Skip to main content

Electric vehicle integration’s impacts on power quality in distribution network and associated mitigation measures: a review


The problem of global warming, along with environmental concerns, has already led governments to replace fossil-fuel vehicles with low-emission electric vehicles (EVs). The energy crisis and environmental problems, such as global warming and air pollution, are essential reasons for the development of electric vehicles (EVs). Electric vehicles are one of the most fascinating and essential fields to emerge in recent years. According to the current report, electric vehicles are attempting to replace older, traditional automobiles. These vehicles not only help to reduce pollution but also to save natural resources. The presence of electric vehicles may cause several problems for the conventional electrical grid due to their grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging and discharging capabilities. With increased EV adoption, many power quality (PQ) issues in the electrical distribution system arise. With the penetration of EVs in distribution networks, power quality issues such as voltage imbalance, transformer failure, and harmonic distortion are expected to arise. The focus of this research is on exploring and reviewing the issues that the integration of EVs poses for electrical networks. The existing and future situations of electric vehicles’ integration, as well as new research on the subjects, have been reviewed in this paper. This study provides a thorough examination of power quality issues and their mitigating approaches.


Due to the emission of harmful gases and environmental pollutants, conventional vehicles are being replaced with plug-in hybrid electric vehicles (EVs). As a result, electric vehicle usage is increasing. Electric vehicle chargers and renewable energy sources such as solar are critical in reducing our reliance on fossil fuels, and they represent the normal progression of our energy structure [1]. Due to the nonlinear nature of the load, the temperature of the transformer and its associated loss rise during EV battery charging, reducing the transformer's lifetime. The non-linear nature of some loads during EV charging induces total harmonic distortion (THD) in the charging current, i.e., the THD of the current influences the power quality of the distribution network [2, 3]. Active power filters and FACTS devices such as shunt and series active power filters, dynamic voltage restorers, and unified power quality conditioners (UPQC), etc. are utilized to overcome these issues. Power quality concerns such as neutral currents, reactive power needs, and THD rise as AC/DC functions on high-frequency switches are changed. Several papers on electricity power quality [3,4,5] and the grid have already been published in the literature. Regarding the optimization objective, Fig. 1 shows the maximization and minimization objective functions of electric vehicle integration into an electric distribution system.

Fig. 1
figure 1

Electric vehicles objective function

An EV battery charger is a device that charges electric vehicles. One or more power electronics circuits that are utilized with a proper converter transform AC electrical energy into DC to charge the batteries at a certain voltage. The battery charging mechanism of electric vehicles (EVs) is a critical power electronic component that introduces nonlinearity into the network, resulting in harmonic distortion of the electric grid side voltage or current [6]. The negative effects of this harmonic distortion include an increased insulation temperature voltage, a lower power factor, decreased insulation life, decreased efficiency, and increased heating losses, as shown in Table 1.

Table 1 Challenges of electric vehicle integration in power quality

Unregulated EV connection and disconnection in the grid cause voltage imbalance. Due to the voltage imbalances in the electric grid, the cost of voltage correction devices may rise. Switching losses in the AC-DC converters of the electric vehicle charging station account for a portion of the power loss [15, 16]. When electric vehicles are integrated into the grid, the distribution transformer is the part of the system that is most susceptible to failure. An electric vehicle’s harmonic level can vary from 3% at the beginning of charging to about 28% at the end [17, 18]. In addition to the charging action, there are various other functions for improved grid integration, especially in terms of electricity quality. The research on charging EVs and their influence on the power grid will assist stakeholders in implementing mitigation strategies and implementing the necessary technologies to reduce the negative impact on the power distribution system. This work illustrates numerous impacts on the power distribution system as a result of EV charging through various research reviews. This work illustrates various impacts on the power distribution system as a result of EV charging through various research reviews. This article investigates the causes of EV-related power quality issues in the distribution system and possible mitigation measures that distribution companies must use to address these issues. These PQ challenges are reviewed as shown in Fig. 2, with a focus on distribution transformer protection.

Fig. 2
figure 2

Power quality issues

Main text

Literature review” section describes the overview of the charging impact of EVs on the distribution grid and EVs controlled and uncontrolled charging. “Issues in electric vehicle’s integration on distribution grid and their mitigation measures” section describes the issues with electric vehicles’ integration on the distribution grid and its mitigation measures. “Calculation of power quality parameters of supply system” section includes the calculation of power quality parameters. “Approaches to mitigate impact of EV on power quality” section describe an approach to mitigating EVs impact on power quality and also describe different types of facts devices. “Some others power quality issue” section describe an overview of some other types of power quality issues in EV integration with the grid. “Discussion” and “Conclusions” sections concludes the overall review of this paper.

Literature review

Charging impact of EVs on the distribution grid (voltage unbalances and fluctuation)

Due to uneven magnitudes and phase angles of voltages in a 3-phase power system (under- or over-voltages), a voltage unbalance problem occurs, and it can be caused by a variety of factors, i.e., a home distribution network in a city, and single-phase loads are imposed heavily, notably in remote areas. The impact of EV charging and discharging on the voltage imbalance in a residential low voltage (LV) distribution grid has been evaluated using the Monte Carlo modelling approach due to the uncertainties in EV charging rates and connection sites [19]. The findings showed that EVs have a negligible effect on the voltage balance at the feeder’s beginning, but they can increase the feeder’s end voltage imbalance factor. Because most domestic loads are connected to only one phase, voltage unbalance is always present in the network, even in the absence of low-carbon technology. Figure 3 shows the pattern of EVs charging on peak load or apparent power. It shows that the graph is acquiring a peak value between a time duration of 16 h and 20 h.

Fig. 3
figure 3

Impact of EVs charging on voltage unbalance

Single-phase photovoltaics (PVs), household battery storage, and EV charging stations can all contribute to system voltage imbalance. Power distribution systems with long power lines have negative sequence voltage components present, resulting in a significant order of unbalance in the system’s line current [20]. Low negative sequence impedance causes 6–10 times the voltage unbalance in a power circuit [21]. As a result, phase currents that flow in excess can damage cable insulation and trip overload protection circuits, shortening the life of the cable, increased losses, additional heating effects, and vulnerability of the system to failures (because an unbalanced system may not be able to feed loads efficiently) are some of the repercussions [22]. As a result, identifying and determining the presence of voltage unbalance in a circuit is critical to deal with it quickly and ensure that the power system and linked loads run smoothly. Figures 4 and 5 show the comparison of charging levels for different levels of AC and DC charging with respect to voltage and current. Level 1 chargers are available in both slow and fast charging modes, depending on whether the power supply is ac or dc. Level 1 ac chargers are 120-V, single-phase ac with power ratings ranging from 1.4 to 1.9 kW. At 6 amps of current, charging the battery takes 10 to 36 h. Level 1 DC chargers with voltage ranges of approximately 200 V to 450 V and up to 36 kW for plug-in hybrid electric vehicles with 80-A current rating level 2 chargers work on both on-board and off-board chargers at different power levels. Level 2 ac chargers rectify 240-V single-phase ac supplies up to 4 kW to 19.2 kW for plug-in hybrid electric vehicles at an 80-A current rating. Level 2 dc chargers have the same voltage level as level 1 dc chargers, but they contain 90 kW at a 200-A current rating for off-board charging [23,24,25].

Fig. 4
figure 4

Comparison of AC and DC level (voltage)

Fig. 5
figure 5

Comparison of AC and DC level (current)

Advancements in electric vehicle chargers require fast charging through the charger. With level 3 chargers, the battery can be charged in less than 30 min. As it reduces the charging time, it comes with level 3 charging or fast charging. Level 3 ac chargers have a power rating greater than 20 kW for single-phase and three-phase ac power at off-board charging. Level 3 DC chargers charge ultra-fast with 600-V DC up to 240 kW at 400 amperes of current [17, 18]. When a utility provides a symmetric load at high power levels, grid stability is improved. Renewable energy grid integration is standardized, ensuring optimum safety as well as maintenance procedures, consistent performance of operation, and capability of testing. As a result, interconnecting renewable sources ensures that cascade failure is avoided [19]. The harmonic current and its matching phase angle are given in Eqs. (1) and (2). The harmonics are introduced into the distributed system and, when charging, produce switching oscillations. Apart from regular EVSEs, focus must be given to renewable energy grid integration with appropriate standardization. Because the voltage imbalance’s effect is determined by the power range of the device.

$${\upphi }_{\mathrm{Harmonics}}={\upphi }_{\mathrm{Harmonics}-\mathrm{Spectrum}}+\mathrm{h}* \left({\upphi }_{\mathrm{Fundamental}}-{\upphi }_{\mathrm{Fundamental}-\mathrm{spectrum}}\right)$$

Electric Vehicle controlled and uncontrolled charging nature

An increase in EV linked to the distribution system affects the voltage profile in the system. Single-phase on-board charging starts at 1.6 kW for families and rises to tens of kW for fast charging. Uncontrolled charging occurs when electric vehicles are allowed to charge and discharge at the same time. If the public is aware of the incentives and penalties that are assessed to the consumer during peak and off-peak hours, they can choose whether to charge their EVs immediately or to consider a good time to charge. The cost of charging power is determined by the ideal charging conditions and the EV driver’s eagerness. For charging EVs, the tariff is higher in the afternoon and evening and lower at night and in the morning.

