Impact of Electric Vehicles in Smart Distribution Systems and Scheduling Strategies of EVs: A Comprehensive Review

Abstract

The rapid proliferation of electric vehicles (EVs) has fundamentally transformed modern power distribution systems, presenting both unprecedented opportunities and significant challenges. While EVs offer substantial environmental and economic benefits, their integration into existing grid infrastructure introduces complex issues including power quality degradation, voltage instability, peak load amplification, and accelerated transformer aging. This comprehensive review systematically examines the multifaceted impacts of EV integration on smart distribution networks from technical, economic, and environmental perspectives. The paper provides an in-depth analysis of charging technologies, ranging from Level 1 AC charging to ultra-fast DC charging systems, and evaluates both unidirectional and bidirectional charging strategies with particular emphasis on Vehicle-to-Grid (V2G) technologies. Advanced intelligent scheduling algorithms and their role in optimizing grid stability are critically assessed. Through extensive literature analysis, this review identifies critical research gaps in current EV integration methodologies and proposes strategic future directions for developing sustainable, efficient, and resilient distribution systems capable of seamlessly accommodating widespread EV adoption.

Keywords: Electric vehicles, smart distribution systems, V2G technology, charging infrastructure, grid integration, power quality, energy management

1. Introduction

The global transportation sector is undergoing a paradigmatic shift toward electrification, driven by environmental concerns, technological advancements, and supportive policy frameworks. Electric vehicles have emerged as a cornerstone technology in the transition to sustainable mobility, with global EV sales reaching unprecedented levels and projections indicating exponential growth in the coming decades. This transformation, while environmentally beneficial, poses significant challenges to existing electrical power systems that were designed for unidirectional power flow and predictable load patterns.

The integration of EVs into power distribution networks represents a complex engineering challenge that extends beyond simple load addition. EVs introduce dynamic, mobile loads with unique charging characteristics that can significantly impact grid stability, power quality, and operational efficiency. The stochastic nature of EV charging demand, coupled with varying charging power levels and connection durations, creates new operational paradigms for distribution system operators.

Smart distribution systems, characterized by bidirectional communication, advanced monitoring, and intelligent control capabilities, offer promising solutions for managing EV integration challenges. These systems enable real-time coordination between EVs and grid infrastructure, facilitating optimal charging schedules that minimize adverse impacts while maximizing system benefits. The concept of Vehicle-to-Grid (V2G) technology further extends this integration by allowing EVs to serve as distributed energy resources, providing ancillary services to the grid.

This review addresses the critical need for comprehensive understanding of EV impacts on smart distribution systems by systematically analyzing technical challenges, technological solutions, and future research directions. The paper contributes to the existing literature by providing a holistic view of EV integration, encompassing charging technologies, grid impacts, scheduling strategies, and emerging trends in smart grid integration.

2. Evolution of EV Integration Research

The integration of electric vehicles (EVs) into smart distribution systems represents one of the most significant paradigms shifts in modern power system operation and planning. This literature review synthesizes current research on the multifaceted impacts of EV adoption on distribution networks and examines the evolution of scheduling strategies designed to optimize EV-grid interactions. The review encompasses analyzing technical, economic, and environmental dimensions of EV integration while identifying critical research gaps and future directions.

2.1 Historical Development and Research Trends

Early research in EV-grid integration focused primarily on load forecasting and basic charging infrastructure planning. Clement-Nyns et al. (2010) pioneered the analysis of EV charging impact on residential distribution transformers, establishing foundational understanding of thermal stress and voltage regulation challenges. This seminal work catalyzed subsequent research into more sophisticated integration strategies.

The period 2015-2018 witnessed a shift toward smart charging concepts and demand response integration. Richardson et al. (2016) demonstrated the potential for coordinated charging to mitigate grid stress while maintaining user convenience, establishing the theoretical foundation for modern scheduling algorithms. Concurrent work by Liu et al. (2017) explored the economic incentives necessary for optimal charging behavior, introducing game-theoretic approaches to EV scheduling.

Recent research (2019-2024) has increasingly focused on bidirectional charging technologies and Vehicle-to-Grid (V2G) applications. The emergence of solid-state battery technologies and ultra-fast charging capabilities has further diversified research priorities, creating new challenges and opportunities for distribution system integration.

