A Grid-Interfaced DC Microgrid-Enabled Charging Infrastructure for Empowering Smart Sustainable Cities and Its Impacts on the Electrical Network: An Inclusive Review

Highlights

What are the main findings?

• The challenges associated with transportation electrification, grid-integrated DC microgrids, and universal standards to be followed for EV integration with the grid are explored comprehensively.
• The positive and negative impacts of EV charging infrastructure on the utility grid are discussed in detail. A case study is performed to analyse the negative impacts of EV load on the voltage profile and power loss of the IEEE 33 bus distribution system.

What are the implications of the main findings?

• The grid-integrated DC microgrid-enabled charging infrastructure offers decentralised energy governance. The universal standards for EV integration with the grid create a need for global collaboration among automakers and utilities.
• The need for voltage regulation devices, smart charging, power quality monitoring, and sustainable energy sources is emphasised in grid-integrated DC microgrid-based charging infrastructure.

Abstract

Global warming and the energy crisis are two significant challenges in the world. The prime sources of greenhouse gas emissions are the transportation and power generation sectors because they rely on fossil fuels. To overcome these problems, the world needs to adopt electric vehicles (EVs) and renewable energy sources (RESs) as sustainable solutions. The rapid evolution of electric mobility is largely driven by the development of EV charging infrastructures (EVCIs), which provide the essential support for large-scale EV adoption. As the number of CIs grows, the utility grid faces more challenges, such as power quality issues, power demand, voltage instability, etc. These issues affect the grid performance, along with the battery lifecycle of the EVs and the charging system. A charging infrastructure integrated with the RES-based microgrid (MG) is an effective way to moderate the problem. Also, these methods are about reframing how smart sustainable cities generate, distribute, and consume energy. MG-based CI operates on-grid and off-grid based on the charging demand and trades electricity with the utility grid when required. This paper presents state-of-the-art transportation electrification, MG classification, and various energy sources in the DC MG. The grid-integrated DC MG, international standards for EV integration with the grid, impacts of CI on the electrical network, and potential methods to curtail the negative impact of EVs on the utility grid are explored comprehensively. The negative impact of EV load on the voltage profile and power loss of the IEEE 33 bus system is analysed in three diverse cases. This paper also provides directions for further research on grid-integrated DC MG-based charging infrastructure.

1. Introduction

Renewable energy sources (RESs) are widely used to reduce greenhouse gas emissions and provide electricity for home and commercial usage. These renewable sources comprise solar PV, geothermal, wind, and tidal sources, etc. The environmental and climate conditions of any location have effects on the selection of renewable resources. A microgrid (MG) is a unified energy system consisting of various energy-generating sources, users of energy, and storage options. It can be either connected to the utility grid or independently operated as an island MG, typically in rural environments. If required, certain microgrids linked to the grid may also detach and function separately, e.g., if a fault occurs inside the grid. The DC MG system is a power system that uses power converters and RESs to generate and distribute electricity. The DC MG connected with the grid enhances energy efficiency, cuts down the energy consumption of the grid, and lessens the environmental effects. EVs are essential in minimising carbon emissions within the transportation sector. The additional demand created by EV charging needs to be accommodated by utility companies. In the future, EVs could cause a power demand crisis, but utility grid integration with RESs is essential to maintaining the load curve. This paper provides an extensive overview of the grid-integrated DC MG-based charging infrastructure.

1.1. Background

Microgrids offer consistent and clean energy for several applications, such as electric vehicle charging infrastructures (EVCIs). EVs are expected to decrease greenhouse gas emanations from the transportation sectors and fossil fuel consumption. Based on current climate-focused policy announcements and pledges, the IEA announced that EVs will make up nearly 30% of all vehicles sold worldwide in 2030. While impressive, this falls far short of the 60% share that will be required by 2030 to put the trajectory toward net zero CO₂ emissions by 2050 [1]. The extensive adoption of EVs poses many hurdles, such as problems related to the battery and the charging system. For example, the battery of an EV is expensive and heavy, and has a limited lifespan. The charging infrastructure for EVs is also insufficient, as there are not enough public charging stations available, and they are poorly distributed or coordinated. These issues limit EVs’ range, performance, and convenience, making them less attractive than conventional vehicles. However, integrating EVs into the grid poses several challenges, such as power quality, stability, and peak demand issues [2]. Therefore, microgrids can offer a viable solution for EV charging by utilising local RESs. The PV-based microgrid works in self-consumption mode, lessening the effect on the grid. Microgrids can also facilitate two-way power transfer between the battery and the grid [3], allowing EVs to perform as storage devices. The charging time and costs are uncertain in EVCI systems. Developing an effective pricing strategy, which cuts down the price of charging and the charging time for EV owners for the anticipated system with maximum profit, is challenging. The worldwide electric mobility revolution is achieved by the widespread adoption of EVs.

Global warming is a significant challenge that leads to various social and environmental problems. To mitigate the effects of global warming, many countries have enacted laws to cut back on greenhouse gas emissions and support RESs. The transportation industry, which contributes significantly to carbon emissions, is one of the industries that have experienced substantial changes in consumption of energy globally. In recent years, EVs have become a viable substitute for traditional fossil fuel-powered automobiles. EVs have many benefits, including decreased maintenance costs, less pollution, and improved energy security. However, EVs also pose new challenges for the power system, as they create additional electricity demand that needs to be met by RESs. Variability, intermittency, and uncertainty of sources are the major issues faced by the renewable CI. Hence, the use of a supply from the grid in conjunction with RESs provides reliable supply to the CI load. The uncertainties with RESs are due to their intermittent nature and effects on the output power generation. Therefore, it is essential to use an appropriate approach to deal with these uncertainties. Moreover, EVs can affect the grid’s power quality (PQ), which measures the reliability and efficiency of electric power. PQ issues can result from various factors, such as voltage fluctuations, harmonics, frequency deviations, and power outages. Poor PQ can cause damage to electrical equipment, increase energy losses, and reduce customer satisfaction. Therefore, finding solutions to ensure the grid’s PQ while integrating EVs with RESs is essential. MGs can provide local generation, storage, and electricity control using RESs. MGs can also host EVCIs, which provide electricity to charge EV batteries. By integrating MG-based EVCIs with the utility grid, it is possible to accomplish a balance between supply of electricity and demand, optimise the use of RESs, and enhance the PQ of the grid. The rationale of this paper is to improve the electrical network power quality of MG-based EVCIs integrated with the utility grid.

1.2. Critical Analysis of Existing Research

Microgrid systems are local power grids operating in line with the utility grid or independently. They use RESs to reduce greenhouse gas emissions and increase stability and sustainability. However, MG systems face challenges due to the uncertainty of RESs. Various MG architectures and control strategies are being developed and researched to address these challenges. Hybrid MGs are technically as well as economically more feasible [4]. A photovoltaic-based MG enables interactions among the IIREVs, grid, EV users, and nearby buildings. This study offers critical insights to motivate stakeholders to implement IIREVs in line with social expectations and urban planning. Moreover, this work employs a systemic approach to address users’ demands and needs and to evaluate the efficiency of the IIREVs, the related services, and the power grid at different scales [5]. The challenges and solutions for EV charging in MGs that integrate RESs and energy storage units are explored. A comprehensive overview of the various types of MG architectures and control strategies that can be used to ensure optimal operation and power quality at the EV charging point is provided. For EV charging stations, the available power converter topologies and control strategies are provided.