Figure 6 shows that around the same time as the peak for the existing demand occurs, the green peak showing the desire for EV charging also appears.

Fig. 6
figure 6

Uncontrolled charging nature of EVs

In Fig. 7, the peak demand from EV charging is compensated by the off-peak consumption from the current demand.

Fig. 7
figure 7

Controlled charging nature of EVs

The average distance traveled by electric vehicle is between 25 and 30 miles. Based on the vehicle mileage analysis, following factors are defined: -

  1. 1.

    Utility factor

  2. 2.

    Electric range utility factor

  3. 3.

    State of charge (SOC)

  1. 1.

    Utility factor (UF): the percentage of daily vehicle miles that are less than or equal to the stated distance is known as UF [22,23,24]. This factor indicates how many miles driven on gasoline would be replaced by miles driven on electricity if all vehicles were converted to PHEVs. Plug-in hybrid electric vehicles (PHEVs) are characterized by their all-electric range (AER). A PHEV-k is one that can travel a certain distance entirely on electricity.

    $$\mathrm{UF}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{D}}_{\mathrm{ei}}}{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{D}}_{\mathrm{i}}}$$

    \({D}_{ei}\) is the distance traveled by electric vehicle, \({D}_{i}\) is the total distance traveled by vehicle \(i.\) N is the number of vehicles, Higher the AER more miles are driven using electricity. A UF of 1 therefore denotes that all miles traveled are powered by electrical energy.

  2. 2.

    Electric range utility factor: the ratio of actual miles driven on electricity to the total miles traveled on electricity is known as the electric range utility factor (ERUF) [25, 26].

    $$\mathrm{ERUF}=\frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{D}}_{\mathrm{ei}}}{\mathrm{N}*\mathrm{k}}$$

    \({D}_{ei}\) is the distance traveled by electric vehicle. N is the no. of vehicles. k is the AER of PHEVs.

  3. 3.

    State of charge (SOC): the amount of charge left in the vehicle when it arrives is known as the state of charge (SOC). Based on distance travelled and the PHEV's AER, one can calculate a vehicle’s SOC. The proportion of the total charge used to represent SOC [27,28,29]. Figure 8 shows the nature of the state of charge for four with respect to electric vehicles, charging time is increasing because the voltage is increasing, and then it becomes constant. The current is also constant for a while, and then it gradually decreases to zero.

Fig. 8
figure 8

Variable nature of charging rates for four EVs

Assume PHEV-k is completely charged and has travelled x miles, the SOC of the vehicle is computed as follows:

$$SOC=\left\{\begin{array}{ll}100*\left(\frac{k-d}{k}\right)&d\le k\\0,&d> k\end{array}\right.$$

Chargers for electric vehicles must be always accessible at all times during the 24-h periods. Therefore, charging infrastructure for EVs battery charging for public location, home and work place is essential [30]. Due to electric vehicle charging load, objective function for optimization of power system demand shows in Eqs. (9), (10), and (12).

$$\mathrm{Max}.\mathrm{EV for charging}=\sum\nolimits_{\mathrm{t}=1}^{24}\left\{{\mathrm{EV}}_{\mathrm{HOME}}\left(\mathrm{t}\right)+{\mathrm{EV}}_{\mathrm{WORKPLACE}}\left(\mathrm{t}\right)+{\mathrm{EV}}_{\mathrm{ROAD SIDE}}\left(\mathrm{t}\right)\right\}*{\mathrm{EV}}_{\mathrm{PENETRATION LEVEL}}$$
$$\mathrm{Power\,req}.\mathrm{for\ charging\ max}.\mathrm{EV}=\mathrm{Max}.\mathrm{EV\ for\ charging}\times \mathrm{Power\ required\ for\ charging\ Single\ EV}$$

During period of light load, we can ignore workplace power so Eq. (9) becomes:

$$\mathrm{Max}.\mathrm E\mathrm V\;\mathrm f\mathrm o\mathrm r\;\mathrm c\mathrm h\mathrm a\mathrm r\mathrm g\mathrm i\mathrm n\mathrm g={\textstyle\sum_{\mathrm t=1}^{24}}\left\{{\mathrm{EV}}_{\mathrm{HOME}}\left(\mathrm t\right)+{\mathrm{EV}}_{\mathrm R\mathrm O\mathrm A\mathrm D\,\mathrm S\mathrm I\mathrm D\mathrm E}\left(\mathrm t\right)\right\}\ast{\mathrm{EV}}_{\mathrm P\mathrm E\mathrm N\mathrm E\mathrm T\mathrm R\mathrm A\mathrm T\mathrm I\mathrm O\mathrm N\;\mathrm L\mathrm E\mathrm V\mathrm E\mathrm L}$$

Issues in electric vehicle’s integration on distribution grid and their mitigation measures

Voltage unbalance mitigation in electric distribution system with EVs

A phase reconfiguration strategy is another way to reduce the voltage imbalance factor. In [31], the financial implications of a phase reconfiguration to lessen the negative effects of the unbalanced EVs on an LV distribution system have been evaluated. The findings confirmed that a phased reconfiguration strategy could reduce the imbalance impact of EVs by utilising the time-of-use tariff. Additionally, managing EV charging and discharging can lessen the phase unbalance issue [32]. A system’s voltage can be balanced by making the best choice possible for connecting points (phases a, b, or c), charging and discharging power rates, and charging and discharging status. A control mechanism is being developed to coordinate all electric vehicles’ smart charging and increase the load profile of the electric vehicle linked to the electrical grid network [33]. The communication system that enables this coordination of charging between the electric vehicles and the aggregator collects and transmits data. In order to standardize the voltage profile, the load profile is smoothed. Voltage regulators can be used to reduce a network’s imbalance index. The PQ for voltage imbalances and variations is improved using energy storage devices, feeder capacitors, and D-STATCOM [34]. Though some EVs would occasionally achieve higher peak voltages. The decentralized controller, unlike the central controller, charges the electric vehicles locally [35]. The use of less communication infrastructure reduces the cost of this dispersed control.

The neutral current that flows as a result of the phase voltage imbalance is not considered by the electric vehicle current charger. Because the input from distributed energy resources is likewise dynamic, the distributed energy resources require coordination in addition to the electric vehicle. While the distributed energy resources and electric vehicles are linked to the distributed network, neutral current and voltage unbalance must all be regulated [36, 37]. The benefits and drawbacks of both the centralized and decentralized EV charging coordination models are examined. For smart charging in the electrical distribution system, active power balancing can be achieved using the droop controller topology. Although the reactive power delivered into the dispersed network by power electronics equipment such as rectification and switching converters adds to the burden on the distribution network, a droop controller-based controller is described in [38] for a balanced system, and reactive power correction is achievable using the auxiliary equipment specified in [39]. To reduce the voltage unbalance, zero and negative sequence voltages are generated.

Harmonics impact and its mitigation technique in distribution grid

Harmonics are the sinusoidal component of a periodic waveform whose frequency is a multiple of the fundamental power frequency's integer [40]. Harmonic power-waveform distortion occurs when there is a mixing of the first, second, third, and other harmonics. Non-sinusoidal voltage and current waveforms are given in Eqs. (12) and (13).

$$V\left(t\right)={v}_{DC}+\sum\nolimits_{H=1}^{{H}_{max}}{v}_{rms}^{H}(cosH{w}_{0}t+{\phi }_{h})$$
$$I\left(t\right)={i}_{DC}+\sum_{H=1}^{{H}_{max}}{i}_{rms}^{H}(cosH{w}_{0}t+{\phi }_{h})$$

Where, \(H\) is the harmonic order, \({w}_{0}\) is the fundamental frequency, \({\phi }_{h}\) is the phase shift of voltage and current for harmonics.

