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Texas A&M University at Qatar

ECEN 403: Electrical Design Lab I

Project Title:

Adaptive Charging Network for Electric Vehicles and its Impact on Power Systems

Course Instructor:

- Dr. Ali Ghrayeb

Mentors: - Dr. Sertac Bayhan - Dr. Haitham Abu-Rub

Team 2 Team members:

- Aisha Al-Marzogi - Dana Badar - Hayfaa Al-Kuwari

Due Date: 21/09/2020

Texas A&M University at Qatar, September 2020 “An Aggie does not lie, cheat or steal or tolerate those who do.”

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Abstract One important topic in engineering is renewable energies conversion and utilization. Accordingly, many studies have revolved around electrical vehicles. Our project is about the impact of the Electric Vehicles Charging Stations (EVCSs) on the electric network in Qatar. The project also aims at developing a region-specific adaptive algorithm that controls Electric Vehicles (EV) charging process. Texas A&M Qatar (TAMUQ) building will be taken as a case study. The project will be realized through three phases. In phase I we will research EV types and the challenges associated with their adoption in Qatar. Phase II is related to learning MATLAB and Simulink and making the simulations case study. The integration of the algorithm in the grid and its analyses are realized in phase III. The project outcome is expected to be a simulation model (software) that runs a smart charging algorithm while minimizing the negative impacts on the grid. The proposed algorithm will depend on various factors such as the capacity of cars that the building can hold, the number of needed charging stations (CSs), charging durations, the installed capacity and load. Our project is essential for Qatar because little research has been made on this topic. Since Qatar is pushing for sustainable transportation, we believe that this project helps motivate the people to invest in this change.

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Table of Contents Section I: Introduction 4

1.1: Motivation 4 1.2: Literature review 5 1.3: Project Description 7

Section II: Problem Statement 8 Section III: Methodology 9

3.1: Research Strategy 9

3.1.1: Charging levels 9 3.1.2: Charging Process Calculation 10 3.1.3: Smart Charging Algorithms 12

3.2: Research Phases 14 3.3: Methodology Chart 16 Section IV: Interview 17 Section V: Budget 17 Section VI: Timeline 18 Section VII: Conclusion 19 References 20

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Section I: Introduction The State of Qatar is considered to be one of the largest emitters per capita in the world, and as stated by the World Health Organization (WHO) Doha is the 12th most polluted city [1]. According to Qatar’s vision of 2030, Qatar is a country with abundant natural resources, and aims for the optimum and responsible exploitation of Oil and Gas. This is possible by establishing a sustainable development for the upcoming generations [2]. Globally, the Electric Vehicles (EV) are growing in popularity. It is expected to become the main mode of passenger transportation by 2050 [3]. It reduces the emissions of greenhouse gases into the atmosphere that causes an increase in the concentration of air pollution and leads to catastrophic climate changes. Such as; the depletion in Ozone layers, rising temperature, glacier melting and sea level rise. Qatar is aiming to adopt 10% of cars by 2030 to be EVs. Which is equivalent to 150000 vehicles [4]. In Qatar, electricity is mainly generated by natural gas power plants, with a total capacity of 8.8 GW [5]. The peak consumption has reached 7.8 GW in July, 2018 [6]. which means the adoption of EVs would require extra demand on the power grid for charging stations. For that reason, sustainable use of power grids is essential. That is why EVs have become one of the headlines topics of recent studies. Researchers are looking into new efficient charging ways, methods to enhance the battery of EVs to make it efficient, accessible and reliable as much as the Internal Combustion Engine (ICE) vehicles can. Our project will be focusing on the study of the impact of EV’s Charging Stations (CSs) at Texas A&M University at Qatar (TAMUQ). It also focuses on developing an algorithm that makes the stations smart and adaptive, which enhances the power production and demand curve. 1.1 Motivation The project is going to be helpful since Qatar is one of the countries advocating sustainable energy and transportation. The motivation of our project aligns with Qatar’s Vision of 2030 [4], where Qatar has set up a goal to reach 10% of vehicles to be green cars (Electric Cars). KAHRAMAA and the Ministry of Energy and Industry already signed an initiative in 2017 called the Green Car intuitive which has a strategic goal to reduce carbon emissions and disseminate awareness, and promote sustainable transportation. Their aim is to launch EVs to the public by 2030 [6]. As EVs are becoming the main focus of many companies and countries these days. It is clear that EVs will become the norm in the transportation sector, which makes it very important to identify problems associated with EVs today to avoid complications in the future. One of the major problems of EVCSs is that it has a negative impact on the grid which is why we are tackling this problem. Since researchers from Caltech University have done similar research on implementing smart charging, where these stations are managed by an outside vendor, through a smartphone [7]. This proved to us that this project is something that could be implemented with specific modification to fit our environment and region. Our project will focus on studying the impacts and create a solution that will reduce the negative effect by using the adaptive charging algorithm. This algorithm is going