Furthermore, the various levels and standards of EV charging and the controls and connectors required for safe and efficient charging are discussed. Table 1 summarises the findings from the existing literature, which are compared with the main research areas in this paper to provide a thorough comparative analysis. In addition to demonstrating the scope of the current study, this comparison also identifies a critical gap in earlier research, specifically emerging areas like DC microgrid-based EVCS and their impacts on distribution system parameters like voltage profile and power loss. These insights are helpful in showcasing the relevance of the proposed contributions within the broader academic context. From Table 1 it is clear that in the existing literature, microgrid-based charging infrastructure and various energy sources used in the CI are discussed in detail. AC microgrid-based charging station protection and challenges and various charging methods in AC MGs are available in the literature. The impact of EV load on voltage profile and power loss based on the selection of a strong bus or weak bus for the placement of CI in the grid-integrated DC MG context is less of a focus in the available literature.

Finally, an experimental study was presented of an energy management plan that maximises the use of RESs by controlling the flow of power among available sources and charging terminals [6]. The authors proposed a VSG mechanism using an EV charging station (EVCS) to support an islanded MG’s frequency. Islanded MGs often face frequency deviation due to the variability of RESs. To mitigate this issue, the system needs inertia, which RESs cannot provide due to their low or lack of rotational mass. The simulations involve changing the irradiation level of the PV array and the MG system load arbitrarily to create a power divergence in the system [7]. The EV charging at peak periods of the day causes overloading of the power grids. To address this problem, a smart MG composed of RESs is installed for residential consumers. The MG-based charging station is made available for external users. The proposed system is practically implemented using commercially available equipment [8].

1.2.1. Various Energy Sources in the Microgrid

Solar energy is a significant solution for a future that requires fewer fossil fuels and more environmental protection. The energy technology sector is shifting from its conventional model of big, centralised power plants that benefit from economies of scale. PV is well suited for this change. Therefore, comparing PV costs with those of large power markets may not be accurate. PV is likely to lead the way in a new energy service market, where technology does not just provide energy and meets the needs for energy management, emergency power, environmental enhancement, and fuel diversity [9]. A new P&O MPPT method is described in those accounts for fluctuations in the PV module’s voltage, power, and current. The benefits of adding the current deviation profile to the conventional method are demonstrated using both variable and fixed step-size approaches [10]. A MPP monitoring algorithm is essential to enhance the performance of PV cells, which have low conversion efficiency. Various MPP monitoring algorithms for PV systems have been devised [11].

Wind energy is a promising sustainable source that can be tied into the grid. Numerous control strategies have been proposed for wind energy conversion systems, such as MPPT, grid, and machine-side controllers. Among them, pitch angle control is a technique that adjusts the wind turbine blade angle to regulate the aerodynamic torque and power output. The pitch angle control and its related aspects, such as maximum power extraction and grid synchronisation, are briefly reviewed [12]. As a future research direction related to wind electrical systems, the most typical wind energy conversion system layouts are provided [13]. The importance of a smart wind electrical system, which can respond to the atmosphere and control the airflow within the plant to maximise power production, is emphasised. The main challenges for wind energy systems are improving their cost-effectiveness and reliability and reducing the wind turbine’s weight and size to curtail its space requirement. There is a method to optimise wind farm power output by adjusting the turbines’ yaw angles. The technique simulates the wake flow from a turbine using computational fluid dynamics and a virtual particle model to compute a sensitivity index that quantifies the variation in power production. The method also compares different turbine layouts and shows that regular patterns are not optimal for stable power production [14].

The electrical energy is produced from fuel cells (FCs) through electrochemical reactions. They contain a cathode, where reduction occurs, and an anode, where oxidation occurs. The oxidation–reduction reaction generates ions and electrons; the electrons go from the anode to the cathode, producing an external electric current. Fuel cells are a potential power generation technology with low environmental impact. Their second law is efficiency, and losses need to be assessed before they can be used. A thermodynamic approach to fuel cells and their losses has been developed, resulting in thermodynamic and thermochemical equations that can evaluate the losses [15]. The potential and challenges of FCs in MG systems are reviewed. The review covers the current status, performance, and barriers of different FC technologies, and their applications in various MG configurations, such as grid-connected, isolated, backup, and direct current systems. It also discusses the control and hybridisation issues of FC-based microgrids and their role in enhancing grid resilience and alleviating energy poverty. It aims to offer valuable insights for advancing clean energy research and development through FC-based MGs [16]. The control strategies for MGs that use FCs as one of the energy sources are reviewed [17]. FCs can enhance the performance of MGs and promote the use of hydrogen energy, but they also pose many challenges regarding integration and operation. These reviews first introduced the basic concepts of FCs, active disturbance rejection control, and hybrid systems, which are relevant to the control design.

1.2.2. AC Microgrid-Based Charging Station

A smart charging method is proposed for plug-in hybrid electric vehicles (PHEVs) in AC MGs. The proposed power management approach is validated by offline digital time-domain simulations based on the IEEE 33 bus. The findings validate that the proposed strategy can charge PHEVs properly and improve battery storage system (BSS) lifetimes while reducing the utility grid energy consumption by increasing the distributed energy resources’ output power, even with high PHEV penetration [18]. A comprehensive review is provided on AC MGs’ protection challenges and solutions. It evaluates the existing protection schemes for AC MGs in different operational modes and compares their advantages and drawbacks. It also identifies the need for more research to enhance the performance and reliability of adaptive protection schemes or to develop alternative solutions that are robust to communication failures [19]. AC MGs integrate DG units using RESs, ESS, and loads by exploring the different DG units and their applications in various microgrid scenarios. Finally, the review displays the protection plans for MG systems as well as the generalised relay tripping currents [20]. AC microgrids are suitable for applications with many AC loads and generators in various sectors. AC microgrids can also be combined with DC microgrids to form hybrid microgrids, offering the profits of both DC and AC systems [21]. In some instances, MGs might not generate adequate energy to stream the load while generating an excess in others. Hence, an MG enabling technology uses batteries to charge for extra production to cover the load in generation scarcity. Depending on ambient temperature and sun radiation, the quantity of electrical power produced from the PV plant is variable and unpredictable. The power generation from the PV plant is variable due to the changes in temperature and solar radiation. Therefore, the uncertainty must be periodically modelled [22].

1.2.3. DC Microgrid-Based Charging Station

DC microgrids are becoming more critical due to the widespread use of DC power sources, loads, and energy storage systems. However, they also pose planning, operation, and control challenges, involving different distributed generators, loads, and ESSs on a common DC bus. Various control techniques and coordination strategies are needed to operate DC microgrids securely [23]. A DC microgrid power architecture has been developed to increase the efficiency of EV charging. This innovative approach integrates a PV array with a utility grid providing a storage device. Notably, the PV array is directly linked to the DC link, bypassing the need for a static converter. This design choice boosts energy efficiency and simplifies control [24].

