Conference PaperPDF Available

Smart Charging of Electric Vehicles According to Electricity Price

Authors:
Morsy Nour at Aswan University
Morsy Nour
Sayed M. Said at Aswan University
Sayed M. Said
Abdelfatah Ali at South Valley University
Abdelfatah Ali
Csaba Farkas at Budapest University of Technology and Economics
Csaba Farkas
Download full-text PDF
Read full-text
Download citation

Abstract and Figures

The growing popularity of private vehicles’ electrification will have a negative impact on the electric power system, especially on the distribution networks, if electric vehicles (EVs) charging is not managed properly. In this paper, a new technique for smart charging of EVs is proposed and tested with simulation. A fuzzy logic controller is used to control and manage the EV charging process to maximize electric utility and EV owner benefits. The electric utility’s benefit is to mitigate the EV charging impacts on the distribution network by shifting EV charging to the off-peak period, while EV owners’ benefit is to charge the EV at low cost. The controller regulates and controls the EV charging power depending on electricity price signal provided by the electric utility and EV battery state of charge (SoC). This controller needs basic communication with the electric utility to receive the electricity price signal every 1 hour. The objective of the controller is to charge EVs at low cost while keeping the normal operating conditions of the distribution network. MATLAB/SIMULINK is used to perform simulations and test the effectiveness of the proposed smart charging method. The results demonstrated that the proposed smart charging method reduced the impacts of EVs charging on the distribution network compared with uncontrolled charging.
Figures - uploaded by Morsy Nour
Author content
All figure content in this area was uploaded by Morsy Nour
Content may be subject to copyright.
Content uploaded by Morsy Nour
Author content
All content in this area was uploaded by Morsy Nour on Feb 25, 2019
Content may be subject to copyright.
2019 INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN COMPUTER ENGINEERING (ITCE’2019)
978-1-5386-5261-9/19/$31.00 ©2019 IEEE
Smart Charging of Electric Vehicles According to
Electricity Price
Morsy Nour1,4, Sayed M. Said2,4, Abdelfatah Ali3,4, and Csaba Farkas4
1Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt
2Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
3Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt
4Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and
Economics, Budapest 1111, Hungary
morsy.abdo@aswu.edu.eg, sayed.said@aswu.edu.eg, abdelfatah.mohamed@vet.bme.hu, farkas.csaba@vet.bme.hu
Abstract— The growing popularity of private vehicles’
electrification will have a negative impact on the electric power
system, especially on the distribution networks, if electric vehicles
(EVs) charging is not managed properly. In this paper, a new
technique for smart charging of EVs is proposed and tested with
simulation. A fuzzy logic controller is used to control and manage
the EV charging process to maximize electric utility and EV
owner benefits. The electric utility’s benefit is to mitigate the EV
charging impacts on the distribution network by shifting EV
charging to the off-peak period, while EV owners’ benefit is to
charge the EV at low cost. The controller regulates and controls
the EV charging power depending on electricity price signal
provided by the electric utility and EV battery state of charge
(SoC). This controller needs basic communication with the
electric utility to receive the electricity price signal every 1 hour.
The objective of the controller is to charge EVs at low cost while
keeping the normal operating conditions of the distribution
network. MATLAB/SIMULINK is used to perform simulations
and test the effectiveness of the proposed smart charging method.
The results demonstrated that the proposed smart charging
method reduced the impacts of EVs charging on the distribution
network compared with uncontrolled charging.
Keywords—Uncontrolled charging; smart charging; electric
vehicles; fuzzy logic control.
I. INTRODUCTION
According to the International Energy Agency (IEA) global
EV outlook of 2017 [1], there is a continuous decrease in the
price of EVs which will result in the acceleration of EV
deployment. The decline of battery cost is the reason for EVs
prices reduction. Mass production in addition to research and
development (R&D) results in rapid decay of battery costs. The
continuous improvement of EVs and battery technology will
narrow the price gap between EVs and conventional internal
combustion engine (ICE) vehicles which will increase
competitiveness. Evaluations of the countries’ targets and
equipment manufacturers’ announcements seem to confirm
these positive signals of the massive increase of the EVs stock
market. EVs stock crossed 1 million in 2015 and surpassed 2
million in 2016. It is expected that the EVs stock market will
be between 9 million and 20 million in 2020 and at 2025 will
be between 40 million and 70 million.
Because this large number of EVs will be charged from the
electric power grid, it is important to develop techniques for the
optimal integration of EVs. There are three main types of EV
charging; uncontrolled charging, delayed charging and smart
charging. Uncontrolled charging is also called unregulated
charging, uncoordinated charging, or dumb charging.
