Enhancing stability and power quality in electric vehicle charging stations powered by hybrid energy sources through harmonic mitigation and load management

Abstract

Growing integration of renewable energy sources (RESs) into power grids has several advantages, including reduced greenhouse gas emissions and improved energy sustainability. Despite the related benefits, RES integration also poses actual challenges, such as power quality, voltage fluctuation, and reliability concerns. This study conducts a comprehensive performance evaluation of a grid-integrated microgrid consisting of an electric vehicle (EV) charging station, wherein the Volkswagen ID4 Crozz has been adopted as the standard EV model. The microgrid will consist of solar panels, a wind energy conversion system (WECS), and a battery energy storage system (BESS), which will be used for the supply of electricity economically with a reliable power supply. ETAP simulates various operating conditions to analyze their impact on voltage stability and power quality. This study investigates the impact of EV integration on power quality and applies advanced load management strategies, such as partial loading, selective disconnection, and coordinated renewable integration. A scenario-based optimization approach is used to minimize harmonic distortion and improve voltage stability. Among the configurations that were being compared, the 60% load configuration was superior, where the 5th harmonic distortion was 0.42% and the 11th was 0.55%, performing superior to full-load and other disconnection configurations of chargers. The configuration also had superior and stable voltage levels, which bear witness to its effectiveness in enhancing power quality as well as grid stability.

Introduction

Motivations and background

Power quality degradation is among the severe challenges that are a result of the intermittent nature of RESs^1,2,3,4,5. Solar and wind power generation experiences fluctuations based on weather, time of day, and seasonality^{6,7,8,9,10,11}. This fluctuation can lead to power quality degradation in the form of the introduction of voltage sags, swells, and harmonics, which can compromise the reliability of the grid and even impact sensitive equipment¹². The shift to renewable energy sources (RESs) and the incorporation of various technologies in microgrids bring advantages as well as challenges to the overall performance of the electrical grid^13,14,15. The shift of power generation to RESs currently needs to be examined closely for its impact on the overall performance of the grid^16,17,18,19. Rapid changes in voltage levels can bring challenges to the stability of the grid. Voltage instability must be tackled by advanced voltage regulation technologies and a keen focus on grid management strategies^20,21,22,23.

The integration of EVs with the electric grid is a complex interaction of opportunities and challenges^24,25,26. Effectively managing this dynamic variable will need a collective effort in the form of grid modernization, smart charging infrastructure, and policy institutions supporting sustainable and grid-friendly utilization of EVs. With continued technological development, the interface between EVs and the electric grid has the potential to make the energy system stronger, more efficient, and more sustainable^27,28. As the number of EVs on the roads rises, their impact on the electrical grid becomes more significant. Extensive use of electric vehicles leads to a rise in the demand for electricity, especially at peak charging times^29,30,31. This higher demand can strain local distribution networks and peak load management, necessitating grid infrastructure reinforcement to handle the increased power requirements. Concentration of charging in specific locations, e.g., public charging stations or residential estates, can cause grid congestion. The congestion can result in voltage reduction and undermine the overall stability of the grid, particularly in areas of high EV density.

The establishment of an extensive and dense charging infrastructure is central to enabling EV adoption. Constraints include calls for higher charging station density, standardization of charging standards, and the introduction of smart charging systems to control grid interactions^32,33,34. The dynamic and generally random character of EV charging loads requires careful grid planning and management. Utilities have to anticipate and keep pace with the changes in demand such that the grid can handle the extra load without compromising reliability. EVs present the potential for demand response programs and smart charging capabilities. With the application of communications technologies, grid operators can influence EV owners to shift their charging towards off-peak hours or during the period of peak renewable generation, thus balancing load and optimizing grid efficiency³⁵. Scheduled charging when there is high generation from RESs aligns EV consumption with cleaner generation, resulting in the reduction of carbon emissions^36,37,38.

Related works

The EV charging into the grid is a dynamic and complex process with system-wide impacts for the majority of the energy system. The charging behavior of EVs can inject variability into the electricity load curve. Uncertain demand peaks, especially during peak charging times, can potentially stress the grid and necessitate generation and distribution changes³⁹. In⁴⁰, a model for an integrated system that combines EV charging and battery storage to operate alongside unpredictable WTs and PV has been developed. The objective was to facilitate the design of an advanced electrical control system capable of generating an appropriate duty cycle for stabilizing and regulating voltage at the DC/DC converter. Extensive simulations were conducted in order to assess in detail the energy management operation and performance of the proposed control system. The system successfully regulated the distribution of electric power, employing energy from the BESSs during peak load hours and charging them during off-peak hours, thereby streamlining energy consumption and enhancing the stability of the grid. This approach reduced the load on the converter and resulted in shorter charging times for EVs.

In⁴¹, an optimization algorithm designed to identify the most effective set of control parameters for a voltage source inverter has been presented. This inverter integrated PV with an EV charging station to a common grid-connected AC bus. The controller parameters were tuned using the Salp Swarm Algorithm to reduce the oscillations in the DC-bus voltage. This was achieved by balancing the active power transfer and controlling the injected harmonics onto the grid. The study simulated a theoretical level 2 AC charging station for electric vehicles under various operating conditions. The controller was evaluated by simulated test cases and real irradiance profiles.

Utilities and grid operators need to plan for load profile changes, considering the geolocation of charging stations and peak hours of charging. The addition of EVs is usually followed by the adoption of time-of-use (TOU) pricing schemes. This encourages owners of EVs to charge their vehicles during off-peak periods, thus load balancing on the grid^42,43,44,45. In⁴⁶, an experiment was performed on different aspects of EV charging under a TOU tariff. The study was on off-peak rates beginning at different times, from 8 P.M. to 3 A.M., and employed a real residential distribution feeder for modeling. The goal was to find the most logical time to initiate off-peak rates, both to constrain the impact of EV charging on secondary service voltage and to ensure that EVs would be charged by 7 A.M. This approach was designed to achieve optimal benefits for the grid and customers. Analysis revealed that the optimum time to initiate off-peak rates was from 11 P.M. to 12 A.M. Moreover, analysis showed that expanding TOU off-peak hours to the latter half of the peak electricity usage period is beneficial.

In⁴⁷, an approach to TOU price-based scheduling for Vehicle-to-Grid (V2G), implemented by an Electric Vehicle Aggregator (EVA), has been introduced. The central goal was to optimize the synchronized charging of EVs and achieve economic benefits for several stakeholders, including EV owners and the system operator. The strategy involved the application of a price-based (PB) DR program to improve the value proposition for EV owners. The V2G scheduling problem optimization for EVA considered real-world factors such as SOC, TOU-PB DR programs, and rate modulation of charging and discharging. Simulation results indicated the effectiveness of this proposed model to efficiently handle peak loads as well as generate monetary gains for all the stakeholders involved. Furthermore, the model played a role in offering regulation services to the system operator, thereby supporting grid stability and averting unforeseen contingencies.

In⁴⁸, a sophisticated charging navigation model has been introduced to optimize benefits for various stakeholders. This optimization was achieved through the implementation of a TOU pricing strategy specifically tailored for fast charging stations (FCSs). The intention was to have EVs charge during off-peak periods and thus save both EV owners and FCs operators funds. The model used was that of a Stackelberg game, with the FCS operator as the leader and EVs as the followers. By analyzing the impact of prices on charging decisions, the study proposed an EV strategy consisting of selecting the optimal charging time, charging energy, charging point, and routes in order to minimize overall EV expenses. In⁴⁹, a TOU pricing structure has been implemented in the electricity market, specifically for the purpose of capturing the time-varying interactions among power plants, generation activities, ESSs, EV charging, and electricity prices.

Research gap and contributions

Despite growing research in EV integration, some essential gaps remain in the existing literature. The majority of existing studies have so far been primarily interested in economic benefits and load management strategies of EVs in the grid, e.g., using EVs as a source of backup power or for demand response^{50,51,52,53,54,55,56,57,58}. This economically oriented approach has a tendency to overlook significant issues of power quality, i.e., how EV charging affects electrical harmonic distortion and voltage stability. EV charging stations employ power electronic converters to extract power, hence becoming non-linear loads that inject current harmonics into the grid. However, there are few works that account for these harmonics in detail and their influence on the grid performance. EV charging causes harmonic distortions and voltage fluctuations that are not well elaborated in most of the works, though excessive distortion can foster inefficient energy transfer, equipment overheating, and voltage instability. Recent studies have pointed out the lack of extensive research on the effect of EV grid integration on power quality, highlighting this wonderful area of research deficiency.

Another deficiency in the literature is the limited analysis of EV integration in conjunction with renewable energy sources such as solar photovoltaics and wind. The variability and intermittency of renewable energy sources can cause voltage swings and power quality degradation, including voltage sags, swells, and harmonic injection from inverters. However, the combined effect of EV charging and renewable variations remains under-examined. The majority of studies treat EV integration and renewable penetration separately or make simplified interaction assumptions between the two. Realistically, an EV charger-enabled grid-connected microgrid with renewables can have compounded effects. Solar farm output variability combined with simultaneous EV fast charging, for instance, could exacerbate voltage stability or harmonics further. Recent studies usually work with static conditions or idealized models, and little is known about such dynamic interactions. Real-time adaptive solutions to handle the fluctuating load of EVs along with the output of renewable energy sources are seldom taken into account. This leaves a void in knowledge on how to maintain power quality at realistic, fluctuating operating levels. In summary, this study demands more comprehensive studies of power quality measurement in EV-integrated grids, especially under renewable generation variability. To address the aforementioned shortcomings, this study presents a comprehensive analysis of EV integration impacts on power quality, supported by in-depth simulations as well as real-time observations. Simulations were carried out using Electrical Transient Analyzer Program (ETAP), Version 20.6, available at https://etap.com/product-releases/etap20-6-release. The new contributions of the proposed study are as follows:

• 1. This paper presents a thorough analysis of power quality in an EV charging microgrid with harmonic distortions and voltage stability in various operating modes. Unlike other papers, which brush technical issues under the carpet, the current paper performs numerical analysis of how various EV charging setups impact the harmonic spectrum. Through an examination of various loading levels as well as on and off conditions of the chargers, this study uncovers the worst-case and best-case scenarios of power quality.

• 2. This study employs the Electrical Transient Analyzer Program, known as ETAP, to conduct detailed simulations of a grid-connected microgrid with integrated EV charging stations. Through various operational scenarios, the research provides valuable insights into the performance of different system configurations, enabling optimized grid design and improved stability measures. Using ETAP, the study captures transient effects and non-linear load behavior with high fidelity.

• 3. This study improves practical relevance by using the Volkswagen ID4 Crozz as a representative EV model, ensuring that all load parameters align with real-world specifications. This technique provides more precise data on the effect on the grid, including voltage distortion and harmonic distortion. Since the simulations are so correlated with actual EV behavior, the outcomes translate directly to actual-world deployment scenarios, enabling utilities and microgrid operators to design more effectively for and operate EV integration.

• 4. The paper considers coordination between charging of EVs and renewables, i.e., photovoltaic panels and wind turbines, in a microgrid. As the paper includes models of renewable energy, it considers their combined effect on harmonics and voltage stability.

• 5. A key contribution of this study is the verification that the EV-integrated microgrid operates within the harmonic limits outlined in IEEE STD 519–2014. All simulation results are assessed in accordance with this industry standard, which establishes acceptable thresholds for total harmonic distortion and individual harmonic order distortion in electric power systems. By designing and operating the system to align with IEEE 519–2014 criteria, the study validates the robustness of the model and proposed solutions.

Organization of the paper

The work is structured in eight sections to provide a comprehensive examination of the impact of EV integration on grid-connected microgrids from the perspective of power quality issues such as harmonic distortions. Section 2 describes the methodology, where the use of ETAP software for the modeling and simulation of the microgrid and the selection of the Volkswagen ID4 Crozz as the test EV model are described. Section 3 gives EV integration to the grid, discussing an overview of EV technology and the impact of EVs on the grid. Section 4 outlines system component models, and particular focus is given to harmonic components induced by EVs. Section 5 discusses thoroughly the exceptional harmonic components introduced by EVs. Section 6 outlines operation modes explored, designed for the purpose of emulating the impact of various configurations on power quality and grid stability. Section 7 gives the results and discussion, summarizing the findings from the simulations. Finally, Sect. 8 concludes with the summary of key findings, highlighting the contribution of the study and offering suggestions on how to enhance microgrid efficiency and stability.