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to be implemented on a simulation that will be formed from several assumptions and case studies. These assumptions will depend on the percentage of EVs present in the building, the charging demands and the capacity and load demand. As for the case studies they will be done with and without the use of the Photovoltaics (PV) panels that are present on TAMUQs rooftop. By creating such an algorithm, the process of implementing EVCSs will become easier, since we will be comparing and looking at the similarities and differences of each scenario. Hence, this project will motivate more people to start using EVs instead of Internal Combustion Engine cars (ICE). Which in turn will help reduce the carbon footprint related to transportations. 1.2 Literature Review Several articles related to EVs were reviewed in order to understand the needs and the importance of considering EVs in Qatar and adaptive charging solutions. In articles [1], the authors created five Monte Carlo based cases that mimicked the sales scenario in Norway, Netherlands, France and China. The fifth one was a scenario of what if 10% of vehicles in Qatar are EV, which is similar to Qatar’s national vision 2030. The problem is with EVs batteries that would not operate properly under high heat. Hence, there is an urgent need to design chargers that can withstand the heat of Qatar. During the summer of 2018 the demand reached its maximum which was close to the generation limits. In addition, the charging stations must not violate the grid operating units. Two questions were raised in this article, can Qatar’s grid handle this amount of Plug-in Electric Vehicles (PEVs) by 2030? The answer is yes if and only if the EV charging methods are controlled, because uncontrolled solutions require additional peak power generators. In such a case a continuous upgrade in the network becomes a requirement. The second question was, is there enough amount of energy required to replace fuel oil vehicles in the country? It was found that the PEVs use energy with four times the efficiency of a combustion engine vehicle. According to Woqod company, 4.31 billions of gas was sold for light duty vehicles in 2017. This amount of gas could be translated into 40.2 TWh of electricity by using the US Energy Information Administration's (EIA) calculation methods. The EV needs around 10.05 TWh of electricity. Article [1], mentioned Al Kharsaah project of developing 800MW PV plant. The authors suggested using Photovoltaic EVCS during the day to reduce the peak demand. This will result in flattening the duck curve, reduce ramping requirements of natural gas generators, and work with minimal interruptions affecting the utility grid. Finally, the paper discussed the difficulties that stand in front of using EVs in Qatar. The high ambient temperature degrades the life time of the used EV battery. Second, the low oil price and the zero tax make the cost of owning a conventional vehicle lower than EVs, which stands against promoting EVs. The third major issue is the power grids in Qatar which are strained by the air conditioning loads, while the adaptation of EVs and their uncontrolled charging stations would cause the power to exceed grid generation capacity. However, the article showed that the current power grid can supply a significant amount of PEVs if smart charging algorithms are developed. The article's main scope is to answer certain questions,