The total harmonic distortion (THD) components, which calculate the effective value of the harmonic contents of a distorted waveform, can be used to quantify the harmonic components of distorted waveforms [41]. THD can be stated as a percentage for voltage and current.


Harmonics are injected by nonlinear devices, i.e., EV chargers, as shown in Fig. 9. Because of the inverter’s semiconductor switches, PV systems are harmonic sources. The inverter technology, solar radiation, temperature, and network parameters all affect total harmonic distortion [42, 43]. The low penetration of EVs and the slow charging rate have little effect on the network’s PQ harmonic distortion. Fast charging rates and the increasing penetration of EVs, however, could cause significant voltage and current harmonic distortion. Furthermore, the author discovered that the charging of arbitrary EVs may deviate from the normal level of the voltage harmonic.

Fig. 9
figure 9

Harmonic distortion where the waveform composed of 3rd and fundamental

Power filter design and control are often part of the grid-tied converters' harmonic mitigation methods. Modulation techniques (SPWM, SVPWM, DPWM, SHE, interleaving, and power filter design (L-, LCL-filter, etc.) are typically used to reduce switching frequency noise [44]. Current harmonic amplification most likely results from the employment of a shunt active power filter. Through the absorption or injection of harmonic currents into or out of the network, other research studies like [45] have deemed EVs to be harmonic compensators. EVs can also engage in the marketplace for ancillary services related to harmonic and reactive power.

On an electrical distribution system, the combined impacts of photovoltaics and electric vehicles on voltage profiles and harmonic distortions were investigated [46]. The findings indicated that while photovoltaic and electric vehicles increase the total harmonic distortion of voltage, they are still able to reduce voltage fluctuation [47].

The influence of current and voltage harmonics produced by photovoltaic systems has been addressed using a variety of strategies. The two most common ways to eliminate harmonics in a system are passive and active filters. However, more compensators are needed for the power system [48]. Utilizing inverter control mechanisms is another strategy for harmonic compensation.

Several techniques, including modelling approaches, system conditions, and solution approaches, can be used to solve the harmonic power flow problem. The two types of solution approaches are coupled and decoupled. Nonlinear systems with strong couplings between harmonics can be accurately solved using a coupled solution approach. Convergence issues may occur in big power systems with numerous nonlinear loads, despite the fact that Newton-based harmonic power flow offers an accurate solution because it incorporates harmonic couplings at all frequencies.

A more uniform load demand profile can result from implementing an off-peak charging plan. In order to deal with the effects of electric vehicle charging on secondary service voltages and transformers, an infrastructure improvement strategy is suggested in [49]. Infrastructure improvements are required for this approach. With this method, the service transformer's kVA rating is increased, and the secondary circuit is rearranged using a second service transformer.

Impacts of transformer in EV charging and its mitigation methods

EV power demand, Plug-in and plug-out time, however, they did not assess the future electric vehicle demand and the possible mitigation by EV demand flexibility or quantify the peak load of the transformer. The future of electric vehicles would greatly benefit from the adoption of solid-state transformers [50]. Its ability to interface with an AC or DC grid system and simplicity of dynamic control are the reasons behind this. As a result, these characteristics would make it simple to integrate DERs like electric vehicles (EVs), high-penetration solar panels (PVs), energy storage, etc. The SSTs have the capacity to enhance communication, protection, and power quality. The medium- and low-voltage transformers on which the charging load is expected are selected based on the parking areas. Since the economics of such a big change are frequently impractical, upgrading the transformer in response to demand is not a good strategy.

By using demand-side control, EV charging can be delayed. EVs with fully charged batteries shouldn't needlessly be charged. To obtain a transformer overloading mitigation effect, those EVs must be encouraged. It is clear that transformer degradation is a result of the system’s increased EV penetration.

Current harmonics produced by electric vehicles can result in increased transformer load loss, a rise in temperature, and decreased transformer lifetime [51, 52]. Transformer losses are classified as either no load loss or load loss.

Transformer load loss consists of copper loss, eddy current losses, and stray losses caused by stray electromagnetic flux in the windings.

$${\mathrm{P}}_{\mathrm{LOAD\ LOSS}}={\mathrm{P}}_{\mathrm{COPPER\ LOSS}}+{\mathrm{P}}_{\mathrm{EDDY}-\mathrm{CURRENT\ LOSS}}+{\mathrm{P}}_{\mathrm{STRAY\ LOSS}}$$
$${\mathrm{P}}_{\mathrm{TOATL\ LOSS}}={\mathrm{P}}_{\mathrm{LOAD\ LOSS}}+{\mathrm{P}}_{\mathrm{NO\ LOAD\ LOSS}}$$

Figure 10 depicts the variation of transformer hourly load demand with respect to time, i.e., the duration of 24 h.

Fig. 10
figure 10

Transformer hourly load vs hours graph

A rule-based system that regulates the charging time in accordance with the client’s preferences, including whether to charge electric vehicles during peak demand or not, reduces loss in the transformer in the electrical distribution network [53, 54]. An optimization technique with the goal of reducing voltage and heat stress on the transformer is described as an objective function in [54]. The algorithm evaluation is based only on the EV batteries’ state of charge (SOC), not the preferences of the client. The algorithm is to be evaluated based on the state of charge (SOC) of the electric vehicle’s batteries, not the customer’s preferences [54]. Even though the objective function only considers thermal stress and voltage, the optimization objective also takes failure hazard into account. The distribution transformer’s ageing factor [55, 56] is as follows:

$$\mathrm{Aging\ acceleration\ factor}=\mathrm{exp}(\frac{15000}{383}-\frac{15000}{{\mathrm{\alpha }}_{\mathrm{H}}+273})$$
$${\mathrm{Insulation}}_{\mathrm{life}}\mathrm{of\ Transformer}=9.8\times {10}^{-18}\mathrm{exp}(\frac{15000}{{\mathrm{\alpha }}_{\mathrm{H}}+273})$$

Distribution transformer loss of life (DTLL) given in Eq. (20)

$$\mathrm{DTLL}=\frac{\mathrm{Aging\ acceleration\ factor}\times \mathrm{time}}{{\mathrm{insulation}\_\mathrm{ life}}_{\mathrm{normal}}}$$

PEV penetration levels as low as 10% may cause distribution transformer overloading [57]. Two residential distribution circuits, and predicts that with significant (60%) PEV deployment, distribution infrastructure costs might rise by 19% and energy losses could rise by 40% [57, 58]. Using the performance indicator, the advanced controller serves as the decision-making mechanism for resolving distribution transformer failure. Decision-making considerations include the battery’s SoC, the SoC needed for the subsequent trip, the amount of time left before departure, and the needs of the EV owner [59].

Power quality issue due to power electronics devices and its mitigation measure in grid

EV chargers convert alternating current to direct current, allowing EV batteries to be charged. Harmonics are introduced into the grid during this power conversion process by high-frequency switching converters used in power electronics, which lower the power quality of line current in the grid as shown in Fig. 11. Such harmonic injection shortens the lifespan of distribution transformers by overloading them electrically and thermally. Other issues include overusing the grid’s current capacity, an imbalance between supply and demand in the utility grid, grid voltage, etc. [60, 61].

Fig. 11
figure 11

Electrical vehicle fast charging station 150 KW

Both the grid structure and the location of the PV capacity inside the grid have an impact on voltage fluctuation. Inconvenient or seeming light flickers may increase in frequency over time due to the rise in EV usage. The voltage profile is improved during fluctuation transients by the algorithmic method described here [62]. Half of the input DC link voltage is used to reduce the switch voltage stress. The connection of large residential loads, the integration of sizable loads like EVs themselves, heat pumps, or unforeseen circumstances like distant problems are a few of the main factors that could result in voltage reductions. Usually, the transformers in low-voltage distribution grids only have offload tap-changers, which allow a voltage drop on the high-voltage side to spread to the low-voltage part.

Flexible ac transmission systems (FACTSs) and voltage-source converters with intelligent dynamic controllers are becoming more common in order to improve power quality. Additionally, distributed FACTS have a significant impact on power quality, energy efficiency, and power factor [63, 64].

A two-stage battery charger is used, with the first stage, the AC-DC stage, being controlled by sinusoidal pulse width modulation and the second stage, the DC-DC stage, being controlled by a predictive duty cycle. Utilizing predictive control, which eliminates the DC bias from the transformer current by managing its peak value, it is possible to quickly control a battery current. At the source of DC-DC converters, power factor correction (PFC) adjustment is possible [65, 66]. The charge efficiency is increased by a proposed bridgeless CUK converter that functions as a PFC-based converter. Total harmonic distortion is decreased in accordance with the regulations, and an improvement in power quality is confirmed by the power quality index. The approach exhibits satisfactory charging efficiency. In the electric vehicle-linked dispersed system, the PFC uses an interleaved converter [67]. Together with the output voltage harmonics, the input current harmonics are also decreased.