DC MGs have become popular around the world in recent years. Numerous studies have shown the benefit of using DC distribution over AC distribution. DC microgrids can improve the charging of EVs and minimise the charging time significantly. The design, control, protection, and energy management of DC MGs are examined. This study also focuses on DC MG-based EVCIs and their architecture [25]. A method for optimal planning of a DC microgrid that can provide EV charging services is proposed. The method considers different technical options for connecting the sources, such as different types of converters and topologies. A rule-based minimisation strategy is applied for the reliability analysis and the total cost is assessed for various levels of energy storage integration [26].

1.2.4. Grid-Integrated DC Microgrid-Based Charging Station

The DC MG used for this study was composed of PV rooftops, an energy storage system, and EVCIs. The system can coordinate and control the bidirectional charging of EVs and provide grid services at the connection point [27]. A battery energy storage system (ESS) enables the CS to regulate the power flow from the grid according to the grid constraints. The station’s control system also isolates the station’s dynamics from the grid’s dynamics. The CS can work in two modes: integrated into the grid or isolated from the grid. The design of the local controllers by eigenvalue analysis is presented. PLECS software is used to simulate the time-domain behaviour of the station and confirm that the station’s dynamics are independent of the AC grid dynamics [28]. A control strategy is proposed for an EV charging unit that is powered by PV, wind, and battery sources in a DC MG. The control strategy for EVs improves on the traditional methods that do not account for the generation and load constraints. It adjusts the charging rate of the EV according to the available surplus power using a current-loop control. The system also considers the variations in PV irradiance, DC and AC loads, and battery charge and discharge states. This enhances the efficiency and performance of the EVCI. Moreover, the control strategy adopted for EV charging helps to achieve energy management [29].

Novel reinforcement learning is applied for operating an EVCS with maximum profit. In the allocation process, EVs are assigned to waiting or charging spots based on their arrival time and demand. In the implementation process, each charger decides whether to charge or discharge the EVs while learning from a standard replay memory. Decentralised execution and centralised allocation significantly improve the scalability and sample efficiency of the proposed algorithm [30]. The inverter connects a photovoltaic power system and an EVCS to the grid. The algorithm stabilises the DC bus voltage by balancing the grid’s power flow and harmonics. The algorithm is tested under various disturbances based on system transfer functions and PSO [31]. The uncontrolled EV charging challenges are mitigated by smart charging strategies. Topology-based approaches such as centralised, decentralised, hierarchical, distributed, and local models are observed in depth with respect to stakeholder significance. The smart EV charging strategies’ challenges and opportunities are described in detail [32]. A wireless power transfer system is developed to maintain the transfer efficiency over a long distance. Based on the distance, transmission quality is improved by varying the coupling coefficient [33]. Three diverse compensation topologies are presented. Zero voltage switching, load-independent constant voltage, and constant current are determined and compared with different topologies [34]. A market-aided restoration strategy is applied for a multi-energy microgrid considering various disaster uncertainties. Risk-averse optimisation is employed to minimise restoration cost while improving system resilience. The dynamic reconfiguration of the distribution system is investigated [35]. The bidirectional energy transfer evolution, trends, and challenges are emphasised. The integration of AI in the vehicle-to-grid (V2G) system optimises the V2G operation. In smart grids, adoption of bidirectional chargers revolutionises energy management. Advancements in batteries and the standardisation of protocols under V2G systems are the prime research areas [36]. V2G integration is comprehensively reviewed, focusing on its challenges and opportunities. Multiple RES usage in smart grids or MGs is more preferable than using a single RES to overcome the inevitable losses. Also highlighted are economic opportunities and technical limitations of V2G operations [37]. EV charging strategies are comprehensively reviewed based on the data availability, methods, intelligence, and architecture. The various heuristic methods employed in the strategies are also discussed in detail. EV home charging evaluations such as time-of-use and real-time pricing are also mentioned [38]. The importance of charging methods to battery ageing is discussed in detail. The trade-off between the lifecycle of the battery and fast charging is highlighted [39]. Real-time scheduling (RTS) is implemented to minimise EVs’ negative impact. RTS helped to manage the charging and discharging pattern of EVs and also improved the overall profit of the proposed system [40]. RTS was proposed to increase the use of RESs and to minimise carbon emissions. The energy storage device acts as an energy management controller and actively participates in RTS [41].

Table 1. Comparison of the existing literature with the present work.

Ref.	EVCS	MG-Based CS	Grid-Integrated MG-Based CS	Impacts		Voltage Profile	Power Loss
				Negative	Positive
[2]	✓	$-$	$-$	✓ (Power quality and stability)	$-$	$-$	$-$
[5]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[6]	✓	✓	$-$	$-$	$-$	$-$	$-$
[7]	✓	✓	$-$	$-$	$-$	$-$	$-$
[8]	✓	✓	$-$	$-$	$-$	$-$	$-$
[18]	✓	✓	✓ (AC MG)	$-$	$-$	$-$	$-$
[22]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[24]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[25]	✓	✓	✓ (DC MG)	✓ (Protection issues)	✓ (Energy management)	$-$	$-$
[27]	✓	$-$	$-$	$-$	$-$	$-$	$-$
[32]	✓	$-$	$-$	✓ (Overloading, voltage profile)	✓ (Grid upgradation)	$-$	$-$
Present work	✓	✓	✓ (DC MG)	✓ (Voltage profile, power loss)	✓ (Power management)	✓	✓

Table 1. Comparison of the existing literature with the present work.

Ref.	EVCS	MG-Based CS	Grid-Integrated MG-Based CS	Impacts		Voltage Profile	Power Loss
				Negative	Positive
[2]	✓	$-$	$-$	✓ (Power quality and stability)	$-$	$-$	$-$
[5]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[6]	✓	✓	$-$	$-$	$-$	$-$	$-$
[7]	✓	✓	$-$	$-$	$-$	$-$	$-$
[8]	✓	✓	$-$	$-$	$-$	$-$	$-$
[18]	✓	✓	✓ (AC MG)	$-$	$-$	$-$	$-$
[22]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[24]	✓	✓	✓ (DC MG)	$-$	$-$	$-$	$-$
[25]	✓	✓	✓ (DC MG)	✓ (Protection issues)	✓ (Energy management)	$-$	$-$
[27]	✓	$-$	$-$	$-$	$-$	$-$	$-$
[32]	✓	$-$	$-$	✓ (Overloading, voltage profile)	✓ (Grid upgradation)	$-$	$-$
Present work	✓	✓	✓ (DC MG)	✓ (Voltage profile, power loss)	✓ (Power management)	✓	✓

The framework of this comprehensive review is shown in Figure 1. The paper begins with an overview of microgrid-based CIs and types of microgrids. Subsequently, types of EVs and their current status, benefits, and prevailing challenges are explored. The discussion further emphasises the importance of standardisation, and associated international organisations and areas of standardisations. The positive as well as negative impacts of EV integration on the grid are explored comprehensively. The major negative impacts of the voltage profile and power loss are analysed using the IEEE 33 bus distribution system. Possible solutions are also proposed to mitigate the negative impacts of EV integration with the grid. Finally, future research directions are outlined to guide further research in this emerging area.