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but it helped us in identifying the main problems with adopting EVCSs. Although this article presented several scenarios but it did not apply the smart charging algorithms to any of its scenarios. Therefore, in this project we will apply similar scenarios with certain improvements. Article [8] was about the study of the resources that constrain the scheduling of EV charging in ACN (Adaptive Campus-scale network) which is similar to our topic. The reason we chose to read this paper is to learn how they came up with their algorithms as we will write our own region-specific algorithm. The way they started to process the program by considering various scenarios. The scenarios had multiple EVs each with a different charging profile that depends on the demand of charging, availability, charging limits and more. They formulated their charging schedule with these goals in mind: 1) to satisfy the charging demands, 2) make an account for the batteries charging limit, 3) to satisfy the global and local peak constraint, 4) to maximize the total revenue. They mentioned that their algorithm is the first algorithm that considers the rate limits of the charging. They considered both the Fractional model as well as the Mixed Integer Linear Problem and the result of both was close to the optimum. Their results outperformed the already existing algorithm of Caltech by 35 percent [7]. They also mention how they formulated and modeled the problem which gave us an idea of what we should look into. They developed and simulated around 7 algorithms each dependent on different theorems for different scenarios, they used around 5 theorems. Some of the mathematical modeling that they used were explained which we hope to get the main idea from and figure out how we are going to write our algorithm. The difference between our project and theirs is the regional aspect. In our project we have to take the lifespan of the battery into consideration, since the heat affects its lifespan. For that we decided to make an algorithm that is region specific for adaptive charging. Article [7], researchers introduced the Caltech Adaptive Charging Network (ACN). which is a large-scale EV charging research that consists of more than 50 connected and controllable EV charging stations. In this paper [7], they explained ACN-Sim, an open-source, data-driven simulation environment. The way it works is by collecting data from ACN-Data, generating different simulation scenarios; and later on, developing them to algorithms that can be converted into codes. Researchers claim that ACN-Sim deals with real-world data that makes the simulations more realistic. Which in turn helps the transition between theory to real-life practice. Their research focuses on the constraints of the infrastructure in the charging facility when developing the algorithms, which is similar to what we are going to work on. However, these constraints are different in Qatar, since most of the load power goes toward operating air conditioning units throughout the year because of the excessive heat in this region. The peak load demand in our region is very different from where this research was done in Pasadena, California. This makes our project significant as there is very little research done in the middle east and especially in Qatar. We are going to contribute to this by developing an algorithm based on simulations that were done using local electrical infrastructure and the different peak loads in power throughout the year at Texas A&M Qatar campus. In ACN-Sim, they are focusing on simulating unbalanced

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three-phase electrical infrastructures which are common in large charging systems [7]. In the article, researchers showed that only considering single-phase constraints can lead to violations in line currents. We could use their research about the effects of only considering the single-phase model when developing our project to improve our algorithm. Although the literature presents themes related to EVs in a variety of contexts, there was not a study of smart charging algorithms done in Qatar. So, in this project we will primarily focus on implementing it with specific requirements that fits Qatar’s grid and development plan. 1.3 Project description Our project will study the power grid in the TAMUQ building by simulating it using MATLAB and Simulink software. It will be divided into two parts, the first part is studying the number of parking spaces in Texas A&M Qatar building, the average number of parked cars during the day, and the data of power consumption and load. By using these data, we will be able to determine the maximum possible number of EV charging stations (EVCSs) that would be implemented in the parking lots with the minimal system update requirements. In the second part of the project we will develop an adaptive charging algorithm that manages the distribution of power between the EVs, along with different charging scenarios that we will take in consideration if more than 10% of cars were EVs. The solution we will be providing is in regards to the problem of inconsistent charging times. As people will be randomly charging their EVs, this will make it hard to predict the load demand on the grid. As a result, power losses, outages, and disruptions may occur. With the algorithm that we will develop, it will calculate the needed power and the best way of distributing this power among the EVs that are plugged in the CS. The final prototype will be a mathematical modeling of the algorithm using MATLAB given several scenarios of EVs charging in the CS corresponds to the peak hours and the different power consumption by the building, and see how it will affect the power quality. This project is organized as follows. In section II, we will discuss the problem statement as well as the solutions proposed to address the problem. The methodology is presented in section III, where we will discuss the detailed steps needed in order to complete this project. Section IV presents the content of our interview. The budget and timeline are presented respectively in section V and VI. Finally, a conclusion is provided in section VII.

