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Contract No. DE-AC36-08GO28308

Conference Paper

NREL/CP-5D00-74838

April 2020

Grid Impact Analysis of Heavy-Duty Electric 

Vehicle Charging Stations

Preprint

Xiangqi Zhu, Barry Mather, and Partha Mishra

National Renewable Energy Laboratory

Presented at the 2020 IEEE Innovative Smart Grid Technologies Conference 

(IEEE ISGT) 

Washington, D.C.

February 17–20, 2020 
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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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Laboratory (NREL) at www.nrel.gov/publications.

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Conference Paper

NREL/CP-5D00-74838

April 2020

Grid Impact Analysis of Heavy-Duty Electric 

Vehicle Charging Stations

Preprint

Xiangqi Zhu, Barry Mather, and Partha Mishra 

National Renewable Energy Laboratory 

Suggested Citation 

Zhu, Xiangqi, Barry Mather, and Partha Mishra. 2020. Grid Impact Analysis of Heavy-Duty 

Electric Vehicle Charging Stations: Preprint. Golden, CO: National Renewable Energy 

Laboratory. NREL/CP-5D00-74838. https://www.nrel.gov/docs/fy20osti/74838.pdf. 
NOTICE

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable 

Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding 

provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies 

Office. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government.

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Energy Laboratory (NREL) at www.nrel.gov/publications. 

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NREL 46526.

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Grid Impact Analysis of Heavy-Duty Electric Vehicle 

Charging Stations 

Xiangqi Zhu and Barry Mather 

Power Systems Engineering Center

National Renewable Energy Laboratory 

Golden, CO, USA

xiangqi.zhu@nrel.gov, barry.mather@nrel.gov

Partha Mishra 

Transportation & Hydrogen Systems Center 

National Renewable Energy Laboratory 

Golden, CO, USA

partha.mishra@nrel.gov 

Abstract—This paper presents a grid impact analysis of heavyduty electric vehicle (EV) charging stations. In this work, heavyduty EVs have battery capacities high enough to provide a range 

of 250–500 miles on a single charge, such as long-haul trucks. 

Heavy-duty EVs will require extremely fast charging rates to 

reduce charging time and will induce very high charging loads (at 

the multiple-megawatt scale) if several vehicles charge at the same 

time. Therefore, analysis is needed to understand the impact of 

charging station loads on the electric power grid and set the 

baseline for developing mitigation plans and necessary system 

upgrades. We develop a systematic procedure to analyze the 

potential impact of the placement of charging stations on the grid. 

Charging load is modeled using a DC fast-charging station model. 

A voltage load sensitivity matrix approach is leveraged to 

investigate the challenges of placing charging stations on the 

feeder. Given the charging load profiles and suggested charging 

station locations, time-series simulations are performed on various 

connection points on the feeder to understand the impact. The 

analysis is performed on both the IEEE 34-bus system and a 

realistic feeder from California. Initial mitigation solutions are

developed based on insights from this analysis. 

Keywords—heavy-duty EV; charging station; grid impact; 

distribution feeder

I. INTRODUCTION 

Electric vehicles (EVs) are increasing in quantity, and more 

types of EVs are entering the market. In the near future, EVs are 

expected to become a sizable share of the overall transportation 

fleet. Companies, including Tesla, are pushing heavy-duty EVs 

such as trucks into the market. Unlike light-duty EVs, electric 

trucks require faster charging rates and demand larger charge 

loads to enable long-distance travel. Therefore, the charging 

stations for heavy-duty EVs might have spiky load profiles with 

large peak loads (>1 MW for a single port) and very high ramp 

rates. Hence, it is important to understand the impact of 

connecting heavy-duty EV charging stations to the grid.

Existing work in the literature mostly focuses on grid 

analysis and grid integration of light-duty EVs, with an 

emphasis on understanding the load behavior of the EVs and 

coordinating them to better aid grid operation [1]–[4]. Few 

studies have been conducted on investigating the impact on the 

grid from heavy-duty EVs and their charging stations. Lightduty EV charging loads are small loads distributed along the 

feeder, and the charging load profiles have small spikes, i.e., 

usually less than 10 kW. In contrast, heavy-duty EV charging 

loads have large loads along the feeder, with load profile spikes 

greater than 1 MW. Because of the differences in load 

characteristics and charging behaviors of heavy-duty and lightduty EVs, existing analyses are insufficient to understand the 

grid impacts from heavy-duty EV charging stations. Therefore, 

we develop a systematic method to analyze and understand the 

grid impacts of heavy-duty EV charging stations. 

