IC, and their modified counterparts—were simulated under standard and dynamically changing irradiance conditions. Results demonstrated that the modified variable step-size algorithms consistently outperformed traditional methods in terms of tracking accuracy, convergence speed, and power stability, confirming their suitability for dynamic solar environments. On the economic front, the proposed system—designed for five Dacia Spring EVs and supported by 140 PV panels—proved to be financially viable. Despite a substantial initial investment of USD 290,560, primarily attributed to the inclusion of energy storage, the system yields annual savings of USD 87,400, leading to a payback period by the end of the fourth year. These results underline the economic and operational benefits of coupling advanced MPPT techniques with energy storage in grid-connected solar EV charging infrastructure. Future work could extend this research by exploring the integration of artificial intelligence-based MPPT controllers to further enhance adaptability under highly volatile conditions. Additionally, incorporating hybrid renewable sources and vehicle-to-grid (V2G) capabilities may unlock new dimensions of flexibility and resilience, paving the way for intelligent, decentralized energy systems. Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author Omar Traibiz on reasonable request via e-mail [email protected] . Ethics statement This article does not contain any studies with human participants or animals performed by any of the authors. All methods were performed in accordance with relevant guidelines and regulations. Competing interests The authors declare no competing interests. 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Photovolt. 10 (6), 1892–1899. https://doi.org/10.1109/jphotov.2020.3019955 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Nov, 2025 Reviews received at journal 09 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviews received at journal 02 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor invited by journal 27 Aug, 2025 Editor assigned by journal 26 Aug, 2025 Submission checks completed at journal 25 Aug, 2025 First submitted to journal 22 Aug, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7434102","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515918582,"identity":"adb30471-3ae3-493c-b628-c4f51f454b50","order_by":0,"name":"Traibiz Omar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDADNgnGBoYPxKtPgGhhnMFgQIIWBgkGBmYeYrTwix1+9oHxxz15Punm5s82FX8SGySSHz5gqLFj0G0/gFWL5Ow04xkMCcWGbTIHG4xzzhgAtaQZGzAcS2YwO5OAVYvB7QRjoMMSGNskEhuSc9tAWhLMJBjYDjCYHcClJf0zSIs9SMthy38gLenffzD8A2o5/wCHlhywLYlALY3NjA0gLTlmDIxtQC03sNsiOTunmCEhLSEZ6Jdmxp5jxsZtPG+KJRL7knnMbmC3hV86fTPDB5sE2/mz2x9/+FEjJ9vPnr7xw4dvdnJm57HbAgYoUmxQER7c6kfBKBgFo2AUEAIAWi1ZACLPYZUAAAAASUVORK5CYII=","orcid":"","institution":"National School of Applied Sciences, Abdelmalek Essaadi University","correspondingAuthor":true,"prefix":"","firstName":"Traibiz","middleName":"","lastName":"Omar","suffix":""},{"id":515918583,"identity":"c23e43d5-75e1-45d9-9fd4-8ce989c46f67","order_by":1,"name":"Rachad Oulad Ben Zarouala","email":"","orcid":"","institution":"National School of Applied Sciences, Abdelmalek Essaadi University","correspondingAuthor":false,"prefix":"","firstName":"Rachad","middleName":"Oulad Ben","lastName":"Zarouala","suffix":""},{"id":515918584,"identity":"ea80e877-6fda-4b4d-9153-ece8404b0194","order_by":2,"name":"Aboubakr EL Hammoumi","email":"","orcid":"","institution":"EST, Abdelmalek Essaadi University","correspondingAuthor":false,"prefix":"","firstName":"Aboubakr","middleName":"EL","lastName":"Hammoumi","suffix":""}],"badges":[],"createdAt":"2025-08-22 11:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7434102/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7434102/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91644395,"identity":"984df1a8-efc4-410e-a982-6dda53b39ca2","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35282,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal EV Sales and Solar PV Capacity Additions, 2010-2023\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/bd0cdb1d1ca807897a0225c1.png"},{"id":91644396,"identity":"ed74e087-81e0-45d8-99e7-898d1d837c56","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110403,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the paper structure and section objectives.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/48010557c4dd6fb83562a125.png"},{"id":91644397,"identity":"4ffd3818-e0e9-496b-9aef-279cb842617d","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73578,"visible":true,"origin":"","legend":"\u003cp\u003eThe Model System of the PV-Powered Charging Station for 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blocs.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/ac1ab5c1df43308afd521f94.png"},{"id":91645244,"identity":"406dbac7-8136-4d8d-a292-17c7357b1355","added_by":"auto","created_at":"2025-09-18 15:39:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":63883,"visible":true,"origin":"","legend":"\u003cp\u003ePV performance under STC: (a) Power with P\u0026amp;O family and (b) Power with IC family.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/d5d2b6d9a961d042cfeb2300.png"},{"id":91644409,"identity":"e5b5bf13-63c6-4965-b86c-f8a805b64ec3","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":111067,"visible":true,"origin":"","legend":"\u003cp\u003eSTC test of the EV performance: (a) Output power with P\u0026amp;O family, (b) Output power with INC family, (c) SOC % with P\u0026amp;O family and (d) SOC % with INC family.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/ae78ef59cf97ca4e282cae8d.png"},{"id":91645239,"identity":"9a63fe0e-49af-4e2b-8a66-de5c815cc2fd","added_by":"auto","created_at":"2025-09-18 15:39:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":64681,"visible":true,"origin":"","legend":"\u003cp\u003eDC Load performance under STC: (a) Power with P\u0026amp;O family and (b) Power with INC family.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/63ccb48aacd4a4b5b9a107a1.png"},{"id":91645245,"identity":"9c3813d7-546e-4852-9052-454c221f4bea","added_by":"auto","created_at":"2025-09-18 15:39:21","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":53434,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in irradiance test of the PV performance: (a) Output power with P\u0026amp;O family and (b) Output power with INC family.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/da02c697bd1126ea52bacc7d.png"},{"id":91644416,"identity":"cf9d27e9-736a-4017-ab2d-66ae381fb10f","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":115110,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in irradiance test of the EV performance: (a) Output power with P\u0026amp;O family, (b) Output power with INC family, (c) SOC % with P\u0026amp;O family and (d) SOC % with INC family.