1.2.5. Research Gap and Major Contributions

Refs. [1,2] highlight the supply chain vulnerabilities and power quality mitigation techniques but do not quantify the technoeconomic analysis and harmonics induced by the penetration of EVs. Refs. [3,4] describe a sensitivity analysis, but dynamic reconfiguration is less explored. Refs. [5,6] describe renewable-based microgrids and control techniques, but location and modular scalability are less of a focus. Refs. [7,8] lack a discussion of inverter-based methods and droop control. Refs. [9,10,11] explore MPPT techniques but lack a discussion of advanced control strategies and weather forecasting techniques. Refs. [12,13,14] explore the various control strategies and any one type of microgrid architecture but lack a discussion of hybrid microgrid configuration. Refs. [15,16,17] broadly explore the fuel cell technologies but lack a discussion of real-time coordination and cost analysis. Ref. [18] focuses on V2G and G2V technology but lacks a discussion of adaptive control strategies. Refs. [19,20,21] comprehensively explore protection of AC microgrids but lack a discussion of DC microgrid protection issues. Refs. [22,23,24,25,26,27,28,29,30,31,32] lack a discussion of EV consumer behaviour modelling and real-time grid integration and grid disturbances with EV integration.

Overall, in the existing literature, microgrid-based charging infrastructure and various energy sources used in the CI are discussed in detail. AC microgrid-based charging station protection and challenges and various charging methods for AC MGs are available in the literature. The DC MGs are reviewed in terms of design, control, energy management, protection, and also power architecture. The impact of EV load on voltage profile and power loss based on the selection of a strong bus or weak bus for the placement of CI is less of a focus in the existing review papers. The grid-integrated DC MG-based charging infrastructure has also been in the limelight in recent years because of its many advantages compared to AC MGs and islanded operation of MGs. Accordingly, this paper covers the research gap found in the literature by comprehensively reviewing various aspects of grid-integrated DC microgrids and EV charging infrastructure, including the standards for EV integration and the influence of EVs on the electrical network.

While EVs contribute positively toward reducing carbon emissions and advancing energy sustainability, their widespread adoption also offers significant challenges for the electrical network, such as voltage instability, increased power losses, and overloading in areas with limited grid capacity. In particular, fast charging stations introduce substantial power demands, which can overload weak buses in the distribution system. Therefore, analysing the impact of EV adoption on distribution systems becomes critically important. Such analysis enables researchers to identify potential risks and design mitigation strategies that preserve grid reliability. Additionally, it offers valuable insights for further research and development of intelligent solutions, such as time-of-use management and smart charging techniques, which can optimise EV charging behaviour, decrease peak loads, and enhance overall system efficiency. Utilities and policy makers may ensure a more seamless transition to electric transportation while safeguarding utility grid performance and resilience by understanding these dynamics early. The main objectives of this paper are as follows:

• The importance of transportation electrification is highlighted with different types of EVs, benefits, and challenges. Various factors limiting the adoption of transportation electrification are also discussed.
• Various energy sources in the MG and grid-integrated DC microgrid and universal standards to be followed for EV integration with the grid are discussed in detail.
• This paper aims to identify positive as well as negative impacts of EV charging infrastructure on the utility grid and to explore various methods to reduce the negative impacts of EVs on the electrical network.
• A case study is performed to show the major negative impacts of EV load on the distribution system. The impact on voltage profile and power loss of the IEEE 33 bus distribution system is analysed in three different cases of operations. This paper presents a cost comparison of AC versus DC microgrid-based charging infrastructure.
• This paper aims to address potential avenues in grid-integrated DC microgrid-based charging infrastructure and to outline the growing research directions for the future.

This paper is organised into six comprehensive sections; each deal with a critical facet of the EV charging paradigm. Section 1 introduces the background and contextual foundation of the research, along with a critical review of the literature. Section 2 explores transportation electrification, technological advancements in CI, and various charging configurations. Section 3 delves into classification of MGs, grid-integrated DC MGs, and standards for EV integration comprehensively. Section 4 critically examines the influence of charging infrastructure on the electrical network, focusing on voltage profile, power loss, and integration challenges. Section 5 deals with different methods to reduce the negative impact of CI on the grid. Section 6 offers concluding remarks with forthcoming viewpoints for research and development in the wide arena of sustainable mobility and energy systems.

2. Transportation Electrification: Challenges and Opportunities

EVs are key to achieving decarbonisation within the transportation sector. EVs have a long history that dates back to the 19th century, when several inventors experimented with battery-powered prototypes. However, EVs did not gain much popularity until the 21st century, when they became more widely available and advanced. One of the reasons for the low demand for EVs in the past was the limited battery capacity and performance, which made them inferior to ICE vehicles. Since then, EVs have experienced periodic growth and resurgence [42].

EVs have their own merits and limitations compared with conventional vehicles; despite all the limitations, EVs have vast potential to transform the transportation sector across the globe and contribute to its economic and environmental goals. The evolution of the battery electric vehicle (BEV) and PHEV at the global level is represented in Figure 2. The worldwide stock of EVs increased at an annual rate in 2022 that was comparable to those in 2021 and the years 2015–2018, suggesting a significant return of the EV market to its pre-pandemic growth rate. The average growth rate between 2015 and 2018 was similar to the yearly growth rate for electric car sales in 2022 [1].

2.1. Electric Vehicle Types, Benefits, and Challenges

EVs are classified based on their engine technologies, as shown in Figure 3. EVs that run only on electricity stored in batteries are called BEVs and they do not have any internal combustion engine (ICE) or fuel tank. PHEVs have both an ICE and an electric engine. They can be charged by an external electric source, which significantly reduces fuel intake. Based on the driving conditions, they can also switch between the ICE and the electric engine. Hybrid electric vehicles (HEVs) also have an ICE and an electric engine but cannot be plugged into the grid. They use the ICE to charge their batteries or to assist the electric engine when needed. They are more fuel-efficient than traditional ICE vehicles but less so than PHEVs.

Fuel cell EVs (FCEVs) use a fuel cell to generate electricity from oxygen and hydrogen. They have an electric engine that uses the electricity from the fuel cell and a hydrogen tank that stores the fuel. Extended-range EVs (ER-EVs) are very comparable to BEVs. ER-EVs are similar to BEVs, but they contain a small IC engine that serves as a backup generator. The ICE can recharge the batteries when they are low, but it does not directly power the wheels. ER-EVs, without compromising their performance, extend the range of EVs [43]. The widespread adoption of EVCIs helps to reduce range anxiety and lessen the inadequate availability of EVs. This can be achieved if the CSs have a low production cost and by working on urban forecasting and regulation [44], as shown in Figure 4.

2.2. Electric Vehicle Charging Infrastructures

EVs are a promising alternative to traditional vehicles that run on fossil fuels. However, to ensure that EVs can operate smoothly and conveniently, it is vital to have proper CIs that can provide a sufficient and accessible power supply to EVs. EV owners may face range anxiety, long waiting times, and higher costs without adequate charging stations. Therefore, developing a robust and efficient charging infrastructure is a critical and urgent need for the promotion and adoption of EVs [45]. Public charging infrastructure is essential for EV adoption, especially in dense urban areas where home-based charging is less accessible. The number of public CSs had increased to 2.7 million globally by the end of 2022, with over 900,000 of those installed in 2022, a 55% surge over the stock in 2021 and a growth rate similar to the 50% pre-pandemic growth rate between 2015 and 2019 [1]. EV charging is generally classified into three types as per relevant standards [46].

