Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms

Machine to machine performance evaluation of grid-integrated electric vehicles by using various scheduling algorithms

Journal Pre-proof Machine to Machine Performance Evaluation of Grid-Integrated Electric Vehicles by Using Various Scheduling Algorithms S. Morsalin, A...

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Journal Pre-proof Machine to Machine Performance Evaluation of Grid-Integrated Electric Vehicles by Using Various Scheduling Algorithms S. Morsalin, A. Haque, A. Mahmud PII:




ETRAN 100044

To appear in:


Received Date: 10 May 2019 Revised Date:

4 January 2020

Accepted Date: 6 January 2020

Please cite this article as: Morsalin, S., Haque, A., Mahmud, A., Machine to Machine Performance Evaluation of Grid-Integrated Electric Vehicles by Using Various Scheduling Algorithms, eTransportation, This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier B.V. All rights reserved.

Machine to Machine Performance Evaluation of Grid-Integrated Electric Vehicles by Using Various Scheduling Algorithms S. Morsalin*, A. Haque, and A. Mahmud School of Engineering, Macquarie University, NSW 2109, Australia Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh School of Engineering, Deakin University, VIC 3220, Australia

[email protected]

Abstract: For smart cities, electric vehicles (EVs) are promisingly considered as a striving industry due to its pollutionless behaviours and easy-to-maintain characteristics. A seamless management system is necessary to manage the energy between EV and various parties participating in the grid operation. To facilitate the energy system in a distributed and coordinated way, a machine-to-machine (M2M) system can be considered as the key component in future intelligent transportation systems. Due to the ubiquitous range and data speed, a fourth generation (4G) cellular-based long-term evaluation (LTE) system inspires us to select it as a potential carrier for M2M communication. However, various simulation and analytical modelling end up with the conclusion that the maximum 250 EVs can be connected under an LTE base station. These limitations or scalability limits may result in a terrible mix-up in future smart cities for over dense roads. In this paper, we measured various M2M quality of services performance for exceeding the number of EVs by using three popular algorithms (proportional fair scheduling, modified largest weighted delay first scheduling and exponential scheduling). The result shows that the proportional fair scheduler has the highest packet loss ratio (PLR) and delay time as compared to other two schedulers. Keywords: DLS; Electric vehicle; Energy management system; EXP; M2M communication; M-LWDF; PF; PLR.

I. Introduction A sustainable city aims to ensure the maximum green energy utilisation with a less environmental footprint. For effective green energy management, a smart grid embedded with various renewable energy sources is considered as a crucial promising technology of future cities [1-2]. As a part of smart cities, electric vehicles (EVs) are now being the most demandable automobile in intelligent transportation systems [2-4], because of its cost-effectiveness and eco-friendly behaviours, which are the primary concerns of developing a sustainable city. With the current increasing trend, the global sales of EVs will be assumed to reach a new massive margin (six million per annum) [2]. The Australian Energy Market Commission (AEMC) also analysed that, in next decades, the share of EVs sale will cross a margin of 40% margin in terms of the purchase of new vehicles [3]. The significant reduction of battery cost (nearly 15% per annum [3]) and the greenhouse gas (GHG) emissionfree behaviours influence the sales; therefore, EVs are on the verge of experiencing swift progress all over the world [4-5]. Different existing literature claimed that around EVs spend 95% of the total running time in parking [6-7]. The idle parking time can be utilised using vehicle to grid (V2G) and grid to vehicle (G2V) operations as these modes allow to exchange the energy in a bidirectional way. With the bi-directional flows, EVs contribute to the network by using their stored energy from battery energy storage systems (BESS) and vice-versa [6-8]. However, the unregulated flows may cause voltage fluctuations, harmonics injections, power outages, overloading the transformer, many more to mention [9-10]. As a primary requirement of a smart grid, it is essential to develop energy management for all EVs in a distributed manner. A sustainable city uses intelligent information and technology, where a machine to machine (M2M) communication system purposely comes into action for the distributed energy management system (DEM). An M2M embedded DEM system can coordinately manage every EV’s trip information (i.e., state of charge, position, destination, etc.) [11-12]. Energy distributors need to aggregate all relevant data of EVs, and these data will then need to be processed to make decisions about optimal charging/discharging schedules. Thus, it is essential to connect all EVs through a wireless communication system considering their coverage areas. Regardless of highlighting the M2M communication system, some previous literature includes various aspects of electric vehicles’ energy management, especially their congestion on a charging station [13-17]. Incorporating with the renewable sources’ impacts, a two-stage stochastic programming model was developed in [13] to make long term and short-term decisions for establishing a new charging station. The developed model with some practical results shows that the rise of load congestion cost decreases the number of existing small charging stations but increases the tendency to open large capacity charging stations. A mixed integer