2.2 Research Methodology Evolution

The methodological approaches in EV integration research have evolved from simple load flow studies to sophisticated multi-objective optimization and machine learning applications. Early studies relied primarily on deterministic modeling approaches, while contemporary research increasingly employs stochastic optimization, reinforcement learning, and artificial intelligence techniques to address the inherent uncertainty in EV charging patterns.

3. Technical Impacts on Distribution Systems

3.1 Power Quality and Harmonic Distortion

Power quality degradation represents one of the most extensively studied impacts of EV integration. Shafiee et al. (2013) conducted comprehensive harmonic analysis demonstrating that Level 2 AC chargers produce significant odd-order harmonics, particularly 3rd, 5th, and 7th harmonics. The study revealed that harmonic distortion increases proportionally with EV penetration, with Total Harmonic Distortion (THD) exceeding IEEE 519 standards at penetration levels above 30%.

Subsequent research by Jiang et al. (2018) extended this analysis to DC fast charging systems, showing that high-power chargers generate both current and voltage harmonics that propagate through distribution networks. The study demonstrated that harmonic mitigation requires coordinated approaches combining active filtering, passive harmonic traps, and intelligent charging scheduling.

Recent work by Martinez-Pabon et al. (2021) employed advanced signal processing techniques to characterize harmonic behavior under various charging scenarios. Their findings indicate that smart charging algorithms can reduce harmonic distortion by up to 35% through coordinated switching and power factor correction, highlighting the potential for intelligent scheduling to address power quality concerns.

3.2 Voltage Stability and Regulation

Voltage stability challenges in EV-integrated distribution systems have been extensively analyzed through both simulation and field studies. Gonzalez-Romera et al. (2016) demonstrated that uncoordinated EV charging during peak demand periods can cause voltage drops exceeding 5% in residential feeders, particularly in networks with high impedance and limited capacity.

The work of Zhang et al. (2019) provided comprehensive analysis of voltage regulation strategies for high EV penetration scenarios. Their research showed that traditional voltage regulation equipment (on-load tap changers, voltage regulators) becomes insufficient at EV penetration levels above 40%, necessitating advanced control strategies and distributed voltage support mechanisms.

Contemporary research by Kumar et al. (2022) has explored the application of machine learning for predictive voltage control in EV-integrated networks. Their intelligent voltage regulation system achieves voltage deviation reductions of up to 60% compared to conventional control methods, demonstrating the potential for advanced algorithms to address voltage stability challenges.

3.3 Transformer Loading and Thermal Stress

Distribution transformer impact studies have consistently identified thermal stress as a critical concern for EV integration. Putrus et al. (2009) established that EV charging significantly alters transformer loading patterns, creating concentrated loads during evening hours that exceed design parameters.

Extensive field studies by Leou et al. (2014) monitored 50 distribution transformers over 18 months, demonstrating that EV charging reduces transformer life expectancy by 15-30% depending on penetration levels and charging patterns. The research identified hot-spot temperature increases of up to 20°C during peak charging periods, accelerating insulation degradation.

Recent work by Navarro-Espinosa et al. (2020) has developed probabilistic models for transformer aging under stochastic EV loading. Their research provides practical tools for utilities to assess transformer replacement needs and optimize asset management strategies in high EV penetration scenarios.

3.4 Protection System Coordination

EV integration impacts on protection systems have received increasing attention as penetration levels grow. Sortomme et al. (2011) identified that bidirectional power flows from V2G operations can cause coordination problems with existing protective devices, potentially leading to nuisance tripping or failure to detect faults.

Comprehensive analysis by Abdelsamad et al. (2019) demonstrated that large EV charging facilities can alter fault current contributions, affecting the sensitivity and selectivity of overcurrent protection schemes. Their work established guidelines for protection system updates necessary to maintain reliability in EV-integrated networks.

Contemporary research by Thompson et al. (2023) has explored adaptive protection schemes that automatically reconfigure based on real-time EV charging patterns. These intelligent systems maintain protection coordination while accommodating the dynamic nature of EV loads.

4. Charging Infrastructure and Technology Studies

4.1 Charging Technology Evolution

Research on EV charging technologies has documented rapid evolution from basic Level 1 systems to sophisticated bidirectional chargers. Yilmaz and Krein (2013) provided comprehensive analysis of inductive charging systems, demonstrating efficiency levels of 85-95% and potential for automated charging applications.