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/a5bcd970cedd2a8ae7d26ecf.png"},{"id":91645226,"identity":"755b0a71-c61d-4472-990c-e8dc25337ef7","added_by":"auto","created_at":"2025-09-18 15:39:20","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":75503,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in irradiance test of the DC Load performance: (a) Output power with P\u0026amp;O family and (b) Output power with INC family.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/f2ee867db6c5f0eb8c3368ff.png"},{"id":91645251,"identity":"1f10139d-1b42-4034-8e08-7333e2384dd8","added_by":"auto","created_at":"2025-09-18 15:39:21","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":14781,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in irradiance test of the Grid performance: Output power.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/6dcfe961e234fa48d8f1de4e.png"},{"id":91645234,"identity":"aae2db94-3d1d-4c9d-ba5c-d78892abb050","added_by":"auto","created_at":"2025-09-18 15:39:20","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":404341,"visible":true,"origin":"","legend":"\u003cp\u003ePV-powered charging station design\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/79518fd2233a9f58b3d6d282.png"},{"id":91644428,"identity":"cdcf1b46-cc43-4af5-a9d1-876a58cb0cc9","added_by":"auto","created_at":"2025-09-18 15:31:19","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":52861,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative cash flow over the first 8 years (in USD).\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/0de7571cbe9fa8d73d92a1d3.png"},{"id":91646455,"identity":"4ced1eed-7e73-49c6-8da2-b5a38299dd81","added_by":"auto","created_at":"2025-09-18 15:55:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2506259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7434102/v1/872a5a43-d39d-4b7d-8c5f-ef4f62701337.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Assessment of a PV-Powered EV Charging Station Using Conventional and Enhanced MPPT Algorithms with Economic Feasibility Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe accelerating shift towards Electric Vehicles (EVs) is driving a growing demand for eco-friendly infrastructure to support the increasing number of EVs on the road. Solar-powered EV charging stations have emerged as a sustainable solution to this demand, leveraging photovoltaic (PV) systems to reduce carbon emissions and power the expanding EV market. As the global energy transition gains momentum, the integration of renewable energy sources into the EV charging network has become critical to achieving sustainability goals.\u003c/p\u003e\u003cp\u003eIn 2022 alone, investments in energy transition technologies reached a record \u003cspan\u003e$\u003c/span\u003e1.3 trillion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], a 19% increase from the previous year. Solar PV have played a significant role in this shift, contributing two-thirds of the growth in renewable power capacity, which surpassed 440 GW in 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The International Energy Agency (IEA) projects that solar PV production capacity could exceed its Net Zero Emissions by 2050 scenario by 2030 if current projects proceed as planned [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Simultaneously, the EV market has experienced exponential growth, accounting for 14% of global auto sales, with numbers doubling every 1.2 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Between 2015 and 2022, solar capacity expanded by over 400%, while EV sales surged by 2,000%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDespite these advancements, solar-powered EV charging stations face a significant operational challenge: efficiently tracking the maximum power point (MPP) of PV systems under varying irradiance and weather conditions. Traditional MPPT techniques, such as Perturb and Observe (P\u0026amp;O) and Incremental Conductance (IC), are widely used to optimize power output, but they exhibit limitations in dynamic environments. These include slow response times, power losses, and fluctuations, which can compromise the overall efficiency of charging stations. To better understand and address these limitations, it is essential to examine prior research on grid integration, advanced MPPT strategies, and the economic viability of PV-powered EV charging systems.\u003c/p\u003e\u003cp\u003eRecent studies have explored several dimensions of this evolving field. A major focus has been the integration of PV-based charging stations with existing electrical grids to ensure system reliability and stability. Studies such as [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] propose models that manage power flow and improve voltage stability through grid integration. This approach ensures load balancing, reduces peak demand, and optimizes overall system stability. Additionally, works like [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] present strategies that combine renewable energy sources, such as solar, to reduce grid load and carbon emissions, offering more efficient infrastructure. Moreover, studies [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] emphasize integrating PV systems with energy storage, highlighting the role of bidirectional Vehicle-to-Grid (V2G) technology for grid stability and cost-effective charging. These systems improve overall grid performance and reduce dependency on conventional power sources.\u003c/p\u003e\u003cp\u003eAlongside grid integration, maximizing energy capture through advanced MPPT control remains a cornerstone of technical development. Several papers, such as [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], explore how AI-based methods, such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), can optimize MPPT performance. These advanced algorithms improve energy output under varying environmental conditions. Furthermore, papers like [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] propose modified Perturb and Observe (P\u0026amp;O) and Incremental Inductance methods that address real-world conditions, optimizing dynamic and steady-state response. Other studies [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] compare these MPPT techniques with traditional algorithms, emphasizing the performance improvement in energy extraction, especially under fluctuating irradiance levels.\u003c/p\u003e\u003cp\u003eIn parallel, the literature also addresses the environmental and economic benefits of solar-powered EV charging infrastructure. Studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] examine how renewable energy sources help reduce overall infrastructure costs, mitigate grid load, and decrease carbon emissions. These papers highlight the financial sustainability of PV charging systems and their contributions to reducing environmental impact.