The proposed methodology has three major steps: 1) 

charging station location investigation, 2) heavy-duty EV 

charging load modeling, and 3) grid impact analysis. We 

leverage the voltage load sensitivity matrix (VLSM) [5]–[6] to 

develop the voltage impact matrix (VIM) to identify the 

better/worse connection points for charging stations on the grid. 

The charging loads are modeled by a DC fast-charging charging 

station model [7]. Various charging load profiles are connected 

to different locations in the system, and the grid impact on 

different locations is demonstrated on both IEEE 34-bus test 

system and a realistic feeder from California. An initial voltage 

impact mitigation plan is proposed and tested on the California

feeder model. 

The rest of this paper is organized as follows: Section II 

introduces the procedure for the analysis methodology. Section 

III discusses the case studies performed on two distribution 

systems, including the IEEE 34-node test system and a realistic 

California feeder. Section IV concludes the paper and presents 

the future work plan. 

II. ANALYSIS METHODOLOGY

 This section first introduces the overall analysis approach, 

then further presents the methods used for each major step of 

the grid analysis. 

A. Approach Overview

As shown in Fig. 1, the analysis methodology comprises

three parts: 1) charging station location investigation, 2) 

charging load modeling, and 3) grid impact analysis. First, the 

VIM is used to partition the feeder into different zones ranging 

from the best locations to the worst locations. Then, the charging 

load is modeled considering various inputs, such as EV arriving 

time and EV charging rates. Finally, several representative 

charger connection points on the distribution system are
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selected, and the modeled charging profiles are applied to the 

connection points for impact analysis. The detailed approach in 

each step is introduced in Part B-D in this section. 

Fig. 1 Analysis approach overview

B. Charging Station Location Investigation 

To identify better connection points for charging stations, 

VLSM is leveraged to evaluate and quantify the voltage impact 

brought by each connection point in the system. Two VLSMs 

are calculated: for real power and reactive power. As shown in 

(1)–(3), the voltage change, δVi, at node ii can be calculated by 

multiplying the real/reactive power change, δδδδ/δδδδ, at all the

1-n nodes with the VVVVVVVVPP/VVVVVVVVQQ . Inside VLSM, pij/qij

represents the real/reactive power sensitivity factor 

representing the change in voltage at bus j for a unit deviation 

of real/reactive power at bus i. 

|δδ | = |VVVVVVVVPP||δδδδ| + �VVVVVVVVQQ�|δδδδ| (1)

i.e.,

�

δδ (1)

⋮

δδ (nn)

� = �

pp11 ... pp1nn

⋮ ⋱ ⋮

ppnn1 ... ppnnnn

� �δδδδ(1)

⋮

δδδδ(nn)

� + �

qq11 ... qq1nn

⋮ ⋱ ⋮

qqnn1 ... qqnnnn

� �δδδδ(1)

⋮

δδδδ(nn)

� (2)

Derived from (2): 

δδ (ii) = ∑ ppiiii

nn

jj=1 δδδδ( ) + ∑ qqiiii

nn

jj=1 δδδδ( ) (3)

The detailed calculation process of the VLSM can be found 

in our previous work [5]–[6]. Because of space constraints, the 

calculation process is not elaborated here. 

Based on the VLSM, the VIM is developed to represent the 

voltage impact of a node with respect to the other nodes in the 

system. As shown in (4)–(5), every node has a voltage impact 

factor, ffpppp/ffqq (Calculated in (6)-(7)), representing the total 

voltage changes in other nodes if the real/reactive power at this 

node changes by one unit. The voltage impact factor is 

calculated as the summation of the absolute values of the 

sensitivity factors in each column of the VLSM. 

|VIMPP|1×nn = �ffpp1, ffpp2, ⋯ ffppnn �

1×nn (4) 

�VIMQQ�

1×nn = �ffqq1, ffqq2, ⋯ ffqq �

1×nn

 (5)

ffpppp = ∑ |ppjj nn

=1 | (6) 

ffqq = ∑ |qqjj nn

=1 | (7) 

 Fig. 2 shows how the good locations and worse locations 

map out in the distribution system. The sum of the voltage 

impact factors for real and reactive power quantify the voltage 

impact of a node to the system. The greater this value is, the 

greater the impact on system voltages if the charging station is 

connected to this node. Thus, nodes with lower values are 

mapped as better locations, and nodes with higher values are 

mapped as worse locations. 