Uncontrolled charging means that the cars start charging at the
instant of arrival to home or workplace. This is the type of EV
charging that is used nowadays. In this case, EV charging
usually occurs at peak load hours which leads to severe grid
impacts. Various studies found that uncontrolled EV charging
may limit the acceptable penetration level of EVs in the
distribution network [2]. The highest impacts on the
distribution networks are expected if this charging technique is
used. Usually, this happens when the utility has fixed tariff
structure, so there are no incentives for EV owners to delay the
charging.
In delayed charging, electricity utility companies use two
tariff structures with high prices at peak period and lower
prices at the off-peak period to motivate EV owners to charge
at these times. With proper choice of the tariff structure, this
can result in a flattened load profile and a decrease of the
voltage drop caused by EV charging. If the off-peak and peak
hours in the tariff structure are not set in an optimal way, the
impact of EV charging may get worse [3]–[5]. This may occur
since the low prices at off-peak period may motivate a huge
number of EV owners to charge simultaneously which may
result in higher voltage drop and load demand with the
possibility of second peak formation at the first hours of off-
peak hours.
Although the use of delayed charging can reduce the EV
charging impacts on the distribution networks, it is observed
that the network capacity is not used in an optimal manner.
Various studies concluded that the controlling of charging start
time and charging rates of EVs using a smart charging
(controlled charging or coordinated charging) algorithm may
use the power system in a more optimal and efficient way.
Many studies presented coordinated charging algorithms [6]–
[8] to control EV charging to maximize EV owners’ benefits
by reducing the charging cost or to maximize the electric utility
benefits by shifting the EV charging to off-peak hours which
reduces the impact on the electric power system. This is a part
of the smart grid concept. In this type, the communication
between EV and electric utility or Distribution System
Operator (DSO) is continuous.
In this paper, three operation scenarios are studied and
compared. In the first scenario, the LV distribution network is
supplying residential consumers only, and no EVs are plugged
in for charging. In the second scenario, EVs with 50%
penetration level are connected to the grid for charging. In this
scenario, the EVs are charged in uncontrolled charging manner.
In the third scenario, the EVs are charging based on the
proposed smart charging method.
II. DISTRIBUTION NETWORK MODELING AND SIMULATION
A. Distribution Network
A 400 V LV distribution network is selected as a case study
in this paper to execute simulations. It is a real distribution
network located in New Toshka city, Aswan, Egypt. Fig. 1
shows the one-line diagram of the distribution network. It has a
500 kVA distribution transformer with 22 kV primary and 400
V secondary voltage. It supplies 96 residential consumers
distributed equally at eight buildings. The system is assumed to
be balanced, and the residential consumer's load is the same in
all the 3 phases. All the system data is provided in [9]. Fig. 2
shows the load profile (power consumption variation) during
the day for each consumer [10]. 1 p.u. is equivalent to 4 kVA
which is the highest power consumption in the 24 hours. The
load power factor is 0.9 lagging.
Nissan Leaf [11] is used to evaluate the impacts of its
charging on the distribution network. It is a battery electric
vehicle (BEV). This type of EVs is powered entirely by
electricity and batteries are used as the electric power source,
and it supplies an electric motor which drives the vehicle. No
internal combustion engine (ICE) is used in this type of EVs.
Its battery capacity is 24 kWh. The EVs are assumed to have
20% SoC when connected to the charger (initial SoC). A three-
Fig. 1 LV distribution network one-line diagram.
0
0.2
0.4
0.6
0.8
1
1.2
0 2 4 6 8 10 12 14 16 18 20 22 24
Demand [pu of maximum]
Time of day [h]
Fig. 2 Daily load curve of residential consumer.
phase charger with 90% efficiency and 6.6 kW power rating is
used to charge EVs.
B. Uncontrolled Charging
Three operation scenarios are studied; base case,
uncontrolled charging, and smart charging. For uncontrolled
charging, EVs start charging at the instant of home arrival (1)
at the maximum charging power of the charger. This case has
no coordination or management of EVs charging and the
highest impacts is expected in this case. Fig. 3 shows that the
start time of EVs charging follows a Gaussian distribution (2)
with a mean (µ) equal to 18:00 and standard deviation(σ) equal
to 5 hours [12]. The actual number of EVs that begin charging
at each hour is shown in Fig. 4. A 50% penetration level of
EVs is investigated which is equal to 48 EV. The penetration
level of EVs can be calculated by (3).
arrivalingch tt =
arg
(1)
et
tf )2(
)(
2
2
2
2
1
),,(

−−
=
(2)
(3)
C. Smart Charging
In this charging method, the EV charging is controlled by a
fuzzy controller with electricity price signal and SoC as inputs
and the charging power as an output. Most of the proposed
smart charging techniques depend on the availability of
sophisticated real-time communication between EVs and
electric utility, but the proposed controller needs only basic
communication to receive the energy price which is provided
by the electric utility every 1 hour. The electricity price is
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
6 8 10 12 14 16 18 20 22 24 2 4 6
Probability
Time of day [h]
Fig. 3 Probability distribution of EVs charging start time.