The load profile, voltage variation, harmonics, power losses, voltage, frequency, and number of EVs charging meet the criteria for oscillatory stability. These effects could be controlled by the application of a feeder capacitor bank, tap transformer replacement, energy D-STATCOM, a filter for the charging station, coordination, and storage, using a distributed generator, charging, and time-of-use (ToU) tax, and so forth [68, 69].

The oscillatory stability of the grid is anticipated to be impacted by the widespread integration of EVs into the power grid. However, because the characteristics of EV loads differ greatly from those of traditional constant impedance, current, and power (ZIP) loads, it is crucial to model EV loads in detail in order to examine their effects on the oscillatory stability of the grid. An EV charging system load model that combines a constant power component and a voltage-dependent negative exponential component has been established, and it includes an AC-DC converter, a DC-DC converter, a filter, and associated control devices.

Calculation of power quality parameters of supply system

Voltage unbalance calculation

Voltage unbalance occurs when the three phase voltages are mismatched in magnitude and/or do not differ in phase angle by 120°, according to IEEE Standards (1995). In a three-phase network, voltage unbalance can be seen as a relationship between negative and positive sequence voltages. The voltage imbalance percent is computed as [20, 21]:

$$\mathrm{Voltage\ unbalance\,}\left(\mathrm{\%}\right)=\frac{{\mathrm{v}}_{2}}{{\mathrm{v}}_{1}}*100$$
$$\Upsilon= \frac{{V}_{ab}^{4}+{V}_{bc}^{4}+{V}_{ca}^{4}}{{({V}_{ab}^{2}+{V}_{bc}^{2}+{V}_{ca}^{2})}^{2 }}$$

Where, \({v}_{2}\) is the negative sequence voltage and \({v}_{1}\) is the positive sequence voltage, respectively.

Based on (PEA, 2009), the acceptable voltage unbalance is limited to 2%.

Voltage deviation calculation

A bus voltage magnitude may differ from the rated value. Small percentages of these variations are frequently accepted, but if they exceed specified thresholds, they are regarded as disturbances.

$$\Delta {V}_{d}= \frac{R{P}_{1}+X{Q}_{1}}{{V}_{1}}={V}_{1}-{V}_{2}$$

The voltage deviation of a line is represented by Eq. (6). It shows that voltage deviation is highly dependent on the reactive power of inductive lines [70, 71]. Because of the lower reactance and resistance ratios, voltage deviation of distribution lines is affected by both active and reactive power. The IEEE 1547–2003 standard recommends that voltage deviation should not be greater than around the base value.

Distribution network parameters

The three most important operating criteria for the distribution network are voltage stability, power losses, and reliability. In this part, the methodology for calculating the distribution network's voltage stability, power losses, and reliability is explained in more detail.

Voltage stability

Voltage stability is the capacity of the power system to maintain consistent, acceptable voltages for all buses when external disturbances occur [72, 73]. When voltage instability occurrences occur, the network’s bus voltage gradually drops. Sudden disturbances, fault circumstances, and line overloading can cause the system to become unstable. The voltage of all system buses must be within acceptable limits, which is a voltage stability requirement utilized in many stability studies.

Based on the estimation of the voltage stability factor (VSF) from the PV curve, the voltage stability is examined. Active power and voltage are graphically represented by the PV curve in Fig. 12. It shows changing voltage with rising active power; finding the voltage of each bus in the distribution network is the first step in designing the PV curve. The Newton–Raphson method and other common load flow analysis techniques have major drawbacks when used to determine the voltage of radial distribution networks due to their large R/X ratios. Compared to transmission systems, the R/X ratio is highly prominent in distribution systems. When analyzing the voltage stability of distribution networks, all buses’ voltages must fall within an acceptable range (6% of their nominal value).

Fig. 12
figure 12

PV Curve

Mathematically it is expressed as:

$$\mathrm{VSF} = \frac{|dv|}{|dP|} \forall \mathrm{ P }<\mathrm{ Pmax}$$

Power losses

Distribution network power losses are \({i}^{2}r\) losses of the line. The mathematical expression for calculating the line losses for the two-bus system shown in Fig. 13 is as provided in Eq. 23.


and total power losses are represented in Eq. 24

Fig. 13
figure 13

Two-bus system single line diagram



Power system reliability analysis has become a challenging area of study. The reliability of generation, transmission, and distribution are all key considerations in power system reliability analysis. Data on the failure rate, repair rate, average outage time, and outages are statistically significant for evaluating the distribution network's reliability indices [74]. The reliability indices of the distribution network are broadly divided into customer- and energy-oriented, as shown in Fig. 5. The three main subcategories of customer-oriented reliability indices are the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), and Customer Average Interruption Duration Index (CAIDI). The energy-oriented reliability indices are further divided into energy not served (ENS) and average energy not served (AENS). The correlation between SAIFI and SAIDI depends on how frequently and for how long interruptions occur. Customer dissatisfaction due to interruption is quantified by CAIDI. AENS is the average load curtailment index as a result of service interruptions.

Approaches to mitigate impact of EV on power quality

The current electric vehicle charging station faces several challenges that must be overcome to provide reliable, high-quality electricity as shown in Table 2, and positive impacts and control features of different type of facts devices in power quality and electric vehicles system as shown in Table 3. Flowchart for power quality issues mitigation and bidirectional converter controlling was created as shown in Fig. 14.

Table 2 Approaches to mitigate impact of EV on power quality
Table 3 Fact devices and their impacts on power quality and electric vehicle charging system
Fig. 14
figure 14

Proposed flowchart for Power quality issues mitigation and bidirectional converter controlling

With a three-phase, three-level rectifier/inverter, the three-phase grid voltage is rectified to a DC voltage (see Fig. 14). The EV-side controller, the grid-side interface controller, and the centralized charging controller level make up the charging station control system. Phase currents and utility voltages are converted from a-b-c coordinates to a d-q frame using the Park transformation, which is then produced by the PLL. A reactive power controller creates the q-axis current reference in the reactive power support mode. However, the PCC voltage controller produces the q-axis current reference signal and voltage support for the AC system. EV is charged and discharged at the EV-side controller using a DC/DC power converter to handle the charge and discharge of the EV batteries. The three-level AC/DC inverter/converter is regulated at the grid-side controller by maintaining a constant DC-bus voltage and managing the reactive power sent to or received from the grid by the three-phase three-level (TPTL) rectifier. The primary connection between the grid and the EVs is the bidirectional AC-DC power converter. This converter functions as a rectifier in the EV charge mode, changing the utility’s AC voltage into the DC bus voltage. When operating in V2G mode, it performs the function of an inverter, converting DC voltage to AC voltage and redistributing electricity to the grid. The EV battery will be charged and discharged using the suggested bi-directional PWM DC/DC converter. Because of the short duty cycle and the lower output power, the current ripple in the inductor will grow in this scenario. Likewise, the converter efficiency will be poorer. It is clear that the TPTL converter performs more effectively in boost and buck operation modes. But after comparing every aspect of these two converters, it becomes clear that the three-level DC/DC converter performs better in almost all areas.

Due to the voltage imbalances in the electric grid and other power quality issues, the cost of voltage correction devices may rise. Switching losses in the AC-DC converters of the electric vehicle charging station account for a portion of the power loss [75,76,77,78,79,80,81,82,83].

Due to the non-linear nature of EV chargers and distribution networks, an effective controller must be used in place of a standard control technique. Using the current references Id and Iq, the regulator determines the necessary reference voltages for the inverter (reactive current). In the case of G2V mode, the Iq reference is set to zero, while in the case of V2G mode, it has a predetermined value. The current regulator’s necessary active current Id reference is established.

To increase the lifespan of the EV battery and keep the battery operating at a higher performance level, a corresponding battery management strategy of charging or discharging should be pursued and created. The procedures of CC and CV charging are combined during the process of charging a battery [27,28,29]. CC charging is typically used to charge an EV battery until it reaches the charge voltage level. Then CV charging is used, enabling the charge current to taper until it is very low.