linear programming was proposed to investigate the economic impacts of energy sharing between commercial buildings and charging stations [14]. To minimize the system cost during the uncertainty of power demand, a similar model examining the energy sharing among EV charging station, commercial building and grid was described in [15]. D. Thomas et al. established a bidirectional energy management system (EMS) and achieved economic benefits from an EV fleet incorporating renewable energy sources and storage systems [16]. By considering the uncertainty of PV farm in their stochastic model, they reduced the energy cost significantly as compared to the cost from the general deterministic approach. In the article [17], a deterministic integer programming model was included to find the optimal number of vehicles to be charged or discharged to supply surplus power to the grid using V2G operation. This paper aims to develop a DEMS where a 4G LTE system is used as a potential carrier for the M2M communication to ensure the exchange of bidirectional information flow between EVs and energy aggregators. However, the limited spectral efficiency and delay times of the LTE may create a significant drawback on the information flow between in-vehicular data logging system and the base station (BS) [18-19]. Moreover, there instantaneously a question arises, “is there any scalability limit for the number of EVs under a base station?”The answer is “yes”. The research in [19] reported that up to 250 vehicles could be capable of connecting to an LTE base station at a time. However, if the number exceeds the scalability limit, the information (or packet) can be discarded by the base station and the delay time of receiving the information can predominantly be increased. As a novelty, the research focuses on the limitations of a 4G network if it participates to the M2M communication of EVs and manages their energy information. The paper investigates the M2M performance of the increased number of electric vehicles in terms of delay, packet-loss, etc. In other words, various aspects of the M2M communication system are investigated to ascertain the bidirectional information flow between EVs and different energy participators (e.g. charging station, stakeholder, utility etc.). The main contributions of this paper are summarised as follows. • A data logging system (DLS) is developed for an M2M communication, which can simultaneously transmit and receive information about the charging profile of EV, such as battery state of charge (SoC), position and destination. • In the absence of any scheduling algorithm, if EVs are connected to a dedicated server to transmit the information data, it is found that more than 250 EV’s under an LTE base station (BS) will face a significant amount of data loss. This research deploys various data scheduling algorithms to find out the impacts of more EVs’ congestion in the M2M communication. • M2M performances or quality of service (QoS) are investigated using three popular scheduling algorithms: proportional fair (PF) scheduling, modified largest weighted delay first (MLWDF) scheduling, and exponential (EXP) scheduling. By numerical simulations, all three algorithms show that if the EVs number exceeds the scalability limit, a significant end-to-end delay and packet-loss are likely to be observed. The layout of this paper is presented as follows. M2M communication system and smart cities are reviewed in Section II. The energy management system and data logging system are described in Section III and IV respectively. Section V explains the scalability limit of EVs under a base station, while Section VI examines the limitations of an LTE network for accommodating grid-integrated electric vehicles using scheduling algorithms. The main contributions of the paper are presented by a flowchart in Figure 1.

Market share of EVs is increasing due to its low operational cost and zero emission.

An in-vehicle data logging system performs M2M communication. A 4G LTE system is a potential carrier for M2M communication. Section IV

What are the challenges?

Increasing number of EVs urges a coordinated distributed energy management system (DEMS).

As a part of DEMS, M2M communication facilitates to connect EVs and energy aggregators to track their information.