Methodology

ETAP software is applied in this research for simulation and modeling the grid-connected microgrid. ETAP has extensive usage for the purposes of power system analysis and simulation and, as such, is also suitable for investigating the complex behaviors within a microgrid. Strong software features of high modeling and simulation ability are exploited for performing a detailed investigation of the behavior of the microgrid in different sets of circumstances. The research capitalizes on the diverse nature of ETAP to strategic effect, emphasizing the value of advanced system modeling and simulation. Scenarios under examination in this study span a set of operating conditions and provide a balanced perspective of microgrid operation. Examination of scenarios with diverse changes in RESs generation makes possible the insight of the response of the system in different conditions. Scenario evaluation with charging station usage supports the evaluation of the impact on the performance of the microgrid during peak demand or specific patterns of charging.

This approach is in accordance with the current industry trends and challenges, particularly in addressing the integration of RESs into current power systems. An integral component of the study is the meticulous analysis of potential power quality issues, voltage stability, and reliability risks likely to be triggered by the increased integration of RESs into the microgrid. This particular attention is essential in ensuring the robustness and sanity of the microgrid, especially considering that RESs are intermittent. The energy system combines PV and WECS to supply power to the loads and charging stations as shown in Fig. 1. The charging station itself is a research area, with a dual configuration where both DC and AC chargers are installed. The presence of a charging station, which is composed of DC and AC chargers, compels the system to be complex. It is termed a critical factor, particularly in terms of diversity in charging technology and its potential impact on microgrid operation. The research places great emphasis on investigating the complexity introduced by the charging station. Investigation is targeted especially on cases in terms of utilization of the charging points so that there is appropriate examination of microgrid resilience at peak load or given charging patterns.

Fig. 1

Configuration of the proposed EVs charging station powered by RESs.

A comprehensive analysis is performed to investigate the impact of EV integration in the grid on power quality with respect to harmonic distortions. The research employs a systematic framework by taking various operating conditions into account to bring out the profound impacts of EV integration on harmonics in the grid. EV integration is a dynamic parameter that can have a far-reaching impact on the electrical grid. When EVs are introduced into the grid, their charging behavior and application scenarios are diversified in a systematic manner to represent different operating conditions. This process enables the study to evaluate how EV integration impacts power quality, with special emphasis on harmonic distortion.

Harmonics, which refer to deviations from the standard sinusoidal waveform in the electrical system, can be influenced by the charging characteristics of EVs. The integration of EVs has the potential to introduce harmonic distortions into the grid, thereby affecting power quality. To assess these effects systematically, this study follows a four-step methodology, as illustrated in Fig. 2. Stage 1 carries out the initial assessment, i.e., grid interaction analysis, power quality concerns, climatological data, system configuration, and load estimation using ETAP software. Phase 2 is concerned with design optimization analysis, considering significant evaluation criteria such as power quality, load management strategies, and renewable energy exploitation with imposed model constraints such as battery state of charge (SOC), harmonic distortion limits, and renewable energy fractions. Various configuration possibilities are investigated, including various charging station configurations and various load levels. Phase 3 is under consideration for investigation by technical, economic, and environmental analysis. Power quality is investigated by harmonic orders, percentage distortion, and voltage waveform deviation, whereas economic issues are with cost implications of power quality issues. Environmental analysis is interested in renewable energy inputs and emissions savings. Stage 4 terminates the power quality assessment by defining the best harmonic suppression methods, voltage stability assessment, and electric vehicle charging network optimization. The ultimate aim is to define the best EV charging method to minimize harmonic distortion, maximize renewable energy management, enhance voltage stability at different loading conditions, and achieve IEEE 519–2014 compliance.

Fig. 2

A Four-Stage Framework for Evaluating EV Integration Impact on Power Quality.

Use of the ETAP software is at the core of this analysis because it provides an interactive simulation platform through which one can scrutinize in-depth how the incorporation of EV affects harmonics in the grid. The proposed model with the unit of ETAP software is shown in Fig. 3. The DC chargers, as shown in Fig. 4, provide the outline for DC charging stations and individual charging units that are linked to a shared DC bus. The design for each unit is to receive various levels of power, thus performing efficiently and effectively in charging. The AC chargers, as shown in Fig. 5, demonstrate the structure of AC charging stations. They are fitted inside an AC bus and are designed to receive power demands for maximum EV charging capacity. The microgrid configuration in the figure is a highly synchronized system that makes use of RESs to provide energy requirements in the current era, such as charging stations for EVs. The use of circuit breakers and converters facilitates seamless control of the flow of energy, protecting the system components as well as power quality.

The BESS is a vital component for supply-demand balancing, storing excess energy and releasing it when needed. This mixed approach allows for the continuous and uniform operation of the microgrid, which is both effective and sustainable as an energy solution. Different operating conditions with fluctuations in EV charging patterns, such as peak loading or specific charging profiles, are investigated to ensure the full range of potential effects on the grid harmonics. Through systematic examination of the modes of operation, this study will provide a comprehensive insight into how the interaction between EVs and the power grid influences the power quality, with special focus on harmonics. The knowledge is significant to power system planners, operators, and stakeholders so they can address the challenges and opportunities that arise with increasing levels of EV adoption and the general shift toward sustainable power systems. Lastly, the research provides worthwhile contributions to the current debate on the integration of EVs into the grid and its implications for power quality.

Fig. 3

ETAP Model of the Grid-Connected Microgrid with EV Charging Station.

Fig. 4

DC charging stations configuration

Fig. 5

AC charging stations configuration.

EV integration to the grid

Overview of EV technologies

In recent times, there has been significant innovation in the manufacture of EVs, with a move towards cleaner and more efficient transport. EVs have branched out into numerous various forms, each characterized by a distinct technology and setup. In general terms, they can be broadly categorized into five types⁵⁹:

• Battery Electric Vehicles (BEVs).

BEVs are fully EVs that operate exclusively on electric power. BEVs are equipped with a sizable traction battery pack for storing electrical energy, along with an electric motor responsible for propelling the wheels. BEVs need to be plugged into an electric charging station to recharge their batteries. They are fully emission-free and famous for being environmentally friendly.

• Plug-In Hybrid Electric Vehicles (PHEVs).

PHEVs integrate an internal combustion engine, usually gasoline-fueled, and an electric motor, with a battery, to enable propulsion. PHEVs are designed to operate on electricity alone for a significant distance, a distance that can be varied based on the model and the size of the battery. This allows drivers to cover short to medium distances entirely on electricity, reducing the demand for gasoline or diesel fuel during normal commuting or other normal driving conditions. Keep in mind that actual real-world fuel consumption of PHEVs can vary under real-world driving conditions.

The fuel economy figures quoted by car manufacturers are often based on standardized test conditions and may not necessarily correspond to the actual fuel economy achieved under real driving conditions. Driving style, road type, temperature, and rate of battery recharging are all factors that can influence actual fuel use. PHEVs can be charged via a standard electrical outlet or charging station and offer flexibility for both short and long trips. PHEVs provide a balance between electric-only driving and the extended range offered by the internal combustion engine. Their suitability depends on individual driving habits, access to charging infrastructure, and environmental considerations.

• Hybrid Electric Vehicles (HEVs).

HEVs incorporate a normal internal combustion engine, along with an electric motor and a compact battery. HEVs are different from PHEVs in the sense that, they cannot be plugged into the grid to recharge. Unlike PHEVs, HEVs do not use power primarily from the internal combustion engine. The electric motor helps the engine during acceleration and can be used to recharge the battery through regenerative braking, which captures energy that would otherwise be lost during braking and converts it into electrical power. HEVs don’t require charging from an external source, such as a charging station or wall socket. They utilize the engine and regenerative braking to maintain the battery charged. HEVs tend to have a smaller battery capacity compared to PHEVs as a limiting factor on their ability to operate in electric-only mode.

HEVs are designed to maximize fuel efficiency through the use of the electric motor to assist the engine during acceleration and other driving conditions, hence reducing the operation of the internal combustion engine to a minimum.

• Fuel Cell Electric Vehicles (FCEVs).

FCEVs use hydrogen to generate electricity by way of a fuel cell that powers an electric motor in return. The only emissions they produce are water vapor, and they typically enjoy greater driving distance than certain BEVs. FCEVs are far less prevalent and will require exposure to hydrogen refueling infrastructure. Refueling at a compressed hydrogen-filling station powers the FCEVs. Filling up an FCEV is as simple as filling up a gasoline- or diesel-driven car, with the process taking only a few minutes. However, refueling infrastructure may be a constraint for the FCEV owners in certain regions. FCEVs are being considered as a future solution to zero-emission transportation, particularly for applications where the large driving distances and rapid refueling are relevant. But expanding hydrogen refueling infrastructure and cleaner ways of producing hydrogen are necessary for mass FCEV adoption and the environmental acceptability of these vehicles overall.

• Extended-Range Electric Vehicles (ER-EVs).

ER-EVs, or “range-extended” EVs, are a unique class of EVs that share some similarities with BEVs. ER-EVs are built on the same principles as BEVs and essentially rely on an electric motor powered by a massive battery pack for power. ER-EVs are equipped with an additional internal combustion engine (typically gasoline or some other similar fuel) that serves as a generator to charge the vehicle’s battery. The engine is not mechanically coupled to the wheels and does not aid in direct mechanical propulsion of the vehicle. When the battery charge falls below a point or when additional power is needed, the auxiliary combustion engine comes on and generates electricity to charge the battery. This boosts the driving range of the vehicle beyond that provided by the battery alone. ER-EVs can operate in all-electric mode when the battery is sufficiently charged. Such a mode offers zero-emission and quiet operation. ER-EVs are designed to reduce range anxiety, which is a concern for some owners of BEVs who worry that they will use up their battery power. Having the backup engine leaves a reserve source of power for long travel.

Impact of integrating EVs into the grid

Integration of EVs will have extensive consequences on various parts of the electrical grid, including power quality and grid stability. While EVs have numerous grid advantages, such as load management and grid stabilization, in certain situations they may turn out to be problems in power quality and stability within the grid. It may result in unstable demand for power during the charging phases and have implications for the continuity of power supplied to the customers. This is mainly applicable to communities with limited grid capacity or poor infrastructure. The ways in which EV integration will affect power quality and grid stability can be enumerated as below:

• Voltage Fluctuations: Rapid charging of EVs at a dense site could create voltage fluctuations, especially if the grid infrastructure is poorly equipped to manage the higher load.

• Harmonics: EV charging can result in harmonic distortion to the grid, affecting the quality of the supply of power. Harmonics can lead to equipment failure and increased losses in the grid.

• Voltage Sags and Swells: High charging loads of EVs can lead to voltage sags (transient voltage drops) or voltage swells (transient voltage increases) if the grid infrastructure is not properly sized to handle the load.

• Grid Resilience: While EVs can potentially enhance grid resilience through V2G capabilities, improper integration or a large number of discharging vehicles during grid outages can impact power quality.

• Peak Load Impact: High concentrations of EVs charging simultaneously during peak hours can result in increased peak electricity demand, potentially requiring additional generation capacity and grid upgrades to maintain stability.

• Grid Congestion: Grid congestion is conceivable where there is a high EV density, with a possibility of destabilizing the voltage and power quality.

• Power Losses: As far as the charging stations’ efficiency and scheduling of charging of EVs are concerned, power losses in the grid are open to effect. Such power quality issues are countered through measures such as voltage regulation devices and advanced control systems, enabling an uninterrupted supply of electricity.