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Section II: Problem Statement Deteriorating in power quality, additional power generation requirements and blackouts are all the results of straining the grid beyond its capabilities. That strain could be the result of an uncontrolled EV charging stations [4]. The objectives of our project are about analyzing the impact of Electric Vehicles Charging Stations on TAMUQs electricity network and finding adapting smart solutions. We will develop a new adaptive and region-specific charging algorithm. Accordingly, what needs to be looked into are:

1. The installed power capacity and additional load that the building can hold. 2. Number of parking spaces in TAMUQs parking lots. 3. Estimating the number of charging stations needed in TAMUQ parking spaces, the number

will be based on the peak hours, power demand and accessibility. The information that we will get from building operations.

4. Studying the impact of the EVCSs on the building’s demand with and without the electricity generated from PV panels, then simulating it using MATLAB Simulink.

5. Writing the smart charging algorithm. This algorithm is going to be a mathematical modeling constructed from several case scenarios. Later the algorithm will be tested on MATLAB simulation. Comparing the results will be conducted with and without smart charging algorithms, and PV panels.

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Section III: Methodology 3.1: Research Strategy

- 3.1.1 Charging Levels Through our research we have found that there are three levels of charging. The amount of driving range and the voltage required is shown in the table below [10]. Table 1: Ranges and Voltages of Different Charging Levels

Type Range Voltage

Level 1 2-5 miles per hour of charging 120 V

Level 2 10-20 miles per hour of charging 240 V

DC Fast charging 60-80 miles per 20 minutes of charging 480V

Level 1 uses normal 120 volts AC connection that can be connected to a standard electrical output, capable of supplying 15- 20 amps. The power drawn in this level is usually around 1.4 KW when charging [11]. The downside of this level is that an EV for example Mercedes B Class 250e with around 87 miles of range would take up to 20 hours to fully charge [12]. On the other hand, the advantages of this level of charging does not require any additional cost and it has low impact on the utility peak demand [11]. Level 2 uses 240 volts AC connection that can significantly reduce the charging time. Uses 30 amps, and the power draw is between 3.3 and 6.6 KW [11]. An EV for example Mercedes B Class 250e with around 87 miles of range would take up to 3 hours to fully charge [12]. That is why EV owners find Level 2 charging stations more convenient. However, the installation costs are higher compared to Level 1 charging stations, and has a higher impact on the utility peak demand [11]. Finally, we have the DC fast charging, sometimes referred to as Level 3. This charging delivers high power directly to the EV’s battery system without the need of rectifiers to convert the AC to DC. It enables rapid charging in the least amount of time. Typically, 80% of the battery can be charged within 30 minutes. It works with almost all recent EV’s that have different connector types [11]. Because it provides more power in less time the installation cost for this kind of station is very high compared to Level 1 and 2 stations [10]. However, it is advised not to overdo the EV with DC charging because it reduces the battery lifespan and efficiency [13]. Not only this, but it also has a huge impact on the utility peak demand [11].

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After learning about the advantages, disadvantages, and the power consumption of each level we have a clear understanding about what kind of charging stations can be implemented at TAMUQ. Since it is a workplace location, people usually spend more than 3 hours there. Especially if they are a faculty or a student then level 2 and DC fast charging serves the needs. In addition, we will be looking at the possibility of charging the EV with the PV electricity, to lower the load on the utility distribution systems.