Development of DLS system

Section V Scalability limitation of M2M communication Section VI

M2M performance evaluation using scheduling

Figure 1: Challenges associated with the M2M communication for EVs

II. M2M Communication System and Smart Cities

A Part of Smart City Network


Machine Core Network



Figure 2: A typical M2M communication system. Here the figure is shown as a part of a smart city. An M2M embedded system aims to interact autonomously with other electronic systems without any human intervention. Based on the European Telecommunications Standards Institute (ETSI), an M2M network consists of some fundamental elements such as an M2M terminal, gateway, server, area network and a communication network. The basic architecture is shown in Figure 2. To facilitate the automated and remote controls, the smart city is currently adopting the internet of things (IoT), where M2M communication is an evolution of an IoT system. However, with the increasing number of M2M devices may exceed the delay threshold [20]. Reasonable signal strength and different quality of services (QoS) also impose challenges [19-20]. Furthermore, the velocity of a mobile system (e.g. EVs) can result in the undesired delay and loss of transmitting the message. Due to the scalability feature, the number of EVs under an LTE base station is also limited [19]. Several studies have been reported in [21-23] to appraise the EVs role in a distributed energy system. In [21], the simulation-based results analyse the EVs’ trajectory by using a probabilistic model and determine their charging profile. The driving patterns and data mining of EVs were extensively investigated in [22] and remarked that for smart infrastructure, the electric vehicles are the replacements of conventional fuel cars. The challenges associated with the integration and the interaction of EVs with the smart grid are extensively reviewed in [5]. The reliability of the distribution network during power interruption was examined for both hybrid and non-hybrid EVs in [23] and suggested that the integration of EVs can improve the reliability performance. To reduce the impact of uncertainty on the grid, a four-quadrant charger has been proposed in [24]. The 40% cost reduction in a renewable source-based microgrid (RMG) has become possible by integrating the parking lot of plug-in EVs (PEVs) [25]. For the same purpose, an energy management system was designed for PEVs [26]. In short, the major concerns associated with the energy side of electric vehicles are covered with many pieces of literature; nonetheless, the communication performances of EVs have not been extensively evaluated [27]. This paper deals with M2M performance in terms of blocking rate and delay, which facilitates the novelty of the work.

III. Energy Management System The uncoordinated charging of EVs may trigger the substantial fluctuations in power flow which can disturb the power quality and the stability of the electricity distribution system. An intelligent scheduling system for the charging/discharging of EVs, which will consider the requirements of EV owners, assess the popular energy demand, and restraints of the distribution network, has become the most essential and desired scheme for the rapidly increasing of EVs. A wide variety of information, like the location of EVs, their state of charge and predictable usage, information about the volume of the electricity distribution network (including battery storage), and electricity pricing info, etc. will be registered as input variables of an energy management system. [12-20]. Figure 3. illustrates the progression path of an upcoming potential distributed energy management system where EVs, including a smart data logging system, will play a key role. The decentralised generation, transmission, and distribution systems are highly expected in the near future where information and power will flow simultaneously. A smart machine-to-machine communication-based data logging system (DLS) can play a pivotal role in the coordinated management of vehicles.

IV. Data Logging System A DLS helps to determine the State of Charge (SOC) and the position of electric vehicles (EVs), and their actions within an energy management system. An endorsement of network authority is required to get the radio access through the cellular network using radio module, which is done by allocating an International Mobile Subscriber Identity (IMSI) and a mobile station International Subscriber Directory Number (MSISDN) to a DLS. After getting authentication, for M2M communication, a server conveys the data and makes decisions by

the packet, sent by EVs, where EVs are considered as clients of the socket communication. Such types of client systems development are shown in Figure 4. Future Utility

Transmission Control Centre

Charging Station

Wind Power Charging Station



EV Utility




Commercial Customer


Hydro Power

Conventional Storage Power Generation Information Flow Power Flow



Distribution Control Centre

Present Transmission Control Centre

Distribution Control Centre


Residential Customer

Substation Wind



Coal Power


Commercial Customer Industrial Customer

Past System Operator

Information Flow Power Flow

Residential Customer

Substation Coal Power Station

Distribution Transmission


Industrial Customer

Commercial Customer

Figure 3: Past, present and future trends of energy management systems This Raspberry Pi-based in-vehicular logging system is interfaced with 4G Modem and GPS, while the software operation is executed by using a Python language-based socket programming. A server can be included by a Private Area Network (PAN) or, consigned to a distinctive static Internet Protocol (IP) address. To link to a dedicated server, Internet Protocol (IP) connectivity via the TCP/IP protocol is used by the logging system. For this work, the server is set up on a computer running with the Linux operating system (Ubuntu).