Fig. 2 Charging station location mapping

C. Charging Load Modeling

The station load profile used in this paper is generated using 

a DC fast-charging station model, developed to analyze the

charging behaviors of EVs at a charging station [7]. A flowchart 

of the DC fast-charging station simulation model is shown in 

Fig. 3, which shows important inputs to the model, the station 

operational steps, and some notable outputs of the model. The 

model uses an agent-based modeling approach, where the 

vehicles and a station are defined by a set of respective 

properties. For a vehicle, these defining properties are its 

battery capacity, arrival time, initial state of charge (SOC), final 

desired SOC or energy demand, and a power acceptance curve. 

Arrival time and initial SOC are stochastic parameters whose 

distributions are obtained through a combination of real-world 

vehicle telemetry data analytics and EV system modeling [8].

Therefore, we run a Monte Carlo simulation of the station 

operation to obtain average performance metrics in the presence 

of these probabilistic parameters. The charge acceptance curve 

of vehicle, which is a map between SOC and maximum 

charging power of the battery pack and is chemistry-dependent, 

is used as a proxy to emulate more complex control algorithms 

of a battery management system (BMS). By using the charge 

acceptance curve, the DC fast-charging model ensures that the 

battery charging power is limited by either the port power 

capacity or by the BMS control action. A station, on the other 

hand, is defined by the number of charging ports, port capacity, 

and station capacity. When a vehicle arrives at the station, it is 

either plugged into a charging port if any port is empty or 
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queued if all the ports are occupied. Charging is completed 

when the battery pack SOC reaches a predefined maximum 

value or a requested level of energy is added to the pack. In each 

Monte Carlo iteration, we simulate the station operation during 

a period of 1 month with time steps of 1 minute. Once the 

Monte Carlo iterations are complete, the DC fast-charging 

station software outputs a number of station performance 

metrics and data, including but not limited to average vehicle 

charging and waiting times, time-series loads of every port and 

the station as a whole, and battery heat generation data during 

charging. Example station load profiles for stations with 1, 5, 

and 10 installed ports and a traffic of 30 vehicles per day are

shown in Fig. 4. Each port has a capacity of 1.2 MW, and the 

station capacity is assumed to be 1.2 MW times the number of 

ports at the station. The similarity between the station loads for 

5 and 10 ports in terms of the magnitude of their peaks indicates 

that for 30 vehicles per day, the charging demand can be met 

by 5 installed ports or fewer. 

Fig. 3 Flowchart of the operation of a DC fast-charging station model

Fig. 4 Station load profiles for stations with different numbers of charging 

ports

D. Grid Impact Analysis and Mitigation 

After the better/worse locations have been mapped out on the 

distribution system, various modeled charging load profiles are

connected to different connection points on these mapped

locations throughout the system. A time-series analysis is

performed to analyze the impact on the system for different 

placements of these heavy-duty EV charging stations. Voltage 

violations and ramps are evaluated in our initial grid impact 

analysis. 

The initial voltage impact mitigation approach using reactive 

power support functions of the smart chargers is proposed and 

validated in this paper. A comprehensive impact mitigation 

plan will be introduced in detail in a following paper. 

III. CASE STUDY 

This section discusses the analysis performed on two 

distribution system models: the IEEE 34-node test system and a 

realistic California distribution feeder model. Voltage violations 

and voltage ramps are analyzed for both the cases. An initial 

mitigation plan is tested on the realistic California feeder model. 

The time-series simulation resolution is 1 minute.

A. Sample Charging Load Profiles 

In the base case, where no charging stations are placed on the 

feeder, the load profiles at each node are modeled using the 

method developed in our previous work [9]–[11]. The load 

profiles along the feeder are diversified with various patterns 

and different variations. Fig. 5 shows three load profile samples. 

Fig. 5. Sample load profiles 

The charging load profiles are modeled with the approach 

discussed in Part C of Section II. Fig. 6 shows the two charging 

load profiles used for grid impact analysis in this paper. Fig. 6(a) 

shows the total charging profiles of five charging ports, which is 

applied to the analysis of the IEEE 34-node testsystem. Fig. 6(b) 

shows the charging profile used for simulation of the realistic 

feeder, which represents the load profile of one charging port. 

(a) (b) 

Fig. 6. Sample charging load profiles 
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B. IEEE 34-Bus System 

Based on the VIM, the nodes at the IEEE 34-node test feeder 

are sorted out and clustered into different groups. As shown in 

Fig. 7, sample nodes in each group are shown in different 

colored zones. Green, yellow, and red zones represent good, 

mediocre, and worst locations, respectively. 

Four case studies are performed, with the base case 

representing the case without any charging stations; and Case 1, 

Case 2, and Case 3 representing good, mediocre, and worst 

locations, respectively. Five nodes inside each level of location

are selected to place the charging stations. Therefore, for each 

case, five different charging loads are connected to the five 

selected locations in each group. The summation of the five 

loads is the profile shown in Fig. 6(a), with a peak charging load 

of approximately 4 MW. 