- 3.1.2 Charging Process Calculations Our team has been looking at the conventional and the smart charging methods for EV. The general charging process is shown in Figure 1 below. The charging process of EVs starts charging at a constant current equal or less than the nominal current of the battery. EV’s battery packs are varied in sizes and dimensions, where the battery determines the amount of energy stored in the vehicle [12]. As the battery charges, the voltage of the battery increases until it reaches the maximum charging current and voltage. These maximum values are for safe operation of the battery; to ensure the lifespan of the battery and its efficiency. Figure 1. Shows the Constant Charging Constant Voltage (CCCV) cycle for a single lithium ion cell. Which is the type of battery used in EVs. The first dashed line rectangle represents the Constant Current (CC) charging region. As it continues to charge, it reaches a region (the second dashed line rectangle) called Constant Voltage (CV) region where the voltage is maintained and the current is reduced to zero. Fast charging occurs in the CC region, the speed decreases when the charging current is reduced in the CV region [14]. The following are general formulas for charging:

1. 𝑃"#$%&'(& = 𝑉,$--.%/ ∗ 𝐼2#$%&'(& 2. 𝐸2#$%&'(& = ∫ 𝑃2#$%&'(&𝑑𝑡 3. 𝐸2#$%&'(& = 𝑃2#$%&'(&𝑡"#$%&'(&

4. 𝐶%$-. =89:;<=>?=@ABC>?;D

Figure 1: CCCV cycle for a single lithium ion cell

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In the first equation, the battery can be charged by controlling the charging current. By increasing the charging current, faster charging will be achieved; because it will operate in the CC region longer. The second equation shows the estimated delivered energy to the battery during charging. It can be calculated by the time integral of the charging power. If the charging power is constant, we can get the amount of energy delivered during charging by multiplying the charging power and charging time (in hours) together, this can be shown in equation three. Finally, the charge rate (C rate) is an important parameter in the charging process. It is the ratio of the charging power over the nominal energy capacity of the battery. There is a direct relationship between the charging current and the C rate. Furthermore, as the C rate increases, the battery losses and temperature increase as well and as a result it reduces the efficiency and the lifespan of the EV battery. Therefore, to increase the lifespan of the battery lower C rates are preferred [14]. When an EV is connected to a charger, charging starts as soon as the EV is plugged in. In conventional charging which is often referred to uncontrolled charging, the power is fixed and continuously supplied to the EV till it is fully charged. This results in peak loading on the utility grid if multiple cars are connected at the same time. Where the charging is just dependent on the time of the connection. In our case, we do not want to upgrade the electricity network to handle more power. We aim to implement a smart charging algorithm that would allow EVs to charge with the minimum impact on the grid. Helping to plan the distribution of power for all EVs connected to the station over the day. Cars could be charged sequentially one after the other or all of them at the same time but with lower power consumption for each car. Another possible solution is to create a system to monitor the electricity network and allow charging based on the demand.

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- 3.1.3 Smart Charging Algorithms During our search on the smart charging algorithm we will utilize the idea in paper [8] where the authors have worked on a similar topic to ours. We believe that their paper will help us learn more on how to start developing an adaptive charging algorithm. In their paper they showed and explained 7 different control charging algorithms depending on 5 different theorems, some are offline and some are online. The charging network model is heavily inspired by the Caltech ACNs network [7]. They used two-level transformer architecture which distributes electricity from 2 base panels to many EV switch panels. To be specific, 25 chargers per panel were used as shown in Figure 2 above. Based on the two papers mentioned above, we will have to get the, which is the power limit of the total power drawn from the main switch (global peak). We should have already acquired this information from the facility operator in the previous steps. We have to obtain several more inputs which are shown in Table 2 below from [8].

In the paper [8], the authors formulated a scheduling problem for Adaptive Charging Network (SPAN) under a Fractional Revenue Model. Which turned to be a Mixed Integer Linear Problem (MILP) as follows:

Figure 2: Caltech ACN network model

Figure 2

Table 2: Summary of Key Annotations

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Where (1a) is at most demand, (1b) is at global demand, (1c) is at a local demand and (1d) is at max charge. At the last one the constraint is equal to the charging rate of EVi is greater than or equal to the max charging rate as well as the equality of the availability window and the demand.