Models for system components

EV modeling using ETAP

EVs in ETAP software are simulated based on advanced battery models that reflect real-world EV battery pack behavior and capacity. For research in this research, the Volkswagen ID4 Crozz is used as the model EV because it is often driven by residents of the study area. ETAP provides a vast library of battery models to ensure proper simulation and analysis. To simulate the Volkswagen ID4 Crozz, a model of one of this library’s ternary lithium batteries is chosen. The chosen battery model with a capacity of 55.7 kWh best represents the Volkswagen ID4 Crozz battery pack specification to enable realistic simulation results that are relevant to real-world use. The ternary lithium battery has high energy density and is efficient and hence the optimum for use in duplicating the Volkswagen ID4 Crozz.

The selection is important in the research because it ensures that the impact of the EV on microgrid stability as well as power quality is well depicted. Table 1 gives a detailed description of the features of the Volkswagen ID4 Crozz, like battery capacity, charging rates, and other factors. Through the inclusion of these specific characteristics within the ETAP model, the research is able to simulate various modes of operation with high accuracy, examining how integration of the Volkswagen ID4 Crozz impacts power quality, particularly harmonic distortions, voltage stability, and overall grid reliability. This approach allows for a systematic analysis of EV-microgrid interaction and one that provides substantial information about challenges and advantages related to high EV penetration. Using a common model that is representative of actual EV specifications guarantees conclusions derived from the study are correct and relevant to industry practice and trends in the present.

Table 1 Volkswagen ID4 Crozz characteristics.

Estimation of the EV population

The quantity of EVs in the study area is ascertained by considering the percentage of EV integration in the area. This ratio, which represents the percentage of EV integration, is computed by comparing the count of households that own EVs ($\:{N}_{hhEV})$ to the overall residential units in the study region ($\:{N}_{hh}$) as represented in the following equation⁶⁰:

$$\:\%EV=\:\frac{{N}_{hhEV}}{{N}_{hh}}*100$$

(1)

The number of residential units within the study area is determined as follows:

$$\:{N}_{hh}=\:\frac{{S}_{Thh}}{{S}_{hh}}$$

(2)

Where $\:{S}_{Thh}$represents the total apparent power consumption of the residential units and $\:{S}_{hh}$ represents the apparent power demand of an individual household.

The EV population in the study area is determined based on the overall apparent power demand of the loads. As previously indicated, the study network consists of a combination of both residential and commercial loads.

Battery energy storage system

Solar power is an RES and a clean energy, but it is highly variable. It depends on the time of day, weather, and location. It leads to fluctuations in output power. During periods of high solar radiation, there is surplus energy generation compared to demand, while during cloudy and nighttime periods, solar output is zero. Battery banks are required for generating a stable and consistent supply of power, especially in situations where the source of energy, e.g., solar radiation, is intermittent. They absorb excess power when available and release it when needed to generate a constant output of power to target loads. The battery bank size determines how much power can be stored and then delivered to the loads. Choosing the proper-sized battery bank is essential so that you have enough storage for as much energy as you need, to power your own special requirement, either backup power on a cloudy day or time-shifting excess generation of solar energy to night loads.

Battery bank capacity is the most significant parameter. It indicates how much energy the system can store and deliver to loads. DOD is the percentage of the stored battery energy that is expended in a single charge-discharge cycle and affects battery life. Battery life is a decisive factor in whether the energy storage system is economically viable. Batteries can have only a limited number of charge-discharge cycles before capacity and performance are degraded. The estimated battery life is an important factor in system design. The degree of discharge of a battery in use impacts its life. More discharging will have fewer numbers of charge-discharge cycles and less total battery life. Monitoring and controlling the DOD is necessary to ensure the best life and performance of the battery. SOC is a significant parameter in battery management. Proper estimation of SOC is crucial for effective operation and maintenance of the battery storage system. The BESS model takes into account numerous factors, such as the charge and discharge power, the rate of self-discharge, and the efficiency of charging and discharging, as described below^61,62:

$$\:{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}+1\right)=\:{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right)\:.\:\left(1-{\varvec{\delta\:}}_{\varvec{s}\varvec{e}\varvec{l}{\varvec{f}}_{\varvec{D}}}\right)+\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{C}\varvec{h}}}\left(\varvec{t}+1\right)\:.\:\varvec{\tau\:}\:.\:{\varvec{\eta\:}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{C}\varvec{h}}}-\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}}\left(\varvec{t}+1\right)\:.\:\varvec{\tau\:}\:/\:{\varvec{\eta\:}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}\:}$$

(3)

$$\:{\varvec{S}\varvec{O}\varvec{C}}_{\varvec{B}}\left(\varvec{t}\right)=\:\frac{{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right)}{{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}^{\varvec{r}\varvec{a}\varvec{t}\varvec{e}}}\:.\:100\varvec{\%}$$

(4)

$$\:{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right)=\:{{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}}_{\varvec{i}\varvec{n}\varvec{i}\varvec{t}}\:,\:\varvec{i}\varvec{f}\:\varvec{t}=1$$

(5)

$$\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{c}\varvec{h}}}^{\varvec{m}\varvec{i}\varvec{n}}\:\le\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{C}\varvec{h}}}\left(\varvec{t}\right)\le\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{c}\varvec{h}}}^{\varvec{m}\varvec{a}\varvec{x}}$$

(6)

$$\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}}^{\varvec{m}\varvec{i}\varvec{n}}\:\le\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}}\left(\varvec{t}\right)\le\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}}^{\varvec{m}\varvec{a}\varvec{x}}$$

(7)

$$\:{{\varvec{S}\varvec{O}\varvec{C}}_{\varvec{B}}}^{\varvec{m}\varvec{i}\varvec{n}}\le\:{\varvec{S}\varvec{O}\varvec{C}}_{\varvec{B}}\left(\varvec{t}\right)\le\:\:{{\varvec{S}\varvec{O}\varvec{C}}_{\varvec{B}}}^{\varvec{m}\varvec{a}\varvec{x}}\:$$

(8)

Where, $\:{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}^{\varvec{r}\varvec{a}\varvec{t}\varvec{e}}$ denotes rated capacity of the BESS in kWh, $\:{\varvec{E}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right)$ denotes the capacity for BESS at time t in kWh, $\:{\varvec{S}\varvec{O}\varvec{C}}_{\varvec{B}}\left(\varvec{t}\right)\:$denotes the SOC for BESS at time t,$\:\:{\varvec{\delta\:}}_{\varvec{s}\varvec{e}\varvec{l}{\varvec{f}}_{\varvec{D}}}$ denotes the self-discharging rate for BESS, $\:{\varvec{\eta\:}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{C}\varvec{h}}}$ and $\:\:{\varvec{\eta\:}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}\:}\:$denote the charging and discharging efficiencies of BESS, respectively, $\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{C}\varvec{h}}}$ and $\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}{\varvec{S}}_{\varvec{D}\varvec{i}\varvec{s}}}$ denote charging and discharging power for BESS in kW, respectively.

PV modelling

PV technology is a major driving force for converting sunlight to electricity using semiconductor material like silicon on solar panels to convert the light energy into direct electric power. Photovoltaic cells are the backbone of this technology, in which when hit by sunlight, photons are absorbed and electrons are driven, thus developing an electric current. Significantly, the efficiency of this conversion is inextricably linked with environmental conditions, and temperature influences performance. However, ongoing innovation in technology and material, as well as the grid integration of PV systems and use of energy storage technology such as batteries, has improved the reputation of solar energy. These advancements not only have improved the overall effectiveness of solar panels but have also helped in making solar power an affordable and universal source of electricity generation, which has been a big leap towards greenifying the energy industry. The quantity of electrical energy generated by a solar panel varies based on a number of parameters and can be calculated using the formula below^63,64,65:

$$\:{P}_{PV}\left(t\right)=\:{P}_{STC}\:\eta\:\:\left(\frac{{G}_{AC}\left(t\right)}{{G}_{STC}}\right)\left[1+\alpha\:\left({T}_{C}\left(t\right)\:-\:{T}_{STC}\right)\right]$$

(9)

$$\:{T}_{C}\left(t\right)\:=\:{T}_{a}+\:\frac{NOCT\:-\:20}{800}\:{G}_{AC}$$

(10)

Grid modelling

As mentioned above, RESs produce power intermittently, and hence there are cases when the net power output of these units and BESS fails to meet the demanded load. In such situations, the utility grid is employed as a backup, providing power to the microgrid to meet the demand. The utility grid power-supplying model is described below:

$$\:{\varvec{P}}_{\varvec{G}\varvec{r}\varvec{i}\varvec{d}}\left(\varvec{t}\right)=\:{\varvec{P}}_{\varvec{L}\varvec{o}\varvec{a}\varvec{d}}\left(\varvec{t}\right)-\:\sum\:{(\varvec{P}}_{\varvec{P}\varvec{V}}\left(\varvec{t}\right),\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right))$$

(11)

Where, $\:{\varvec{P}}_{\varvec{G}\varvec{r}\varvec{i}\varvec{d}}\left(\varvec{t}\right)$ denotes the power supplied by the grid, $\:{\varvec{P}}_{\varvec{L}\varvec{o}\varvec{a}\varvec{d}}\left(\varvec{t}\right)$ represents the load power and $\:\:{\varvec{P}}_{\varvec{B}\varvec{E}\varvec{S}\varvec{S}}\left(\varvec{t}\right)$ represents the power provided by BESS.

Harmonic components generated by EVs

EVs are gaining increasing popularity, and their integration into microgrids raises some interesting considerations, particularly in terms of harmonic components and power quality. Harmonic components are sinusoidal voltage or current waveforms with frequencies that are integer multiples of the fundamental frequency. EVs are typically powered by power electronic converters that can inject harmonics into the microgrid. The chargers are normally connected to the grid, and the injected harmonics have the potential to affect power quality. EVs also utilize power electronics for battery charging, DC-AC inversion in hybrid and EVs, and motor control. The power electronics can inject harmonic distortion into the grid since they are non-linear. The frequency and amplitude of harmonic components from EVs depend on charging infrastructure type, power electronics, and EV design. EVs employ inverters to transform the battery’s DC power to AC power to power the electric motor.

The inverters, by their switching operation, can produce harmonics in the microgrid. The order and amplitude of harmonics are determined by the inverter configuration and the modulation strategy. When a number of EVs are charging from a microgrid, unbalanced load currents and unbalanced loads can be present, and they can inject harmonic components. Unbalanced loads can produce harmonics, particularly in the zero-sequence (neutral current) components. EVs that are charging from a microgrid can get coupled with the existing grid harmonics. If the grid already contains harmonic distortion, the EVs can compound the issue by introducing more harmonics into the system. There are regulations and standards in most countries to restrict the level of harmonic distortion within the power grid. These regulations are applied to all devices that are connected to the grid, including EVs and charging stations. Adherence to these standards is important in order to provide power quality. Excessive harmonic components can lead to negative impacts on other microgrid elements, such as transformers and capacitors. Components are subjected to additional stress and heating by harmonics that can reduce their life expectancy.

Harmonic components in waveforms of current are greater than in voltage waveforms, and the percentage of total harmonic distortion (THD) plays a major role in impacting power quality of microgrids. This is of specific significance in EV charging stations. The increasing use of EV non-linear loads can disturb the sinusoidal nature of voltage and current signals because these non-linear waveforms consist of harmonic components. AC/DC and DC/AC converters of EV charging systems are some of the prominent sources of such harmonics. EVs, driven by electric motors and battery banks, possess notable environmental advantages in the form of zero emissions and renewable energy sources, thereby reducing dependence on fossil fuels. Harmonics in the power system can lead to a variety of risks in a smart microgrid:

• Overheating of Power Distribution Lines: Harmonics can result in excessive heat generation in power distribution lines, leading to increased losses, reduced efficiency, and even potential harm to the electrical infrastructure.

• Resonance in Smart Microgrid: Effects of resonance may be caused within the smart microgrid through harmonics, causing interference to the stability and performance of the microgrid. This equipment damage and voltage and current distortion are other alternatives provided by resonance.

• Reduced Transformer and Electric Equipment Life: Harmonics shorten the life of transformers and electric equipment installed on the microgrid, leading to future premature failure and increased maintenance.

• Reactive Capacitor Damage: Destructive effects may be induced on reactive power compensating capacitors by harmonics, which will destroy the capacitors. The process will decrease power factor correction as well as system efficiency.