\u003c/p\u003e\u003cp\u003eDespite extensive research on the integration of photovoltaic systems into electric vehicle charging infrastructure, there remains a significant gap in optimizing MPPT performance under real-world conditions. Prior studies have predominantly concentrated on aspects such as grid integration, energy storage solutions, and the economic feasibility of PV systems. However, limited attention has been given to the efficiency challenges posed by rapidly fluctuating irradiance, which often results in suboptimal MPPT performance, reduced energy output, and inefficient charging of EVs.\u003c/p\u003e\u003cp\u003eThis research aims to address this critical gap by evaluating six well-known MPPT algorithms tailored for PV-powered EV charging stations. The selected algorithms include well-established methods -Perturb and Observe (P\u0026amp;O) and Incremental Conductance (IC)- as well as more sophisticated Variable Step-Size (VSS) algorithms and their modified variants. By testing these algorithms under varying irradiance levels and Standard Test Conditions (STCs), the study seeks to assess their effectiveness in enhancing power tracking speed, reducing fluctuations, and improving overall energy output efficiency. The ultimate objective is to identify the most efficient MPPT algorithm for real-world applications, thereby contributing to the optimization of solar energy utilization in EV charging systems and supporting the transition to sustainable energy solutions.\u003c/p\u003e\u003cp\u003eAdditionally, the research will evaluate the economic feasibility of implementing these optimized MPPT algorithms in real-world scenarios. This includes a cost-benefit analysis to determine the potential savings in energy costs and the overall return on investment for PV-powered EV charging stations. The ultimate objective is to identify the most efficient MPPT algorithm while ensuring that the economic implications support the viability of solar energy utilization in EV charging systems, thus contributing to the transition to sustainable energy solutions.\u003c/p\u003e\u003cp\u003eThe remainder of this paper is organized as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e focuses on the materials and methods, incorporating insights from recent studies on the modeling of the PV-powered EV charging stations, additionally it delves into the MPPT algorithms. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the simulation results and discusses the findings comprehensively, emphasizing their relevance to real-world applications. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e evaluates the economic feasibility of the proposed system, while Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes the paper with a summary and recommendations for future research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eTo evaluate the performance of PV-powered EV charging stations under different MPPT algorithms, a comprehensive system model was developed using MATLAB/Simulink. This methodology section outlines the system architecture and component configurations. The model replicates real-world operating conditions and incorporates grid interaction, energy storage, and EV charging dynamics to reflect practical application settings.\u003c/p\u003e\n\u003cp\u003eA key element of the MPPT process is the DC-DC converter connected to the output of the PV array. This converter adjusts its duty cycle based on feedback from the MPPT controller, ensuring efficient power transfer by regulating the array\u0026rsquo;s voltage and optimizing power generation. The PV-Powered EV charging system integrates grid connectivity along with energy storage options to enhance flexibility. Excess energy can be stored for future use or fed back into the grid, supporting both grid stability and resilience.\u003c/p\u003e\n\u003cp\u003eAs illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, the primary goal is to utilize grid connectivity, allowing the PV system to charge both the load and the EV battery. If the EV is disconnected from the grid, an automatic disconnection occurs, enabling the PV system to power the load directly and charge the stationary battery. This approach maximizes the use of solar energy while providing operational adaptability, depending on the presence of the EV.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. PV Modeling\u003c/h2\u003e\n \u003cp\u003eThe single-diode equivalent circuit, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, is used to simulate the PV array, accurately representing the nonlinear I-V properties of solar cells. A current source is used to represent the photogenerated current in this model, a diode is used to account for junction behavior, internal losses are represented by a series resistance (Rs), and leakage paths are modeled by a shunt resistance (Rsh). Dynamic simulations and MPPT algorithm testing can benefit from the single-diode model\u0026apos;s ability to reconcile computational simplicity and accuracy. The array output is predicted to reach roughly 500 V under standard test conditions (STC) when modules are connected in series, with a maximum power point voltage of about 30 to 31 V per module. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the specifications of the PV module used.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecifications of the PV module and total PV array used in the study (under STC*)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSymbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModule Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArray Value (16 in series)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModule Model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoltech 1STH-250-WH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParallel Strings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeries Modules per String\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum Power\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4003.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVoltage at Maximum Power Point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVmp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e491.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrent at Maximum Power Point\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOpen-Circuit Voltage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVoc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e596.