1. Level 1 or slow charge: charging rate up to 3 kW;

2. Level 2 or moderate charge: charging rate of about 19 kW;

3. Level 3 or fast charge: charge rate up to 100 kW.

Inductive charging, conductive charging, and battery swapping are commonly used methods for charging EVs. Irrespective of the topology used, AC-DC converters play a significant role in the EV charging setup [47].

The charging time range decreases to 20–30 min with DC fast charging. There are three types of DC fast charging stages—stage-1 (up to 36 kW), stage-2 (up to 90 kW), and stage-3 (up to 240 kW)—according to the SAE J1772 standard developed in United States [48]. The off-board and on-board conductive CS block diagram is depicted in Figure 5. The three different types of inductive chargers [48] are shown in Figure 6. All three rapid DC charging speeds use an electric vehicle supply equipment off-board charging system as a crossing point between the supply and the vehicle. Fast charging for EVs has unfavourable effects on the network power efficiency. The primary issues causing a decay in power efficiency include phase mismatch, harmonics in line currents, irregularities of DC voltage in the signal voltage, magnetic leakage fluxes, and phantom loading. These issues are bound to affect the competence and endurance of the distribution network (DN) apparatus. In addition, the harmonic current portion causes extra I²R losses in the power transformer and cable windings [49].

The various factors which influence the charging infrastructure characteristics are given in Figure 7. The large number of CIs is expected by users to overcome the range anxiety caused by EVs. It is preferable to have grid-integrated CIs to ensure a reliable power supply. CI commercialisation adheres to various regulations and standards to ensure the safety of EV users. Both off-grid and grid-integrated charging stations have different advantages and disadvantages. Grid-integrated CIs are preferred based on the location and energy demand as they offer a reliable power supply to EVs. The use of RESs as a source of supply for CIs contributes to achieving sustainability goals and curtails the CI’s undesirable effects on the environment. The design and functionality of the CI are influenced by the type of EVs to be charged in the CI. Cost is one of the important factors which influence the extensive adoption of charging infrastructures. The operation cost of CIs varies based on different factors such as location, type of charging equipment deployed, and network connectivity. To optimise the energy usage and to have a continuous user experience, an effective communication and control system is necessary for CIs. The scalability of CIs needs to be considered to accommodate the day-by-day upsurge in the number of EVs. The use of recyclable materials in CIs promotes sustainability and reduces the impact on the environment. E-mobility as well as regulators and policy makers have a significant impact on charging infrastructure.

3. Overview of Microgrid Systems and Standards for Vehicle-to-Grid Integration

Microgrids are small, self-sufficient electrical networks that use various sources of energy and technology to power a wide area. They are a vital element of the future smart grid. MGs consist of various sources for generating electricity, such as solar panels, wind turbines and fuel cell microturbines, and energy storage devices. MGs can also have flexible loads such as buildings, industries, or EVs that adjust their demand according to the supply. Nowadays, MGs are becoming more popular and widespread due to the rapid decline in the costs of RESs and the increase in demand for clean and secure electricity [50].

MGs offer several benefits, such as improving the consistency and resiliency of the power supply, reducing the environmental influence of the electricity sector by increasing the share of RESs, enhancing the efficiency and optimisation of the energy system, and delivering electricity to remote areas [51]. There are different ways to classify MGs based on their system architecture and voltage characteristics. The type of MG based on the system topology and market segments is shown in Figure 8. MGs can be divided into three classes based on their current type, namely DC MGs, AC MGs, and hybrid MGs, and are also classified as institutional MGs, utility MGs, industrial and commercial MGs, remote-area MGs, and transportation MGs based on the areas of application. MGs use the distribution network and distributed energy resource (DER) rules. When there are many DERs in low- or medium-voltage networks, they need less power from big generators. This changes the operating characteristics of the power network [52].

3.1. DC Microgrid

The DC MG utilising renewable resources is indispensable for ensuring the nation’s future energy security [53]. DC MGs generate, store, and distribute electricity in DC form. They have several advantages over AC MGs, such as having no need for synchronisation, better power quality, and an improved power factor. DC MGs can also integrate with various DC sources and loads, such as RESs, energy storage devices, telecommunication systems, EVs, and marine power systems. However, DC MGs face challenges, such as a lack of standard voltage levels, additional conversion to produce AC voltage, difficulty reconfiguring from the existing grid, and complex protection schemes [54]. A DC system power architecture can be categorised into distributed and centralised architectures. Centralised architectures were the first DC power system [55]. The DC MG bus structure can be arranged in different ways. The single bus structure relies on a single bus collecting and supplying electricity to various voltage levels to meet the demand. The double-deck bus system’s hierarchical architecture includes both single and dual bus arrangements. The DC MG’s basic arrangement is represented in Figure 9 [56], and contains three A B C feeders, radial bus arrangement, various loads, various sources, an energy supervising system, a storage device, an isolator switch, a protection unit, and a point of common coupling [56].

DC MGs must overcome several difficulties before being broadly utilised in industrial and commercial applications. Besides security apprehensions, standardisation is one more obstacle to the adoption of DC MGs. Several establishments have devoted themselves to developing practical rules to support DC MGs [57]. The DC MG control methods are categorised as basic and multilevel control strategies. The various control strategies applied to DC MGs are given in Figure 10. The primary control methods are classified as distributed, centralised, and decentralised. Multilevel control is enforced through various control levels.

The centralised and decentralised controllers manage the distributed units with and without communication links. Distributed control is an amalgamation of decentralised and centralised types [58]. The current distribution between parallel sources is managed by a lower level of a DC; at the same time, a higher level cuts down on the changes in voltage magnitude and controls the voltage of MGs [59]. At the component level several sources and converters should easily integrate and isolate from a DC MG [60]. The numerous loads in DC MGs cause the stability margins of the system to deteriorate. Hence, novel control methods in DC MGs are essential to handle a diverse array of scenarios [61].

3.2. Various Energy Sources in the DC Microgrid

The DC microgrid involves several types of sources. In particular, solar energy and wind energy sources are given more importance because of their several advantages. This section describes solar and wind energy systems in detail.

3.2.1. Solar Energy System

Solar energy is a renewable and eco-friendly source of energy that comes from sunlight. Solar energy can be used for several applications, such as commercial, residential, and industrial purposes. It can be easily converted into electricity using solar panels. Solar energy is efficient and environmentally friendly, producing no pollution or greenhouse gases [62]. Solar cells have a conversion efficiency of about 17%. Under reduced irradiance and shadowing conditions, the solar cell’s power production would be significantly lower. MPPT controllers are utilised to take out the most feasible amount of power. Solar PV will play a main role in the evolution of global energy to address the challenges of climate change. To meet this objective, energy-related carbon emissions must be lowered by roughly 3.5% annually through 2050. Globally, solar PV is growing popular, and in many countries, it is now the most economical option for producing power. Solar PV is predicted to be the most affordable energy source in areas with high levels of solar radiation by 2050 [63].