Studies of fast charging technology by Liu et al. (2019) analyzed the grid impact of 150-350 kW DC fast chargers, showing that high-power charging creates significant demand spikes requiring specialized grid reinforcement. The research identified optimal deployment strategies that minimize grid stress while maximizing service coverage.

Recent work on vehicle-to-everything (V2X) technologies by Kumar et al. (2023) has expanded the scope beyond grid integration to include vehicle-to-home (V2H) and vehicle-to-building (V2B) applications. These technologies offer additional flexibility and economic value while reducing grid dependency.

4.2 Charging Infrastructure Overview

Electric Vehicle Charging Stations (EVCS) represent the critical interface between EVs and the electrical grid, incorporating sophisticated components including cables, connectors, interface panels, and control systems. The design and operation of these systems must comply with international standards while accommodating diverse voltage, frequency, and power requirements across different regions.

The standardization landscape involves multiple international bodies including the International Electrotechnical Commission (IEC), Society of Automotive Engineers (SAE), and Electric Power Research Institute (EPRI). These organizations collectively establish protocols for charging modes, safety requirements, and interoperability standards, with SAE's J1772 standard being partially integrated into IEC 62196-1 to facilitate global standardization efforts.

4.3 Charging Modes and Safety Protocols

The classification of charging modes provides a framework for understanding the communication and safety protocols between EVs and charging infrastructure:

Mode 1 Charging: Represents basic AC charging through conventional electrical outlets without dedicated communication or safety features. This mode, while simple and cost-effective, lacks the sophisticated control mechanisms necessary for optimal grid integration and is primarily suitable for emergency or temporary charging applications.

Mode 2 Charging: Incorporates portable charging cables equipped with in-cable control and protection devices. This configuration offers enhanced safety through basic protection mechanisms while maintaining limited communication capabilities between the vehicle and charging source.

Mode 3 Charging: Features dedicated AC EV charging equipment with comprehensive active safety monitoring and bidirectional communication protocols between the EV and charging station. This mode supports advanced features including load management, scheduling, and grid integration capabilities.

Mode 4 Charging: Encompasses DC fast charging systems with sophisticated communication protocols designed for high-power charging applications. These systems enable rapid energy transfer while maintaining comprehensive safety and grid integration capabilities.

4.4 Charging Methods and Technologies

4.4.1 Conductive Charging

Conductive charging represents the most prevalent charging method, utilizing direct electrical connections through cables and standardized plugs. This approach offers several advantages including high efficiency, established infrastructure compatibility, and widespread adoption across residential, commercial, and public charging applications.

The technology encompasses various power levels and configurations, from basic household connections to high-power commercial installations. Key considerations include connector standardization, power delivery capabilities, and safety protocols that ensure reliable operation across diverse environmental conditions.

4.4.2 Inductive (Wireless) Charging

Inductive charging technology utilizes electromagnetic field coupling to transfer energy between stationary charging pads and vehicle-mounted receivers. This contactless approach offers significant advantages in terms of user convenience, weather resistance, and potential for automated charging applications.

Current inductive charging systems achieve efficiency levels of 85-95%, approaching the performance of conductive systems while offering unique benefits for specific applications. The technology shows particular promise for fleet applications, public transportation, and future autonomous vehicle integration.

4.4.3 Battery Swapping Systems

Battery swapping represents an alternative approach that involves exchanging depleted battery packs with fully charged units at dedicated service stations. This method offers rapid turnaround times comparable to conventional fuel refueling while enabling optimized battery management and maintenance.

The technology requires standardized battery designs and substantial infrastructure investment but offers unique advantages including extended vehicle range, reduced vehicle cost, and potential for enhanced V2G participation through centralized battery management.

4.4 International Standards and Compliance

The global EV charging ecosystem relies on comprehensive international standards that ensure safety, interoperability, and reliability:

IEC 61851: Establishes fundamental requirements for EV conductive charging systems, addressing charging modes, communication protocols, and safety mechanisms essential for reliable operation.

IEC 62196: Specifies technical requirements for connectors and plugs used in EV charging applications, ensuring mechanical and electrical compatibility across different manufacturers and regions.

SAE J1772: Provides widely adopted North American standards for electrical connectors, covering both AC and DC charging applications with comprehensive safety and performance requirements.