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eShort-Circuit Current\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Cells per Module\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*STC: Standard Test Conditions (irradiance\u0026thinsp;=\u0026thinsp;1000 W/m\u0026sup2;, cell temperature\u0026thinsp;=\u0026thinsp;25\u0026deg;C, air mass\u0026thinsp;=\u0026thinsp;1.5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. DC\u0026ndash;DC Converter Modeling\u003c/h2\u003e\n \u003cp\u003eAn interleaved buck converter topology, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, is used to control the voltage level between the PV array and the DC connection. By using numerous parallel buck converter phases, the interleaving technique enhances transient response, distributes thermal stress, and lowers input and output current ripple. This type optimizes the interaction between the PV system and the storage or inverter stage by stepping down the PV voltage from approximately 500 V to roughly 400 V. Pulse-width modulation (PWM) controls a fast-recovery diode, an inductor, and a high-speed MOSFET in each converter stage. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Converter Specifications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConverter Specifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Resistance, Ron (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSnubber Resistance, Rs (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026times;10⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResistance, Ron (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward Voltage (V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSnubber Resistance, Rs (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSnubber Capacitance, Cs (F)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u0026times;10⁻⁹\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Inverter Modeling\u003c/h2\u003e\n \u003cp\u003eTo convert the regulated DC voltage into AC power that would satisfy the grid and EV charging requirements, a three-phase voltage-source inverter (VSI) is modeled. To achieve balanced AC output, the inverter\u0026apos;s insulated gate bipolar transistors (IGBTs) are arranged in a bridge configuration, with sinusoidal PWM controlling each leg. To represent realistic inverter performance, the model as shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e incorporates switching losses and delay characteristics. The output can be fed directly into the EV interface or synchronized with the grid using a nominal 300 V AC supply. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Inverter Specifications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThree-Phase Voltage-Source Inverter (VSI) Specifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue / Description\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInverter Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThree-phase Voltage-Source Inverter (VSI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSwitching Devices\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulated Gate Bipolar Transistors (IGBTs)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Technique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinusoidal Pulse Width Modulation (SPWM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutput Voltage (Nominal)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 V AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrid Synchronization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Bi-directional Converter Modeling\u003c/h2\u003e\n \u003cp\u003eA bi-directional DC\u0026ndash;DC converter is introduced to manage the power exchange between the battery energy storage system (BESS) and the DC link (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). It operates in buck mode during charging (reducing the DC bus voltage to match battery voltage) and in boost mode during discharging (raising battery voltage to supply the DC bus). The converter includes a bidirectional switch, realized using synchronous MOSFETs, and operates under dual-mode PWM control depending on system demand and battery SOC. This dual functionality enhances energy flow flexibility and ensures efficient charge/discharge cycles. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the bidirectional DC\u0026ndash;DC converter specifications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBidirectional DC\u0026ndash;DC Converter Specifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Resistance, Ron (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u0026times;10⁻\u0026sup3;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSnubber Resistance, Rs (Ω)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u0026times;10⁵\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Stationary Battery Modeling\u003c/h2\u003e\n \u003cp\u003eTo capture dynamic charge/discharge behavior, the stationary battery system is modeled using an equivalent circuit technique that includes an open-circuit voltage source, internal resistance, and a parallel RC branch. Because of its extended lifespan, high energy density, and efficiency, lithium-ion technology was chosen. When PV output is low, the battery powers the charging station, and when irradiance is at its highest, it stores excess energy. SOC-based control, load balancing, and voltage regulation are important features that are modeled to ensure the best possible integration with the power management system. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the stationary battery specifications.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStationary Battery Specifications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithium-Ion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNominal Voltage (V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 (12 \u0026times; 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRated Capacity (Ah)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial State of Charge (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBattery Response Time (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. MPPT control\u003c/h2\u003e\n \u003cp\u003eA comprehensive study [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] identifies eight categories of MPPT techniques, which are essential for optimizing PV power systems. These techniques continuously adjust the operating point to adapt to changing environmental conditions. Among these, Perturb and Observe (P\u0026amp;O) and Incremental Conductance (IC) are two prominent MPPT methods. While P\u0026amp;O can take longer to reach the MPP due to external disturbances, IC provides accurate and efficient tracking of rapidly changing irradiance conditions. Enhancements are necessary to address the limitations of traditional P\u0026amp;O and IC methods, such as drift issues and constraints in managing various irradiation settings.