0
1
2
3
4
12345678910 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Number of EVs
Time of day [h]
50% penetration
Fig. 4 EVs charging start time.
Fig. 5 Electricity tariff structure.
Fig. 6 Inputs and output membership functions.
assumed to vary during the day as in Fig. 5. The electricity
price is 5 p.u. from 24:00 to 17:00, 7.5 p.u. from 17:00 to 19:00
and from 22:00 to 24:00, and 10 p.u. from 19:00 to 22:00.
According to the controller design, the EV can charge at the
maximum charging power when electricity price is low (5
p.u.). In addition, it charges at medium charging power
depending on battery SoC when electricity price is medium
(7.5 p.u.) and stop charging when electricity price is high (10
p.u.). The controller is designed to maximize EV owner benefit
by charging the EV at low cost. Also, the electric utility
benefits (operating the system within the acceptable limits) can
be maximized by proper choice of electricity tariff structure.
EV owners can be motivated to charge at off-peak hours which
have low electricity prices and stop charging at peak hours
which have high electricity price.
TABLE I. FUZZY CONTROLLER KNOWLEDGE BASE RULES
Price
SoC
L
M
H
VL
VH
H
VL
L
VH
H
VL
M
VH
M
VL
H
VH
M
VL
VH
VH
M
VL
Fig. 6 shows that the universe of discourse of the output
(charging power) and first input (SoC) is divided into five
membership functions sets (three triangular and two
trapezoidal); Very High (VH), High (H), Medium (M), Low
(L), and Very Low (VL). Furthermore, the second input
(energy price) is divided into three trapezoidal membership
functions sets; Low (L), Medium (M) and High (H). Because
the controller output signal is in p.u. so, it should be multiplied
by EV charger rated charging power. A saturation limit block
is added to the fuzzy controller output to guarantee the
unidirectional flow of power from the power grid to the
vehicle. Fig. 7 shows the MATLAB/SIMULINK fuzzy
controller model with electricity price and SoC inputs and per
unit charging power output which is multiplied by 6.6 kW gain.
Table I shows the controller knowledge base rules. Fig. 8
illustrate a three-dimensional (3-D) visualization of the control
space. The surface viewer shows the inputs and output relation.
The proposed fuzzy controller can be easily extended to
have more than two inputs or to include other distribution
network parameters such as total power, transformer loading or
feeders loading as well as EV owner preferences.
III. RESULTS AND DISCUSSION
A. Total Power Demand
For uncontrolled charging, the peak demand increased to
500 kVA with 100 kVA increase in peak demand at 20:00
compared with the base case. In opposition, for smart charging,
the peak demand was almost the same as the base case because
EVs stopped charging during the peak hours from 19:00 to
Fig. 7 MATLAB/SIMULINK model of fuzzy controller.
Fig.8 relation between inputs and output in 3-D.
Fig. 9 Total power demand for the base case, uncontrolled charging,
and smart charging.
Fig. 10 Transformer loading for the base case, uncontrolled charging,
and smart charging.
22:00 due to the high electricity price in these hours. EV
charging was shifted to off-peak period as shown in Fig. 9
which is called valley filling.
B. Transformer Loading
The transformer loading increased with EV integration and
reached 100% of its power rating at 20:00 as shown in Fig. 10
when uncontrolled charging was used to charge EVs. On the
other hand, for smart charging, the transformer loading was
limited to 80% of its power rating as in the base case. This
means a 20% reduction in transformer loading compared with
uncontrolled charging.
C. Cable Loading
The cable loading was very low during the whole day even
at peak demand hours as shown in Fig. 11. The cable loading
increased with uncontrolled EV charging and reached more
than 50% of its capacity at 20:00. For smart charging, the
highest loading recorded during the day was 40% of its power
rating similar to the base case. This means more than 10%
reduction in cable loading compared to uncontrolled charging.
D. Voltage at Cable Endpoint
Fig. 12 shows that uncontrolled charging of EVs results in
a worse voltage profile compared with the base case. The
voltage at the furthest point from the transformer reached the
lower limit (0.95 p.u. according to ANSI standard [13]) of the
acceptable operating conditions at peak time (20:00). On the
contrary, smart charging improved the voltage profile because
no EVs were charging during peak hours from 19:00 to 22:00
and the lowest voltage recorded was more than 0.96 p.u.
a.