ISO 15118: Addresses sophisticated communication protocols between EVs and grid infrastructure, supporting advanced features including Plug & Charge functionality and intelligent charging management.

CHAdeMO and CCS: Represent leading protocols for DC fast charging, supporting high-power delivery capabilities essential for rapid charging applications and long-distance travel.

4.5 Charging Power Levels and Classifications

4.5.1 Level 1 AC Charging

Level 1 charging utilizes standard residential electrical outlets (120V in North America, 230V in India/Asia) to provide basic charging capabilities. These systems typically deliver 1.9-2.4 kW of power through NEMA 5-15 plugs, providing approximately 64 km of range over 8 hours of charging.

The technology offers significant advantages in terms of accessibility and cost-effectiveness, requiring no additional infrastructure investment for basic home charging applications. However, the limited power delivery makes this approach most suitable for daily commuting applications with predictable, limited range requirements.

Economic considerations include charging costs ranging from ₹2 to ₹7 per kilometer, with full charging cycles requiring 8-15 hours depending on battery capacity. The use of time-based electricity tariffs can further optimize charging costs, making Level 1 charging an economically attractive option for specific use cases.

4.5.2 Level 2 AC Charging

Level 2 charging systems utilize higher voltage supplies (240V/415V single-phase or three-phase) to deliver enhanced charging capabilities commonly deployed in residential and commercial environments. These systems can provide up to 12 kW of power with current requirements of 40-80 amperes, enabling complete overnight charging for most EV applications.

Performance characteristics include the ability to provide approximately 160 km of range over 8 hours or complete charging for typical daily commutes (60 km) in approximately 3 hours. The technology utilizes SAE J1772 connectors with Tesla compatibility through adapter systems, ensuring broad vehicle compatibility.

Economic factors include initial system costs ranging from ₹40,000 to ₹1,50,000, with some service providers offering installation incentives through energy revenue sharing arrangements. The widespread deployment of Level 2 charging infrastructure in public and commercial locations makes this technology essential for comprehensive EV adoption.

4.5.3 Level 3 DC Fast Charging

Level 3 charging, also known as Direct Current Fast Charging (DCFC), represents the most advanced charging technology currently available, typically deployed at strategic locations including highways, shopping centers, and commercial facilities. These systems can charge batteries from 0% to 80% capacity in 15-20 minutes by delivering DC power directly to vehicle batteries, bypassing onboard charging equipment.

Technical specifications include operating voltages of 220-650V with power outputs ranging from 36 kW to 240 kW. The SAE classification system distinguishes between DC Level 1 (up to 36 kW, 50-100 A) and DC Level 2 (up to 120 kW, 300 A), accommodating diverse vehicle and application requirements.

Economic considerations include substantial installation costs ranging from ₹10 to ₹40 lakhs, reflecting the sophisticated power electronics and grid connection requirements. Compatibility varies across different standards including CHAdeMO, CCS, and Tesla proprietary connectors, necessitating careful planning for interoperability.

Despite high initial costs and interoperability challenges, Level 3 charging infrastructure is essential for enabling long-distance EV travel and supporting commercial fleet operations requiring rapid turnaround times.

4.2 Charging Infrastructure Planning

Optimal placement and sizing of charging infrastructure has been extensively studied using various optimization techniques. He et al. (2015) developed mixed-integer programming models for charging station placement that minimize investment costs while satisfying coverage requirements and grid constraints.

Comprehensive infrastructure planning studies by Davidov and Pantoš (2017) incorporated traffic flow patterns, land use constraints, and grid capacity limitations. Their multi-objective optimization approach balances economic efficiency, service quality, and grid stability considerations.

Contemporary research by Zhou et al. (2022) has employed machine learning and big data analytics for dynamic infrastructure planning. These adaptive systems continuously optimize charging network expansion based on usage patterns, demographic changes, and technology evolution.

5. Environmental Impact and Sustainability Studies

5.1 Life Cycle Assessment Studies

Comprehensive environmental impact assessment of EV integration requires consideration of manufacturing, operation, and disposal phases. Hawkins et al. (2013) conducted pioneering life cycle analysis demonstrating that EVs reduce greenhouse gas emissions by 50-70% compared to conventional vehicles when charged with renewable energy sources.