Some others power quality issue

Phantom loading effect in power quality

After the initial data gathering phase, it was discovered that some of the charging stations were using energy even when there were no EVs attached to those stations, which was an oddity. There turned out to be two types of this “phantom” loading [77, 78]. The digital circuitry, LCD panels, and indicator lights present in most of charging stations are responsible for a modest degree of phantom loading. Whether an EV is charging at the station or not, these ancillary circuits always use a small amount of power. Level 3 DC rapid chargers with a battery bank are the second type of phantom loading. The battery bank, which is separate from the charger at the site, allows the charger to direct power from both the utility and the battery bank, reducing current surges on the utility feeder.

Load imbalance and DC charger

Systems are often created with the loads distributed evenly among the three phases. By balancing the loads, the terminal voltages produced on each of the three branches and the current in each are about equal. The system was found to be loaded substantially heavier for one phase or the other depending on which units were in operation at any given time at electric avenue due to the vast number of level I and II charging stations, which are single-phase equipment [79]. Currents might flow through the neutral line as a result of unbalanced loading. In severe situations, these neutral currents can result in excessive heating since neutral lines frequently have smaller diameters than hot lines. A voltage imbalance brought on by a load imbalance might cause issues for three-phase loads reliant on equal phase voltages. The size of the negative sequence component divided by the magnitude of the positive sequence component, stated as a percentage, is the ratio that characterizes an unbalanced three-phase system (IEEE Std. 1159–1995). It was discovered that the system's voltage imbalance never went above 1% at any given time. This is far less than the IEEE’s suggested maximum of 3% [80].

DC offset in power quality

Due to a rise in transformer saturation and the resulting heating, additional stress on the insulation, and other negative effects, DC in AC networks can be harmful (IEEE Std 1159–1995). Many of the charging events occurred at varied times during the cycle, and in all three cases, the DC offset was comparable [81].


In this paper, we analyzed the power quality issue and its mitigation approach. From the simulation graph of Fig. 3, which shows the graph of EV charging on peak load with respect to time. The load curve is studied throughout the entire week and it is suggested that EV charging can be accomplished when the load is around 50% of the peak load demand during the hours of midnight to 3:00 a.m. At different times during the day, the voltage drop on the grid exceeds the permitted limit of 0.93 pu, which leads to a voltage imbalance between the system’s three phases. It could be analyzed from the result amount of voltage drop mostly occurs during peak load. A comparison of all the levels of AC and DC charging shown in Figs. 3 and 4 with respect to voltages and current.

A simulation graph of Figs. 6 and 7, which show a typical daily demand profile with base and intermediate loads supplying enough generation to meet most of the demand. This graph shows that with these peak alignments, once the two demands are added up, the present peal would be amplified. This gives information on how well the controlled charge scenario levels the demand profile, which is important if the network operator depends on grid imports to meet demand. The uncontrolled weekly total electrical demand is shown in the graph with 100% EV adoption.

Issues in electric vehicle’s integration on the distribution grid, such as voltage unbalance issues, harmonic impact issues, transformer impact, and issues related to power electronic devices are analyzed in this paper. Voltage imbalances and variations are improved using energy storage devices, feeder capacitors, and D-STATCOM. Phase reconfiguration strategy and droop controller-based controller etc., improved the voltage unbalance problem on the distribution grid. Variation of transformer hourly load demand with respect to time as shown in Fig. 10. In order to enhance power quality, flexible ac transmission systems (FACTSs) and voltage-source converters with sophisticated dynamic controllers are becoming more widespread. Furthermore, distributed FACTS significantly affect power factor, energy efficiency, and quality of power.

An increase in industrial and commercial consumption, the Morning Peak, which occurs between 8 and 9am, follows the Night Lean at 4am to 5am. Daytime leaning can be categorized as occurring between 1 and 2 pm, with evening peaking occurring between 5 and 8 pm. Hence, it may be concluded that India has a peak in the evening (5 pm–7 pm, approximately), about 225,034 MVA at 9 pm. Residential lights, HVAC, and manufacturing industries' usage make up a sizable portion of this, but commercial consumption is essentially at a standstill at this time. In order to enhance power quality, flexible ac transmission systems (FACTSs) and voltage-source converters with sophisticated dynamic controllers are becoming more widespread. Furthermore, distributed FACTS significantly affect power factor, energy efficiency, and quality of power.

A proposed flowchart presents the bidirectional control, mitigation of power quality issues, MPPT technique and battery management system using a three-level converter on both the grid and EV sides. At the grid interface point, or the point of common coupling (PCC), the three-level grid side converter (GSC) can take part in reactive power support. It is necessary to create an efficient controller that can implement the transformer disturbance controller, harmonic reduction, and voltage imbalance management. Future implementation must move towards the efficient controller because it is feasible at the EVSE end. A comparison is made between the facts devices as shown in Table 3. which shows their benefits, drawback, and control features in power quality and electric vehicles.


This paper’s context effectively covered a variety of power quality problems that arise from the integration of EVs in electric distribution networks and mitigation strategies. From this review, it is evident that high penetration of EVs can have a negative effect on the grid’s stability and power quality because energy sources are intermittent and EV loads are unpredictable. These negative effects on power quality and grid stability, however, can be mitigated by coordinating or combining the operational strategies of integrating EV systems. Large-scale EV loads are also considered significant participants in the future energy market because of their high penetration and potential to influence the price and outcome of the wholesale energy market in the future smart grid. Utilizing strategies for battery charge scheduling, supporting renewable energy sources, and a few other techniques, the voltage unbalance issue is reduced. A composite controller is designed that can implement transformer disturbance, harmonics reduction, and voltage imbalance management. Active harmonic filters and fact devices can be designed and put into use in addition to EV integration to reduce harmonics produced by non-linear devices and supply highly reactive power that satisfies the specifications. Due to their ease of refit and scale, as well as their direct efficacy in reducing the harmonic voltage, parallel-coupled active harmonic filters have various benefits. The offered active filters provide dynamic reactive power adjustment and can filter harmonics up to the 50th order. To reduce the loss of transformer life, the ant colony optimization, particle swarm optimization, and Firefly algorithm’s optimization problem can be expanded.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Electric vehicles


Grid to vehicles


Vehicle to grid


Power quality


Total harmonic distortion


Photo voltaic


All electric range


Electric range utility factor


Time of use


Voltage stability factor


Battery storage system


Direct voltage restorer


Static VAR compensator


  1. Zhao J, Xi XI, Na QI, Wang S, Kadry SN, Kumar PM (2021) The technological innovation of hybrid and plug-in electric vehicles for environment carbon pollution control. Environ Impact Assess Rev. 86:106506.

    Article  Google Scholar 

  2. Filote C, Felseghi R, Andreea R, Maria S, AÅ chilean Ioan (2020) Environmental impact assessment of green energy systems for power supply of electric vehicle charging station. Int J Energy Res er.5678.

  3. Johnson OD, KHassan A (2016) Issues of power quality in electrical systems”. Int J Energy Power Eng. 5(4):148–154

    Article  Google Scholar 

  4. Singh B, Jain V, Chandra SA, Al-Haddad K (2021) Power Quality Improvement in a PV Based EV Charging Station Interfaced with Three Phase Grid. Toronto: IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society; pp. 1–6.

  5. Farahani HF (2017) Improving voltage unbalance of low-voltage distribution networks using plug-in electric vehicles. J Clean Prod 148:336–346.

    Article  Google Scholar 

  6. X. Zhou, L. Zou, Y. Ma and Z. Gao (2017) Research on impacts of the electric vehicles charging and discharging on power grid, 2017 29th Chinese Control And Decision Conference (CCDC). 1398–1402,

  7. Fernández P, Luis et al (2011) Assessment of the impact of plug-in electric vehicles on distribution networks. IEEE Trans Power Syst. 26:206–213

    Article  Google Scholar 

  8. de Hoog J, Muenzel V, Jayasuriya DC, Alpcan T, Brazil M, Thomas DA, ... Jegatheesan R (2015) The importance of spatial distribution when analysing the impact of electric vehicles on voltage stability in distribution networks. Energy Syst Optimization Model Simul Econ Asp. 6(1); 63–84.