IV. CONCLUSIONS
In this study, maximizing EV owners’ benefit by
controlling EV charging based on electricity price was
executed. Fuzzy controller with electricity price and SoC inputs
and charging power output was used to control EV charging. It
was concluded that:
• Smart charging reduced the maximum power demand by
100 kVA which is a 20% reduction compared with
uncontrolled charging. Apparently, the highest power
demand recorded during the day was the same as in the
base case. Smart charging resulted in valley filling.
• Smart charging reduced the maximum transformer loading
during the day by 20% compared with uncontrolled
charging. The highest transformer loading recorded for
smart charging was 80% which was the same as the base
case.
• Smart charging reduced the cable maximum loading by
more than 10% compared with uncontrolled charging.
• Smart charging improved the voltage profile compared with
uncontrolled charging and the lowest voltage recorded was
higher than 0.96 p.u. similar to the base case.
REFERENCES
[1] International Energy Agency, “Global EV Outlook 2017: Two
million and counting,” IEA Publ., p. 66, 2017.
[2] Y. Chen, A. Oudalov, and J. S. Wang, “Integration of electric
vehicle charging system into distribution network,” in 8th
International Conference on Power Electronics - ECCE Asia, 2011,
pp. 593–598.
[3] S. Shao, T. Zhang, M. Pipattanasomporn, and S. Rahman, “Impact
of TOU rates on distribution load shapes in a smart grid with PHEV
penetration,” 2010 IEEE PES Transm. Distrib. Conf. Expo. Smart
Solut. a Chang. World, pp. 1–6, 2010.
[4] Yajing Gao, Chen Wang, Zhi Wang, and Haifeng Liang, “Research
on time-of-use price applying to electric vehicles charging,” in IEEE
PES Innovative Smart Grid Technologies, 2012, pp. 1–6.
[5] S. Schey, D. Scoffield, and J. Smart, “A First Look at the Impact of
Electric Vehicle Charging on the Electric Grid in The EV Project,”
EVS26 Int. Batter. Hybrid Fuel Cell Electr. Veh. Symp., vol. 1.
[6] C. Jin, J. Tang, and P. Ghosh, “Optimizing Electric Vehicle
Charging: A Customer’s Perspective,” IEEE Trans. Veh. Technol.,
vol. 62, no. 7, pp. 2919–2927, Sep. 2013.
[7] J. de Hoog, T. Alpcan, M. Brazil, D. A. Thomas, and I. Mareels,
“Optimal Charging of Electric Vehicles Taking Distribution
Network Constraints Into Account,” IEEE Trans. Power Syst., vol.
Fig. 11 Cable loading for the base case, uncontrolled charging,
and smart charging.
Fig. 12 Voltage profile for the base case, uncontrolled charging,
and smart charging.
30, no. 1, pp. 365–375, Jan. 2015.
[8] E. Sortomme, M. M. Hindi, S. D. J. MacPherson, and S. S. Venkata,
“Coordinated Charging of Plug-In Hybrid Electric Vehicles to
Minimize Distribution System Losses,” IEEE Trans. Smart Grid,
vol. 2, no. 1, pp. 198–205, Mar. 2011.
[9] M. Nour, A. Ali, and C. Farkas, “Evaluation of Electric Vehicles
Charging Impacts on A Real Low Voltage Grid,” Int. J. Power Eng.
Energy, no. April, pp. 837–842, 2018.
[10] S. Papathanassiou, H. Nikos, and S. Kai, “A benchmark low voltage
microgrid network,” Proc. CIGRE Symp. power Syst. with dispersed
Gener., no. April, pp. 1–8, 2005.
[11] “NISSAN LEAF,” 2013. [Online]. Available: http://www.auto-
brochures.com/makes/Nissan/Leaf/Nissan_US Leaf_2013.pdf.
[12] Y. Cao et al., “An optimized EV charging model considering TOU
price and SOC curve,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp.
388–393, 2012.
[13] ANSI Standard Publication, “Electric power systems and
equipment—Voltage ratings (60 Hertz),” Natl. Electr. Manuf.
Assoc., p. ANSI C84.1-2011, 2011.
... HC reference, scaled with respect to some fixed network parameter, helps in analysing EV addition in the network, and five reference definitions are generally employed to represent the percentage value of EV penetration as shown in Figure 3. It has been defined as a ratio of EVs to the total number of customers in the network subject to network performance constraints [17][18][19][20][21][22][23][24][25][26][27]. ...