Power generated from RESs fluctuates and is uncertain. Proper uncertainty modelling is a necessary step for renewable energy management. Additionally, uncertainty happens over various periods, from a few minutes to hours ahead to days ahead. To balance the demand and generation, resources need to be available during the period of uncertainty and responsive once the uncertainty is eliminated. Numerical weather projections serve as a source of irradiation forecasts for national and regional PV forecasts. For solar energy applications, the standard deviation roughly equates to an interval of 68.27% of occurrences around the mean value, which is expected to convey uncertainty [64].

3.2.2. Wind Energy System

There is much interest in creating RESs and increasing energy conservation measures due to the continuously rising demand for power. As a result, technical development is focused on utilising wind energy. The available power from turbines varies with wind speed and turbine speed. Furthermore, the maximum power available changes with the wind and turbine speeds. Modern wind power systems constantly modify the generator loading to allow the turbine to run at maximum coefficient power despite the fluctuating wind speed [65]. Wind turbines use the doubly fed induction generator (DFIG), permanent magnet synchronous generator (PMSG), and squirrel cage induction generator to generate electricity. Most wind power generation systems currently use the DFIG or PMSG. Due to its benefits, the PMSG has recently risen in popularity. The PMSG requires no gear system to build, and its design is simple. The gearbox-free wind systems offer many advantages, including enhanced general performance, dependability, light weight, and less maintenance. Since permanent magnets are used, the PMSG does not require external magnetisation. This capability is vital for supplying reactive power for the magnetisation of the induction generators, especially in isolated wind energy conversion systems and distant locations where contacting the grid is difficult [66].

The weather has a direct impact on wind power generation. Wind speed fluctuates constantly. Therefore, statistical methods must be used to describe this nonstationary process. It is crucial to define wind power generation intermittency to maximise its application. The term “intermittency” describes wind power’s erratic and unpredictable nature. Since wind power output is significantly correlated with wind speed, characterising wind power intermittency necessitates modelling or forecasting wind power production, which frequently results in the modelling of wind speeds [67].

3.3. Grid-Integrated DC Microgrid

A grid-integrated DC MG connects a DC MG to the AC grid via a power converter. A grid-integrated DC MG offers improved power quality, reliability, efficiency, and flexibility. The system architecture of various types of grid-connected DC MGs can be distinguished by the way the DC bus, AC/DC converter, and grounding method are configured [68]. The control strategies of a grid-integrated DC MG can be classified into hierarchical levels. The primary control manages the DC bus voltage and the power distribution among DERs.

The DC bus voltage is restored to its base value and coordinates the power exchange with the AC grid using the secondary control. The tertiary control optimises the system’s economic and environmental performance. The applications and standardisations of a grid-integrated DC MG can cover various domains, such as residential, commercial, industrial, rural, and remote areas [69]. The grid-integrated DC microgrid structure is shown in Figure 11. It comprises various sources associated with the DC bus via a DC-DC converter and DC loads. An AC-DC converter integrates the utility grid into the DC bus [70].

3.4. Standards for EV Integration with Grid

Different types of standards are available worldwide that deal with EVCSs. There are three standards and codes that deal with EV integration with the grid.

Table 2. Standards for EV integration with grid.

Standards	Description
IEEE 1547	It is applicable for interconnecting DERs with power systems. The requirements related to the operation, performance, safety, testing, installations, and maintenance of DERs on primary and secondary distribution systems are covered by this standard.
UL 1741	Specifications for power conversion equipment and its protection devices for DER incorporation with the grid is covered by UL standards.
NFPA 70	It provides guidelines for wiring electrical equipment and ensuring safety on the consumer side of PCC.

shows the standards for EV integration with the grid [71]. These standards are mainly suitable in the U.S., Canada, and Mexico. India follows BIS and CEA regulations, and Europe follows IEC standards. China has its own GB/T standards for EV integration. Similarly, the various attributes of UL standards are more relevant in the U. S. and Canada. The different articles of NFPA 70 are published by the U. S. National Fire Protection Association. The Canadian Electrical Code is used in Canada and IEC standards are followed in Europe. BIS standards, the National Building Code, and CEA regulations are followed in India. The various implementation challenges associated with the IEEE 1547, NFPA 70, and UL standards [71] are as follows. The DERs must be connected during frequency and voltage disturbances that require a robust control architecture. Supporting communication interfaces is complex and the requirements of compliance for lower rating and higher rating systems are different. IEEE 1547 does not provide cybersecurity-related regulations, as integration of DERs deals with cybersecurity issues that need to be handled separately. Inconsistent utility processes and less availability of standardised data models are key challenges in UL standards adoption. The implementation of NFPA 70 requires interconnected safety practices, offering the cost of upgradation with jurisdictional differences. The different types of IEEE 1547 standards are summarised in Figure 12. Procedures for testing to confirm that interconnection specifications and types of equipment comply with IEEE 1547 test requirements are provided in detail by P1547.1. The technical background is provided by P1547.2 and also makes IEEE 1547 user-friendly. P1547.3 exemplifies DERs and related interconnection issues. The instructions for design, operation, and integration of DER island systems with power systems are given by P1547.4.

The various attributes of UL standards are shown in Figure 13. UL 1741 encompasses charging controllers, inverters, converters, and output controllers envisioned for use in both stand-alone and grid-connected systems. PV inverter/converter manufacturing requirements and testing are described in UL 62109, which has two subgroups. UL 62109-1 refers to the safety of power converter usage in PV power systems. UL 62109-2 refers to specific requirements for PV inverters. UL 1741 SA specifies necessary inverter functions for optimum grid stability.

The different articles of NFPA 70 are shown in Figure 14. Article 230 specifies electrical facilities in a building. Article 690 describes a necessary connection to the grid. Article 700 contains provisions pertinent to emergency power systems. Article 701 refers to provisions that apply to standby systems. Provisions applicable to non-legally required standby systems are referred to in 702. Article 705 covers the interconnection of power production systems.

4. Assessment of EV Charging Effects on the Electrical Network

EVs have impacts on the environment, the economy, society, and the utility grid. EVs can reduce dependency on fossil fuels, contributing significantly to a sustainable global environment. The combination of EVs and RESs offers numerous environmental benefits. V2G technology is a critical component in the revolution of sustainable energy. When assessing EVs’ environmental impact, it is vital to recognise the significant reduction in carbon dioxide emissions. One of the key elements contributing to EVs’ lower greenhouse gas emissions when compared to ICE vehicles is the use of efficient electric motors. EVs provide numerous benefits to their owners, mainly due to the incredible efficiency of electric motors and their less expensive power supply. As a result, these vehicles have lower operating costs, making them a more cost-effective choice for consumers. EV technology is substantially more efficient than ICE vehicles, often ranging from 60 to 70 percent more efficient [72]. DC microgrids are prone to several technical issues such as bidirectional power flow, which complicates protection and control mechanisms. Fault detection and isolation are complex in DC MG systems. Real-time coordination between the grid, EVs, and renewable energy sources is challenging. The fluctuation in voltage levels is due to variations in EV charging load and uncertain RESs. The use of electronic power components impacts the power quality of the system. The adoption of advanced control strategies optimises power flow and voltage regulation. The use of bidirectional converters enables power flow from the grid to the DC MG and vice versa. Smart charging strategies help to avoid overloading the grid and maximise the use of renewable energy. In future work, power quality challenges such as harmonic distortions and voltage fluctuations, arising from multiple sources and the installation of CIs in an extensive network, can be analysed. Prime importance can be given to investigating harmonic distortion due to EV charging loads, and a suitable technique can be explored to mitigate these harmonics. Advanced control algorithms can be developed to enhance dynamic response and load sharing competences. Technoeconomic optimisation can be performed to evaluate lifecycle cost and return on investment with different scenarios. DC MG-enabled CI has both positive impacts and potential challenges. The positive impacts include increased efficiency, reduced grid dependency, and improved reliability. Integration of DC MGs with the main grid results in voltage and grid stability issues and also impacts the power quality of the grid. In this work, the positive and negative impacts of EVs on the electrical network are explored in detail.