  9. Dharmakeerthi CH, Mithulananthan N, Saha TK (2011) Overview of the impacts of plug-in electric vehicles on the power grid. 2011 IEEE PES Innovative Smart Grid Technologies, 1-8

  10. Masoum MA, Moses PS, Smedley KM (2011) Distribution transformer losses and performance in smart grids with residential Plug-In Electric Vehicles. ISGT 2011:1–7

    Google Scholar 

  11. Li HL, Bai XM, Tan W (2012) Impacts of plug-in hybrid electric vehicles charging on the distribution grid and smart charging. in Proceedings of the IEEE POWERCON 2012: International Conference on Power System Technology. IEEE

  12. Rautiainen A, Markkula J, Repo S, Kulmala A, Jarventauta P, Vuorilehto K (2013) Plug-in vehicle ancillary services for a distribution network. 2013 Eighth International Conference and Exhibition on Ecological Vehicles and Renewable Energies (EVER), 1-8

  13. Yong JY, Ramachandaramurthy VK, Tan KM, Mithulananthan N (2015) Bi-directional electric vehicle fast charging station with novel reactive power compensation for voltage regulation. Int J Electr Power Energy Syst 64:300–310

    Article  Google Scholar 

  14. Kim K, Song CS, Byeon G, Jung H, Kim H, Jang G (2013) Power demand and total harmonic distortion analysis for an EV charging station concept utilizing a battery energy storage system. J Electrical Eng Technol 8(5):1234–1242.

    Article  Google Scholar 

  15. Lucas Alexandre, Bonavitacola Fausto, Kotsakis Evangelos, Fulli Gianluca (2015) Grid harmonic impact of multiple electric vehicle fast charging. Electric Power Syst Res. 127:13–21.

    Article  Google Scholar 

  16. Delgado Joaquim, Faria Ricardo, Moura Pedro, de Almeida Aníbal T (2018) Impacts of plug-in electric vehicles in the portuguese electrical grid. Transport Res Part D: Transport Environ. 62:372–385.

    Article  Google Scholar 

  17. Williamson SS, Rathore AK, Musavi F (2015) Industrial electronics for electric transportation: current state of-the-art and future challenges. IEEE Trans Ind Electron 62:3021–3032

    Article  Google Scholar 

  18. Grunditz EA, Thiringer T (2016) Performance analysis of current BEVs based on a comprehensive review of specifications. IEEE Trans Transp Electr 2:270–289

    Article  Google Scholar 

  19. Freire R, Delgado J, Santos JM, de Almeida AT (2010) Integration of renewable energy generation with EV charging strategies to optimize grid load balancing, In 13th International IEEE Conference on Intelligent Transportation Systems. 2010, pp. 392–396,

  20. von Jouanne A, Banerjee B (2001) Assessment of voltage unbalance. IEEE Trans Power Delivery 16(4):782–790.

    Article  Google Scholar 

  21. Pillay P, Manyage M (2001) Definitions of voltage unbalance. IEEE Power Eng Rev 21(5):49–51.

    Article  Google Scholar 

  22. Wu Xing, Aviquzzaman Md, Lin Zhenhong (2015) Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data. Transport Res Part C: Emerging Technol. 57:1–12.

    Article  Google Scholar 

  23. Seshadri Srinivasa Raghavan, Gil Tal (2020) Plug-in hybrid electric vehicle observed utility factor: Why the observed electrification performance differ from expectations. Int J Sustain Transport.

  24. Srinivasa R, Seshadri T, Gil (2019) Influence of user preferences on the revealed utility factor of plug-in hybrid electric vehicles. World Electric Vehicle J. 11(1):6.

    Article  Google Scholar 

  25. Hao X, Yuebo Y, Hewu W, Yingbo S (2021) Actual electricity utility factor of plug-in hybrid electric vehicles in typical Chinese cities considering charging pattern heterogeneity. World Electric Vehicle J. 12(4):169.

    Article  Google Scholar 

  26. Duoba, Michael. Developing a utility factor for battery electric vehicles (2013) SAE International Journal of Alternative Powertrains, vol. 2, no. 2, pp. 362–68. JSTOR, Accessed 25 Jul 2022

  27. Neubauer J, Brooker A, Wood E (2013) Sensitivity of plug-in hybrid electric vehicle economics to drive patterns, electric range, energy management, and charge strategies. J Power Sources 236:357–364.

    Article  Google Scholar 

  28. Zhang L, Li Y (2017) Optimal management for parking-lot electric vehicle charging by two-stage approximate dynamic programming. IEEE Transactions Smart Grid. 8(4):1722–1730.

    Article  Google Scholar 

  29. Espedal IB, Asanthi J, Odne SB, Jacob JL (2021) Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles. Energies. 14(11):3284.

    Article  Google Scholar 

  30. Bakhshinejad A, Tavakoli A, Moghaddam MM (2021) Modeling and simultaneous management of electric vehicle penetration and demand response to improve distribution network performance. Electr Eng. 103:325–340.

    Article  Google Scholar 

  31. Farahani HF (2017) Improving voltage unbalance of low-voltage distribution networks using plug-in electric vehicles. J Clean Prod 148:336–346.

    Article  Google Scholar 

  32. Verma A, Singh B (2019) Multi-objective reconfigurable three-phase off-board charger for EV. IEEE Trans Ind Appl 55(4):4192–4203.

    Article  Google Scholar 

  33. Ahn C, Li CT, Peng H (2011) Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid. J Power Sources 196(23):10369–10379.

    Article  Google Scholar 

  34. Kabir ME, Assi C, Alameddine H, Antoun J, Yan J. Demand-aware provisioning of electric vehicles fast charging infrastructure. IEEE Transactions Vehicular Technol. 69(7):6952–6963.

  35. García-Villalobos J, Zamora I, Knezović K, Marinelli M (2016) Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks. Appl Energy 180:155–168.

    Article  Google Scholar 

  36. Bozalakov DV, Laveyne J, Desmet J, Vandevelde L (2019) Overvoltage and voltage unbalance mitigation in areas with high penetration of renewable energy resources by using the modified three-phase damping control strategy. Electric Power Syst Res. 168:283–294.

    Article  Google Scholar 

  37. Kim S, Cha H, Kim H-G (2021) High-efficiency voltage balancer having DC–DC converter function for EV charging station. IEEE J Emerg Selected Topics Power Electron 9(1):812–821.

    Article  Google Scholar 

  38. Knezović K, Marinelli M (2016) Phase-wise enhanced voltage support from electric vehicles in a Danish low-voltage distribution grid. Electric Power Syst Res 140:274–283.

    Article  Google Scholar 

  39. Leemput N, Geth F, Van Roy J, Büscher J, Driesen J (2015) Reactive power support in residential LV distribution grids through electric vehicle charging. Sustainable Energy Grids Networks 3:240–335.

    Article  Google Scholar 

  40. Maier V, Pavel SG, Beleiu HG, Farcas V (2019) Aspects on harmonics analytical identification of a periodic non-sinusoidal wave, 2019 8th International Conference on Modern Power Systems (MPS), 2019, pp. 1–6.

  41. Alhafadhi L, Teh J (2019) Advances in reduction of total harmonic distortion in solar photovoltaic systems: A literature review. Int J Energy Res.

  42. Graham T, Taghizadeh S, Deilami S (2022) Review of fast charging for electrified transport: demand, technology, systems, and planning energies. 15;4:1276.

  43. Alshahrani S, Khalid M, Almuhaini M (2019) Electric vehicles beyond energy storage and modern power networks: challenges and applications. IEEE Access 7:99031–99064.

    Article  Google Scholar 

  44. Ali K, Muhammad Y, Haoming L, Zhihao Y, Xiaoling Y (2020) A comprehensive review on grid connected photovoltaic inverters, their modulation techniques, and control strategies. Energies. 13:(16)4185.

  45. Basha CHH, Bhanutej JN, Rani C, Odofin S (2019) Design of an LPF based slider controller for THD reduction in solar PV B-4 inverter, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–9,

  46. Tovilović DM, Rajaković LJN (2015) The simultaneous impact of photovoltaic systems and plug-in electric vehicles on the daily load and voltage profiles and the harmonic voltage distortions in urban distribution systems. Renewable Energy 76:454–464.

    Article  Google Scholar 

  47. Peter AG, Saha KA (2018) Comparative study of harmonics reduction and power factor enhancement of six and 12-pulses HVDC system using passive and shunt APFs harmonic filters. Int Conf Domestic Use Energy (DUE) 2018:1–10.

    Article  Google Scholar 

  48. Alame D, Maher A, Narayan K (2020) Assessing and mitigating impacts of electric vehicle harmonic currents on distribution systems. Energies. 13(12):3257.

    Article  Google Scholar 

  49. Knezović K, Marinelli M, Codani P, Perez Y (2015) Distribution grid services and flexibility provision by electric vehicles: A review of options, 2015 50th International Universities Power Engineering Conference (UPEC), pp. 1–6,

  50. Tahir Y, Khan I, Rehman S, Nadeem MF, Iqbal A, Xu Y, Rafi M (2021) A state of the art review topologies and control techniques of solid state transformers for electric vehicle extreme fast charging. IET power electronics 14(9):1560–1576

    Article  Google Scholar 

  51. Soleimani M, Kezunovic M (2020) Mitigating transformer loss of life and reducing the hazard of failure by the smart EV charging. IEEE Trans Ind Appl 56(5):5974–5983.