• Protection Switch Tripping: Harmonic current may result in premature or unjustified tripping of the protective switches of the microgrid, leading to microgrid operation instability and finally power interruption.

• Communication Infrastructure Disturbance: Harmonics can lead to noise or interference within the communication infrastructure of the smart microgrid, impacting the reliability and performance of communications networks utilized for monitoring and control.

THD is a typical parameter that can be utilized in order to quantify the extent of harmonic distortion in smart microgrids. $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ refers to a measure used to measure the overall distortion of the voltage waveform within a microgrid. The measure of $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ is typically taken as a percentage and represents the quality of the voltage supply within the microgrid. A lower percentage of $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ defines a purer and cleaner sinusoidal voltage waveform, while a higher percentage of $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ indicates that there is a greater level of harmonic distortion, and this can lead to equipment failure and power quality issues in the microgrid. It is required to monitor and maintain $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ at low levels to ensure a stable and safe electric supply in smart microgrids. It is determined by analyzing the harmonic components of the voltage signal with respect to the fundamental frequency. Mathematically, $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}$ is determined by the formula⁶⁶:

$$\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{V}}=\:\frac{\sqrt{{{\varvec{V}}_{2}}^{2}+\:{{\varvec{V}}_{3}}^{2}+\:{{\varvec{V}}_{4}}^{2}+\dots\:\:+\:{{\varvec{V}}_{\varvec{n}}}^{2}}}{{\varvec{V}}_{1}}$$

(12)

Where, $\:{\varvec{V}}_{\varvec{n}}$ represents RMS voltage of $\:{\varvec{n}}_{\varvec{t}\varvec{h}}$ harmonic, $\:{\varvec{V}}_{1}$ represents the magnitude of the fundamental frequency component of the voltage waveform.

Current Total Harmonic Distortion ($\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}$) is one of the significant measurements, which is used to ascertain what percentage of the harmonic distortion has the current waveform of an electricity system. It measures the way the current waveform deviates from the ideal sinusoidal waveform. The $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}$ is normally expressed in percentage. The smaller the percentage of $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}$, the nearer the current waveform will be to an ideal sinusoidal waveform, which indicates good power quality. Conversely, the higher the percentage of $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}$, the more the harmonic distortion of the current waveform, and this has negative effects on the power distribution system, such as increased losses, overheating, and the potential to damage sensitive equipment. $\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}$ is calculated using the formula:

$$\:{\varvec{T}\varvec{H}\varvec{D}}_{\varvec{I}}=\:\frac{\sqrt{{{\varvec{I}}_{2}}^{2}+\:{{\varvec{I}}_{3}}^{2}+\:{{\varvec{I}}_{4}}^{2}+\dots\:\:+\:{{\varvec{I}}_{\varvec{n}}}^{2}}}{{\varvec{I}}_{1}}$$

(13)

$\:{\varvec{I}}_{\varvec{n}}$ represents the effective current of $\:{\varvec{n}}_{\varvec{t}\varvec{h}}$ harmonic, $\:{\varvec{I}}_{1}$ represents the fundamental frequency component of the current waveform.

Effect of EV charger operation on harmonic dynamics

EV charging stations are becoming increasingly widespread in modern power systems, and their integration into microgrids introduces new challenges, particularly in terms of power quality. Among these, harmonic distortion is a significant concern due to the nature of EV chargers as nonlinear loads. However, it is not only the presence of chargers that matters. The operational settings and technical configurations of these chargers play a decisive role in determining the level and characteristics of harmonic distortion they inject into the grid.

One of the primary factors influencing harmonic emissions is the type of charger employed. Level 1 and Level 2 AC chargers generally operate with single-phase or three-phase rectifiers that introduce lower-order harmonics, especially the 3rd, 5th, and 7th. These harmonics arise due to the switching action of diodes or thyristors in uncontrolled or semi-controlled rectifier configurations. Meanwhile, DC fast chargers employ more complex converter topologies, such as two-level or three-level inverters combined with high-frequency switching. These systems are capable of injecting both low-order and high-order harmonics, depending on their switching strategy and load conditions. The faster the charger delivers power, the more rapidly its power electronics must switch, which in turn broadens the spectrum of harmonics generated.

Another critical aspect is power factor correction (PFC). Many modern EV chargers are equipped with PFC circuits, either passive or active. Passive PFC typically includes inductors and capacitors that smooth out current waveforms to reduce harmonic content. Active PFC, which uses power electronic converters, can dynamically shape input current to mimic a sinusoidal waveform. Chargers without any form of PFC tend to draw highly distorted current waveforms, especially at partial loads. The presence or absence of PFC, along with its implementation quality, significantly alters the THD and the harmonic current spectrum.

Switching frequency and modulation technique are also important contributors. Chargers that use pulse-width modulation (PWM) with lower switching frequencies tend to concentrate energy in the lower harmonic orders, which have a more detrimental effect on voltage quality and equipment lifespan. On the other hand, chargers using high-frequency PWM spread the harmonic energy over a wider range of higher-order harmonics, which are often easier to filter but may pose challenges for electromagnetic compatibility. The choice of modulation strategy, such as sinusoidal PWM or space vector modulation, impacts not only the efficiency and control performance of the charger but also the shape of the current waveform and harmonic content.

SOC of the EV battery also influences the harmonic behavior. At lower SOC levels, the battery demands higher charging currents, leading to greater distortion. As charging progresses and the battery nears full capacity, the current demand diminishes and the charger transitions into constant-voltage mode, often resulting in different harmonic patterns. This dynamic behavior introduces time-varying harmonic characteristics, which complicates the prediction and management of harmonics in real-time grid operations.

Operational coordination among chargers introduces another layer of complexity. In public or fleet charging stations, multiple chargers may operate simultaneously. If they are uncoordinated, each charger may start or stop independently, creating random harmonic superpositions. Moreover, if chargers operate on the same or similar switching frequencies without phase-shifted control strategies, they may produce harmonic resonance or amplification at certain frequencies. Even chargers from different manufacturers may have unique harmonic signatures due to proprietary control algorithms, resulting in intermodulation harmonics when used in the same location.

User behavior and environmental conditions also influence harmonic levels. Peak-hour charging, especially in residential areas where many users plug in vehicles at the same time in the evening, can create clusters of harmonic generation. Additionally, climatic factors such as ambient temperature can affect charger efficiency and internal thermal management systems, indirectly influencing their switching characteristics and, consequently, their harmonic output.

Furthermore, smart charging algorithms and demand-side management strategies can significantly mitigate or worsen harmonic issues. Chargers that implement delayed start, power throttling, or adaptive current control based on grid signals can help in reducing harmonic surges. In contrast, unmanaged or opportunistic charging, where vehicles begin charging at maximum capacity as soon as they are plugged in, is likely to cause significant harmonic spikes.

The harmonic impact of EV charging infrastructure is not only a function of the number of vehicles or chargers connected to the grid, but also of the intricate and dynamic operational behaviors of those chargers. Detailed modeling of these behaviors, along with coordinated grid control strategies, is essential to accurately assess and mitigate harmonic pollution in EV-integrated power systems. Future research should focus on developing standardized harmonic profiles for charger types, advancing harmonic-aware scheduling strategies, and designing robust grid monitoring systems that account for the evolving nature of EV charging operations.

Moreover, selective disconnection of EV chargers represents a promising operational strategy for mitigating harmonic distortion in modern power systems. In large-scale charging infrastructures, simultaneous operation of multiple chargers can result in the aggregation of harmonic currents, especially if these chargers share similar switching frequencies or modulation techniques. This overlap can lead to constructive interference, harmonic amplification, or even resonance at specific harmonic orders, particularly in weak or lightly damped distribution systems. By implementing selective disconnection protocols, system operators can disconnect specific chargers based on their harmonic contribution, operating phase, or real-time power quality indices.

This approach can be particularly effective during peak charging periods, where the harmonic load is elevated due to high utilization of chargers. For instance, disconnecting chargers with poor power factor correction, high THD, or those operating on harmonically sensitive buses can help flatten the overall harmonic spectrum and improve voltage waveform quality. Additionally, this strategy can be integrated with other demand-side management measures, allowing for dynamic reallocation of charging load while minimizing power quality impacts. Advanced control systems equipped with harmonic analyzers and real-time monitoring can support automated decision-making processes for charger disconnection and reconnection. These systems can assess the harmonic contributions of individual chargers and apply rule-based or optimization-based algorithms to determine the optimal subset of active chargers that balances energy demand with power quality requirements. In this context, the integration of machine learning and predictive analytics may further enhance the responsiveness and effectiveness of selective disconnection strategies by anticipating harmonic trends and proactively managing charger operation.

Ultimately, the selective operation of EV chargers offers a practical, scalable, and low-cost method to address the growing challenge of harmonic distortion in EV-integrated grids. When combined with smart scheduling algorithms and coordinated grid control, this approach contributes to a more stable and resilient energy infrastructure, capable of accommodating high penetration levels of electric mobility.

Studied operational scenarios

To analyze the effect of EV integration on grid power quality for harmonic distortions, some operating scenarios were defined. The operating scenarios vary ratios of loads, AC chargers, and DC chargers to assess their effect on the microgrid. The primary purpose is to use ETAP software to identify and implement solutions filtering out harmonics, enhancing power quality, and optimizing load flow for smooth microgrid operation. Every scenario presents other configurations to compare their impact on energy consumption and network load. The scenarios include an in-depth study of charger configuration versus attendant energy consumption. Load management and strategic disconnection reveal a nonlinear effect between the energy consumption at a station and charging chargers quantity. Peak load management is the most critical factor towards the success of energy efficiency and a healthy charging system, with power quality optimization and grid stability.

• Scenario 1: AC Stations Off.

AC chargers are turned off here. Here, the intention is to study the impact of having only DC chargers on the microgrid’s power quality. In this case, there is an awareness of how the lack of AC charging generates harmonic distortions and the grid’s general performance.

• Scenario 2: DC Stations Off.

This situation involves the closure of all the DC chargers, with AC chargers standing alone to work. The idea is to observe the levels of power quality and harmonic distortion when DC charging is not used. It provides an understanding of how the grid looks like when there are only AC chargers in operation.

• Scenario 3: Full Load (100%).

AC and DC charging are both charged to capacity in this instance, a station maximum load case. This is a comparison point for observing worst-case energy consumption and harmonic distortion, an end-to-end picture of the microgrid at the top loads.

• Scenario 4: 60% Load.

The load is reduced to 60% of the peak capacity by intentionally disconnecting half of the chargers. This case helps to determine the effect of partial load operation on power quality and grid stability. It analyzes how minimizing the load can optimize system performance and minimize power quality issues.

• Scenario 5: 40% Load.

The load is then brought down to 40% of the total capacity. The aim is to explore the effect of degree load reduction on microgrid operation, primarily power quality and energy efficiency, and harmonic distortions. It evaluates the effectiveness of aggressive load management techniques in maintaining power quality.

Results and discussion

The section provides a comprehensive discussion of grid-connected microgrid and operation of an EV charging station. Various situations were taken into account for evaluating the impact of various operation conditions on power quality, harmonic distortion, and voltage stability. The simulation results obtained from the ETAP software under consideration of voltage waveform, harmonics orders, and voltage spectrum for various scenarios are discussed elaborately. To investigate the impact of EV integration into the grid on power quality, specifically on harmonic distortion, some simulation scenarios have been performed. Five operation scenarios have been designed, with various amounts of loads, AC chargers, and DC chargers. The distortion of voltages imposed in this study adhere to the IEEE STD 519–2014 standards, which provide acceptable individual harmonic distortion and THD values for different voltage levels at the point of common coupling (PCC). The limits are listed in Table 2.

Table 2 Voltage distortion limits in IEEE STD 519–2014⁶⁷.

Scenario analysis

• i. Scenario 1: AC Stations Off.