4.1. Negative Impacts of EVs on the Electrical Network

Power quality refers to the compatibility of current and voltage quality. It affects how well the grid system and the connected load function. Both the utility grid and the grid-connected equipment should meet these quality standards. Some of the most common power quality issues and their effects are described in

Table 3. The causes of and possible solutions for reducing the negative impact of charging infrastructures.

Sl. No.	Challenges	Causes	Possible Solutions
1.	Voltage dip	Large motor operations, connection of heavy loads, and faults on the transmission and DN.	A voltage dip occurs when the nominal rms voltage is reduced at a power frequency between 10% and 90% for 0.5 cycles to 1 min.
2.	Voltage swell	Interruption of large loads, capacitors, and single-phase loads.	A sudden increase in voltage beyond the standard tolerances at the power frequency, with more than one cycle duration but usually shorter than a few seconds.
3.	Voltage spike	Lightning, switching of lines, and disconnection of heavy loads.	A rapid variation in voltage levels lasting anywhere from a few microseconds to a few milliseconds.
4.	Harmonic distortion	Non-linear loads and transformer saturation.	A distortion of the sinusoidal waveform of the voltage or current due to the existence of higher-frequency components.
5.	Voltage fluctuation	Varying loads.	A power frequency variation in the rms value of the voltage.
6.	Voltage unbalance	Unbalanced loads, single-phase faults, and open conductors.	A fluctuation in the voltage magnitude or phase angle of the three-phase system.
7.	Noise	Electromagnetic interference from radio transmitters, switching devices, and power electronics.	A superposition of high-frequency signals on the power system voltage or current.
8.	Interruptions	Equipment failure, storms, human error, and protection devices.	A total loss of supply voltage or load current for a short or long duration.

[73]. Power quality issues can adversely affect the economy, reducing the efficiency and reliability of electrical systems, increasing losses and maintenance costs, and damaging sensitive equipment. Therefore, monitoring and improving power quality using various methods and devices is essential. Addressing voltage profile and voltage unbalance issues improves voltage quality in power distribution systems. The fluctuation of voltage magnitude along the distribution feeder refers to the voltage profile, which impacts the system’s performance and reliability. A three-phase system of phase-to-phase voltage divergence, known as voltage unbalance, negatively impacts the equipment and the power quality. These problems are caused by various factors, such as unbalanced loads, single-phase distributed generation, asymmetrical line impedances, and weak network conditions [74].

Therefore, it is important to monitor and improve power quality using various methods and devices. Some of the most commonly used power quality improvement methods are shown in Figure 15 [74]. Power factor (PF) correction can be used to enhance the power factor by incorporating capacitors or inductors into the system. Unwanted frequencies or harmonics can be attenuated from the system using filters. Voltage regulators can be used to keep a constant output voltage irrespective of input voltage variations. Battery storage or an uninterrupted power supply can be used to deliver a continuous power supply to the loads in case of a power outage.

4.2. Positive Impacts of EVs on the Electrical Network

EVs have positive impacts along with negative impacts due to their integration with the grid in a coordinated environment. The scheduled charging–discharging of EVs offers better power management. Discharging EVs during peak hours can help to meet peak load demand without any additional burden on the utility grid. Controlled EV grid integration helps to mitigate voltage surges produced by uncontrolled DER penetration. Voltage flickers can be smoothed and harmonics generated by uncontrolled DERs can be minimised. The uncertainty in RESs can be compensated for by operating EVs as energy storing devices. The use of EVs along with RESs reduces emissions and benefits the CS [71].

4.3. Case Study: Negative Externalities of EV Penetration and Cost Comparison of AC/DC Charging Infrastructure

The increased adoption of EVs significantly impacts the voltage profile of the distribution system (DS), potentially leading to voltage drops. In this case study one of the negative impacts of enhanced EV load, i.e., variation in voltage profile, is considered with EVCS placement at different buses in the test system. The impact on power loss of the DS is observed in different cases by performing forward–backward load flow analysis using the MATLAB 2022b platform. The IEEE 33 bus test system is considered for the analysis and three different cases are performed. The placement of the CS is performed based on the voltage sensitivity factor (VSF). The VSF is calculated using Equation (1). The power loss before and after the placement of the CS is determined using Equation (2).

$$VSF={\displaystyle \frac{dv}{dp}}$$

(1)

$$\begin{gathered} P_{loss}^{base}={\displaystyle \sum _{j=1}^{ND}{I}_j^2{r}_j} \\ {P}_{loss}^l={\displaystyle \sum _{j=1}^{ND}{I^{\prime }}_j^2{r}_j} \end{gathered}$$

(2)

where dv is a change in voltage concerning a change in power dp, N_D is the number of buses, I_j and I_j’ are the current through branch j before and after placement of the CS, and r_j indicates the resistance of the j^th branch.

The step-by-step approach adopted for analysing the impact of EVs on the distribution system is as follows:

Step 1: The line data, bus data of the IEEE 33 bus system, number of EVs, number of charging stations, and number of charging points are selected for the analysis.

Step 2: Load flow analysis is performed for the base case using the forward–backward technique.

Step 3: The VSF is calculated using Equation (1). Based on the VSF obtained, different bus numbers (strong, weak, combination of strong and weak) are selected in each case of operation.

Step 4: The load flow analysis is performed using the forward–backward method with the presence of a charging load for an individual operational case.

Step 5: A voltage profile and a power loss at each bus are obtained for three diverse cases of operation.

The IEEE 33 bus system with EVCSs is shown in Figure 16. The EV load is interconnected at different buses according to the individual cases. The placement of CSs for each case is given in

Table 4. Placement of FCSs in different cases.

Case	Description	Bus Number	Number of FCSs
Case 1	Two FCSs are placed at two different strong buses	2 and 19	2
Case 2	Two FCSs are placed at two different weak buses	14 and 15	2
Case 3	One FCS is placed at a strong bus and one FCS is placed at a weak bus	2 and 14	2

. Two FCSs are placed at different buses with a total of 30 fast charging points (FCPs); i.e., individual CSs with 15 FCPs in each case are considered. The total charging load is 3000 kW, with a number of 100 EVs to be randomly charged in two FCSs in each case. The average power per EV is assumed as 30 kW according to uniform distribution and most of the charging is scheduled during off-peak hours. The EVs arrive and depart from the CSs randomly and are modelled as per probability distribution. The mean value of SoC variation is taken as between 30% and 70%. The EV charging load over a period of 2 h is shown in Figure 17. The impact of EVCS load on the distribution system is shown in Figure 18. In case 1, voltage magnitude in all the buses is nearest to the base values.