    Article  Google Scholar 

  52. Hilshey AD, Rezaei P, Hines PDH, Frolik J (2012) Electric vehicle charging: Transformer impacts and smart, decentralized solutions. IEEE Power Energy Soc Gen Meeting 2012:1–8.

    Article  Google Scholar 

  53. Khalili T, Nojavan S, Zare K (2019) Optimal performance of microgrid in the presence of demand response exchange: a stochastic multi-objective model. Comput Electr Eng 74:429–450.

    Article  Google Scholar 

  54. Olsen J, Sarker MR, Ortega-Vazquez MA (2018) Optimal penetration of home energy management systems in distribution networks considering transformer aging. IEEE Transactions Smart Grid 9(4):3330–3340.

    Article  Google Scholar 

  55. Mobarak MH, Bauman J (2019) Vehicle-directed smart charging strategies to mitigate the effect of long-range EV charging on distribution transformer aging. IEEE Transactions Transportation Electrification 5(4):1097–1111.

    Article  Google Scholar 

  56. Hilshey AD, Hines PDH, Rezaei P, Dowds JR (2013) Estimating the impact of electric vehicle smart charging on distribution transformer aging. IEEE Transactions Smart Grid 4(2):905–913.

    Article  Google Scholar 

  57. Hilshey P, Hines PR, Dowds J (2013) Estimating the impact of electric vehicle smart charging on distribution transformer aging. IEEE Transactions Smart Grid 4(2):905–913.

    Article  Google Scholar 

  58. Abdelsamad SF, Morsi WG, Sidhu TS (2015) Probabilistic impact of transportation electrification on the loss-of-life of distribution transformers in the presence of rooftop solar photovoltaic. IEEE Transactions Sustainable Energy 6(4):1565–1573.

    Article  Google Scholar 

  59. Zhao Y, Lin Z, Ding Y, Liu Y, Sun L, Yan Y (2018) A model predictive control based generator start-up optimization strategy for restoration with microgrids as black-start resources. IEEE Trans Power Syst 33(6):7189–7203.

    Article  Google Scholar 

  60. Khalid M, Rizwan A, Mohammad S, Sarwar A, Jamil Asghar MS (2019) A Comprehensive review on electric vehicles charging infrastructures and their impacts on power-quality of the utility grid. eTransportation 1:100006.

    Article  Google Scholar 

  61. Habib S et al (2020) Contemporary trends in power electronics converters for charging solutions of electric vehicles. CSEE J Power Energy Syst 6(4):911–929.

    Article  Google Scholar 

  62. Liang X (2017) Emerging power quality challenges due to integration of renewable energy sources in IEEE Transactions on Industry Applications. vol. 53, no. 2, pp. 855–866.

  63. Hossain E, Tür MR, Padmanaban S, Ay S, Khan I (2018) Analysis and mitigation of power quality issues in distributed generation systems using custom power devices. IEEE Access 6:16816–16833.

    Article  Google Scholar 

  64. Gandoman FH, Ahmadi A, Sharaf AM, Siano P, Pou J, Hredzak B, Agelidis VG (2018) Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renew Sustain Energy Rev 82(1):502–514.

    Article  Google Scholar 

  65. Verma AK, Singh B, Shahani DT, Jain C (2016) Grid-interfaced solar photovoltaic smart building with bidirectional power flow between grid and electric vehicle with improved power quality. Electric Power Components Syst 44(5):480–494.

    Article  Google Scholar 

  66. Gupta J, Singh B (2019) A bidirectional home charging solution for an electric vehicle, 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019, pp. 1–6.

  67. Kushwaha R, Singh B (2018) Interleaved landsman converter fed EV battery charger with power factor correction, In 2018 IEEE 8th Power India International Conference (PIICON), pp. 1–6.

  68. Bollen MHJ et al (2017) Power quality concerns in implementing smart distribution-grid applications. IEEE Transactions Smart Grid 8(1):391–399.

    Article  Google Scholar 

  69. Aleem SK, Abdul SM, Suhail H, Taha SU (2020) A review of strategies to increase PV Penetration Level in Smart Grids Energies 13, no. 3: 636.

  70. Li L, Wang L, Sheng C, Sun W, Li Y (2014) Analysis on voltage deviation inactive distribution network and active voltage management. Chin Int Conference Electricity Distribution (CICED) 2014:1610–1614.

    Article  Google Scholar 

  71. Ahmed Haytham MA, Awad Ahmed SA, Ahmed MH, Salama MMA (2020) Mitigating voltage-sag and voltage-deviation problems in distribution networks using battery energy storage systems. Electric Power Syst Res. 184:106294.

    Article  Google Scholar 

  72. Terriche Y et al (2019) Power quality and voltage stability improvement of shipboard power systems with non-linear loads, 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), pp. 1–6,:

  73. Nguyen Tung Linh, (2009) Voltage stability analysis of grids connected wind generators, 2009 4th IEEE Conference on Industrial Electronics and Applications, pp. 2657–2660 .

  74. Brumsickle WE, Divan DM, Luckjiff GA, Freeborg JW, Hayes RL (2005) Power quality and reliability. IEEE Ind Appl Mag. 11(1):48–53.

    Article  Google Scholar 

  75. Dharmakeerthi CH, Nadarajah M, Saha T (2011) Overview of the impacts of plug-in electric vehicles on the power grid. Innovative Smart Grid Technologies Asia (ISGT), 2011 IEEE PES.

  76. Luo X, Chan KW (2014) Real-time scheduling of electric vehicles charging in low-voltage residential distribution systems to minimise power losses and improve voltage profile. Generation Trans Distribution, IET 8:516–529.

    Article  Google Scholar 

  77. Amin A, Wajahat Ullah KT, Muhammad U, Haider A, Inam B, Ben H, Saad M, Muhammad A, Saeed A, Anzar M (2020) A review of optimal charging strategy for electric vehicles under dynamic pricing schemes in the distribution charging network. Sustainability. 12(23):10160.

    Article  Google Scholar 

  78. Siti W, Nicolae DV, Jimoh ABDUL-GANIYU, Ukil A (2007) Reconfiguration and load balancing in the LV and MV distribution networks for optimal performance. Power Delivery, IEEE Transactions on 22:2534–2540.

    Article  Google Scholar 

  79. Gray Matthew K, Morsi Walid G (2016) Economic assessment of phase reconfiguration to mitigate the unbalance due to plug-in electric vehicles charging. Electric Power Syst Res, S0378779616302164.

  80. Devi S, Geethanjali M (2014) Optimal location and sizing determination of Distributed Generation and DSTATCOM using Particle Swarm Optimization algorithm. Int J Electrical Power Energy Syst. 62:562–570.

    Article  Google Scholar 

  81. Yong JY, Fazeli SM, Ramachandaramurthy VK, Tan KM (2017) Design and development of a three-phase off-board electric vehicle charger prototype for power grid voltage regulation. Energy 133:128–141.

    Article  Google Scholar 

  82. Wang Q, Zhou N, Wang J, Wei N (2015) Harmonic amplification investigation and calculation of electric vehicle charging stations using three-phase uncontrolled rectification chargers. Electric Power Syst Res. 123:174–184.

    Article  Google Scholar 

  83. Tavakoli A, Saha S, Arif MT, Haque Md Enamul, Mendis N, Oo Aman M.T (2020) Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: a review. IET Energy Systems Integration 2(3):243–260.

    Article  Google Scholar 

  84. Bai S, Lukic SM (2013) Unified active filter and energy storage system for an MW electric vehicle charging station. IEEE Trans Power Electron 28(12):5793–5803.

    Article  Google Scholar 

  85. Saber C, Labrousse D, Revol B, Gascher A (2016) Challenges facing PFC of a single-phase on-board charger for electric vehicles based on a current source active rectifier input stage. IEEE Trans Power Electron 31(9):6192–6202.

    Article  Google Scholar 

  86. Karthik M, Usha S, Venkateswaran K, Panchal H, Suresh M, Priya V, Hinduja KK (2020) Evaluation of electromagnetic intrusion in brushless DC motor drive for electric vehicle applications with manifestation of mitigating the Electromagnetic Interference. Int J Ambient Energy. 1–12.