Scenario 1 identifies the challenges that the microgrid faces in the case of shutting down AC stations. The significant harmonic distortion levels observed in this scenario reflect the need for appropriate load management, efficient system design, and advanced monitoring systems such that the microgrid follows correct power quality standards. These issues need to be addressed in order to ensure the reliability, efficiency, and lifespan of the microgrid equipment. The conclusions emphasize that harmonics not only affect power quality but also create other impacts on system operation and maintenance. Figure 6 illustrates the voltage waveform with respect to time for Scenario 1, where AC stations are turned off. The waveform shows large fluctuations, representing harmonic distortions.

The distortions are caused by the nonlinear loads created by the EV charging stations. The waveform shows deviation from the pure sinusoidal shape, with clear peaks and troughs typical of harmonics. The waveform amplitude is highly fluctuating, with both greater and lesser peaks compared to those of a pure sinusoidal waveform. Such oscillations are typical of harmonic interference, which is typically present in systems with high non-linear loads, e.g., EV chargers. The waveform shape is a deformed waveform with high distortion from the ideal sinusoidal waveform. Distortion is caused by the harmonics produced due to the nonlinear loads of the EV charging stations. The fundamental cycle is not altered, but the occurrence of harmonics provides variability in each cycle and affects the voltage stability.

Fig. 6

Voltage waveform percentage over time in scenario 1.

Table 3 presents a comprehensive analysis of harmonic voltage distortion across four major buses in the microgrid under Scenario 1, where all AC charging stations are switched off. The buses include the main loads bus at 0.40 kilovolts, the WT bus at 4.16 kilovolts, the grid bus at 0.40 kilovolts, and the PV bus at 0.60 kilovolts. The voltage spectrum is analyzed across various harmonic orders, ranging from the 1 st to the 49th, focusing on identifying the dominant harmonics and assessing their compliance with the IEEE standard 519–2014.

Among all harmonic orders, the fifth harmonic exhibits the highest magnitude, recorded at 0.7935% on both the main loads and grid buses. This value is notably higher than that on the WT bus and the PV bus, which show 0.4438% and 0.4191% respectively. The prominence of the fifth harmonic is characteristic of non-linear loads and power electronic converters, particularly those found in EV charging stations, which often operate using six-pulse rectifiers. The slightly lower values at the generation-side buses, such as the WT and PV buses, indicate that harmonic propagation is more significant near load centers than at generation points. Despite being the most dominant, the fifth harmonic remains well below the IEEE 519–2014 individual limit of 5%, indicating that the microgrid maintains harmonic levels within acceptable boundaries. Another significant component is the eleventh harmonic, with values of 0.6338% at both the main loads and grid buses, 0.5604% at the WT bus, and 0.5451% at the PV bus. The presence of this harmonic is likely due to resonance phenomena and the cumulative interactions between non-linear sources distributed throughout the microgrid. The eleventh harmonic is not typically as dominant in simple systems, but in interconnected renewable-based microgrids with varying impedance and multiple converter interfaces, it becomes more pronounced. Again, all values remain within IEEE-recommended limits.

The thirteenth harmonic also appears with non-negligible magnitudes across all buses, ranging from 0.3529 to 0.4321%. While lower than the fifth and eleventh, its consistent presence confirms that multiple harmonic orders are actively propagating in the network. These medium-order harmonics, if left uncontrolled, can lead to voltage waveform distortion and degradation in the performance of sensitive electronic devices. The third and seventh harmonics show relatively minor distortion, with values around 0.0642–0.1850% depending on the bus. These lower-order odd harmonics generally arise from asymmetrical loading or line impedance mismatch and are also influenced by system grounding. Their minimal values reflect effective load balancing and grounding practices in the model. For harmonic orders above the fifteenth, the voltage spectrum shows a clear declining trend. The seventeenth harmonic registers up to 0.1995%, and subsequent harmonics such as the nineteenth, twenty-third, twenty-fifth, and so on, reduce progressively below 0.07%. By the time the system reaches the forty-ninth harmonic, distortion is as low as 0.0084%. This tapering off of higher-order harmonics is expected due to natural attenuation in the system and the frequency-selective impedance of transformers and lines, which suppress high-frequency components. These results also suggest that no resonant conditions exist that would otherwise amplify specific high-order harmonics.

A comparison across the four buses reveals a consistent pattern. The main loads and grid buses typically exhibit the highest harmonic distortion values, indicating that these points are the most exposed to non-linear load effects. This observation is particularly relevant for microgrid planners, as it highlights the need for local harmonic filtering or improved converter designs at the load end. In contrast, the WT and PV buses, which represent renewable generation points, experience comparatively lower harmonic content. This pattern confirms that harmonic propagation in microgrids is directional and that loads are the principal sources of distortion. Overall, all harmonic levels recorded in Table 3 fall well within the individual harmonic distortion limit of 5% and the THD limit of 8% defined by IEEE standard 519–2014 for voltage levels below 1 kilovolt. Although the total THD is not explicitly presented in this table, the low values across all harmonic orders suggest that the aggregate THD remains well below the threshold.

Scenario 1 effectively demonstrates the capability of the proposed model to manage harmonic distortion even in the presence of non-linear loads, and without the influence of active AC stations. The data shows that harmonic emissions from EV charging infrastructure and power electronics are well controlled through embedded control strategies, appropriate network impedance design, and possibly passive or active filtering mechanisms. This level of performance indicates a high degree of robustness and reliability in the microgrid model and supports its suitability for real-world applications where power quality is critical. The findings of Table 3 validate the model’s strength in harmonic suppression, system resilience, and adherence to regulatory standards. These attributes are essential for ensuring the stable and efficient operation of microgrids integrating renewable energy sources and modern electric loads.

Table 3 Harmonic order with voltage spectrum percentage in scenario 1.

• ii. Scenario 2: DC Stations Off.

Scenario 2 shows the results when DC stations are turned off. This scenario is important for understanding the impact of the absence of DC charging stations on the microgrid’s performance. Figure 7 shows the voltage waveform for Scenario 2, where DC stations are off. The voltage variations indicate the system’s response to the absence of DC charging stations, which affects the harmonic distortions and overall power quality. Similar to Scenario 1, the amplitude of the waveform in Scenario 2 exhibits significant variations, indicating harmonic presence. The model is well representing these variations, demonstrating its potential for reflecting the real-world consequences of different operation conditions. The waveform is also distorted, but the pattern of distortion is a bit different than Scenario 1 due to the changing nature of loads (absence of DC stations). The distinction verifies the model’s responsiveness to changing types of loads. The consequences of harmonics introduce additional oscillations in each cycle, which influence voltage stability.

Table 4 provides a detailed overview of the harmonic voltage distortion across four key buses in the microgrid under Scenario 2, where all DC charging stations are deactivated. The buses analyzed include the main loads bus operating at 0.40 kilovolts, the WT bus at 4.16 kilovolts, the grid bus at 0.40 kilovolts, and the PV bus at 0.60 kilovolts. Harmonic analysis was conducted across a wide range of odd harmonic orders, from the third up to the forty-ninth, with the fundamental frequency excluded. A dominant characteristic observed in this scenario is the significant amplitude of the fifth harmonic component. Both the main loads and the grid bus report a maximum distortion of 0.9762% at this harmonic order, making it the most pronounced distortion across the system. The WT bus and PV bus also show notable fifth harmonic content, registering 0.4975% and 0.4443% respectively. The elevated level of the fifth harmonic is strongly associated with the non-linear operation of EV charging systems and other power electronic interfaces, such as inverters, rectifiers, and variable speed drives. These components are known sources of low-order harmonics due to their switching behavior.

Despite the relatively high magnitude of the fifth harmonic in Scenario 2 compared to Scenario 1, it remains significantly below the individual harmonic distortion limit of 5% as prescribed by the IEEE standard 519–2014. This compliance demonstrates the capacity of the model to maintain power quality, even under conditions where DC charging infrastructure is unavailable and non-linear load interactions become more concentrated at the alternating current (AC) end of the system. The eleventh harmonic represents another significant distortion component. The main loads and grid bus both record distortion levels of 0.6346% at the eleventh harmonic, with the WT bus and PV bus registering 0.5522% and 0.5385% respectively. This harmonic is not typically associated with isolated single sources, but rather with complex interharmonic interactions, system resonances, and the aggregated effect of distributed non-linear devices. Although the values are lower than the fifth harmonic, they are consistent across all buses, indicating a network-wide propagation of this component. Once again, all values remain comfortably within the IEEE-defined individual limits, ensuring no breach of voltage waveform quality.

The thirteenth harmonic follows a similar trend, showing voltage distortions in the range of 0.3303–0.4250% across all buses. The presence of this medium-order harmonic is noteworthy as it often arises due to switching overlap in converters and the harmonic multiplication effects of interlinked non-linear sources. While not as impactful as the fifth and eleventh harmonics, its non-negligible magnitude points to a layered harmonic profile within the microgrid, demanding careful monitoring and control. Lower-order harmonics such as the third and seventh also appear in the results. The third harmonic, while minimal at the main loads and grid buses (0.0641%), reaches up to 0.3918% at the PV bus. This is indicative of the presence of triplen harmonics, which may accumulate in neutral lines and are typically generated by single-phase non-linear loads or unbalanced three-phase systems. However, the values remain low enough to suggest that phase imbalance and zero-sequence harmonic propagation are not critically affecting the microgrid in this scenario.

The seventh harmonic records moderate levels across all buses. The main loads and grid buses show distortion of 0.2103% each, while the WT and PV buses display 0.1962% and 0.1935% respectively. These values, although measurable, are well below the 5% limit and pose little risk to system stability or equipment functionality. Higher-order harmonics beyond the fifteenth order exhibit a clear pattern of exponential decay in magnitude. The seventeenth harmonic is the last notable one, with values around 0.2015% at the main loads and grid buses, and slightly lower at the WT and PV buses. From the nineteenth to the forty-ninth harmonics, distortion levels continue to decrease, falling below 0.15% in all instances. For example, the twenty-fifth harmonic measures approximately 0.0368% at the main loads, and even the forty-ninth harmonic is reduced to just 0.0093%. This attenuation pattern reflects the natural impedance filtering effect of the network, and possibly the presence of designed passive filters or careful layout of cable and transformer configurations to suppress high-frequency harmonic propagation.

A comparative review between the different buses confirms that the main loads and grid buses experience the highest distortion levels. These buses, being the primary delivery points for consumer power and the connection points for downstream non-linear loads, are more exposed to harmonic interference. On the other hand, generation-side buses such as the WT and PV points demonstrate lower harmonic penetration, reinforcing the directional nature of harmonic flow and the local generation-load dynamics in microgrids. Importantly, all harmonic magnitudes in Table 4 fall within the permissible limits defined by IEEE standard 519–2014, which stipulates that the total harmonic distortion in voltage for systems below one kilovolt should not exceed 8%, and individual harmonic components should be kept below 5%. Although the total harmonic distortion value is not provided explicitly in the table, the cumulative sum of individual harmonic contributions suggests that THD remains safely under this threshold. The highest individual harmonic value recorded is the fifth harmonic at 0.9762%, which is less than one-fifth of the permissible maximum.

Scenario 2 successfully demonstrates the model’s capability to suppress harmonic distortions despite the absence of DC charging stations. Typically, DC charging systems with high-speed converters are major sources of harmonics; yet, their deactivation in this scenario shifts harmonic burden to other non-linear AC-based devices. The fact that the system continues to perform within compliance parameters illustrates the model’s effective harmonic mitigation strategy. This is crucial for maintaining voltage waveform quality, preventing overheating or malfunction in sensitive equipment, and ensuring operational continuity of protection systems.

In conclusion, the results presented in Table 4 confirm the resilience and stability of the microgrid model in managing harmonics under real-world-like operating conditions. The ability of the model to remain within IEEE standard thresholds, even in a high non-linear load environment without the support of DC charging regulation, showcases its reliability, adaptability, and readiness for deployment in modern microgrids that integrate renewable energy and electrified transport infrastructure.

Fig. 7

Voltage waveform percentage over time in scenario 2.

Table 4 Harmonic order with voltage spectrum percentage in scenario 2.

• iii. Scenario 3: Full Load.