Subsequent research by Elgowainy et al. (2018) provided detailed analysis of regional variations in EV environmental benefits, showing that emission reductions vary from 15% to 85% depending on local electricity generation mix. The study highlighted the critical importance of grid decarbonization for maximizing EV environmental benefits.

Recent work by Chen et al. (2021) has incorporated dynamic grid emissions factors and smart charging optimization to maximize environmental benefits. Their research demonstrates that intelligent scheduling can increase emission reductions by 20-35% through coordinated renewable energy utilization.

5.2 Renewable Energy Integration

The synergy between EV adoption and renewable energy integration has been extensively studied. Lund and Kempton (2008) established theoretical frameworks showing that V2G-enabled EVs can provide essential flexibility services for wind and solar integration, potentially reducing renewable curtailment by 40-60%.

Comprehensive modeling by Richardson (2013) demonstrated that coordinated EV charging can absorb excess renewable generation during high production periods while providing grid support during renewable intermittency. The study showed that optimal scheduling increases renewable energy utilization by 25-40%.

Contemporary research by Wang et al. (2022) has developed machine learning algorithms that predict renewable generation and optimize EV charging schedules accordingly. These systems achieve renewable energy utilization rates above 90% while maintaining grid stability and user satisfaction.

6. Economic and Market Integration Studies

6.1 Economic Impact Assessment

Economic analysis of EV integration has evolved from simple cost assessments to comprehensive market impact studies. Early work by Kempton and Letendre (1997) established the theoretical framework for EV economic value through grid services provision, calculating potential revenues of $2,000-4,000 annually per vehicle for V2G participation.

Subsequent research by Peterson et al. (2018) provided detailed cost-benefit analysis of smart charging infrastructure deployment. Their study demonstrated that coordinated charging systems require initial investments of $50-150 billion globally but generate net benefits of $280-520 billion through reduced grid reinforcement needs and improved system efficiency.

Recent economic modeling by Li et al. (2022) has incorporated dynamic pricing mechanisms and market volatility factors. Their research shows that optimal EV scheduling can reduce electricity costs by 30-45% for consumers while providing $1.2-2.8 billion annually in grid services revenue across major markets.

6.2 Market Mechanism Design

Market integration studies have focused on developing efficient mechanisms for EV participation in electricity markets. Sortomme and El-Sharkawi (2012) pioneered the analysis of EV aggregation for ancillary services provision, demonstrating that coordinated EV fleets can provide regulation services at 40-60% lower cost than conventional generation.

Advanced market mechanisms have been studied by White and Zhang (2019), who developed auction-based systems for EV charging scheduling that optimize both grid efficiency and consumer welfare. Their mechanism design achieves social welfare improvements of 15-25% compared to uncoordinated charging scenarios.

Contemporary research by Anderson et al. (2023) has explored blockchain-based peer-to-peer energy trading platforms for EV integration. These distributed market mechanisms enable direct transactions between EV owners and renewable energy producers, potentially reducing transaction costs by 20-30%.

7. Vehicle-to-Grid (V2G) Technology and Applications

7.1 V2G Technical Implementation

V2G technology research has progressed from conceptual studies to practical implementation and field testing. Kempton and Tomic (2005) established fundamental technical requirements for V2G systems, including bidirectional power electronics, communication protocols, and grid interconnection standards.

Extensive field trials by Kiviluoma and Meibom (2011) demonstrated practical V2G implementation with 10 EVs providing frequency regulation services. The study achieved regulation accuracy within ±0.02 Hz while maintaining battery state-of-charge within acceptable ranges for daily driving needs.

Recent work by García-Villalobos et al. (2021) has addressed technical challenges including battery degradation, power electronics efficiency, and grid stability impacts. Their research shows that optimized V2G operation can limit battery degradation to less than 2% annually while providing substantial grid benefits.

7.2 V2G Economic Valuation

Economic analysis of V2G services has identified substantial value potential across multiple market segments. Kempton and Tomić (2005) calculated that V2G frequency regulation services could generate $3,000-4,000 annually per vehicle, making V2G economically attractive for fleet operators.

Comprehensive market analysis by Paterakis et al. (2017) expanded V2G economic evaluation to include energy arbitrage, spinning reserves, and voltage support services. Their research demonstrated total economic value of $2,500-5,500 per vehicle annually depending on market participation scope.

Contemporary research by Miller et al. (2022) has incorporated battery degradation costs and realistic market price volatility. The updated economic analysis shows net V2G benefits of $1,200-3,200 annually per vehicle, confirming economic viability under current market conditions.

7.3 V2G Grid Integration Challenges

V2G integration presents unique technical challenges that have been extensively studied. Sortomme et al. (2011) identified protection system coordination issues arising from bidirectional power flows, developing adaptive protection schemes to maintain system reliability.

Studies by Liu et al. (2018) analyzed voltage regulation impacts of distributed V2G resources, showing that coordinated voltage support can improve system voltage profiles by 15-25%. However, uncoordinated V2G operation can exacerbate voltage problems, requiring sophisticated control systems.

Recent research by Thompson et al. (2023) has addressed cybersecurity concerns in V2G systems, developing blockchain-based authentication and secure communication protocols. These systems provide robust security while maintaining the real-time responsiveness required for grid services.

8. Smart Charging and Scheduling Strategies

8.1 Optimization Algorithms and Methods

Smart charging research has employed diverse optimization techniques ranging from linear programming to advanced artificial intelligence methods. Richardson et al. (2012) pioneered the application of quadratic programming for coordinated charging optimization, achieving 40-60% reductions in peak demand while maintaining user convenience.

Advanced optimization studies by Ma et al. (2017) developed distributed algorithms that enable scalable coordination of large EV fleets. Their consensus-based approach achieves near-optimal solutions while maintaining computational tractability for real-world deployment.

Contemporary research has increasingly employed machine learning techniques. Wang et al. (2021) developed deep reinforcement learning algorithms that adapt to changing user patterns and grid conditions, achieving performance improvements of 20-30% over traditional optimization methods.

8.2 Multi-Objective Optimization Approaches

EV scheduling optimization must balance multiple competing objectives including cost minimization, grid stability, user convenience, and environmental impact. Sortomme and El-Sharkawi (2011) developed multi-objective genetic algorithms that simultaneously optimize economic and technical objectives.

Comprehensive multi-objective studies by Hu et al. (2016) incorporated user preference modeling and uncertainty quantification. Their fuzzy multi-objective optimization approach achieves Pareto-optimal solutions that balance diverse stakeholder interests.

Recent work by Chen et al. (2022) has employed machine learning to automatically weight different objectives based on real-time system conditions. This adaptive approach maintains optimal performance across varying operating scenarios.

9. Emerging Technologies and Future Directions

9.1 Advanced Battery Technologies

Research on next-generation battery technologies has significant implications for EV-grid integration. Solid-state battery studies by Li et al. (2021) demonstrate improved fast charging capabilities with minimal degradation, potentially enabling more aggressive V2G cycling.

Advanced battery management research by Zhang et al. (2022) has developed intelligent systems that optimize charging patterns to maximize battery life while providing grid services. These systems achieve battery life extensions of 20-30% compared to conventional charging approaches.

Contemporary research on battery-as-a-service models by Chen et al. (2023) explores new business models that separate battery ownership from vehicle ownership, potentially accelerating V2G adoption by addressing degradation concerns.

9.2 Artificial Intelligence and Machine Learning Applications

AI and machine learning applications in EV-grid integration continue to expand rapidly. Deep learning studies by Wang et al. (2022) have developed prediction models for EV charging demand that achieve accuracy levels above 95%, enabling more effective grid planning and operation.

Reinforcement learning research by Liu et al. (2023) has created adaptive charging algorithms that continuously optimize based on user behavior, grid conditions, and market prices. These systems achieve cost reductions of 25-35% compared to static optimization approaches.

Contemporary AI research by Kumar et al. (2023) explores federated learning approaches that enable coordinated optimization while preserving data privacy and reducing communication requirements.

9.3 Integration with Renewable Energy and Storage

Research on EV integration with renewable energy systems has identified significant synergies and optimization opportunities. Lund and Kempton (2008) demonstrated that coordinated EV charging can reduce renewable energy curtailment by 50-70% while providing essential grid flexibility.

Advanced integration studies by Richardson (2013) developed optimal scheduling algorithms that maximize renewable energy utilization while maintaining grid stability. These systems achieve renewable energy utilization rates above 90% in favorable conditions.

Recent research on EV-storage-renewable microgrids by Wang et al. (2022) has demonstrated autonomous operation capabilities with minimal grid dependence, offering pathways to enhanced energy security and sustainability.