  87. Somayajula D, Crow ML (2015) An integrated dynamic voltage restorer-ultracapacitor design for improving power quality of the distribution grid. IEEE Transactions Sustainable Energy 6(2):616–624.

    Article  Google Scholar 

  88. Changjiang Z. et al (2001) Dynamic voltage restorer based on voltage-space-vector PWM control, in IEEE Transactions on Industry Applications, vol. 37, no. 6, pp. 1855–1863.

  89. Zhan C-J, Wu XG, Kromlidis S, Ramachandaramurthy VK, Barnes M, Jenkins N, Ruddell AJ (2003) Two electrical models of the lead-acid battery used in a dynamic voltage restorer. 150(2)175.

  90. Tolbert LM, Peng FZ, Habetler TG (1998) Multilevel inverters for electric vehicle applications, Power Electronics in Transportation (Cat. No.98TH8349), pp. 79–84.

  91. Venkittaraman Aishwarya, Kesari Gnana Sheela (2021) Review of reduced‐switch multilevel inverters for electric vehicle applications. Int J Circuit Theory Appl.

  92. Sheir A, Youssef MZ, Orabi M (2019) A novel bidirectional T-type multilevel inverter for electric vehicle applications. IEEE Trans Power Electron 34(7):6648–6658.

    Article  Google Scholar 

  93. Radhakrishnan G, Gopalakrishnan V (2020) Applications of internet of things (IOT) to improve the stability of a grid connected power system using interline power flow controller. Microprocessors Microsystems 76:103038.

    Article  Google Scholar 

  94. Vasquez-Arnez RL, Fernando AM (2008) Main advantages and limitations of the interline power flow controller: a steady-state analysis

    Google Scholar 

  95. Vasquez-Arnez RL, Moreira FA (2008) [IEEE Exposition - Chicago, IL, USA (2008.04.21–2008.04.24)] 2008 IEEE/PES Transmission and Distribution Conference and Exposition - The Interline Power Flow Controller: Further aspects related to its operation and main limitations.

  96. Singh B, Singh S (2019) GA-based optimization for integration of DGs, STATCOM and PHEVs in distribution systems. Energy Rep. 5:84–103.

    Article  Google Scholar 

  97. Rassõlkin A, Kallaste A, Hõimoja H (2014) Power factor correction with vehicle-to-grid STATCOM implementation. Electr Power Qual Supply Reliability Conf (PQ) 2014:177–180.

    Article  Google Scholar 

  98. Mathew B, Varghese J (2014) Electric vehicle as STATCOM and real power flow controller for wind energy conversion system, 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), pp. 1–4,

  99. Marjani SR, Gheibi M, Talavat V, Farsadi M (2015) A novel hybrid intelligent method for static var compensator placement in distribution network with plug-in hybrid electrical vehicles parking, 2015 Intl Aegean Conference on Electrical Machines & Power Electronics (ACEMP), 2015 Intl Conference on Optimization of Electrical & Electronic Equipment (OPTIM) & 2015 Intl Symposium on Advanced Electromechanical Motion Systems (ELECTROMOTION). pp. 323–330.

  100. IEEE Guide for Design (2011) Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems, in IEEE Std 1547.4–2011. vol., no., pp.1–54, 20.

  101. Gholami Farkoush, Saeid Wadood, Abdul Khurshaid, Tahir Kim, Chang-Hwan Rhee, Sang-Bong (2019) Minimizing static VAR compensator capacitor size by using SMC and ASRFC controllers in smart grid with connected EV charger. Int J Electrical Power Energy Syst, 107:656–667.

  102. Yunus K, De La Parra HZ, Reza M (2011) Distribution grid impact of Plug-In Electric Vehicles charging at fast charging stations using stochastic charging model, Proceedings of the 2011 14th European Conference on Power Electronics and Applications, pp. 1–11

  103. Zheng S, Yan H, Chen L, Fang S, Ge L (2013) Research on static VAR generator with direct current control strategy. 534–537.

  104. Fang ZP, Jih-Sheng L (1997) Dynamic performance and control of a static VAr generator using cascade multilevel inverters. IEEE Transactions Ind Appl. 33(3):748–755.

    Article  Google Scholar 

  105. Percis ES, Nalini A, Rama ST, Bhuvaneswari S, Jayarajan J, Jenish T (2019) Reactive power compensation using fuzzy logic controlled UPFC in a hybrid microgrid. Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) 2019:1–5.

    Article  Google Scholar 

  106. Sunil Kumar AV, Prakash R, Shivakumara Aradhya RS, Lamsal M (2022) A Review on Social Group Optimization Technique for Power Capability Enhancement with Combined TCSC-UPFC. In: P., S., Prabhu, N., K., S. (eds) Advances in Renewable Energy and Electric Vehicles. Lecture Notes in Electrical Engineering, vol 767. Springer, Singapore.

  107. Sanjeevikumar P, Zand M, Nasab MA, Hanif MA, Bhaskar MS (2021) Spider community optimization algorithm to determine UPFC optimal size and location for improve dynamic stability, 2021 IEEE 12th Energy Conversion Congress & Exposition - Asia (ECCE-Asia). pp. 2318–2323.

  108. Yuan Z, de Haan SWH, Ferreira B (2009) Utilizing distributed power flow controller (DPFC) for power oscillation damping. IEEE Power & Energy Soc Gen Meeting 2009:1–5.

    Article  Google Scholar 

  109. Dai J et al (2019) Optimal configuration of distributed power flow controller to enhance system loadability via mixed integer linear programming. J Modern Power Syst Clean Energ 7(6):1484–1494.

    Article  Google Scholar 

  110. Wu Z, Jiang D, Lao D, Ying Q, Du Y (2017) A novel topology of DC distribution network with fault current limiting static synchronous series compensator, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), pp. 1–5.

  111. Lionel Leroy Sonfack, Godpromesse Kenné & Andrew Muluh Fombu (2018) A new static synchronous series compensator control strategy based on RBF neuro-sliding mode technique for power flow control and DC voltage regulation, electric power components and systems, 46:4, 456–471.

  112. Mahapatra S, Raj S, Krishna SM (2020) Optimal TCSC Location for Reactive Power Optimization Using Oppositional Salp Swarm Algorithm. Innovation in Electrical Power Engineering, Communication, and Computing Technology. Springer, Singapore, pp 413–424

    Chapter  Google Scholar 

  113. Mahajan V (2007) Thyristor Controlled Series Compensator. 182-187.

  114. Niu Y, Zhang D, Su Y, Zhang H (2016) Application of cubic spline interpolation in var compensator with thyristor controlled reactor, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 389–392,

  115. Zhang D, Guacci M, Kolar JW, Everts J (2020) Synergetic Control of a 3-Φ Buck-Boost Current DC-Link EV Charger Considering Wide Output Range and Irregular Mains Conditions, 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), pp. 1688–1695,

  116. Li S, Ozpineci B, Tolbert LM (2009) Evaluation of a current source active power filter to reduce the dc bus capacitor in a hybrid electric vehicle traction drive. IEEE Energy Conversion Congress and Exposition 2009:1185–1190.

    Article  Google Scholar 

  117. Crosier R, Wang S (2013) DQ-frame modeling of an active power filter integrated with a grid-connected, multifunctional electric vehicle charging station. IEEE Trans Power Electron 28(12):5702–5716.

    Article  Google Scholar 

  118. Zhou N, Wang J, Wang Q, Wei N, Lou X (2014) Capacity calculation of shunt active power filters for electric vehicle charging stations based on harmonic parameter estimation and analytical modeling. Energies 7:5425–5443.

    Article  Google Scholar 

  119. Anam G, Sindhu MR (2021) A bidirectional power converter with shunt active filter for electric vehicle grid integration. In: Ranganathan, G., Chen, J., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 145. Springer, Singapore.

  120. Li F, Guo L, Liu L, Li X, Wang Q (2019) Method to improve charging power quality of electric vehicles. J Eng

Download references


I would like to express my deep and sincere gratitude to my research supervisors, Dr. Munish Manas and Dr. Rajesh Kumar Dubey, at the Central University of Haryana, Mahendragarh, for providing me guidance throughout the preparation of this review article.


Not applicable.

Author information

Authors and Affiliations



AS carried out the studies, collected and analyzed the data, and drafted the manuscript. MM and RKD revised and contributed to the final manuscript. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Munish Manas.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srivastava, A., Manas, M. & Dubey, R.K. Electric vehicle integration’s impacts on power quality in distribution network and associated mitigation measures: a review. J. Eng. Appl. Sci. 70, 32 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: