ArticlePDF Available

Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data

Authors:

Abstract and Figures

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.
Content may be subject to copyright.
energies
Article
Simulation of Electric Vehicle Charging Stations Load
Profiles in Office Buildings Based on Occupancy Data
Semen Uimonen and Matti Lehtonen *
School of Electrical Engineering, Aalto University, P.O. Box 15500, 00076 Espoo, Finland;
simo.uimonen@gmail.com
*Correspondence: Matti.Lehtonen@aalto.fi
Received: 12 October 2020; Accepted: 26 October 2020; Published: 31 October 2020


Abstract:
Transportation vehicles are a large contributor of the carbon dioxide emissions to the
atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO
2
emissions which,
however, requires the right electric power production mix for the largest impact. The increase in the
electric power consumption caused by the EV charging demand could be matched by the growing
share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular
sustainable mean of transportation and the expansion of EV units due to the stochastic nature of
charging behavior and increasing share of RES creates additional challenges to the stability in the
power systems. Modeling of EV charging fleets allows understanding EV charging capacity and
demand response (DR) potential of EV in the power systems. This article focuses on modeling of
daily EV charging profiles for buildings with various number of chargers and daily events. The article
presents a modeling approach based on the charger occupancy data from the local charging sites.
The approach allows one to simulate load profiles and to find how many chargers are necessary
to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival
time, duration of charging, and maximum charging power. Additionally, to better understand the
potential impact of demand response, the modeling approach allows one to compare charging profiles,
while adjusting the maximum power consumption of chargers.
Keywords: electric vehicles; load modeling; load profiling; demand response; load aggregation
1. Introduction
The transport sector is responsible for almost a quarter of worldwide total CO
2
emissions,
while about three quarters of these emissions are attributable to cars and trucks. According to the
IEA report, car ownership, trucking activity, and air travel would increase substantially by 2050 [
1
].
Appropriately, the IEA expects the energy use to increase by 70%, and greenhouse gas emissions
to increase by 50%, if no new policies are introduced [
2
]. Besides promoting the climate change,
the increasing number of combustion engine vehicles adds to the problem of airborne particle pollution.
According to WHO report, it already affects the health of more than 90% of the world’s population [
3
].
Thus, one of the challenges is to enable mobility without accelerating the climate change and prevent
adding up to the already existing pollution problem [
4
]. Due to absence of emission during operation,
Electric Vehicles (EVs) have become a promising technology that offers practical reduction of the CO
2
emissions and air pollution if the increased power demand necessary to charge EVs is sustainable.
To successfully implement the solution and maximize the technological advantage, it requires a
multilevel approach that involves car manufacturers, car owners, building owners, and power system
authorities to collaborate.
EV deployment has been growing rapidly over the past ten years. The global stock of EV
passenger vehicles passed 5 million in 2018, with an increase of 63% since 2017, and the rising
Energies 2020,13, 5700; doi:10.3390/en13215700 www.mdpi.com/journal/energies
Energies 2020,13, 5700 2 of 16
trend continues. At the same time, there is also a growing trend of installation of EV charging
points. Charging infrastructure follows the EV trend. In 2018, according to IEA in the global EV
outlook, the number of charging points was estimated to be 5.2 million, which is 44% more than in
2017, where 90% were in the private sector [5].
A large number of Electric Vehicles (EVs) and the growing trend in installation of EV charging
points may create more challenges for electrical power system. The EV charging patterns are stochastic
due to uncertainties in the travel behavior of each individual driver and charging preferences.
Coupled with increasing share of Renewable Energy Sources (RES) in the generation mix and their
intermittent nature, large-scale EV charging can lead to grid overloading, especially during the peak
loading hours [
6
8
]. With well-designed incentives for EV users and charging service providers,
EVs may be used as flexible loads that help to mitigate the load variations and peak demand in the
power system [8].
Previous research supports the importance of conducting EV modeling, investigating EVs and
the related impact on the electric power system. Reference [
9
] proposes an optimization model for
determining the capacity of RES, while utilizing EVs with other sources to capture fluctuations of
RES. Reference [
10
] utilizes Support Vector Regression (SVR) approach to create a charging load
forecasting model based on various historical data. Reference [
11
] describes a model that forecasts
the daily load profile of EV charging stations in commercial building premises. Reference [
12
]
models power profiles using a variational auto-encoder. The research emphasizes that peak load will
rise due to uncontrolled charging of EVs [
13
16
]. If it is possible to impact the charging behavior,
using different charging strategies, this flexible capacity could be used to keep the grid stable with
an increased amount of variable renewable energy. With higher penetration of EVs, their batteries
in an aggregate become a flexible capacity in the power system [
8
]. This is an opportunity to use
them as individual and flexible loads which may be considered for grid-support to mitigate load
variation and load peaks. Reference [
17
] describes how various EV charging strategies can help to
reduce the peak demand and improve system load factor. If there are well-designed incentives for
EV users to take part in grid-support, the value of driving an EV and having EVs in the electrical
system increases. The opportunity of using EVs as grid ancillary services was studied in [
18
22
].
While modeling approaches in the literature focus on modeling average hourly power profiles,
modeling the demand-side management and demand response events requires minutes or even
seconds resolution. For example, the technical requirements for participation in the frequency
containment reserve in Finland require the activation time of the reserve from sub-minute values to
3 min, depending on the type of the reserve [
23
]. Taking into consideration that aggregated EV load is
an intermittent and a stochastic source of flexibility, new data-driven modeling approaches require
smaller resolution for modeling of power profiles.
The present article is prepared for a special issue on the subject of load modeling in power
systems, where the general theme is modeling of loads in the context that includes analysis and
control of existing electricity supply networks and future “smart grids”, at all voltage levels and
in a variety of applications. Applications include measurement-based and component-based load
modeling approaches, modeling and representation of aggregate loads and evaluation of their
impact, load modeling in related “smart grid” applications, e.g., demand-side management and
demand–response schemes, functionalities, and services.
This article presents a modeling approach that allows one to use EV charger occupancy data
for simulation of load power profiles. In the context of analysis and control of existing electricity
supply networks, the current approach allows one to extend the existing modeling methods by creating
seconds resolution load profiles of EV charging infrastructure, while utilizing the data straight from
the service provider and mapping the possible expectations of load profiles during the demand-side
management and demand response events. It is used to model various charging scenarios, e.g., with an
increasing charging demand in the future it helps finding the necessary number of chargers and the
peak load with the corresponding number of daily events, or use it on historical data obtainable from
Energies 2020,13, 5700 3 of 16
the local service provider. This is important to the building owner, as it is necessary to understand
the approximate charger demand and peak power demand to be able to carry out a decision on,
e.g., upgrading the charging infrastructure. Simulating scenarios of using EVs as a flexible load for
mitigation of load variation and load peaks by reducing or increasing the maximum charging load in
the charging infrastructure allows one to showcase the pros of EVs as a flexible load and a necessity
of a robust charging infrastructure, which implicates larger levels of EV penetration. Object-oriented
design of the simulation tool allows one to incorporate real data, zoom in, and find possible bottlenecks
in the availability of the service on standalone chargers. Aggregators and flexibility service providers
want to understand the implications of adjusting the available charging power on the probability
of having enough available procurement for delivery in DR events. It is beneficial to prepare a set
of scenarios before creating a pilot to have expectations about the aggregate load and individual or
collective response.
The remainder of the article is organized as follows. Section 2describes the types of EVs,
relevant for modeling parameters, field EV charging data, and the modeling approach of the EV
charging power. Section 3presents the modeling results. Section 4discusses the implications of
utilizing the modeling approach for creating scenarios of EV profiles during the demand response
(DR) events. It also discusses the bottlenecks for extending the research, as well as the future research
directions. Section 5provides the link to the source code for possible updates and documentation.
2. Methodology
Electric vehicles can be mainly subdivided into plug-in electric vehicles (PEVs), hybrid electric
vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). PEVs have an electric motor and a
battery that can be recharged from the power grid. HEVs have both an electric motor and an internal
combustion engine; however, the battery cannot be charged from the grid, while the combustion
engine recharges it. PHEVs have both an electric motor with a battery that can be externally charged
from the grid and, additionally, a combustion engine [
8
]. The original dataset described the PEV and
PHEV arrival times and charging duration; however, the available electric vehicle parameter data
describes only PEVs; thus, in this simulation, we used only PEVs, which, in the rest of the article, are
denoted as simply EVs.
2.1. Electric Vehicle Parameters
EV power consumption depends on battery temperature, utilizing of heating, ventilation, driving
speed, road friction, etc. [
4
], and battery utilization requires regular charging. Charging is carried
out by connecting the EV to the power grid through an off-board EV charging station, and additional
charging happens through regenerative breaking. Perhaps the most popular option for charging is
charging at home. It is practical because majority of people do not use their cars at night and it is
easy to charge it for the next day [
24
]. Additional economic incentive is the cheaper electricity tariff
during the night [25]. Otherwise, there is an expanding possibility to charge in public places, such as
business premises, universities, hospitals, shopping malls, among others [
11
]. Adequate investments
in charging infrastructure are necessary to make EVs a practical alternative to internal combustion
engine cars [
4
]. According to Reference [
26
], the lack of EV charging infrastructure is one of the most
critical barriers to successful deployment of EVs at a large scale. In addition, charging strategies play
an important role in the effectiveness and sustainability of EVs. Following the growing trends of EV
vehicles and charging facilities [
5
], with high levels of penetration, uncontrolled charging of large EV
fleets may coincide with the peak power demand [
4
,
27
29
]. Uncontrolled charging may lead to phase
imbalances, current harmonics, and transformer and fuse failures in the distribution
system [4,30]
.
One solution besides reinforcing the distribution grid is to manage the EV load optimally to avoid the
negative impacts. For example, it may help to schedule the time intervals where probable charging
peak coincides with the peak load in the power systems and reduce the charging power or the number
Energies 2020,13, 5700 4 of 16
of available chargers in the area, while EV owners may benefit from cheaper electricity [
4
,
31
]. In further
text, it is referred to as a demand response (DR) event.
EV charging power is determined by several factors, such as: battery voltage, maximum current,
number of available phases, and maximum allowed power of the charging pole. Potentially, there also
might be further limitations, for example, the total combined charging power of all charging poles
in the area, such as building premises, might be capped. In Finland, the phase voltage is 230 V.
Thus, the charging power is about 3.6 kW and 7.4 kW for single-phase charging with 16 A and 32 A,
respectively. Charging power is about 11 kW and 22 kW for three-phase charging with 16 A and 32 A,
respectively [4].
According to Reference [
32
], currently, the distribution of EVs and plugin hybrids in Espoo
municipal area in Finland is 19% and 81%, respectively. The distribution of the most common EVs in
the area is adapted from Reference [
33
] and presented in Table 1, as well as the maximum values for AC
charging power, battery capacity, and charging times. The names of the models and manufacturers are
omitted. Maximum charging power and the share in the EV mix is used during the modeling process.
Table 1.
Electric vehicle (EV) model mix and corresponding parameters, adopted from Reference [
33
].
EV Model Letter A B C D E F G H I
Charge Power AC (kW) 17 6.6 6.6 7.2 22 11 7.4 7.4 7.2
Share of All EVs (%) 42.82 26.46 8.66 8.62 4.79 4.58 1.49 1.38 1.21
Charge Time (h) 6.75 7 5 5.25 2 4.25 12.5 13.5 10.5
Battery (kWh) 94 38 28 32 37 37.9 78 84.7 64
Range (km) 490 225 185 190 235 230 350 390 375
2.1.1. Electric Vehicle Charging Data Description
Occupational data utilized during the modeling was gathered from a university campus area in
Otaniemi, Espoo, Finland. At the moment, there are five publicly available charging places for EVs.
Of these places, three have just two chargers, one has four chargers, and the other has eight public
charging spots. All of the spots have the chargers installed with the maximum charging power capped
at 11 kW. With the introduction of charging-time-based pricing some time ago, the maximum available
charging power was changed from 22 kW. As a result, the use of the charging facilities by hybrids
decreased substantially.
The original dataset included the following parameters:
Charging duration
Transaction energy
Timestamps of charging event start and stop
Since there was no information provided that would reflect the unique identifiers for EVs, it was
not possible to track individual EVs in between the campus charging stations and implement spacial
coefficients between them. Thus, the data was used to prepare the probability distributions of the
charging start hour and duration.
Figure 1presents a distribution of charging duration after removing the outliers for weekdays
and weekends, respectively. The duration value is a difference between two timestamps when the car
was first connected to the charger and then disconnected from it. Thus, the reason for removing the
outliers from the dataset is to remove obviously flawed data points where the vehicle occupied the
charger for an improbable time larger than the cars maximum battery charging time from Table 1.
Energies 2020,13, 5700 5 of 16
Figure 1.
Distribution of charging duration in the dataset for weekdays and weekends after removing
the outliers.
Figure 2depicts the distribution of a charging start hour for weekdays and weekends in the
dataset. The data shows that there is a difference in the charging starting hours during the weekend,
which is mostly shifted by several hours during the weekend. Figure 3depicts the distribution plot of
transaction energy for weekdays and weekends, where the difference is less obvious: weekday mean
is 8.9 kWh, and weekend mean is 7.6 kWh.
Figure 2.
Distribution of the charging start hour for weekdays and weekends in the dataset. Time set
to UTC.
Main parameters that are used during the simulation modeling are drawn values of the charging
start hour and the duration of charging, depending on the day type. The following subsection elaborates
how these parameters fit in the simulation model.
Energies 2020,13, 5700 6 of 16
Figure 3.
Distribution of the transaction energy for weekdays and weekends in the dataset. Time set
to UTC.
2.2. Modeling of EV Charging
The article presents a modeling approach which produces power profiles and impact of DR events
on the peak power consumption of an aggregate of EV chargers. The approach is based on deriving
parameters from distributions of the arrival time and the duration of charging, depending on the day
type. In the contrast to the majority of the studies about EV charging power this data does not include
prior knowledge about the state of charge (SOC) of the vehicle. Due to the present communication
protocols between EVs and chargers, this parameter is not communicated during charging between
the EV and the charging station. Thus, it is unavailable in the most datasets. In the presence of the
described parameters, it is not as relevant to the objective of modeling power consumption profiles of
aggregated chargers.
The simulation approach can be subdivided into two parts. Figure 4depicts the first part of the
simulation, where we set the simulation parameters and create the relevant simulation objects, such as
building chargers, EVs, and charging events.
Figure 4. Simulation steps.
First, events are created where event parameters are derived according to the distributions
specified in the data section. Each event is, essentially, an EV of a specific model, which arrives at a
certain time to the charger and charges for a certain duration. When an event is created, the EV model
is derived according to the distribution in Table 1. The selected model has a maximum charging power
Energies 2020,13, 5700 7 of 16
parameter which is later used during the simulation to create the power profile. After the EV model is
selected for the event, the arrival time and the duration of charging are derived from Figures 1and 2
with a specified day type. Next, the events are arranged into a timeline with seconds resolution, which
is used in the simulation algorithm. The last preparation step prior to the simulation is creating a
specified number of chargers. Each of the chargers has the maximum power, which is a constant value
of charging power that it can provide to an EV at that particular time. The power profile of the charger
at each time
t
will have a value smaller or equal to the charger maximum power. The next step is to
execute the simulation algorithm depicted in Figure 5.
Figure 5. Schematic representation of the simulation algorithm.
Power profiles are in seconds resolution; thus, the algorithm has 86,400 iterations which result
in a calculated a daily profile of power consumption. The algorithm starts by finding all of the free
and the occupied chargers from a vector of all chargers
C
. It proceeds to check if there are any events
starting at the time
t
, e.g., if there are any EVs willing to get charged. If such is the case, the algorithm
checks if there is a free charger from the previously found vector
Oc
to provide the charging service
to the EV. If there are none, then the charging event is registered as an unsuccessful in the counter
Energies 2020,13, 5700 8 of 16
and the algorithm proceeds. Otherwise, the event is registered as a successful, the EV is assigned to
a random free charger if there are more than one free in
Oc
, and the charger instantaneous load is
updated according to the Equation (1).
Pck:=Pmax_c,Pmax_cPmax_ev
Pmax_ev,Pmax_c>Pmax_ev
, (1)
where
Pck
is the instantaneous load of charger
k
,
Pmax_c
is the charger maximum charging power, and
Pmax_ev is the maximum charging power of the EV.
In case there were no events starting at the time
t
, the algorithm proceeds to deal with finishing
events. It checks at each time
t
if there are any events that are finishing. If there are, the instantaneous
load of affected chargers from vector Fcis updated to 0 since they become unoccupied.
Once all of the instantaneous powers,
Pc
, of all chargers are updated and set for time
t
,
the algorithm fills (updates) the value of the power profile Pk,tfor each charger kat time t.
2.3. Modeling of Demand Response
We refer to a demand response event as the adjusting of the maximum charging power of chargers
for a time interval, as measure opposite to the uncontrolled charging of EVs. When translated into
the modeling approach, the simulation of Figure 4would include additional steps, as presented in
Figure 6.
Figure 6.
Simulation steps with inclusion of demand response (DR) events. Additions to Figure 4are
highlighted in green.
Essentially, DR events represent a signal that is sent to a specific charger or across all chargers
which indicates the change in the charger maximum power. A DR event has its start time, duration and
an adjustment coefficient for the charger maximum power. Thus, before arranging events into the
timeline, the approach includes creating DR events. Figure 6indicates these additional steps in color.
In turn, the addition of DR events would also affect the simulation algorithm, since now it needs
to be aware of DR events that might take place and affect the charger maximum power. Figure 7
depicts the changes in the algorithm caused by the addition of DR events in color.
The absence of behavioral data when adding DR events to the simulation forces to make
assumptions about an unknown parameter of how many charging events’ duration gets affected by a
DR event. The assumption in this work about potential EV owners behavior is that, with a changing
maximum charging power during DR events, the owners can be subdivided in the following way:
Those who arrive at the charging premises strictly for a specific interval of time, thus, cannot
afford staying longer than the initially derived duration of charging. This category of EVs can be
referred to as ’time-based’.
Those who arrive at the charging premises for an indefinite interval of time and will charge their
EV until it reaches its predefined target. This category of EVs can be referred to as ’energy-based’.
Energies 2020,13, 5700 9 of 16
Figure 7.
Schematic representation of the simulation algorithm with inclusion of DR events.
Additions to Figure 5are highlighted in green.
Therefore, in an occasion of a DR event when the maximum charging power is affected,
the ’energy-based’ EVs charging event duration gets affected, as well. This happens when:
Pmax_ev <Pmax_c·CP_DR, (2)
Energies 2020,13, 5700 10 of 16
where CP_DR is the maximum power coefficient from Figure 6.
The described simulation model accommodates this with an energy counter, which is calculated
from the initial duration of the charging. When the charging duration is derived prior to the simulation
loop, the target of the energy charged for the car is saved in the event information, according to
Equation (3).
Eev =Pck·tev, (3)
where
tev
is the initial duration of the charging, and
Pck
is the nominal charger load, selected according
to Equation (1). Thus, the charging events ending time is offset until all of the energy is charged.
3. Results
The simulation approach allows one to simulate power consumption profiles for each of the
chargers and their aggregate, as well as adjusting the charger maximum power and counting successful
charging events. The results section illustrates how the simulation approach helps to visualize and
plan the charging infrastructure, e.g., number of chargers and maximum charging power. Additionally,
it allows one to model DR events to understand the consequences, such as the reduced power and the
decrease in the level of service.
3.1. Simulating Various Number of Chargers
To show how crowded is the charging infrastructure in situations with various number of
events and chargers, Figure 8depicts the average values of successful events against the number of
daily events for four sets of chargers: 2, 4, 8, and 16. The values are calculated over 100 iterations,
which was selected arbitrarily. The mean percentage of successful events shows borders of the installed
charger capacity event-wise, which could be used, for example, as acceptable levels of provided
charging service.
The figure shows mean values for successful charging events and the 95% confidence interval of
the mean value. The figure could be interpreted the following way. A building has two chargers in
the premises, with 5 charging events per day it is possible to successfully service on average 80–90%
of the vehicles with the current distributions of arrival time and charging duration as depicted in
Figures 1and 2. With a rising trend in EVs the building owner decides to install more chargers. Figure 8
shows that, if they would like to retain the 80–90% successful charging service level, to accommodate
10–15 EVs daily, they would require to double on chargers, while, to service 20–30 EVs daily, they would
need to install 6 additional chargers.
Installation of infinite number of additional chargers is not possible in case there is not enough
power capacity. Figure 9depicts the mean values of maximum charger load for four sets of chargers
for various number of daily events in 100 iterations.
For each set of chargers, after a certain number of daily events, the EV flow starts to be
overwhelming for the installed capacity of chargers, and the increasing number of daily events
causes the average maximum load to saturate and become asymptotic to the sum of the maximum
charging power of the charger set. The simulation helps to identify the magnitude of the average
maximum loads for various numbers of daily events. These should be taken into account when
decisions about installation of new chargers are made from the point of view of technical specification,
e.g., connection to the power source, selecting phases, etc.
Energies 2020,13, 5700 11 of 16
Figure 8.
Mean values of successful charging events in percentages for four sets of chargers and various
number of daily events. Vertical black bars illustrate the 95% confidence interval of the mean value.
Figure 9.
Mean values of maximum power consumption in 100 iterations for four sets of building
chargers and various number of daily events. Vertical black bars illustrate the 95% confidence interval
of the mean value.
3.2. Modeling Demand Response Events
We modeled a DR event as a reduction of maximum charging power of all building chargers
by 30% for 1 hour from 10:00 to 11:00, the value of 30% was selected arbitrarily, while 10:00–11:00 is
close to the power peak, which is the busiest time. The simulation algorithm described in Section 2.3
allows one to portray the effect of reducing charger maximum power by comparing the load profile in
two situations. Figure 10 depicts the comparison of average power profiles with and without a DR
event in 100 iterations for a building with 8 chargers with an average of 40 charging events per day.
Changing the chargers maximum available power affects those EVs that have their maximum
charging power greater than the adjusted maximum value during DR. Figure 10 shows a clear reduction
in the average power during the DR event. The quality of service is decreased and those users who
were planning to get a strict amount of energy from the charging event will have to sacrifice more
time, while those who cannot afford to wait would undercharge. Additionally, less EVs will be able to
charge during that time.
The following two figures illustrate this by showing two simulated cases with a DR event when
chargers maximum power is reduced by 30% for the duration of the event. The set has 8 chargers,
11 kW each, the duration of the event is 1 hour from 10:00 till 11:00. The figures feature various number
of daily events, where in each time the probability of EV being time- and energy based was equal
to 50%.
Energies 2020,13, 5700 12 of 16
Figure 11 depicts the the average power consumption during the time of the DR event (10:00–11:00)
in cases when there is a DR event and when there is none. The figure additionally depicts the differences
between the average values. The average power consumption grows until the number of daily events
starts to be overwhelming, and the average power saturates towards the sum of the maximum available
charging power. The average reduced power as a result of the DR event follows similarly.
Figure 12 depicts percentage values of the successful events for the same simulation. The figure
shows that, for a larger number of daily events, there starts to be a difference in successful charging.
The impact is not large in percentage, because the simulated DR event is only 1 hour long, while the
average charging time is 2 hours, and there are 50% time-based events. Thus, not that many events fit
in the affected time bin, and the effect is limited.
Figure 10.
An illustration of the average aggregated load profile for scenarios with and without
adjusting the maximum charging power of all chargers in 100 iterations, 8 chargers 11 kW each,
40 daily events.
The decrease in the number of successful events becomes more prominent with more DR events,
with larger duration of time intervals, with the increasing number of daily events and with a larger
share of energy-based events.
Figure 11.
The effect of a DR event on the average value of power consumption during 10:00–11:00 for
a building with 8 chargers of 11 kW each. DR event power reduction is 30% for 1 hour with 50 to 50%
of time- and energy-based events, respectively. Black vertical bars indicate a 95% confidence interval of
the mean value. The difference between bars mean values is presented on top.
Energies 2020,13, 5700 13 of 16
Figure 12.
The effect of a DR event on the average value of successful charging events during the day
for a building with 8 chargers, 11 kW each. DR event power reduction is 30% for 1 hour from 10:00 to
11:00 with 50 to 50% of time- and energy-based events, respectively. Black vertical bars indicate a 95%
confidence interval of the mean value. The difference is presented on top.
4. Discussion and Conclusions
This article presents an algorithm that allows one to incorporate measured EV occupancy data
for modeling EV charging power consumption. The proposed algorithm keeps track of successful
charging events and allows one to compare various scenarios of charger configuration, e.g., number
of chargers and number daily events. The main focus is on modeling the aggregated daily charger
load profiles and comparing them with the load profiles during a demand response event when the
maximum available charging power is reduced.
Modeling demand response (DR) allows one to peek into future scenarios of an abundance of
EVs, to study the available flexibility of EV fleets, and to understand the necessity of installation of
additional chargers and the building technical restraints in terms of the peak load across charging
infrastructure. One of the main obstacles in the modeling of future scenarios is the uncertainty in EV
owner behavioral patterns, e.g., how large of a group of EV owners permits their maximum charging
power to diminish during charging in a public domain, such as in the office building. Another large
obstacle is unavailability of EV vehicle labeling in the dataset, which constitutes the inability to model
spatial coefficients, so that it is possible to take into consideration the geographical location of the
charging infrastructure. With a large level of EV penetration, there is a corresponding growing trend
in expanding the charging infrastructure, which would result in a more sophisticated relationship
between the unavailable charger in the respective building and the possibility of an EV owner to charge
the car at the neighboring building. Reference [
34
] showed that not only the utilization rate of the
charging infrastructure depends on their geographical location but also the idle times. Idle time is the
period when the EV is plugged in but not charging. The study concluded that utilization of charging
stations at the residential level was the highest, followed by the utilization in the office buildings.
Additionally, these locations showed the highest idle time. Reference [
34
] said that there was no
specific penalty for the occupation of the charging station without charging. Our original dataset lacks
idle time information, which would help to improve the understanding of how time-based payment
system would affect the idle times.
We illustrated how the proposed algorithm can be utilized to understand if additional charger
infrastructure is necessary, depending on the desired average percentage of successful events compared
to the daily number of events. Modeling of DR events, e.g., adjusting the maximum allowed power
consumption in a time interval, demonstrates that EV flow in coup with the existing charging capacity
are the two main factors that affect the existing flexibility potential. Specifically, a larger charging
infrastructure potentially provides the largest potential for power adjustment, however, requires an
adequate flow of vehicles throughout the studied time interval. At the same time, a large number
Energies 2020,13, 5700 14 of 16
of daily events that restricts the maximum charging power decreases the number of successful
chargings; thus, this ratio is a subjective preference of a building owner or a technical designer.
Additional understanding of incoming EV flows is required to study the probabilities of EVs to be
in place for DR events, so that aggregators can utilize that information during modeling process, for
example, for the frequency containment markets. Perhaps one of the greatest obstacle is scarceness of
available data and limited possibility to identify single EV units to create spatial models.
5. Materials and Methods
The proposed modeling approach was developed using Python, and the source code with
full documentation and example code is available https://version.aalto.fi/gitlab/power_systems_
research_group_aalto/ev_charging_sim.
Author Contributions:
Conceptualization, S.U. and M.L.; methodology, S.U.; software, S.U.; validation, S.U., M.L.;
formal analysis, S.U.; investigation, S.U.; resources, S.U.; data curation, S.U.; writing—original draft preparation,
S.U.; writing—review and editing, S.U.; visualization, S.U.; supervision, M.L.; project administration, M.L.;
funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Business Finland in the frame of Smart Otaniemi Project.
Acknowledgments:
The original dataset was provided by Plugit Finland. Additionally, authors acknowledge the
support of VTT, Nokia and other Smart Otaniemi project members.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
RES Renewable Energy Sources
DR Demand Response
EV Electric Vehicle
PEV Plug-in Electric Vehicle
PHEV Plug-in Hybrid Electric Vehicle
HEV Hybrid Electric Vehicle
SVR Support Vector Regression
SOC State of Charge
References
1.
Fulton, L.; Cazzola, P.; Cuenot, F.; Kojima, K.; Onoda, T.; Staub, J.; Taylor, M. Transport, Energy and CO
2
:
Moving Toward Sustainability; OECD/IEA: Paris, France, 2009.
2.
Dulac, J. Global Transport Outlook to 2050. 2012 . Available online: https://iea.blob.core.windows.net/
assets/imports/events/252/DULAC_23052013.pdf (accessed 24 August 2020).
3.
World Health Organization. Ambient Air Pollution: A Global Assessment of Exposure and Burden of
Disease. 2016. Available online: https://www.who.int/phe/publications/air-pollution- global-assessment/
en/ (accessed 24 August 2020).
4.
Lindgren, J. Charging of Plug-in Electric Vehicle Fleets in Urban Environment. Ph.D. Thesis, School of
Science, Aalto University, Aalto, Finland, 2017.
5.
Global EV Outlook 2019; Technical Report; IEA: Paris, France, 2019. Available online: https://www.iea.org/
reports/global-ev-outlook-2019 (accessed 24 August 2020)..
6.
Koch, A.K.; Fowler, M.W.; Fraser, R.A. Implementation of a fuel cell plug-in hybrid electric vehicle and
factors affecting transportation policy. Int. J. Energy Res. 2011,35, 1371–1388. [CrossRef]
7.
García-Villalobos, J.; Zamora, I.; San Martín, J.; Asensio, F.; Aperribay, V. Plug-in electric vehicles in electric
distribution networks: A review of smart charging approaches. Renew. Sustain. Energy Rev.
2014
,38, 717–731.
[CrossRef]
8.
Grahn, P. Electric Vehicle Charging Modeling. Ph.D. Thesis, Electric Power Systems, KTH Royal Institute of
Technology, Stockholm, Sweden, 2014.
Energies 2020,13, 5700 15 of 16
9.
Farrokhifar, M.; Aghdam, F.H.; Alahyari, A.; Monavari, A.; Safari, A. Optimal energy management and sizing
of renewable energy and battery systems in residential sectors via a stochastic MILP model. Electr. Power
Syst. Res. 2020,187, 106483. [CrossRef]
10.
Sun, Q.; Liu, J.; Rong, X.; Zhang, M.; Song, X.; Bie, Z.; Ni, Z. Charging load forecasting of electric vehicle
charging station based on support vector regression. In Proceedings of the 2016 IEEE PES Asia-Pacific Power
and Energy Engineering Conference (APPEEC), Xi’an, China, 25–28 October 2016; pp. 1777–1781.
11.
Islam, M.S.; Nadarajah, M. Daily EV load profile of an EV charging station at business premises.
In Proceedings of the 2016 IEEE Innovative Smart Grid Technologies—Asia (ISGT-Asia), Melbourne,
Australia, 28 November–1 December 2016; pp. 787–792.
12.
Pan, Z.; Wang, J.; Liao, W.; Chen, H.; Yuan, D.; Zhu, W.; Fang, X.; Zhu, Z. Data-Driven EV Load Profiles
Generation Using a Variational Auto-Encoder. Energies 2019,12, 849. [CrossRef]
13.
Darabi, Z.; Ferdowsi, M. Aggregated Impact of Plug-in Hybrid Electric Vehicles on Electricity Demand
Profile. IEEE Trans. Sustain. Energy 2011,2, 501–508. [CrossRef]
14.
Qian, K.; Zhou, C.; Allan, M.; Yuan, Y. Modeling of Load Demand Due to EV Battery Charging in Distribution
Systems. IEEE Trans. Power Syst. 2011,26, 802–810. [CrossRef]
15.
Pieltain Fernández, L.; Gomez San Roman, T.; Cossent, R.; Mateo Domingo, C.; Frías, P. Assessment of the
Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Trans. Power Syst.
2011
,26, 206–213.
[CrossRef]
16.
Soares, F.; Lopes, J.A.; Almeida, P.; Moreira, C.; Seca, L. A Stochastic Model to Simulate Electric Vehicles
Motion and Quantify the Energy Required from the Grid. 2011. Available online: http://repositorio.inesctec.
pt/handle/123456789/2210 (accessed on 31 August 2020) ).
17.
Dogan, A.; Kuzlu, M.; Pipattanasomporn, M.; Rahman, S.; Yalcinoz, T. Impact of EV charging strategies
on peak demand reduction and load factor improvement. In Proceedings of the 2015 9th International
Conference on Electrical and Electronics Engineering (ELECO), Bursa, Turkey, 26–28 November 2015;
pp. 374–378.
18.
Tomi´c, J.; Kempton, W. Using fleets of electric-drive vehicles for grid support. J. Power Sources
2007
,
168, 459–468. [CrossRef]
19.
Kempton, W.; Tomi ´c, J. Vehicle-to-grid power implementation: From stabilizing the grid to supporting
large-scale renewable energy. J. Power Sources 2005,144, 280–294. [CrossRef]
20.
Han, S.; Han, S.; Sezaki, K. Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation.
IEEE Trans. Smart Grid 2010,1, 65–72.
21.
Andersson, S.L.; Elofsson, A.; Galus, M.; Göransson, L.; Karlsson, S.; Johnsson, F.; Andersson, G. Plug-in
hybrid electric vehicles as regulating power providers: Case studies of Sweden and Germany. Energy Policy
2010,38, 2751–2762. [CrossRef]
22.
Wei, W.; Guo, X.; Li, P.; Jian, G.; Zhan, K.; Tan, Q.; Meng, J.; Jin, X. The effect of different charging strategies
on EV load frequency control. In Proceedings of the 2016 International Conference on Smart Grid and Clean
Energy Technologies (ICSGCE), Chengdu, China, 19–22 October 2016; pp. 161–165.
23.
Fingrid Oyj, Transmission System Operator. Frequency Containment Reserves, Technical Requirements.
2017. Available online: https://www.fingrid.fi/en/electricity-market/reserves_and_balancing/\frequency-
containment-reserves/#technical-requirements (accessed 25 October 2020).
24.
Hess, A.; Malandrino, F.; Reinhardt, M.B.; Casetti, C.; Hummel, K.A.; Barceló-Ordinas, J.M. In Optimal
Deployment of Charging Stations for Electric Vehicular Networks; UrbaNe ’12; Association for Computing
Machinery: New York, NY, USA, 2012; p. 1–6.
25.
Paterakis, N.G.; Erdinç, O.; Pappi, I.N.; Bakirtzis, A.G.; Catalão, J.P.S. Coordinated Operation of
a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed
Generation. IEEE Trans. Smart Grid 2016,7, 2736–2747. [CrossRef]
26.
Wang, G.; Xu, Z.; Wen, F.; Wong, K.P. Traffic-Constrained Multiobjective Planning of Electric-Vehicle
Charging Stations. IEEE Trans. Power Deliv. 2013,28, 2363–2372. [CrossRef]
27.
McCarthy, R.; Yang, C. Determining marginal electricity for near-term plug-in and fuel cell vehicle demands
in California: Impacts on vehicle greenhouse gas emissions. J. Power Sources
2010
,195, 2099–2109. [CrossRef]
28.
Paramonova, S.; Thollander, P.; Ottosson, M. Quantifying the extended energy efficiency gap-evidence from
Swedish electricity-intensive industries. Renew. Sustain. Energy Rev. 2015,51, 472–483. [CrossRef]
Energies 2020,13, 5700 16 of 16
29.
Waraich, R.A.; Galus, M.D.; Dobler, C.; Balmer, M.; Andersson, G.; Axhausen, K.W. Plug-in hybrid electric
vehicles and smart grids: Investigations based on a microsimulation. Transp. Res. Part C Emerg. Technol.
2013,28, 74–86. [CrossRef]
30.
Liu, R.; Dow, L.; Liu, E. A survey of PEV impacts on electric utilities. In Proceedings of the ISGT 2011,
Anaheim, CA, USA, 17–19 January 2011; pp. 1–8.
31.
Su, W.; Chow, M.Y. Computational intelligence-based energy management for a large-scale PHEV/PEV
enabled municipal parking deck. Appl. Energy 2012,96, 171–182. [CrossRef]
32.
Traficom, Finnish Transport and Communication Agency. Public Vehicle Data. 2020. Available online:
https://www.traficom.fi/en/news/open-data (accessed 24 August 2020).
33.
Electric Vehicle Charging Solutions in Apartment Houses. 2019. Available online: https://wiki.aalto.fi/display/
AEEproject/Electric+vehicle+charging+solutions+\in+apartment+houses (accessed 24 August 2020).
34.
Flammini, M.G.; Prettico, G.; Julea, A.; Fulli, G.; Mazza, A.; Chicco, G. Statistical characterisation of the real
transaction data gathered from electric vehicle charging stations. Electr. Power Syst. Res.
2019
,166, 136–150.
[CrossRef]
Publisher’s Note:
MDPI stays neutral with regard to jurisdictional claims in published maps and institutional
affiliations.
c
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... A recursive challenge for the optimisation of energy storage systems has to do with dynamic management of demand. A demand response (DR) model has been proposed [14] to characterise the usage of a set of 9 chargers that represent different characteristics in terms of power and charging speeds. The model reproduces the DR of the charging system to the EVs, keeping track of successful charging events and comparing various scenarios of charging configuration. ...
... When a DR approach is implemented, EVs play a key role in increasing the flexibility of microgrids [5,14], contributing to relieving fluctuations in renewable energy generation [15]. For example, a Swedish case study [16] provides a comparison between distributed and centralised charging approaches to determine optimal EV charging schedules, with the aim of increasing photovoltaic (PV) self-consumption and reducing peak loads. ...
... The study's findings show that self-consumption is improved when community level strategies are taken into consideration. In this regard, recent contributions [14,18,19] stress the importance of evaluating the impact of EVs at district or city level. It is also recognisable the effect of environmental variables at the charging speed of batteries. ...
Article
Full-text available
A rise in the number of EVs (electric vehicles) in Europe is putting pressure on power grids. At an urban scale, Positive Energy Districts (PEDs) are devised as archetypes of (small) urban districts managing a set of interconnected buildings and district elements (lighting system, vehicles, smart grid, etc.). This paper offers a comprehensive analysis of the impact of e-mobility in a PED, simulated using MATLAB-Simulink software. The PED, a small district in northern Spain, is assessed in five scenarios representing varying requirements in terms of energy efficiency of buildings, type of street lighting and number of EVs. The results suggest that the energy rating of the buildings (ranging from A for the most efficient to E) conditions the annual energy balance. A PED with six interconnected buildings (3 residential and 3 of public use) and 405 EVs (as a baseline) only achieves positivity when the buildings have a high energy rating (certificate A or B). In the most efficient case (A-rated buildings), simulation results show that the PED can support 695 EVs; in other words, it can provide nearly 9 million green kilometres. This result represents a potential 71% saving in carbon emissions from e-mobility alone (as compared to the use of fossil-fuel vehicles), thus contributing a reduction in the carbon footprint of the district and the city as a whole.
... The various types of EVs constitute essential factors that affect the charging patterns of EV users. Each type of EV model has a different battery size and energy consumption rate [17]. The different power consumption rates and battery sizes lead to different charging requirements in terms of charging duration and energy required. ...
... First, the system operator must maintain the voltage magnitudes at all buses within prespecified limits expressed by (16). Second, the active power of all load buses must be maintained within a permissible value given by (17). Third, the power flow on the lines must not exceed their loading capability limits expressed by (18). ...
Article
Full-text available
The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.
... This technology is easy to be integrated in urban scenarios [9]. There is plenty of research that shows the importance of EVs for demand-side management so as to increase the flexibility of microgrids [10] and also relieve the fluctuations of renewable energy generation [11]. ...
Conference Paper
Full-text available
This article presents preliminary results that assess the effect of electromobility in an archetype Positive Energy District (PED). We present a PED modelling approach that represents renewable energy generation, an energy storage system, the consumption of residential and non-residential buildings, smart lighting services, and the inclusion of electric mobility. We consider renewable energy generation from photovoltaic panels and annual irradiation patterns of the North of Spain to accomplish the electric demands of a synthetic PED. In this general case study, we build up four scenarios where we evaluate at which degree the consumption of EVs would be covered by local Renewable Energy Sources (RES). The simulation results show that the urban areas with great efficiency (in terms of buildings) may support the demand of EVs and even provide a relevant amount of green kms out of PED boundaries.
... In the final stage, based on the desired window width and time interval, for example for each minute for each month of a year, the total energy consumption was computed using the proposed algorithm. The dependency of EV power consumption on the driving speed, temperature, auxiliary services, traffic, and covered distance, which are the most influential factors, are taken into account in this framework [28]. In the rest of the article, the detailed descriptions for each step are discussed. ...
Article
Full-text available
The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel esti- mation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow. Moreover, the pattern identifies Thursdays and Fridays as the days of peak energy usage, while weekend days and holidays present the lowest range.
Article
The study of the behavior of smart distribution systems under increasingly dynamic operating conditions requires realistic and time-varying load profiles to run comprehensive and accurate simulations of power flow analysis, system state estimation and optimal control strategies. However, due to the limited availability of experimental data, synthetic load profiles with flexible duration and time resolution are often needed to this purpose. In this paper, a top-down stochastic model is proposed to generate an arbitrary amount of synthetic load profiles associated with different kinds of users exhibiting a common average daily pattern. The groups of users are identified through a preliminary Ward’s hierarchical clustering. For each cluster and each season of the year, a time-inhomogeneous Markov chain is built, and its parameters are estimated by using the available data. The states of the chain correspond to equiprobable intervals, which are then mapped to a time-varying power consumption range, depending on the statistical distribution of the load profiles at different times of the day. Such distributions are regarded as Gaussian Mixture Models (GMM). Compared with other top-down approaches reported in the scientific literature, the joint use of GMM models and time-inhomogeneous Markov chains is rather novel. Furthermore, it is flexible enough to be used in different contexts and with different temporal resolution, while keeping the number of states and the computational burden reasonable. The good agreement between synthetic and original load profiles in terms of both time series similarity and consistency of the respective probability density functions was validated by using three different data sets with different characteristics. In most cases, the median values of synthetic profiles’ mean and standard deviation differ from those of the original reference distributions by no more than ±10% both within a typical day of each season and within the population of a given cluster, although with some significant outliers.