\u003c/p\u003e\n \u003cp\u003eModified versions of P\u0026amp;O and IC algorithms have been developed to improve tracking accuracy and peak power extraction in PV systems. These modifications address the shortcomings of conventional methods, resulting in more reliable and efficient MPPT solutions across diverse environmental conditions.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eModified P\u0026amp;O Algorithm\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe Modified P\u0026amp;O technique is an enhancement of the traditional P\u0026amp;O. It adjusts the duty cycle more responsively to changes in the power-voltage characteristics of the PV system. When both power and voltage increase, a positive offset causes a decrease in duty cycle and voltage, leading to a quicker shift towards the new MPP. This modification improves tracking speed and efficiency. Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e (a) illustrates the flowchart form of the modified P\u0026amp;O algorithm.\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eModified IC Algorithm\u003c/strong\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eThe Modified IC algorithm enhances the traditional IC method by incorporating adjustments to improve performance under various environmental conditions. These modifications optimize the MPPT process for more efficient power extraction from PV systems under changing solar conditions. Figure 8 (b) illustrates the flowchart form of the Modified IC algorithm.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThis section evaluates the performance of various MPPT methods, including traditional approaches like P\u0026amp;O and IC, as well as enhanced methods such as Variable Step Size P\u0026amp;O/IC and Modified Variable Step Size P\u0026amp;O/IC. Using MATLAB/Simulink, simulations are performed under both stable and dynamic conditions, encompassing standard and variable solar irradiation scenarios. The analysis focuses on the efficiency of these algorithms in tracking the maximum power point, handling steady-state power fluctuations, and optimizing converter performance.\u003c/p\u003e\n\u003cp\u003eThis section explores the effectiveness of various MPPT methods while an EV is being charged. It evaluates how these approaches manage EV-related factors such as charging load and power variations, and how effectively they extract power from solar panels. This analysis provides insights into the algorithms\u0026apos; ability to optimize power generation and distribution between the EV, DC load, and grid during real-time charging scenarios. The system configuration is detailed in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Test under STC\u003c/h2\u003e\n \u003cp\u003eFigure 10 presents the performance of the PV system using six distinct MPPT algorithms under STC of 1000 W/m\u0026sup2; irradiance and 25\u0026deg;C temperature. Conventional methods such as P\u0026amp;O and IC successfully reach the MPP but exhibit significant power oscillations. Enhanced algorithms improve efficiency by reducing these oscillations. Among them, the Modified Variable Step Size IC algorithm demonstrates the fastest response in reaching the MPP, while Modified Variable Step Size P\u0026amp;O achieves 100% power efficiency with minimal steady-state oscillations.\u003c/p\u003e\n \u003cp\u003eThe analysis of EV performance using six different MPPT algorithms, as shown in sections (a) and (b) of Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e, reveals that the traditional P\u0026amp;O and IC methods are less efficient compared to other algorithms. These conventional methods also experience more significant oscillations while reaching the MPP. In contrast, the Modified Variable Step Size P\u0026amp;O and Variable Step Size IC algorithms demonstrate superior efficiency in tracking the MPP.\u003c/p\u003e\n \u003cp\u003eThe simulations illustrated in sections (c) and (d) of Figure 11 show the EV\u0026apos;s state of charge (SOC), starting at 20%. Notably, both the Mod. VSS P\u0026amp;O and VSS IC algorithms exhibit the fastest response in achieving the highest SOC, with their timings being comparable to other evaluated algorithms.\u003c/p\u003e\n \u003cp\u003eIn accordance with the established methodology and based on the data presented in Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, an additional experiment was conducted under STC to evaluate the effectiveness of the DC load using different MPPT techniques. Previous analyses indicated that the conventional P\u0026amp;O and IC algorithms exhibited significant oscillations while tracing the MPP. In contrast, alternative strategies, specifically the Modified Variable Step Size P\u0026amp;O and Modified Variable Step Size IC algorithms, demonstrated superior efficiency in achieving the MPP with minimal oscillations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Irradiance Variation Test at Constant STC Temperature\u003c/h2\u003e\n \u003cp\u003eExpanding the investigation of the six MPPT techniques under STC, we conducted a study addressing a sudden change in irradiance, as shown in Fig.\u0026nbsp;13. Initially, power output was 3800 W, but at t\u0026thinsp;=\u0026thinsp;0.6 s, it sharply dropped to 2000 W. Another shift occurred at t\u0026thinsp;=\u0026thinsp;1.2 s, reducing the power to 0 W. Power stayed at 0 W until t\u0026thinsp;=\u0026thinsp;1.7 s, then rose back to 2000 W, ultimately reaching 3800 W by the end of the simulation at t\u0026thinsp;=\u0026thinsp;3 s.\u003c/p\u003e\n \u003cp\u003eSimulation tests confirmed the efficiency of the algorithms, especially the enhanced versions designed to improve MPP tracking. In Fig. 13, the six MPPT techniques were evaluated under STC. While traditional algorithms could detect the MPP during the sudden drop to 2000 W, they exhibited considerable oscillations. In contrast, enhanced algorithms significantly reduced steady-state oscillations near the MPP. The modified variable step-size P\u0026amp;O and IC methods demonstrated superior performance, effectively handling abrupt irradiance changes with quick responses and minimal oscillations near the MPP.\u003c/p\u003e\n \u003cp\u003eExamining the transition to EV performance, we observe in Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e that the EV\u0026apos;s power initially began at 2800 W. At t\u0026thinsp;=\u0026thinsp;0.6 s, the power unexpectedly dropped to approximately 1000 W, remaining there until t\u0026thinsp;=\u0026thinsp;2.4 s, before rising again to 2800 W, where it remained until the simulation ended at t\u0026thinsp;=\u0026thinsp;3 s.\u003c/p\u003e\n \u003cp\u003eIn Figures 14-a and 14-b, the six MPPT methods were tested under STC. During a sudden drop in irradiance, traditional algorithms were able to detect the MPP but exhibited considerable oscillations, especially around 1000 W. In contrast, enhanced algorithms significantly reduced steady-state oscillations near the MPP.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;In Figures 14-c and 14-d, the EV\u0026apos;s SOC is depicted, starting at 20%. Notably, the Modified Variable Step-Size P\u0026amp;O and IC methods demonstrated fast response times in achieving peak SOC levels, performing comparably with other algorithms.\u003c/p\u003e\n \u003cp\u003eFurthermore, the performance of the DC load is evaluated using the previously mentioned MPPT techniques, as shown in Figure 15. Traditional methods, P\u0026amp;O and IC, exhibit noticeable oscillations during MPP tracking. In contrast, enhanced approaches, including the Modified Variable Step-Size P\u0026amp;O and Modified Variable Step-Size IC, prove significantly more effective, achieving the MPP with minimal oscillations and improved stability.\u003c/p\u003e\n \u003cp\u003eDuring periods of zero solar radiation, the grid is essential for maintaining a stable power supply. It acts as a reliable backup, ensuring continuous electricity to both the EV and the DC load when solar irradiance drops to zero. However, grid support is constrained to a brief window, only occurring between 1.2 s and 1.7 s, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e. This highlights the critical importance of grid reliability and fast response, as it must swiftly compensate for the loss of solar energy to meet the power demands.\u003c/p\u003e\n \u003cp\u003eThis study examines six MPPT methods: P\u0026amp;O, VSS P\u0026amp;O, Modified VSS P\u0026amp;O, INC, VSS INC, and Modified VSS INC. These techniques are compared across several criteria, including oscillation levels, tracking effectiveness, response time to sudden irradiance changes, implementation complexity, and cost, as summarized in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The comparison highlights the strengths and limitations of each method, providing a comprehensive understanding of their performance under varying operating conditions. In addition, Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e provides a summary of the comparison results, highlighting the overall performance of the evaluated MPPT algorithms.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative Analysis of MPPT Algorithms Based on Key Performance Criteria.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriterion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026amp;O and INC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVSS P\u0026amp;O and VSS INC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM VSS P\u0026amp;O and M VSS INC\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOscillation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher oscillations near MPP under changing conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduced oscillations via adaptive step size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal oscillations by dynamically adjusting step size based on power change rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffective in stable conditions; poor under rapid changes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImproved performance in variable irradiance and temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuperior tracking performance across all weather conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponse Time (Irradiance Changes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlower due to fixed step size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFaster response via dynamic step size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFastest response due to advanced dynamic adjustment mechanisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow complexity; easy to implement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate complexity from variable step size logic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh complexity due to advanced calculations for dynamic control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow cost; minimal hardware and processing needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate cost due to added control features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher cost from sophisticated logic and increased computational demands\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab7\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of MPPT Algorithms Based on Performance, Complexity, and Cost.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlgorithm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOscillation Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall Efficiency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReaction Time in the Event of Abrupt Irradiance Changes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplexity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP\u0026amp;O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVSS P\u0026amp;O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM VSS P\u0026amp;O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeglected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery fast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpensive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVSS INC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM VSS INC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeglected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery fast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpensive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Economic Evaluation of the On-Grid Energy Storage System","content":"\u003cp\u003eIn this paper, two complementary yet distinct studies are conducted. The first one (In sections \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3\u003c/span\u003e) focuses on the technical modeling and simulation of a PV-powered EV charging station configured with a single 16-module series string, aimed at evaluating system performance under various MPPT control strategies. The second current study (In section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e) evaluates the economic viability of an on-grid energy storage system. By comparing the initial costs, operating expenses, savings generated, and return on investment (ROI) with those of a system without storage, we demonstrate that while the initial costs are higher, the economic benefits of optimized energy management justify the investment.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e17\u003c/span\u003e illustrates a conceptual design of the proposed PV-powered electric vehicle charging station. The system integrates a rooftop PV array into a modern and streamlined canopy structure, designed to simultaneously accommodate and charge five Dacia Spring electric vehicles. Behind the charging station, a bi-directional connection is established with a stationary Battery Energy Storage System, ensuring energy buffering and stability. Additionally, the station maintains grid connectivity for reliability during low solar irradiance or high demand periods. While visually demonstrating a futuristic architectural approach, this image serves as a preliminary design representation and not a real-world implementation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe assumptions made for selecting the number of components is based on projected energy needs and operational requirements. The choice of five Dacia Spring cars is rooted in the anticipated transportation demands of the study, considering average daily travel distances and the efficiency of EVs. Similarly, the installation of 140 PV panels is determined by the estimated energy consumption of the study and the available roof space for solar installation, ensuring sufficient energy production to meet both direct usage and charging needs for the vehicles. These assumptions are integral to the feasibility analysis and provide a framework for optimizing the system\u0026rsquo;s design.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Initial Costs\u003c/h2\u003e\u003cp\u003eThe evaluation of initial costs for the on-grid energy storage system includes the addition of batteries and modifications to components. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a detailed estimate of the necessary investments. This initial cost estimate, totaling 290,560 USD, highlights the significant impact of batteries on the overall cost. It serves as a basis for evaluating the project's financial viability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation of the necessary investments (in USD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit Cost (USD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Cost (USD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDacia Spring EVs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e109,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharging Stations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7,100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePV Panels (320W)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17,500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInverters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmart Logger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmart Meter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStorage Batteries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWiring, Grounding, Breakers, Enclosures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfrastructure\u0026thinsp;+\u0026thinsp;Labor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePermits, Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e290,560\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Operating Costs\u003c/h2\u003e\u003cp\u003eAnalyzing operating costs is crucial for understanding recurring expenses. With proper battery sizing, the system will not require electricity purchases. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, annual maintenance costs total 3,000 USD, indicating a significant reduction in operating expenses due to the absence of electricity costs from the grid.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual maintenance costs (in USD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Cost (USD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaintenance (Panels, Inverters, etc.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonitoring \u0026amp; Repairs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal OPEX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3,000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Savings Generated\u003c/h2\u003e\u003cp\u003eSavings come primarily from using surplus energy to power secondary loads, thereby reducing energy billing costs. Annual savings generated by the on-grid energy storage system total 87,400 USD. Although slightly lower than a system without storage, these savings reflect effective energy management. Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents the annual savings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual savings (in USD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Saving (USD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurplus Energy Offset\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13,400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEV Usage Cost Avoidance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74,000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Annual Benefit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e87,400\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Return on Investment (ROI)\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e18\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e present the cash flow analysis over an eight-year period.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual cash flow over an eight-year period (in USD)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual Cash Flow (USD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCumulative Cash Flow (USD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStart\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;290,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;290,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;205,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;120,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;35,560\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e304,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e389,440\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\u003eThe economic analysis of the on-grid energy storage system shows an initial investment of 290,560 USD, annual operating costs of 3,000 USD, and annual savings of 87,400 USD. The ROI is projected to be achieved by the end of the fourth year, confirming the financial viability of the project. This system represents a cost-effective solution for adopting renewable energy, enhancing energy efficiency, and contributing to sustainability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study presented a dual analysis involving both technical and economic evaluations of a PV-powered EV charging station integrated with an on-grid energy storage system. Six MPPT algorithms\u0026mdash;P\u0026amp;O, IC, VSS-P\u0026amp;O, VSS-IC, and their modified counterparts\u0026mdash;were simulated under standard and dynamically changing irradiance conditions. Results demonstrated that the modified variable step-size algorithms consistently outperformed traditional methods in terms of tracking accuracy, convergence speed, and power stability, confirming their suitability for dynamic solar environments.\u003c/p\u003e\u003cp\u003eOn the economic front, the proposed system\u0026mdash;designed for five Dacia Spring EVs and supported by 140 PV panels\u0026mdash;proved to be financially viable. Despite a substantial initial investment of USD 290,560, primarily attributed to the inclusion of energy storage, the system yields annual savings of USD 87,400, leading to a payback period by the end of the fourth year. These results underline the economic and operational benefits of coupling advanced MPPT techniques with energy storage in grid-connected solar EV charging infrastructure.\u003c/p\u003e\u003cp\u003eFuture work could extend this research by exploring the integration of artificial intelligence-based MPPT controllers to further enhance adaptability under highly volatile conditions. Additionally, incorporating hybrid renewable sources and vehicle-to-grid (V2G) capabilities may unlock new dimensions of flexibility and resilience, paving the way for intelligent, decentralized energy systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author Omar Traibiz on reasonable request via e-mail [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003eAll methods were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThe International Renewable Energy Agency (IRENA) \u0026amp; Investment \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.irena.org/Energy-Transition/Finance-and-investment/Investment\u003c/span\u003e\u003cspan address=\"https://www.irena.org/Energy-Transition/Finance-and-investment/Investment\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; (2023). 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PV-Powered Electric Vehicle Charging Stations: Preliminary Requirements and Feasibility Conditions. \u003cem\u003eAppl. Sci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app11041770\u003c/span\u003e\u003cspan address=\"10.3390/app11041770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarah, M. T. et al. Solar Powered EV Charging Station with G2V and V2G Charging Configuration. \u003cem\u003eJ. Green. Eng. (JGE)\u003c/em\u003e. \u003cb\u003e10_4\u003c/b\u003e, 1704\u0026ndash;1731 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaleh Cheikh-Mohamad. PV-Powered Charging Station with Energy Cost Optimization via V2G Services. \u003cem\u003eAppl. 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Photovolt.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (6), 1892\u0026ndash;1899. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/jphotov.2020.3019955\u003c/span\u003e\u003cspan address=\"10.1109/jphotov.2020.3019955\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Photovoltaic systems, MPPT, Electric Vehicle Charging, Economic Feasibility, MATLAB Simulation","lastPublishedDoi":"10.21203/rs.3.rs-7434102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7434102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the world moves toward cleaner transportation solutions, photovoltaic (PV)-powered electric vehicle (EV) charging stations present a promising solution to reducing greenhouse gas emissions and relieving pressure on conventional power grids. However, the intermittent nature of solar energy poses significant challenges to maintaining a consistent and efficient power supply. To address this, Maximum Power Point Tracking (MPPT) controllers are crucial for continuously extracting the maximum available power from PV systems under dynamic environmental conditions. This study presents a comprehensive evaluation of six well-known MPPT algorithms: Perturb and Observe (P\u0026amp;O), Incremental Conductance (IC), Variable Step-Size P\u0026amp;O (VSS-P\u0026amp;O), Variable Step-Size IC (VSS-IC), and their modified variants, aimed at enhancing the performance of PV-powered EV charging stations. The feasibility and effectiveness of these methods are validated through MATLAB/SIMULINK simulations. Results carried out under both stable and rapidly changing irradiance conditions demonstrate that the modified variable step-size algorithms provide better tracking accuracy, faster convergence, and enhanced power stability, making them well-suited for dynamic scenarios. These improvements contribute to more reliable and energy-efficient solar EV charging infrastructure. Additionally, the study also evaluates the economic viability of an on-grid energy storage system, based on projected energy needs and system design assumptions, including five Dacia Spring EVs and 140 PV panels sized to meet expected consumption. The return on investment analysis reveals a favorable payback period, with the initial investment anticipated to be recovered by the end of the fourth year. Overall, the findings support the development of reliable, economically viable, and environmentally sustainable PV-integrated EV charging solutions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Performance Assessment of a PV-Powered EV Charging Station Using Conventional and Enhanced MPPT Algorithms with Economic Feasibility Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 15:31:14","doi":"10.21203/rs.3.rs-7434102/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-27T08:27:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T08:42:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-04T18:15:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T12:40:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85045277824564868274454899581138179031","date":"2025-09-30T10:41:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38867344217315818081413309795992232846","date":"2025-09-16T11:45:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304736663291651474396621930467069314821","date":"2025-09-16T05:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153616450554801305122321185496546150617","date":"2025-09-13T19:33:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323290163099694807412158688276150136498","date":"2025-09-11T11:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-10T18:22:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-27T10:27:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-26T04:54:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-25T08:24:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-22T11:22:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"545ce9f1-36c9-4d71-b7a3-13baa5e4bd2c","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54808899,"name":"Physical sciences/Energy science and technology"},{"id":54808900,"name":"Physical sciences/Engineering"}],"tags":[],"updatedAt":"2026-01-25T15:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 15:31:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7434102","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7434102","identity":"rs-7434102","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}