Scenario 3 examines the behavior of the microgrid when both AC and DC EV charging stations are operating concurrently at their maximum rated capacity. This scenario simulates the most demanding condition, where the charging infrastructure introduces the highest possible load on the system. In this context, the contribution of EV charging stations to power quality issues becomes most pronounced, particularly in terms of harmonic distortion. EV charging stations are a major source of harmonic distortion due to the use of power electronic interfaces, including rectifiers, inverters, and DC-DC converters. These components exhibit nonlinear behavior by drawing current in short pulses rather than in a smooth sinusoidal manner. As a result, they inject harmonic currents into the grid, leading to distortion of the voltage waveform across the entire network. The severity of this distortion increases proportionally with the number of chargers in use and their rated capacity, both of which are maximized in this scenario.

At full load, each charging unit operates at its maximum switching frequency, which intensifies the generation of harmonics, particularly in the lower orders such as the 3rd, 5th, 7th, and 11th. These harmonics are reflected back into the grid and affect voltage and current waveforms at all nodes. The superposition of multiple harmonic sources from simultaneously charging EVs leads to harmonic resonance, waveform notching, and voltage flattening, which collectively degrade power quality. This is particularly problematic at the point of common coupling, where the aggregation of harmonic sources results in elevated THD levels. Figure 8 presents the voltage waveform during full load conditions. The waveform exhibits evident deviation from the ideal sinusoidal shape, with exaggerated peaks and valleys indicating significant waveform distortion. This distortion arises from cumulative nonlinear loading by multiple high-power EV chargers operating simultaneously.The presence of such distortions confirms that EV charging stations, particularly when operating in large numbers and at full capacity, are dominant contributors to harmonic pollution in the microgrid.

Table 5 provides a detailed breakdown of harmonic content for this scenario. The results show that the 5th harmonic distortion is 0.72% and the 11th harmonic distortion is 0.63% at the main loads and grid bus. These values are the highest among all five evaluated scenarios. The 5th harmonic is especially significant because it is commonly produced by six-pulse rectifiers used in most Level 2 and DC fast chargers. The 11th harmonic is often associated with high-frequency switching in inverters and pulse-width modulation techniques. The fact that these two harmonics dominate the distortion profile under full-load conditions highlights the role of EV charger electronics in generating repetitive waveform deviations. Compared to Scenario 4, which uses only 60% of the charging infrastructure, the full-load scenario presents noticeably higher harmonic levels. The marginal increases may appear numerically small, but even slight percentage increases in lower-order harmonics can produce substantial electromagnetic interference, voltage flicker, and overheating of system components such as transformers and neutral conductors.

WT AC bus and PV AC bus also register increased harmonic levels at 0.42% and 0.41% respectively for the 5th harmonic, and 0.56% and 0.55% respectively for the 11th harmonic. These measurements reflect the distributed impact of EV-induced harmonics throughout the microgrid. The 7th harmonic remains low at all buses, with values ranging from 0.14 to 0.18%. Although these higher-order harmonics are less severe, they still indicate the broader harmonic spectrum caused by high switching frequencies. The consistent presence of harmonics across the microgrid nodes indicates that distortion from EV chargers is not localized but diffuses system-wide through coupling paths. These findings strongly support the conclusion that EV charging stations, especially when operating at full load, are not only active sources of harmonics but also dominant contributors to power quality deterioration in interconnected microgrids. This underscores the need for careful consideration of EV load modeling, harmonic behavior, and system-wide impacts during microgrid planning and operation. Moreover, understanding the harmonic signature of EV chargers is essential to maintaining compliance with standards such as IEEE STD 519–2014, ensuring voltage quality, and avoiding long-term equipment degradation.

Fig. 8

Voltage waveform percentage over time in scenario 3.

Table 5 Harmonic order with voltage spectrum percentage in scenario 3.

• iv. Scenario 4: 60% Load.

Scenario 4 examines the system performance under the 60% capacity load. The partial load condition allows for the examination of the behavior of the microgrid under a modest loading condition. Figure 9 illustrates the voltage waveform as a percentage versus time (cycle) for scenario 4, portraying the voltage stability and oscillations faced under the 60% load condition. This is a key observation since it illustrates the ability of the system in maintaining stable voltage levels despite the variation in the load. In this instance, the voltage waveform undergoes minimal fluctuations, showing that the microgrid is able, in most instances, to maintain voltage stability with medium load levels. These fluctuations, although minimal, are significant to note since they may affect the performance of sensitive equipment connected to the grid. Voltage stability is among the performance measures, and any deviation from the nominal voltage can be a sign of issues with the microgrid’s ability to process the load efficiently. The variations in voltage observed are within acceptable limits as per the IEEE STD 519–2014 standards. These standards stipulate that at voltages less than 1.0 kV, THD should be lower than 8%, and individual harmonic distortion should be lower than 5%.The findings indicate that the microgrid performs well within these specifications, demonstrating its feasibility to drive a 60% load without significantly deviating from the standard voltage levels. The stability in voltage waveform, as noted, contributes to overall power quality. Steady voltage levels minimize the threat of equipment breakdown and enhance power delivery efficiency.

Table 6 presents the harmonic order and the corresponding voltage spectrum percentages at key buses in the microgrid under Scenario 4, where the system operates at 60% load. The buses analyzed include the main loads bus at 0.40 kilovolts, the WT bus at 4.16 kilovolts, the grid bus at 0.40 kilovolts, and the PV bus at 0.60 kilovolts. This scenario captures harmonic performance under partial loading conditions, which often represent real operational states for microgrids in practice. The harmonic analysis for the main loads bus reveals that the fifth harmonic remains the most dominant, with a voltage distortion level of 0.7184%. This value clearly indicates the presence of non-linear loads that inject low-order harmonics into the system, such as electric vehicle chargers, power electronic converters, and other switching-based devices. The significant amplitude of the fifth harmonic in this scenario reinforces the fact that such non-linear components continue to influence waveform distortion, even when the system is not operating at full capacity.

Following the fifth harmonic, the eleventh harmonic registers a voltage distortion of 0.6267% at the main loads. This relatively high value suggests that, while the fifth harmonic is the strongest, higher-order harmonics also contribute notably to the overall distortion. The presence of the eleventh harmonic typically arises from cumulative harmonic interactions, waveform asymmetries, and resonance effects in the network. This order is particularly sensitive to combined effects of distributed energy resources and the configuration of network impedances. The thirteenth harmonic also appears as a relevant contributor, showing a distortion of 0.3577% at the main loads bus. Though lower in magnitude than the fifth and eleventh harmonics, its consistent presence indicates that harmonic pollution extends beyond just the low-order components, requiring attention in the harmonic mitigation strategy. These three harmonic orders together form the primary contributors to the total harmonic distortion and shape the harmonic profile of the system under partial loading.

At the WT bus, the harmonic profile shows a different pattern. The fifth harmonic distortion is lower at 0.4201% compared to the main loads, indicating that the wind turbine connection contributes less to low-order harmonic injection, or that some attenuation occurs along the connection path. This difference may also result from better filtering or smoother control interfaces in the WT systems. Interestingly, the eleventh harmonic at the WT bus measures 0.5584%, which, although lower than the main load side, still reflects a significant impact. The thirteenth harmonic at the WT bus is relatively higher than at the main loads, recorded at 0.4332%. This highlights that wind energy systems, despite producing cleaner power at the fundamental frequency, may still contribute to higher-order harmonics through their inverter switching and control dynamics. The grid bus exhibits harmonic distortion values nearly identical to the main loads bus. The fifth harmonic distortion again appears at 0.7184%, with the eleventh harmonic at 0.6267%, and the thirteenth harmonic at 0.3577%. These values suggest that the harmonic sources are not isolated but are widely distributed across the microgrid. The similarity in distortion between the main loads and the grid bus confirms that the same sets of non-linear loads influence both points, and harmonic currents are circulating through the grid connection point, indicating a bidirectional flow of distortion.

The PV bus reveals a slightly different harmonic profile, with generally lower values across key orders. The fifth harmonic distortion is recorded at 0.4074%, which is lower than at the main loads and grid bus. This suggests that the photovoltaic generation system contributes less to fifth-order harmonics, potentially due to lower current magnitudes or advanced converter design. The eleventh harmonic distortion at the PV bus is 0.5445%, and the thirteenth harmonic is 0.4207%. These values are notable but still lower than those observed at the WT bus for the same harmonic orders. This comparison implies that PV systems may have a relatively milder harmonic impact, but they are nonetheless contributors to the overall distortion landscape. Across all buses, the harmonic magnitudes decrease progressively with increasing harmonic order beyond the thirteenth. For example, the seventeenth harmonic measures around 0.1998% at the main loads and grid buses, slightly lower at the WT and PV buses. The nineteenth harmonic follows with distortion levels under 0.15% at all buses. By the twenty-third harmonic and beyond, values continue to drop steadily, with the forty-ninth harmonic recording just over 0.0092% at the main loads and less than 0.0085% at the PV bus. This steady decay of high-order harmonics is consistent with the expected frequency response of network impedance and the attenuation effects of line impedance, transformer filtering, and possibly passive filters integrated into the system.

The overall harmonic profile in Scenario 4 reflects that the primary sources of distortion originate from the load side rather than from generation. The consistent presence of harmonics at both the main loads and grid bus supports the conclusion that non-linear loads are distributed across the network and not concentrated at a single bus. The wind and PV generation units, while also exhibiting harmonics, show relatively lower contributions, likely due to their controlled interface characteristics or reduced active power contribution at 60% system loading. Crucially, all observed harmonic values across all buses in this scenario remain within the acceptable range defined by the IEEE standard 519–2014. The standard mandates that the maximum individual harmonic voltage distortion should not exceed 5% for systems with voltages below one kilovolt, and the total harmonic distortion should remain below 8%. In Scenario 4, the highest individual harmonic observed is the fifth harmonic at 0.7184%, which is significantly below the limit. This confirms that the power quality is preserved even under moderate loading conditions.

Scenario 4 thus highlights the effectiveness of the proposed microgrid model in maintaining harmonic performance within industry standards under partial load conditions. The model successfully controls harmonic emissions from distributed non-linear loads and renewable generation sources, ensuring stable operation and protection for sensitive equipment. The low harmonic distortion levels across all buses reinforce the reliability and robustness of the microgrid control strategy, making it suitable for real-world deployment where maintaining power quality under varying operational states is critical.

Fig. 9

Voltage waveform percentage over time in scenario 4.

Table 6 Harmonic order with voltage spectrum percentage in scenario 4.

• v. Scenario 5: 40% Load.

In Scenario 5, being a 40% load condition, the voltage waveform analysis is applied to check the performance of the microgrid under moderate load. The voltage waveform in percentage versus time in cycles reflects the deviations at this load level. This test is required to find out the stability and reliability of the system under lower loads and also at higher loads. Figure 10 is a fairly stable voltage waveform with very little fluctuation, which indicates the voltage stability offered by the microgrid at 40% load. The variations are within acceptable levels as defined in IEEE STD 519–2014. According to the standard, for bus voltages below 1.0 kV, THD should not be greater than 8%, and individual harmonic distortion should not exceed 5%. The testing confirms the microgrid’s satisfactory operation under these limitations and that it can handle a 40% load without significant deviations from normal voltage levels.

Table 7 illustrates the harmonic order and corresponding voltage spectrum percentages measured at four critical buses of the microgrid under Scenario 5, in which the system operates at 40% loading capacity. The buses analyzed include the main loads bus at 0.40 kilovolts, the WT bus at 4.16 kilovolts, the grid bus also at 0.40 kilovolts, and the PV bus at 0.60 kilovolts. This scenario simulates a reduced demand condition, which is vital to evaluate as harmonic behavior can vary depending on the load level due to changes in power flow, inverter switching behavior, and system impedance. Under this lightly loaded scenario, the harmonic analysis continues to highlight the fifth harmonic as the most dominant distortion component. At both the main loads and the grid bus, the fifth harmonic distortion reaches 0.7173%. This similarity suggests a strong correlation in harmonic propagation between consumer load points and the utility interface, confirming the widespread influence of non-linear loads across the microgrid. Despite its dominance, this level of distortion remains significantly below the individual harmonic limit of 5% as defined by IEEE standard 519–2014, affirming that system compliance is maintained even under light loading.

At the WT bus, the fifth harmonic distortion is reduced to 0.4187%, indicating that the wind turbine and its associated converters contribute less significantly to harmonic injection. This reduction may be attributed to the smoother operation of power electronic interfaces in wind systems or to the filtering effects of transformer and line impedance between the WT interface and the rest of the network. The PV bus follows a similar trend with the fifth harmonic measured at 0.4065%, confirming that both renewable energy generation units are comparatively fewer dominant sources of fifth-order harmonics under the given operating conditions. The eleventh harmonic emerges as the second most influential order in all locations. The main loads and grid bus report distortion levels of 0.6231%, while the WT bus and PV bus register 0.5558% and 0.5430% respectively. This consistent presence indicates a network-wide harmonic influence, possibly arising from cumulative interactions among distributed converters and waveform resonance. While the values are lower than the fifth harmonic, they remain significant and are essential to monitor, particularly in systems with sensitive voltage control or high penetration of distributed energy resources.

The thirteenth harmonic, although smaller in magnitude, remains a considerable contributor to the overall harmonic profile. The distortion at the main loads and grid bus stands at 0.3557%. At the WT and PV buses, the values are slightly elevated, reaching 0.4324% and 0.4200% respectively. The appearance of this medium-order harmonic suggests the presence of harmonic multiplication and non-sinusoidal waveform effects caused by inverter switching harmonics and possibly network resonances. The trend of higher thirteenth harmonic levels at generation points may also reflect the harmonic interaction between the renewable sources and system impedance characteristics. Lower-order harmonics such as the third and seventh are also evident. The third harmonic at the main loads and grid bus is 0.0642%, while it is slightly higher at the PV bus at 0.3929%, likely due to single-phase loading imbalances or the influence of triplen harmonics in transformer windings. The seventh harmonic is moderate across all buses, ranging from 0.1338% at the load side to 0.1810% at the PV bus. These values are relatively low and not of major concern individually, though they contribute to the overall THD.

As harmonic order increases, the magnitude of distortion diminishes, exhibiting a typical decay profile due to the frequency-dependent attenuation effects of line inductance and transformer impedance. For instance, the seventeenth harmonic measures approximately 0.2005% at the main loads, decreasing further in subsequent higher orders. From the nineteenth harmonic upward, distortion levels continue to fall, staying well below 0.15% for all cases. By the time the system reaches the forty-ninth harmonic, the distortion at all buses is below 0.0093%. This indicates that the system has no significant resonance or amplification at higher frequencies, and the harmonic spectrum is well-contained through design and operational strategies. The similarity in harmonic values between the main loads and the grid bus confirms that harmonic sources are not localized but rather distributed across the microgrid, with harmonics being injected from multiple points. The relatively lower harmonic content at the WT and PV buses reflects the lesser contribution of generation sources compared to consumer loads, particularly under reduced load conditions where the proportion of converter-fed renewable energy may be lower.

Importantly, all harmonic distortion values reported in Scenario 5 are well within the limits established by IEEE standard 519–2014. The maximum observed individual distortion is the fifth harmonic at 0.7173%, which is significantly lower than the 5% threshold for individual harmonics in systems operating below one kilovolt. While the total harmonic distortion is not explicitly computed in this table, the summation of the major harmonic components suggests that the THD remains comfortably below the 8% limit, thereby ensuring compliance. The results in Table 7 validate the microgrid’s harmonic performance under low-load conditions, demonstrating effective harmonic mitigation and control despite the presence of multiple non-linear elements. This performance is critical for maintaining voltage stability, minimizing equipment stress, and preventing misoperation of protection systems. Scenario 5 confirms that the model is capable of delivering high power quality and system stability across a range of operating conditions, fulfilling both regulatory and operational expectations.

Fig. 10

Voltage waveform percentage over time in scenario 5.

Table 7 Harmonic order with voltage spectrum percentage in scenario 5.

Integration of renewable energy sources and harmonic impact

This section provides a comprehensive analysis of the impact of RESs on harmonic distortions in the microgrid, paying particular attention to the necessity of dedicated measures for mitigating the effects to provide power quality. The inclusion of RESs such as PV systems and WTs in the microgrid is one of the most important factors for enhancing sustainability while reducing greenhouse gas emissions. However, this integration also comes with unique challenges, particularly with regards to harmonic distortions and their implications on power quality. The incorporation of PV systems and wind turbines into the microgrid has an impact on the harmonic profile of the system. As described in the scenarios above, each of the sources of renewable energy has various characteristics that affect the levels of harmonic distortions. PV systems, due to their power electronic interfaces, are sources of harmonic distortions, but at varying degrees depending on the design and operation of the inverters used. For instance, in Scenario 4 under 60% loading, PV Bus at 0.60 kV recorded 5th harmonic distortion of 0.407415%, which is lower than the distortions at the principal loads and Grid Bus. The 11th harmonic at the PV Bus had 0.544487% distortion, while the 13th harmonic was at 0.420681%. Despite being harmonic distortion levels, they are within the acceptable limits of IEEE STD 519–2014. The results suggest that although PV systems contribute to harmonic distortions, the impact is partially mitigated by the quality of the inverters used and the natural nature of operation of PV systems.

The harmonic profile is also influenced by the integration of wind turbines. For Scenario 4, the WT bus at 4.16 kV showed 5th harmonic distortion of 0.420051%, less than what was observed at the main loads. It could be stated that the wind turbine connection and its associated power electronic interfaces are either better managed or less contributing to harmonic distortion. The 11th harmonic distortion in the WT bus was 0.558383%, and the 13th harmonic distortion was 0.433211%. These are slightly higher than those at the PV Bus but also within IEEE limits. That there are higher-order harmonics indicates that wind turbines, like PV systems, inject harmonic distortions primarily through their power electronic interfaces.

Results analysis and discussions

Harmonic distortion analysis suggested substantial effects in various scenarios. In Scenario 1, when AC stations were deactivated, the 5th harmonic distortion was 0.79% and 11th harmonic was 0.63%. The percentages reflected the impact of non-linear loads generated by EV charging stations. Scenario 2 with isolated DC stations saw an increase in the 5th harmonic to 0.98%, reflecting increased predominance of nonlinear loads compared to Scenario 1. The distortions, however, were still within IEEE standards, thereby establishing the strength of the model in coping with harmonic levels. In full-load Scenario 3, the distortion of the 5th harmonic was 0.72%, while the 11th harmonic remained unchanged at 0.63%. These results verified that higher loads generated more harmonic distortions. Scenario 4 for 60% load showed the same harmonics, such as the 5th at 0.72% and the 11th at 0.63%, with consistent occurrence of harmonics far below the IEEE standards. Scenario 5 in a 40% load condition showed relatively lower harmonic distortions, showing the stability of the microgrid at lower loads. Voltage stability analysis in all the cases revealed that the voltage levels in the microgrid were within acceptable limits. There were minor fluctuations, but these were within IEEE standards, and operation was stable irrespective of the varying conditions. The fact that this was done successfully shows the stability of the proposed model. Harmonic distortions were successfully controlled by the microgrid model, and voltage stability was realized in all cases of operation. The highest recorded individual harmonic distortion (5th order at 0.98%) and THD values were IEEE STD 519–2014 compliant, validating the strength and applicability of the model in real-world microgrid implementation.

This study conducts a comprehensive investigation into the impact of EV charging integration on the power quality of a grid-connected microgrid, with a particular focus on harmonic distortion and voltage stability. To capture the variability of EV charger behavior under realistic grid conditions, multiple operational scenarios were developed and simulated using ETAP. These include full load (all chargers active), partial load (60% of chargers active), and selective disconnection (targeted removal of certain chargers). Advanced load management strategies were implemented in each scenario to observe how control over EV charging patterns can be used to improve overall system performance. The adoption of partial loading, as seen in Scenario 4, proved especially effective. In this case, the harmonic distortion levels at key system buses were significantly reduced, most notably, the 5th and 11th harmonic voltages on the PV Bus were brought down to 0.407% and 0.544% respectively, well below the IEEE 519–2014 thresholds. Moreover, voltage profiles across the microgrid remained within the acceptable deviation limits, confirming the scenario’s effectiveness in maintaining grid stability.

In addition to charger control, the system also employed coordinated integration of renewable energy sources, particularly PV and wind power units, supported by high-performance inverters. These inverters played a key role in conditioning power output and mitigating the harmonics typically generated by intermittent sources. Their role was especially apparent in stabilizing the PV Bus, where voltage quality was consistently higher under coordinated scenarios. While this study did not employ mathematical optimization techniques such as particle swarm optimization (PSO) or genetic algorithms, it implemented a scenario-based optimization framework. This approach allowed for the systematic evaluation and comparison of each operational condition based on empirical performance metrics such as THD, voltage deviation, and system reliability.

The scenario-based framework functioned as a practical and intuitive optimization strategy, enabling the selection of the most effective control configuration without introducing algorithmic complexity. As a result, Scenario 4 was identified as the optimal case, balancing energy delivery, power quality, and stability. The use of the Volkswagen ID.4 Crozz as a standard EV model further ensured that load characteristics were realistic and consistent with actual EV charging demands, thereby enhancing the accuracy of the simulation outcomes. This approach underscores the significance of intelligent load scheduling, charger disconnection logic, and renewable coordination in maintaining power quality in EV-integrated microgrids.

Impact of EV charging configurations on harmonic distortions

The connection of EV charging stations to a microgrid generates various amounts of harmonic distortions depending on operating modes. Different scenarios are considered in this research for evaluating the impact of the startup and shutdown of AC and DC chargers and varying load levels on power quality. Harmonics are an important consideration in power quality because high harmonic distortion results in inefficient energy transfer, equipment overheating, and voltage instability. By thorough research of the effects of different modes of EV charging, it is possible to identify ways of minimizing these effects and maximizing performance and reliability of renewable-powered microgrids.

In Scenario 1 with all the AC charging stations shut down, 5th harmonic distortion was 0.79% and had a strong influence on the main loads and grid bus, as shown in Fig. 11. WT and PV buses experienced comparatively less harmonic distortion levels, thereby indicating that the mere existence of DC chargers resulted in moderate harmonic impact. Scenario 2, with no DC chargers operational and AC chargers only operating, had the highest 5th harmonic distortion of approximately 0.98% from Fig. 12. This indicates that the AC chargers generate a higher number of harmonics than DC chargers, possibly due to their different power electronic converter topologies and operation characteristics.

Under full-load operation in Scenario 3, where the AC and DC chargers were operated at maximum load, the level of the 5th harmonic distortion was 0.72% as shown in Fig. 13. This result affirms that while maximum charging station utilization causes apparent harmonic impacts, overall levels remain below IEEE STD 519–2014 acceptable standards. The research also indicates that the harmonic presence is greater when both the DC and AC chargers are run in parallel, and therefore demands rigorous countermeasures in the shape of active filtering or control measures.

The optimal results were achieved in Scenario 4, where the load was reduced to 60% of the total capacity. The 5th harmonic distortion decreased significantly to 0.42% in this case, which shows that operating EV chargers at low-to-medium loads helps in enhancing power quality without compromising charging needs, as shown in Fig. 14. Scenario 5, with an additional reduction of load to 40%, yielded similar results, though the additional reduction in load did not increase harmonic mitigation so much as under the 60% scenario depicted in Fig. 15. This implies that while load reduction can improve power quality, there is a point at which an additional reduction contributes diminishing returns.

Fig. 11

Harmonic Spectrum for Scenario 1.

Fig. 12

Harmonic Spectrum for Scenario 2.

Fig. 13

Harmonic Spectrum for Scenario 3.

Fig. 14

Harmonic Spectrum for Scenario 4.

Fig. 15

Harmonic Spectrum for Scenario 5.

Comparative analysis of harmonic distortions

Comparative evaluation of harmonic distortions in some EV charging configurations reveals the dominating role of AC compared to DC chargers, and the influence of varying load levels on microgrid power quality. Harmonic distortions, particularly of the 5th and 11th orders, have adverse effects on system stability, power losses, and equipment performance. Proper management of the distortions is crucial to guarantee an efficient and stable microgrid.