Figure 4: Set up for a Raspberry Pi-based DLS system The packet data comprises information about the SOC, position, temperature, etc. of EVs, which are sent by these EVs to a server at a regular interval. After manipulating the recorded data, the server sends back the required decision to the client EVs. The overall process of this DLS system has been illustrated in Figure 5.

Internet Cloud

Port Forwarding

LTE eNodeB

Dtata Logging System RaspberryPi+Modem Intelligent Server

Figure 5: Schematic of the automated M2M electric vehicle monitoring system

V. Scalability Limit For an analytical model, we assumed X as the number of Electric Vehicles (EV) that can be controlled by an LTE base station, M as the number of EVs that are ready to communicate, and N as the number of EVs that succeed to transmit data. A time-frame is constituted by Transmission Period (TP) and Contention Period (CP), where CP allocates all of the M vehicles to transmit on the contention-based random-access basis and TP allows N vehicles on a reservation basis. If refers to the interval between two successful events and refers to the number of collisions, it can be written as: =




Where shows the indolent time duration of the th incident for a vehicle while crossing the LTE base station during a busy channel period and represents the duration of the th collision. In this paper, is mentioned as a random variable which can be written as follows:

= Therefore, the mean value of this random variable for N successful contentions can be written as !

# !


" + 1%. !


! "


" +

". !




After a few assumptions and doing some manipulations, the number of vehicles that can successfully communicate with a base station can be written as [19]: =



Where /3455 =


/012+ + /3455 + 6

+ EFG + HIJE +


'()*+ -. 7 89:;<=

and /[email protected] =



− 1? /[email protected] A +



In the above equations, (+D is the required message length which is forwarded as the main message to the base station. A collision happens as a result of the concurrent data transmission of two or more vehicles. Furthermore, )5 is referred to as acknowledgement time for the base station. The other variables, SWP and BIFS, denote as switching period and back-off time-frame space, respectively. For a 100 ms LTE time-frame, with a probability of (p = 1/500) and a 0.5 ms transmission time interval as set by the 3rd Generation Partnership Project (3GPP) standard, the number of EVs capable of transmitting a packet at a time is around 200-250. In Figure 6, blocking rate, standard deviation of delay, maximum and minimum protocol data unit (PDU) delay considering the number of EVs under a basestation are shown graphically. For 200 to 250 EVs, the delay time varies between 0.6s to 0.7s, whereas for a lesser amount of EVs, it fluctuates between 0.2s to 0.6s. On the other hand, the data collision increases sharply which results in more number of vehicles to go in the sleeping mode. It has been observed that the blocking rate jumps from 0 to 95% if it exceeds the scalability limit (200 to 250 EVs).

Figure 6: Performance of EVs under a single base station [19]

VI. M2M Communication with Scheduling As discussed in previous, more than 250 EV's are not admissible to run under a base station. However, on a busy road, an LTE base station has to control more than this limit, simultaneously maintaining the best Quality of Services (QoS) for M2M communication. This can be done by applying various scheduling algorithms which allow efficient sharing of radio resources among EVs. Over the years, a great deal of research has been done on

the packet scheduling to increase the capacity of EVs and maintain the consistent quality. The packet scheduler is a traffic control module that regulates how much data for an application (or flow) [28]. Several algorithms are proposed to achieve those requirements and this paper consider only three of these methods. The simplest implemented algorithm is the Round Robbin (RR) algorithm scheduling. The execution of RR can be done in two ways, one is Time Domain Round Robbin (TDRR), and another is Time and Frequency Domain Round Robbin (TFDRR). In both cases, it ensures great fairness to the all user equipment’s (UE) without considering channel quality information (CQI), which in return reduces the throughput for transmission and recognised as a disadvantage [29]. Best CQI scheduling algorithm works as its name suggests. In the LTE downlink model, the eNodeB transmits a reference signal to the UE, based on which, terminals send the CQI information to the base station. A higher CQI value is considered as a better channel quality. Therefore, the system capacity is increased in trade with the fairness. [30]. Hence, in this paper we have included some popular scheduling algorithms, which will maintain fairness with a better rate of throughput. A.LTE Scheduling LTE has a unique time and frequency frame structure as compared to other generations of mobile communications. A radio frame of LTE, consisting of ten 1 ms subframes, last as long as 10 ms where each subframe is formed by two 0.5 ms long slot. These 0.5 ms slots are called Transmission Time Interval (TTI). The scheduling procedures for the uplink can be summarised as follows: •