Fig. 7. Partition example for IEEE 123-bus feeder 

 Representative time-series voltage results in the three cases 

are plotted in Fig. 8, with (a)–(c) representing good, mediocre, 

and worst location, respectively. The location on the feeder is 

also shown in each caption, where 816, 840, and 844 represent 

bus numbers; and .2, .3, and .2 represent phases B, C, and B, 

respectively. Comparing the three figures shows that the voltage 

profile is not greatly affected when the charging stations are 

placed at the good locations; however, connecting charging 

stations to other locations, especially worst locations, causes 

very large voltage sags and induces a lot of voltage violations. 

Fig. 9 shows the statistical analysis of the 1-day time-series 

voltage results. Figs. 9 (a)–(c) show the distributions of the 

minimum/maximum daily voltage of all the nodes in the system, 

for all the cases, including the base case and cases 1–3. From 

Case 1 to Case 3, the number of voltage violations (greater than 

1.05 and less than 0.95) increases, which shows that case 1 (good 

locations) have the best capability to accommodate heavy-duty

charging stations. The voltage ramp distribution shown in Fig. 

9(d) shows that Case 1 has smaller voltage ramps, whereas cases 

2 and 3 have larger voltage ramps. 

(a) Good location (816.2) (b) Mediocre location (860.3)

(c) Worst location (844.2)

Fig. 8 Voltage profile after charging station connected

 (a) Base case (b) Daily maximum voltage distribution 

(c) Daily minimum voltage distribution (d) Voltage ramp distribution 

Fig. 9 Statistical analysis of voltage results 

C. California Realistic Feeder 

Using the method discussed in Part B of Section II, the nodes 

in this California feeder are sorted, and each node is marked as

a good, mediocre, or worst connection point. In this realistic 

feeder case, one charging port is connected to one node at a time. 

As shown in Fig. 10, three locations are selected along the 

feeder. 

Fig. 10 Charging station placement on California feeder

The time-series voltage profiles for the three cases are shown 

in Fig. 11. Location 1 has better capability to incorporate the 

charging station than locations 2 and 3; however, even Location 

1 cannot handle the heavy and spiky charging loads: the voltages 

decrease to less than 0.95 p.u. 
5 

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An initial mitigation plan is tested on this feeder to help the

feeder better accommodate a charging station. In this mitigation 

case, smart chargers provide reactive power support while 

pulling real power heavily from the grid. Fig. 12 shows the 

testing results of two mitigation cases. Fig. 12(a) shows the case 

where a smart charger provides reactive power support, which 

equals 60% of the power amount it pulls from the grid; whereas 

Fig. 12(b) shows a 100% support case. The 100% support case 

is able to maintain the voltage within the [0.96 1.05] limit, 

whereas the 60% support case is still struggling around the 

minimum limitation edge. 

(a) Location 1 (b) Location 2

(c) Location 3

Fig. 11. Voltage profiles for three charging station locations 

(a) 60% Q support (b) 100% Q support 

Fig. 12. Q support for voltage sag mitigation 

IV. CONCLUSION 

This paper presented a systematic methodology for grid 

impact analysis of heavy-duty EV charging stations. Three 

major steps were introduced: investigation of charging station 

location, charging load modeling, and grid impact analysis. The 

proposed method was tested on two distribution systems: the

IEEE 34-bus system and a California realistic feeder model. The 

results demonstrated the voltage impact that the charging station 

might bring to the system. The proposed analysis approach is not 

system-dependent and can be generalized to other feeders. 

Based on the analysis results, an initial mitigation plan using 

smart charger was proposed and tested. A comprehensive 

impact mitigation plan using both a smart charger and on-site 

distributed energy resources will be developed in the future. 

ACKNOWLEDGMENT

This work was authored by the National Renewable Energy 

Laboratory, operated by Alliance for Sustainable Energy, LLC, 

for the U.S. Department of Energy (DOE) under Contract No. 

DE-AC36-08GO28308. Funding provided by U.S. Department 

of Energy Office of Energy Efficiency and Renewable Energy 

Vehicle Technologies Office via the 1+ MW Medium 

Duty/Heavy Duty Vehicle Project. The views expressed in the 

article do not necessarily represent the views of the DOE or the 

U.S. Government. The U.S. Government retains and the 

publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a nonexclusive, 

paid-up, irrevocable, worldwide license to publish or reproduce 

the published form of this work, or allow others to do so, for 

U.S. Government purposes. 

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