The most salient conclusion derived from this study is that the maximum 5th harmonic distortion happened in Scenario 2 at around 0.98%, under the sole operation of AC chargers as depicted by Fig. 16. This suggests that AC chargers inject more harmonic content into the network compared to DC chargers, on the likely basis of variation within rectification and conversion stages. On the other hand, the most optimal power quality overall was achieved in Scenario 4 at 60% Load, with 5th harmonic being minimized to 0.42%, showing that average charging loads produce a balanced harmonic spectrum.

Operating the operation at full load in Scenario 3 had a 5th harmonic distortion of 0.72%, demonstrating that charging loads are linearly correlated with higher harmonic content. Nevertheless, at full loading, the distortions were still not over IEEE standards, demonstrating the system can sufficiently constrain propagation of the harmonic. In the comparison of lowered load levels in Scenarios 4 and 5, it was observed that reducing the load below 60% did not yield significant additional improvements in harmonic mitigation. This is important for microgrid operators because it shows that maintaining EV charging at approximately 60% load capacity can optimize power quality without sacrificing effective energy utilization.

The radar chart in Fig. 17 provides a visual comparison of harmonic distortion performance in different EV charging conditions. There is one axis on the radar chart for each of the five operational modes, and the indicated points are the measured 5th harmonic distortion levels in each mode. The further from the center of the chart that a point is, the greater the harmonic distortion in the mode. The graph easily reveals that Scenario 2 is highest in harmonic distortion, supporting the conclusion that AC chargers have a greater contribution to power quality degradation. Scenario 4 is closest to the center of the graph, as one would expect, confirming that operation at moderate loads produces the least harmonic distortion and overall power quality. Scenario 3 falls somewhere in between and says that using the maximum number of AC and DC chargers produces harmonics but not to levels above tolerance.

Fig. 16

Comparison of 5th and 11th Harmonic Distortions Across Scenarios.

Fig. 17

Radar Chart for Harmonic Performance Across Scenarios.

Voltage stability assessment of EV-Integrated microgrid scenarios

To assess the power quality and voltage stability of a renewable energy-integrated microgrid with EV charging infrastructure, five distinct operational scenarios were simulated. Each scenario emulated specific charger configurations and load conditions to reflect real-world variability in microgrid performance. This section presents a detailed comparative analysis of each scenario, focusing on voltage stability and overall system behavior. The results provide valuable insights into how strategic charger operation can mitigate power quality issues and optimize microgrid performance.

Scenario 1 simulated a condition where all AC charging stations were turned off, and only DC fast chargers were operational. This configuration reflects a reliance on high-speed DC charging, which, while efficient in terms of time, introduces substantial switching transients and harmonic disturbances. The voltage performance in this scenario was relatively weak. As shown in Table 8, the minimum voltage dropped to 91.107%, significantly below the IEEE Std 1159–2019 recommended threshold of 95% for steady-state operation. The average voltage was 97.02%, with a standard deviation of 3.80%, indicating substantial voltage fluctuation. These metrics highlight the challenges of DC-only configurations in maintaining stable voltage levels and suggest potential risks to grid-connected devices.

Scenario 2 deactivated all DC fast chargers and relied exclusively on AC Level 2 chargers. This setup, common in residential and public slow-charging stations, resulted in slightly better voltage regulation than Scenario 1. The minimum voltage improved marginally to 91.231%, while the average voltage was 97.02%, nearly identical to Scenario 1. However, the standard deviation decreased to 3.47%, indicating improved voltage consistency (Table 8). Despite this improvement, the minimum voltage remained below the IEEE-recommended limit, and thus the system still exhibited signs of voltage instability. This suggests that while AC-only charging reduces harmonics, it may not be sufficient on its own to ensure compliance with voltage standards.

Scenario 3 tested a worst-case full-load condition, with both AC and DC chargers operating simultaneously at 100% capacity. This peak-demand scenario imposed the greatest stress on the microgrid. As expected, it produced the lowest voltage performance: the minimum voltage dropped to 90.448%, with an average of 97.25%, and a standard deviation of 3.92% (Table 8). These results indicate severe voltage deviations, reflecting the grid’s vulnerability under full charging load. The voltage instability observed underscores the necessity for mitigation measures such as smart load control, reactive power support, or enhanced storage coordination to stabilize grid performance under extreme demand.

Scenario 4 implemented a 60% load condition by partially disconnecting a balanced mix of AC and DC chargers. This strategy was designed to evaluate whether moderate demand reduction could yield more stable voltage performance. The results were highly favorable. The voltage profile remained well within the IEEE Std 1159–2019 acceptable range (95–105%), with a minimum voltage of 95.598%, a maximum of 101.465%, and an average of 98.48%—the highest among all scenarios. Moreover, the voltage standard deviation was only 2.50%, the lowest across all configurations (Table 8). Additionally, harmonic content was minimal, with the 5th and 11th harmonic distortions measured at 0.42% and 0.55%, respectively—both well below the IEEE Std 519–2014 limit of 5%. These findings confirm that operating the system at a 60% load level significantly improves both voltage stability and power quality.

Scenario 5 further reduced the charging load to 40% to assess the effects of deeper load curtailment. Although this scenario achieved slightly better performance than Scenarios 1 and 3, it did not outperform Scenario 4. The minimum voltage recorded was 90.711%, while the average was 97.38%, and the standard deviation increased to 4.31%, the highest among all scenarios (Table 8). These results suggest that while load reduction can improve voltage behavior, excessive curtailment may lead to inefficiencies or increased voltage variability if not coupled with intelligent management of generation and storage.

Among all configurations evaluated, Scenario 4, which operates at 60% of the total charging station capacity, clearly emerges as the optimal strategy for enhancing grid stability and ensuring high power quality. This scenario satisfies all the essential criteria outlined in the IEEE standards, particularly IEEE Standard 1159 of 2019 and IEEE Standard 519 of 2014, which specify the acceptable voltage limits and harmonic distortion thresholds for reliable grid operation. In Scenario 4, the voltage levels were consistently maintained within the recommended operational range of 0.95 to 1.05 per unit, corresponding to 95–105% of nominal voltage. The minimum recorded voltage was 95.60%, and the maximum reached 101.46%. These values demonstrate that the voltage profile was not only fully compliant but also stable across all operating intervals. In addition to voltage compliance, Scenario 4 achieved the lowest harmonic distortion levels among all five scenarios analyzed. The measured fifth and eleventh harmonic components were 0.42% and 0.55% respectively. These values fall significantly below the IEEE recommended limit of 5% for Total Harmonic Distortion at the Point of Common Coupling. The low harmonic content observed in this case indicates that the waveform quality was well preserved and that the power electronics of the charging infrastructure operated efficiently in coordination with the microgrid’s renewable energy sources and inverters. Voltage consistency further reinforces the superiority of Scenario 4. The scenario recorded an average voltage of 98.48%, the highest among all tested cases. Additionally, the voltage standard deviation was only 2.5%, which reflects minimal voltage fluctuation and reliable operation under a reduced load. This result confirms that moderate load control strategies can improve voltage behavior, preventing both overloading and underutilization of energy resources. Avoiding such extremes helps reduce power losses and maintain optimal regulation.

The effectiveness of Scenario 4 lies in its balanced approach to load reduction. By selectively deactivating a portion of the EV chargers, especially during periods of lower demand or variable renewable output, the microgrid is able to reduce harmonic interference and maintain voltage stability without compromising the availability of charging services. This equilibrium between energy demand and supply is essential for the success of smart microgrids in urban and semi-urban settings, where EV penetration continues to rise. At the systems level, these findings also support the integration of adaptive control mechanisms into microgrid operations. Real-time load management, charger prioritization, and predictive power flow control are among the strategies that can help replicate the stability observed in Scenario 4 under fluctuating conditions. For example, a smart controller can redistribute charging loads or apply load shedding based on real-time voltage and harmonic measurements. This would ensure continuous operation within IEEE-compliant limits without the need for manual intervention. Scenario 4, therefore, establishes itself as a reference model for optimal microgrid configuration under variable EV charging conditions. It demonstrates that a thoughtfully implemented partial-load operation can yield significant improvements in both voltage stability and harmonic performance. Looking ahead, future designs of EV-integrated microgrids should include intelligent control algorithms and operational constraints that enable operation within this load range. Such strategies would contribute to the development of resilient and efficient systems that deliver high power quality while complying with international reliability standards. This approach ultimately ensures safe, stable, and sustainable microgrid operation in the face of increasing electrification and renewable energy integration.

Table 8 Summary of voltage stability metrics across five EV charging scenarios.

Conclusions

With the increasing use of EVs and integration of RESs into power grids, ensuring power quality and system stability is the major challenge. Microgrids with EV charging stations offer a clean means of managing energy, reducing greenhouse gas emissions, and enhancing the efficiency of energy utilization. However, the non-linear nature of EV loads and the intermittent operation of RES raise the added challenges of harmonic distortions, voltage fluctuation, and system reliability concerns. The current study evaluated the performance of a grid-connected microgrid with an EV charging station using ETAP software to analyze different operating conditions in determining their impact on power quality and voltage stability.

One of the most important of these findings was the impact on power quality through non-linear EV charging station loads. The tested reference EV vehicle was the Volkswagen ID4 Crozz with an installed battery pack capacity of 55.7 kWh. Insights into the influence of current on-grid EV charging were provided through this vehicle. In other scenarios, harmonic distortion levels were different, the maximum 5th harmonic distortion of 0.98% being observed when DC chargers were in off-state, while Scenario 4 (60% load) recorded the lowest harmonic distortions, which indicated effective load management. Moreover, the influence of RES in shaping the harmonic profile of the microgrid was also visible. Though PV and wind systems introduced harmonics, those were balanced by good-quality inverters and appropriate grid connection. Voltage stability was according to IEEE standards with hardly any oscillations; thus, the system was stable. The conclusions can be tabulated as below:

• 1. EV charging points are a prime source of harmonic distortions, especially at full load. The research proved that the settings of the chargers have a direct bearing on the severity of harmonics, where complete operation of chargers kept the distortion levels under tolerable bounds, and selective disconnection of chargers had a greater harmonic effect.

• 2. Various charging conditions had different impacts on the power quality. Scenario 4 (60% load) was the most favorable configuration with the best group of harmonic distortions and voltage stability. Ideal charger operating control minimizes non-linear load impact on the grid.

• 3. The harmonic content generated by the PV and wind sources was alleviated using high-performance inverters and coordinated integration. For Scenario 4, harmonic distortion on the PV bus was reduced to the minimum (5th harmonic 0.42%, 11th 0.55%), demonstrating the effectiveness of inverter-based power conditioning.

• 4. The voltage deviations were within IEEE standards in all the cases, and the system was proved to be very reliable. The small deviations, which occurred mainly due to harmonics, were well within control, and microgrid operation was always stable irrespective of charger and load variations.

• 5. Smart load management techniques like schedule-based charger disconnection and load balancing were responsible for the removal of voltage stability harmonic distortion. These techniques must be implemented for extracting maximum efficiency and reliability from microgrid-integrated EV charging systems.

To further strengthen the practical applicability of the proposed microgrid system, future research will focus on extending the current simulation-based analysis with real-time validation and experimental implementation. In particular, hardware-in-the-loop (HIL) testing will be conducted using embedded control platforms to verify the system’s dynamic performance under actual operating conditions. Moreover, detailed small-signal and transient stability analyses will be performed to complement the voltage stability evaluation introduced in this study. These efforts will enable a more robust assessment of the microgrid’s resilience to EV charging behavior, renewable fluctuations, and grid disturbances. Additional work will also explore the integration of advanced energy management algorithms and real-time harmonic mitigation strategies to enhance grid interaction in diverse load environments. In addition, Future work will explore the integration of formal optimization techniques, including heuristic algorithms and intelligent load scheduling methods. These will complement the current scenario-based load management approach and enhance the system’s adaptability, efficiency, and stability under varying operational conditions.