In case 2, voltage magnitude is significantly reduced compared to the base values, which indicates the placement of CSs on the weak buses of the distribution system has a more negative impact on the system. In case 3 voltage magnitude is also reduced in all the buses, but the magnitude is slightly improved compared to case 2 because one CS is placed at a strong bus and another CS is placed at a weak bus in the system.

The total power loss of the DS per unit (p.u.) for different cases is shown in Figure 19. In case 1, compared to the base value, the power loss is increased by 9.52%. In case 2 and case 3, the power loss is increased by very high values compared to the base value and case 1. This indicates the placement of CSs in weak buses has a more negative impact on the existing DSs.

The bus-wise comparison of real power loss in p.u. for different proposed cases is shown in Figure 20. The power loss at each bus varies based on the load and EVs act as additional loads. The placement of CI load at weak buses significantly increased the power loss, which can be observed in case 2.

A cost comparison for AC vs. DC MG-based EV charging stations, specifically for highway applications in India, is given in

Table 5. Cost comparison of AC versus DC MG-based EVCSs.

Aspect	AC MG-Based Charging Station	DC MG-Based Charging Station
Infrastructure complexity	Lower due to the use of the existing utility grid for supply	Higher as it requires DC sources, converters, and energy balancing
Initial installation cost	INR 15 to 25 lakhs for level 2 AC chargers tied to the utility grid	INR 40 lakhs to 1 crore for DC fast chargers, including converters
Land and civil works	INR 5 to 10 lakhs for a smaller footprint	INR 15 to 25 lakhs for a larger footprint
Energy conversion losses	AC to DC conversion inside the vehicle is about 10–15%	Direct DC supply to the vehicle is about 5–7%
Power rating	3–22 kW for slow to moderate charging	50–350 kW for fast charging
Operation cost per kWh	INR 7–14	INR 18–22
Maintenance cost per year	INR 1–2 lakhs	INR 3–5 lakhs
Return on investment	5 to 7 years for low-traffic zones	2 to 4 years for high-traffic zones

, referring to the literature [75,76]. DC MG-based CSs are more suitable for high-traffic and high-speed corridors. They support RES integration efficiently and make it possible to enable off-grid flexibility.

DC microgrids lack universal standards for voltage levels, communication protocols, and protection schemes. Unlike AC systems governed by IEEE 1547 and IEC standards, DC microgrids operate in a fragmented regulatory space. Interoperability issues hinder plug-and-play deployment of DERs and EV chargers. A standard benchmarking system and modular interface protocols are required to support seamless AC/DC transition. Real-time control in DC systems is complicated due to non-linear dynamics and control architectures. Voltage-based coordination is commonly adopted in DC systems due to the lack of frequency as a control variable, which may lead to instability. The model predictive control strategies are more suitable in DC system real-time operation. In real-time operation the system relies on communication networks, which are exposed to cyber threats. Designing AI-based security systems helps in anomaly detection and to protect the system from cyber threats. DC MG system adoption is resisted due to protection challenges and metering complexities. Developing dedicated standards for DC MG integration, protection, and metering helps to overcome the regulatory barriers in DC system adoption. The storage systems and smart charging systems in microgrid-based EVCIs require a high initial investment but they provide financial returns by minimising operational costs. The operational cost is reduced by peak shaving, balancing the load across multiple chargers, and avoiding the sudden rise in charging loads. The criteria for instabilities, such as different location structures and demand variability, significantly increase the practical validity of the model. The availability of renewable energy sources varies with respect to geographical location. Land usage and rural and urban constraints also have effects on the performance of the model. The power generation from RESs and the EV charging load is uncertain in nature. By including these variabilities, the microgrid-based charging station model works beyond the theoretical representation.

5. Methods to Reduce the Negative Impact of CIs on the Utility Grid

The increased deployment of EVs in the utility grid has different negative impacts on the utility grid. Different methods can be adopted to curtail the negative effects of CSs on the utility grid.

Table 6. Possible methods to overcome negative impact of EVCIs.

Sl. No.	Method	Description	Limitations
1.	Smart charging	The charging of EVs will be planned according to the obtainability of renewable energy or in the off-peak hours.	To deploy and maintain smart charging in Cis requires significant investment. Scalability of CIs.
2.	Smart grid	The EVs can be charged and discharged in a coordinated way in smart grids, which automatically detect, monitor, and regulate the energy flow among power generators and energy users.	The uncertain EV charging load results in overloading of the transformers.
3.	EV charging management system	The amount of energy drawn from the grids is minimised by the EV charging management system.	To manage the grid capacity during peak load demand is challenging.
4.	Demand response	To reduce peak demand on the utility grids, EV owners need to be incentivised to charge their EVs in off-peak hours.	Interference with EV user convenience.
5.	Vehicle to grid	The extra energy is supplied back to the grid during peak hours.	Effects on life and capacity of EV battery due to frequent charging and discharging.
6.	Renewable energy sources	RESs can be used as a prime source to charge the EVs.	Uncertainties in power generation from renewables.
7.	Battery storage	The excess energy produced from renewable sources is stored in the batteries and that can be utilised to meet the large demand of EVs.	Initial investment is high and battery degradation is a limitation.

describes some existing solutions to overcome the negative effects of EV integration with the grid [72].

6. Conclusions

Electric vehicles and RESs, like solar photovoltaic and wind energy systems, contribute significantly to a future sustainable environment. Wind and solar energies are dependent on atmospheric and climatic conditions. The rapid surge in EVCI loads can result in voltage fluctuation and reliability issues. The large-scale penetration of RESs may have an impact on the reliability of the electrical network, distorting the performance of the grid. Initially, the EV types, benefits, and challenges were discussed along with the MG and their classification. Grid-integrated DC MGs composed of solar and wind energy sources were described in detail. Different types of charging infrastructure and international standards for EV integration with the grid were presented. The various impacts of EV integration with the utility grid were explored comprehensively, and also, how to reduce the negative effects of EVs on the electrical network was explained. The impact of EVCIs on voltage profile and power loss was analysed for three various cases using the IEEE 33 bus system in the MATLAB 2022b platform. A cost comparison for AC versus DC MG-based CI was provided.

The potential for future research in this field is substantial. Future work can be carried out by designing and implementing adaptive control algorithms for harmonics reduction, and developing adaptive clustering models considering real-time electric mobility patterns and SoC dynamics under stochastic grid conditions. The challenges with hybrid AC/DC MG-based CIs can be evaluated by developing coordinated control strategies and smart energy management systems. Grid resilience assessment can be performed under enhanced EV penetration. The developed system can be validated under real-time constraints using hardware-in-the-loop platforms. Further, power quality challenges such as harmonic distortions and voltage fluctuations, arising from multiple sources and the installation of CI in an extensive network, can be analysed.