... In addition, the combined objective to increase the benefits for EV owners and utility was investigated by researchers working on multi-objective problems. A fuzzy logic (FL) controller was used with inputs as EV SOC and the price signal from the utility and output as the charging power in [24] for assessing charging coordination. This study strengthened the concept that smart charging can maximise the financial benefits of EV owners by scheduling off-peak charging and enabling utilities to design optimal tariff structures for charging within network limits. ...
A review of electric vehicle hosting capacity quantification and improvement techniques for distribution networks
Article
Full-text available
The electrification of the transport sector to control the carbon footprint has been gaining momentum over the last decade with electric vehicles (EVs) seen as the replacement for conventional internal combustion engines. Economic incentives, subsidies, and tax exemptions are also paving the way for rising EV penetration in the power distribution networks. However, the exponential EV adoption requires careful technical and regulatory analysis of traditional networks to satisfy the network reliability constraints. Therefore, it would be vital to find EV hosting capacity (HC) limits of networks from a multifaceted approach involving various market players, mainly distribution system operators and EV owners. This review provides a systematic categorization of EV hosting capacity evaluation and improvement methods, thus enabling researchers and industry personnel to navigate the advancing landscape of EVs. This novel framework extends beyond the theoretical implication of diverse objective functions and HC improvement methods to the actual numerical values of EV HC across varying geographical settings. Therefore, this unique synthesis of varying aspects of EV HC facilitates the in‐depth understanding of the integration of sustainable energy and transport sector.
View
... Smart charging, i.e., advancing charging processes to times when electricity prices are low or renewable energy is available, is a common approach to running managed charging infrastructures. There are publications that examine smart charging on a theoretical [4][5][6] and practical basis [7]. The authors of [5] predict potential cost savings of 200 EUR/EV/year if smart charging based on variable prices is applied. ...
On the Aggregation and Monetization of Flexible Loads in the Context of EV Fleets
Article
Full-text available
  • May 2024
In this paper, we present an approach to the price-optimized charging of electric vehicles (EVs) based on energy flexibility. Fleet operators determine the minimum and the maximum power demand to charge EVs at a specific time and share this information as so-called power corridors (PCs) with an energy aggregator. The energy aggregator collects the predicted PCs from the fleet operators located in the same market area and aggregates the PCs. The energy provider periodically sends energy prices from the market to the energy aggregator, which purchases energy when its price is opportune. The energy aggregator calculates and delivers charge plans for each fleet operator involved and thus can pass along the purchase prices. The incentive design must ensure that fleet operators are better off by disclosing their flexibility data to the aggregator. This study can contribute to a new data-driven energy market communication system by providing insights on how to leverage the energy flexibility that EVs can offer to the energy system.
View
... As a result of Action Plan implementation of this Concept by 2035, it is planned to reduce electricity losses in the power grids in Ukraine as a whole from 11.6% to 7.5% or by 6 billion kWh, which is equivalent to 3 million tons of burned coal at thermal power plants [12]. ...
SWOT analysis of electric transport and V2G implementation for power system sustainable development in the terms of Ukraine
Article
Full-text available
  • Oct 2023
An analysis was carried out to examine the prospects for the integration of smart charging technologies, such as Vehicle-to-Grid (V2G), into the power system in Ukraine. The country is experiencing a growing concern regarding greenhouse gas emissions. The transport sector in Ukraine is one of the most energy-intensive sectors of the economy and contributes significantly to environmental degradation. The use of internal combustion engines in transport also increases the country’s dependence on imported fuel due to the high demand for petroleum products. The integration of electric transport and smart charging technologies, particularly V2G, is seen as a critical solution with immense potential to mitigate climate change. V2G technology is becoming increasingly relevant as the world transitions towards a more sustainable energy future. V2G refers to the bi-directional flow of energy between electric vehicles (EVs) and the grid. With V2G, EVs can not only consume energy from the grid but also supply energy back to the grid during peak demand periods, helping to stabilize the grid and increase its resilience. Therefore, it is imperative to develop a stable policy to promote these technologies and compare the features of the policy of integrating electric vehicles and the energy system in Ukraine. A qualitative strategic planning methodology, Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis, was used to evaluate the diverse measures and initiatives related to electric transport development and infrastructure while integrating them into the power system. This analysis identified problems associated with the impact of EV charging on the power system, such as the inability to provide increased energy needs or limiting the EV charging infrastructure load. However, a high percentage of renewable energy sources, increased social awareness of climate change, and reduced prices of electric vehicles could provide the necessary opportunities for developing electric transport in Ukraine.
View
CHARGEEV: AN EV CHARGING STATION FINDER BRIDGING THE GAP IN EV CHARGING INFRASTRUCTURE
Research Proposal
Full-text available
  • Apr 2024
In the past decade, significant advancements in electric vehicle (EV) technology and charging infrastructure have revolutionized the automotive industry. Electric vehicles offer not only environmental benefits by reducing emissions but also superior power delivery and efficiency through innovations like regenerative braking. However, despite these advantages, the challenge of locating charging stations remains a critical issue for EV drivers. Unlike conventional vehicles, EV owners must plan their routes carefully to ensure access to charging points, highlighting the urgent need for robust charging infrastructure. To address this challenge, we present ChargeEV, an innovative EV charging station finder application developed using ReactJS. ChargeEV aims to empower EV drivers by providing real-time access to nearby charging stations and enabling seamless reservation of parking slots for charging. By integrating user-friendly features such as interactive maps and personalized journey planning, ChargeEV facilitates efficient trip management for EV owners. Users can simply input their starting point and destination, and the application generates a comprehensive roadmap displaying all available charging stations along the route. ChargeEV not only enhances the convenience of EV ownership but also contributes to the wider adoption of sustainable transportation solutions. Through its intuitive interface and advanced functionalities, ChargeEV represents a significant step towards optimizing EV charging infrastructure and promoting the transition to cleaner mobility options.
View
View
Heuristic Techniques and Evolutionary Algorithms in Microgrid Optimization Problems
Chapter
Full-text available
  • Jun 2024
This study introduces an optimization model and evaluates the performance of various metaheuristic optimization techniques in solving the sizing problem for an on-grid hybrid renewable energy system (HRES), comprising solar photovoltaic (PV) panels, diesel generators (DG), wind turbines (WT), and battery storage systems (BSS). A university campus was selected as a case study to assess the effectiveness of the proposed HRES. The optimization focuses on minimizing the annual system cost (ACS), the levelized cost of energy (LCOE), and the total net present cost (TNPC), while ensuring that the annual load demand is met and the load power supply probability (LPSP) remains within acceptable limits. The decision variables include the sizes of the PV and WT sources, the power transfer capacity of the inverter to the load, and the capacities of the BSS and DG. The snake optimization algorithm (SOA) was employed to optimize the objective function, taking into account constraints including the maximum and minimum sizes of system components, the renewable energy fraction, and reliability measures such as LPSP. The performance evaluation of on-grid hybrid systems is based on their costs, reliability, and greenhouse gas emissions reduction. Detailed mathematical models are provided to estimate the power output of the hybrid system. The results indicate that the combination of PV, WT, and BSS offers the best performance in terms of ACS, LCOE, TNPC, and reliability indices. Furthermore, a comparison of SOA with other algorithms, including gray wolf optimization (GWO), reptile search algorithm (RSA), firefly algorithm (FFA), battle royale optimization (BRO), and ant colony optimization (ACO), shows SOA’s superior capabilities in system design, achieving lower costs and better reliability indices. The findings also reveal that TNPC, ACS, and LCOE are approximately 33% lower than those of a standalone hybrid system in an on-grid setup, with values reaching $3,340,600, $383,020, and $0.1342/kWh, respectively. Additionally, significant grid interaction was observed, with up to 801,040 kWh purchased and 1,105,800 kWh sold. The proposed system is economically viable, with an LCOE of $0.1511/kWh, below the commercial electricity rate of $0.35/kWh in Turkey. All analyses were conducted using MATLAB 2022b.
View
View
View
Vehicle-to-Grid Fleet Service Provision Considering Nonlinear Battery Behaviors
Article
  • Jan 2023
The surging adoption of electric vehicles (EV) calls for accurate and efficient approaches to coordinate with the power grid operation. By being responsive to distribution grid limits and time-varying electricity prices, EV charging stations can minimize their charging costs while aiding grid operation simultaneously. In this study, we investigate the economic benefit of vehicle-to-grid (V2G) using real-time price data from New York State and a real-world charging network dataset. Our V2G approach incorporates nonlinear battery models and price uncertainty to provide a realistic estimation of cost savings from different V2G options. The proposed control method is computationally tractable when scaling up to real-world applications. We show that our proposed algorithm leads to an average of 21% charging cost savings compared to uncontrolled charging when considering unidirectional charging, and bi-directional V2G enables additional 17% cost savings compared to unidirectional smart charging. Our result also shows the importance of using more accurate nonlinear battery models in V2G controllers and evaluating the cost of price uncertainties over V2G.