First of all, the scheduler of packet scheduling scheme, residing in eNodeB generates a list of flows containing uplink packets which need to be transmitted in the contemporary subframe.

During the scheduling operation, the scheduler intermittently measures the channel quality experienced by each M2M device. After the measurement, each M2M device generates some reference symbols associated with the quality of the channel, commonly known as a Channel Quality Indicator (CQI), and sends it to eNodeB, which acknowledges these CQI symbols. After that, to maximise the spectral efficiency, CQI feedbacks are mapped with the channel link adaptation module to select the most suitable modulation and coding scheme (MCS) for the corresponding M2M device at the physical layer. This approach is popularly known as Adaptive Modulation and Coding (AMC) [28].

Finally, according to the scheduling strategy, K0 , a characteristic indicating the matrix’s elements for the user equipment, is computed for each flow in the list. The eNodeB assigns a certain sub-channel resource for the flow to the channel, which has the highest K0 . Once all packets waiting for a typical flow are transmitted, the record is erased from the list in the MAC layer.

B. LTE Popular Scheduling Algorithms Proportional Fair Scheduling The adjustment between throughput and user equipment (i.e., EVs) is the primary purpose of Proportional Fair algorithm, which attempts to enlarge the capacity of M2M communication in conjunction with ensuring a minimal amount service to all users. This algorithm considers not only the CQI of the UE but also the previous data rate of the consumer. For a PF scheduler, the metric K0 is defined as the ratio between the instantaneous available data rate and the average data rate. Therefore, K0 can be written as:

K0 =


Where MON is the average data rate and L0 is the instantaneous data rate computed by the AMC scheme mapped with CQI feedback [28]. A user with a maximum value of K0 is selected by the scheduler. On the other hand, for the th sub-channel in every TTI, the parameter MON can be defined as: MON 7P: = 0.8MON 7P − 1: + 0.2MON 7P:

where MON 7P: and MON 7P − 1: represent the achievable data rate for the th flow in the Pth TTI and the 7P − 1:th TTI, respectively. Proportional scheduling algorithm does not contemplate QoS requirement while scheduling RB to the UE.

Modified Largest Weighted Delay First Scheduling The Modified-Largest Weighted Delay First (M-LWDF) algorithm defines the precedence of each user according to some essential characteristics, like channel quality, average throughput, and packet delay information. It is one of the prominent packet scheduling algorithms for hybrid real-time services over wireless networks. For the th flow, with a packet delay threshold T0 , the M-LWDF scheduler defines the metric K0 as: K0 = U0 ∆W

(XY \\\ Z[

with U0 = −

]^_ `X aX

where ∆W denotes the delay of the first packet in the queue and /0 is defined as the maximum probability that the first packet exceeds the delay threshold. During the time of scheduling, the th flow will be scheduled if the value of K0 is the superlative among all service queues. Exponential Scheduling In the exponential (EXP) scheduler, the head-of-line packet (i.e., the first packet) delay is very close to the delay threshold. The resource metric is computed as eX ∆f 8g (XY : \\\ hig Z[

K0 = exp 7

with χ =


U0 ∆W .

For both M-LWDF and EXP schedulers, packets belonging to a certain flow will be dropped from the scheduling queue if they are not transmitted before the deadline threshold.

C. Performance Evaluation The vehicular M2M performances for the above-mentioned PF Scheduler, the M-LWDF Scheduler, and the EXP Scheduler have been investigated in this section. The simulation is performed by LTE-Sim version 5 software for the Manhattan highway scenario in the USA. This software is an opensource framework coded with the C++ language and allows researchers to measure the performance in a 4G cellular network for various scheduling algorithms. For measuring the M2M performance with these scheduling algorithms, we have considered parameters listed in Table I for 300 seconds flow duration. Table I. Vehicle Communication Simulation Parameters System bandwidth

20 MHz

Cell radius

1.5 km

EVs maximum velocity

120 km/h

EVs inter-spacing distance

10 m (at least)

Inter packet interval

20 s

Packet size

1.5 kB, UDP

Maximum transmission unit

1500 Bytes



Time frame

100 ms

Buffer size


Frame delay

2.5 µs

Switching period

0.5 µs

Fast fading model


In this numerical simulation, we have taken two constant speeds (60 kmh-1 and 120 kmh-1). Figure 7 illustrates average end-to-end packet delays from M2M vehicles to the base station for varying numbers of vehicles. We have simulated the delay of M2M communications up to 500 vehicles and observed that, for transmitting a 1.5 KB packet, the average delays are 11 seconds and 17 seconds for 60 kmh-1 and 120 kmh-1 speeds, respectively. This simulation is carried out for only the PF Scheduler since the other two schedulers always maintain a threshold time delay of 0.5 seconds. The higher speed results in higher delay because of the fast fading effect, and radio link adaption procedures need more time for radio access with the increasing velocity. Another point of observation is that if the number of EVs exceeds the scalability limit, the slope of delay becomes approximately zero.

Figure 7. Delay vs number of EVs plot for 60kmh-1 and 120kmh-1 speeds

Figure 8. Packet-loss ratio plot for 60 kmh-1 and 120 kmh-1 speeds Figure 8 shows the packet loss ratio (PLR) of these three scheduling algorithms. The packet loss ratio is given by the ratio of the number of packets lost by the base station to the number of packets transmitted by M2M vehicles. From the figure, the PLR is higher for the PF scheduler than for the other two schedules. This is because as with the increasing number of concurrent real-time flows, the probability of discarding packets due to deadline expiration increases. For the simulation, we have considered a maximum 10% packet loss as a threshold for an ideal M2M communication. It is seen that for every scheduler, at both 60 kmh-1 and 120 kmh-1 speeds, the PLR is around 8-9%. We have further investigated the PLR ratio up to 800 vehicles to see how far PLR can go and noticed that it is going near the threshhold value, and resulting in poor performance of M2M communications. The results are shown in Figure 9.

Figure 9: Packet-loss ratio histogram for 120 kmh-1 speed

VII. Conclusion In this paper, the machine to machine (M2M) performance of electric vehicle (EV) are investigated using three popular scheduling algorithms. From the results, if the packet is not properly scheduled and also, if the vehicle number exceeds the scalability limit, there will be a reasonable amount of delay and packet loss, which reflects the poor performance in the M2M communication system. In addition, to increase the system capacity, if we apply various scheduling algorithms, it is found that the proportional fair (PF) algorithm provides the poorest performance in terms of packet loss ratio (PLR) and delay time. Further improvement is possible if an appropriate data scheduling algorithm can be developed which will reduce both packet loss and delay time while accommodating more number of vehicles. In the era of ‘Internet of things (IoT)’, it is assumed that more devices will come under M2M technology; therefore, it is also important to evaluate EVs’ M2M performance in the IoT system. On the other hand, the higher data transfer rate of 5G network makes it more easy to accommodate large number of EVs under a base station. As the 5G network is growing, it is also recommended to extend the research for 5G communication network. VIII. Acknowledgements The authors gratefully acknowledge the financial support of Singtel Optus Pty. Ltd. and the Australian Research Council.

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Machine to Machine (M2M) Performance Evaluation of Grid-Integrated Electric Vehicles by Using Various Scheduling Algorithms S. Morsalin*, A. Haque, and A. Mahmud

Highlights: •

Machine to machine (M2M) performance evaluation of grid-integrated electric vehicles.

For exceeding the scalability limit, there will be a delay and packet loss.

The proportional fair (PF) algorithm provides the poorest performance.

5G network for accommodating EVs is recommended.