Article
Full-text available
Since e-Mobility is on the rise worldwide, large charging infrastructure networks are required to satisfy the upcoming charging demand. Planning these networks not only involves different objectives from grid operators, drivers and Charging Station (CS) operators alike but it also underlies spatial and temporal uncertainties of the upcoming charging demand. Here, we aim at showing these uncertainties and assess different levers to enable the integration of e-Mobility. Therefore, we introduce an Agent-based model assessing regional charging demand and infrastructure networks with the interactions between charging infrastructure and electric vehicles. A global sensitivity analysis is applied to derive general guidelines for integrating e-Mobility effectively within a region by considering the grid impact, the economic viability and the Service Quality of the deployed Charging Infrastructure (SQCI). We show that an improved macro-economic framework should enable infrastructure investments across different types of locations such as public, highway and work to utilize cross-locational charging peak reduction effects. Since the height of the residential charging peak depends up to 18% on public charger availability, supporting public charging infrastructure investments especially in highly utilized power grid regions is recommended. Full-Text of article available at: https://www.mdpi.com/1996-1073/14/23/7992
Article
The mode of battery replacement will become the main means of urban public transport energy supply, which will have a significant impact on the energy cost of public transport companies and the stability of the power system. Therefore, this paper proposes a charging and discharging optimization model for electric buses to participate in the carbon trading market and the peak shaving auxiliary service market. The upper model aims to minimize the total daily energy cost of the bus company, and the lower model is optimized based on the upper optimization results to minimize grid load fluctuations. Then, the scheme proposed in this paper is compared with disordered charging and orderly charging based on time of use price. Finally, a bus company in Beijing is taken as an example of simulation. The results show that compared with the disordered charging and the orderly charging based on time of use price, the charging and discharging schemes proposed in this paper can reduce the daily energy cost of 6052.68 yuan and 617.42 yuan respectively, reduce the fluctuation of power grid load by 92.14% and 13.85%, and reduce the carbon emission of the "bus power" combined system by 3930 kg and 2080 kg.
Article
Full-text available
Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.
Conference Paper
Full-text available
Due to the quick power reaction characteristic, the electrical vehicles (EVs) are used for load frequency control. An EV frequency control strategy is proposed considering the charging and discharging characteristics of the EV battery. Different from battery energy storage system, the availability of EVs largely depends on the charging behaviors. With different charging power and different charging time, three charging strategies are studied (dumb charging, smart charging and constant charging). The effects of EV load frequency control with three charging strategies are compared under a severe frequency event. The simulation results show that the EV load frequency control effect is closely related with the charging strategy. Considering the system load curve along a whole day, “smart charging” is the most suitable for preventing severe frequency event.
Conference Paper
Full-text available
A rapid growth in Electric Vehicle (EV) penetration is expected in the near future. EV presents economic and environmental advantages over traditional gasoline vehicles. However, a number of EVs, if charged simultaneously, may pose some serious challenges for electric utilities especially at the distribution level. This paper investigates selected EV charging strategies to mitigate the adverse effect of charging EV on the peak demand. These charging strategies are implemented for different EV penetration levels and charging modes. Simulation results indicate that EV charging time, charging power and penetration levels are significant factors impacting system peak load. Examining different EV charging strategies can help reduce the negative impact of EV penetration on the peak demand and thereby improve system load factor.
Book
Car ownership is set to triple by 2050, trucking activity will double and air travel could increase fourfold. This book examines how to enable mobility without accelerating climate change. It finds that if we change the way we travel, adopt technologies to improve vehicle efficiency and shift to low-CO2 fuels, we can move onto a different pathway where transport CO2 emissions by 2050 are far below current levels, at costs that are lower than many assume. The report discusses the prospects for shifting more travel to the most efficient modes and reducing travel growth rates, improving vehicle fuel efficiency by up to 50% using cost-effective, incremental technologies, and moving toward electricity, hydrogen, and advanced biofuels to achieve a more secure and sustainable transport future. If governments implement strong policies to achieve this scenario, transport can play its role and dramatically reduce CO2 emissions by 2050.
Article
Energy supply through integrated renewable energy sources (RESs) and battery systems will be of higher importance for future residential sectors. Optimal energy management and sizing for the components of residential systems can enhance efficiency, self-suffiency, and meanwhile can be cost-effective by reducing investment as well as operating costs. Accordingly, this paper proposes an exhaustive optimization model for determining the capacity of RESs, namely: wind turbines and photovoltaic (PV) systems. In this study, batteries and electric vehicles (EVs) are utilized in line with other sources to capture fluctuations of RESs. To model the uncertainties of RESs, energy prices, and load demands a linearized stochastic programming framework is applied. The proposed framework involves long-term and efficient resource development alongside with short-term management and utilization of these resources for supplying the demand load. In our study, we utilize the roulette wheel mechanism (RWM) method as well as proper probability distribution functions (PDFs) to generate scenarios for all sources of uncertainties, including wind turbines, PV systems, demand, and electricity market price. The approach is verified in two different cases, including an individual home and a larger micro-grid (MG). The results of multiple numerical simulations demonstrate the effectiveness of the proposed stochastic model.
Article
Despite the many environmental benefits that a massive diffusion of electric vehicles (EVs) could bring to the urban mobility and to society as a whole, numerous are the challenges that this could pose to the electricity distribution grid, particularly to its operation and development. While uncoordinated management of EVs can lead to load imbalances, current or voltage variation excess and steep power requests, properly designed and well-coordinated integration approaches can in contrast provide flexibility, hence value, to the whole electrical system. Such step can be achieved only if real data are available and real drivers’ behaviours are identified. This paper is based on a real dataset of 400,000 EV charging transactions. It shows and analyses an important set of key figures (charge time, idle time, connected time, power, and energy) depending on driver's behaviour in the Netherlands. From these figures, it emerges a key role of the uncertainty of the relevant variables due to the drivers’ behaviour. This requires a statistical characterisation of these variables, which generally leads to multi-modal probability distributions. Thereby, this paper develops a Beta Mixture Model to represent these multi-modal probability distributions. Based on the emerged statistical facts, a number of results and suggestions are provided, in order to contribute to the important debate on the role of EVs to move to a fully decarbonised society.
Article
In this paper, the optimal operation of a neighborhood of smart households in terms of minimizing the total energy procurement cost is analyzed. Each household may comprise several assets such as electric vehicles, controllable appliances, energy storage and distributed generation. Bi-directional power flow is considered both at household and neighborhood level. Apart from the distributed generation unit, technological options such as vehicle-to-home and vehicle-to-grid are available to provide energy to cover self-consumption needs and to inject excessive energy back to the grid, respectively. The energy transactions are priced based on the net-metering principles considering a dynamic pricing tariff scheme. Furthermore, in order to prevent power peaks that could be harmful for the transformer, a limit is imposed to the total power that may be drawn by the households. Finally, in order to resolve potential competitive behavior, especially during relatively low price periods, a simple strategy in order to promote the fair usage of distribution transformer capacity is proposed.