View
Review on Possible Impact of Mass EVs Charging on the Power System and Ways to Mitigate It
Chapter
  • Aug 2023
The growing number of plug-in electric vehicles (EVs) represents an increasing load on the power system. And the higher the charging power is, the more likely it is that serious problems will occur in power systems. There is a significant increase in electric vehicles implementation in many countries due to the reduction of EVs costs, incentives and subsidies on EVs, as well as environmental requirements and obligations. It is expected that in the years to come EVs will lead on private passenger cars market. These EVs charge their batteries from the power grid and may cause significant effects if not controlled in proper way. On the other hand, they potentially can provide many benefits to the power grid and owners if managed properly. The study examined the effects of unregulated charging of electric vehicles, which included increased peak demand, voltage deviation, phase imbalance, harmonic distortion, equipment overloading, and increased power losses. Additionally, the research also reviewed the potential benefits of regulated charging and discharging of EVs, including the provision of auxiliary services. Principals and advantages of the most effective charging strategies especially smart charging like Vehicle-to-Grid (V2G) also are reviewed. The development of electric grids with electric vehicles, the development and use of Smart Grid technologies for them was carried out taking into account additional requirements and restrictions regarding the charging modes of EVs batteries in order to ensure their effective integration into the hybrid power supply system.KeywordsElectric vehiclesCharging strategiesUncontrolled chargingSmart chargingV2GLoad shiftPower qualityGrid stability
View
Evaluation of Electric Vehicles Charging Impacts on A Real Low Voltage Grid
Article
Full-text available
  • Apr 2018
Electric Vehicles (EVs) showed environmental and efficiency advantages compared to gasoline vehicles. The number of EVs is expected to increase crucially in all over the world. These EVs can be charged from a large charging stations, street chargers, and private home chargers. This new load will have effects on the distribution grid, so utility companies should execute comprehensive studies to understand the possible effects of this new load on the distribution grid and plan for it. This paper presents a study of the EVs charging impacts on a real residential low voltage(LV) distribution grid located in New Toshka city, Aswan, Egypt for different penetration levels (e.g., 12.5%, 25%, and 50%). Two charging scenarios are investigated 1) uncontrolled charging, and 2) delayed charging. DIgSILENT Power Factory simulation software is used to perform the study. 24-hour time series simulations are executed with a load flow every 15 minutes. The effect on transformer loading, cables loading, voltage profile at the furthest point from the transformer, and daily energy losses were evaluated. The results demonstrate that the delayed charging has lower impacts on the LV grid compared with uncontrolled charging.
View
A First Look at the Impact of Electric Vehicle Charging on the Electric Grid in The EV Project
Article
Full-text available
  • Sep 2012
ECOtality was awarded a grant from the U.S. Department of Energy to lead a large-scale electric vehicle charging infrastructure demonstration, called The EV Project. ECOtality has partnered with Nissan North America, General Motors, the Idaho National Laboratory, and others to deploy and collect data from over 5,000 Nissan LEAFsTM and Chevrolet Volts and over 10,000 charging systems in 18 regions across the United States. This paper summarizes usage of residential charging units in The EV Project, based on data collected through the end of 2011. This information is provided to help analysts assess the impact on the electric grid of early adopter charging of grid-connected electric drive vehicles. A method of data aggregation was developed to summarize charging unit usage by the means of two metrics: charging availability and charging demand. Charging availability is plotted to show the percentage of charging units connected to a vehicle over time. Charging demand is plotted to show charging demand on the electric gird over time. Charging availability for residential charging units is similar in each EV Project region. It is low during the day, steadily increases in evening, and remains high at night. Charging demand, however, varies by region. Two EV Project regions were examined to identify regional differences. In Nashville, where EV Project participants do not have time-of-use electricity rates, demand increases each evening as charging availability increases, starting at about 16:00. Demand peaks in the 20:00 hour on weekdays. In San Francisco, where the majority of EV Project participants have the option of choosing a time-of-use rate plan from their electric utility, demand spikes at 00:00. This coincides with the beginning of the off-peak electricity rate period. Demand peaks at 01:00.
View
View
Impact of TOU rates on distribution load shapes in smart grid with PHEV penetration
Conference Paper
Full-text available
  • May 2010
A smart grid introduces new opportunities and challenges to electric power grids especially at the distribution level. Advanced metering infrastructure (AMI) and information portals enable customers to have access to real-time electricity pricing information, thus facilitating customer participation in demand response. The objective of this paper is to analyze the impact of time-of-use (TOU) electricity rates on customer behaviors in a residential community. Research findings indicate that the TOU rate can be properly designed to reduce the peak demand even when PHEVs are present. This result is insensitive to seasons, PHEV penetration levels and PHEV charging strategies. It is expected that this paper can give policy makers, electric utilities and other relevant stakeholders an insight into the impacts of various TOU pricing schemes on distribution load shapes in a smart grid with PHEV penetration.
View
View
Optimal Charging of Electric Vehicles Taking Distribution Network Constraints Into Account
Article
  • Jan 2015
The increasing uptake of electric vehicles suggests that vehicle charging will have a significant impact on the electricity grid. Finding ways to shift this charging to off-peak periods has been recognized as a key challenge for integration of electric vehicles into the electricity grid on a large scale. In this paper, electric vehicle charging is formulated as a receding horizon optimization problem that takes into account the present and anticipated constraints of the distribution network over a finite charging horizon. The constraint set includes transformer and line limitations, phase unbalance, and voltage stability within the network. By using a linear approximation of voltage drop within the network, the problem solution may be computed repeatedly in near real time, and thereby take into account the dynamic nature of changing demand and vehicle arrival and departure. It is shown that this linear approximation of the network constraints is quick to compute, while still ensuring that network constraints are respected. The approach is demonstrated on a validated model of a real network via simulations that use real vehicle travel profiles and real demand data. Using the optimal charging method, high percentages of vehicle uptake can be sustained in existing networks without requiring any further network upgrades, leading to more efficient use of existing assets and savings for the consumer.
View
Research on time-of-use price applying to electric vehicles charging
Conference Paper
  • May 2012
With problems of environmental pollution and energy crisis, because of the advantages of zero emissions and low noise level, electrical vehicles (EV) are gaining more and more attention. Large number of EVs connected to power systems for charging will have significant impact on power system, especially massive uncontrolled charging will cause bottlenecks in supplying capacity and expose power system to severe security risks. This paper establishes a charging load model of EVs, and V2G is also included. Based on the EVs' response to the change of power price, a Time-of-use (TOU) price model is proposed to influence the EVs' charging habits in the perspectives both of grid and EV owners.
View
Optimizing Electric Vehicle Charging: A Customer's Perspective
Article
  • Sep 2013
Electric vehicles (EVs) are considered to be a promising solution for current gas shortage and emission problems. To maximize the benefits of using EVs, regulated and optimized charging control needs to be provided by load aggregators for connected vehicles. An EV charging network is a typical cyber-physical system, which includes a power grid and a large number of EVs and aggregators that collect information and control the charging procedure. In this paper, we studied EV charging scheduling problems from a customer's perspective by jointly considering the aggregator's revenue and customers' demands and costs. We considered two charging scenarios: static and dynamic. In the static charging scenario, customers' charging demands are provided to the aggregator in advance; however, in the dynamic charging scenario, an EV may come and leave at any time, which is not known to the aggregator in advance. We present linear programming (LP)-based optimal schemes for the static problems and effective heuristic algorithms for the dynamic problems. The dynamic scenario is more realistic; however, the solutions to the static problems can be used to show potential revenue gains and cost savings that can be brought by regulated charging and, thus, can serve as a benchmark for performance evaluation. It has been shown by extensive simulation results based on real electricity price and load data that significant revenue gains and cost savings can be achieved by optimal charging scheduling compared with an unregulated baseline approach, and moreover, the proposed dynamic charging scheduling schemes provide close-to-optimal solutions.
View
Integration of electric vehicle charging system into distribution network
Article
  • May 2011
Transportation electrification is one of the most promising ways to limit the CO2 emission from the transportation sector so as to avoid catastrophic climate changes in the future. However, a large-scale integration of electric vehicles will introduce new challenges to electric power systems operation, especially at distribution network level. This paper aims to analyze possible impacts of large-scale electric vehicle charging system on the distribution network, propose corresponding control methods for impact mitigation, and investigate potential benefits of system performance improvement. In this paper, simulation model development of electric vehicle charging system is described firstly. After that simulation studies for both steady-state and dynamic performance analysis is introduced respectively. Simulation results show that with properly designed control strategies, the impact of large-scale charging system on power grid can be mitigated effectively; furthermore, distribution network can also benefit from electric vehicles due to the improved controllability of active and reactive power.
View
Coordinated Charging of Plug-In Hybrid Electric Vehicles to Minimize Distribution System Losses
Article
  • Apr 2011
As the number of plug-in hybrid vehicles (PHEVs) increases, so might the impacts on the power system performance, such as overloading, reduced efficiency, power quality, and voltage regulation particularly at the distribution level. Coordinated charging of PHEVs is a possible solution to these problems. In this work, the relationship between feeder losses, load factor, and load variance is explored in the context of coordinated PHEV charging. From these relationships, three optimal charging algorithms are developed which minimize the impacts of PHEV charging on the connected distribution system. The application of the algorithms to two test systems verifies these relationships approximately hold independent of system topology. They also show the additional benefits of reduced computation time and problem convexity when using load factor or load variance as the objective function rather than system losses. This is important for real-time dispatching of PHEVs.
View