After the mathematical modelling in article [8] the authors designed their algorithms. Here we are going to attempt to explain how they developed one of their algorithms and learn how to develop our own algorithm. They came up with their algorithm from a case scenario. If there are two EVs in a single charging station, both cars plugged in car 2 have a time limit but car 1 doesn't. If the algorithm is as follows: charge the EVs at each time slot with highest unit-value and process others if they cannot allocate the selected remaining resources. Car 2 will not charge due to the time limit and car one will charge fully. The main tool we will be using in this project is MATLAB and Simulink modeling. MATLAB is a graphical interface and since our project mainly consists of simulations and creating algorithms. We are going to use MATLAB to simulate the TAMUQ power grid and the effect of adding an EV charging station to it. Then we will develop an algorithm that will improve on the power consumption of the CS at which it will reduce the load on the power grid. Therefore, using MATLAB simulation would give us a better presentation of visual graphs to our project and it will help us in the analysis process.

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3.2: Research Phases Phase I: Planning & researching In this phase, we will be reading about EVs obstacles and challenges of adopting them in Qatar. We will be looking at what kind of research has been done in that area and what can we do to improve. Since this is a wide topic, we have decided to narrow it down and use it mainly at TAMUQ. However, it still can be implemented in any other building. We will hold several meetings with our mentors to fully understand the importance of this project, how such algorithms can be implemented and what kind of things we will be learning throughout this project. This phase will be divided into tasks as follow:

- Identifying the problem. - Identifying the customers/Area. (How often people will be using these Charging stations,

how will it be accessible). - Identifying the duration, costs, and risks of the project. - Resource and Data Collection (Building of Operation and KAHRAMAA). - Interviews and surveys.

Phase II: Execution & Analyzing In the second phase of the project, we should have gathered enough information from the previous phase that will give us a more precise idea on what we are going to work on. We are planning to search and learn how to use MATLAB and MATLAB Simulink. As this is very important at this stage of the project since we are going to simulate TAMUQ building’s power network to know its power capacity. We will also need this information to be able to execute the third phase of our project. We should have calculated in the first phase the number of parking slots needed to be converted to charging stations according to different case scenarios. Then, using the acquired numbers, we will simulate these different scenarios using MATLAB to analyze the effect on the TAMUQ power network. This phase will be broken down into the following tasks:

- Learn how to use MATLAB simulation. - Given the data that we collected in phase one, simulate TAMUQ power grid without CSs. - Simulate the TAMUQ power grid with CSs given the different scenarios (if 10% of cars

were EVs, 20%, or 30%). With the integration of PV systems and without. - Analyze the impact of the different scenarios on the power grid.

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Phase III: Execution & Monitoring This phase is where the algorithm is going to be developed. At first, we are going to read and research a lot of works related to smart charging like [7] and [8] as an example. From the research we are going to learn how to develop our own region-specific algorithm. The algorithm is going to be based on several things which are how many cars, how many charging stations, what is the charging duration and many more that we have to look into. This algorithm is going to be a mathematical model that is going to be developed in a method that is similar to how [7] came up with their solution. After developing the adaptive algorithm, we are going to apply it on the MATLAB simulation that we should have worked on in the previous phase. The final prototype will be the best charging case scenario that is running the algorithm in a form of simulation. The results will show us an estimation of what it would be like when smart EV stations are actually installed.

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3.3 Methodology chart This flowchart represents how the final solution will be formulated based on the three phases that were mentioned above. Which consists of 1) Research and Planning, 2) Execution and analysis and 3) Execution and Monitoring.

Figure 3: Methodology Phases Chart

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Section IV: Interview We have contacted Dr. Islam Safak Bayram, he is currently an Associate Professor in the United Kingdom at the University of Strathclyde. Previously, Dr. Islam have worked on several research about electric vehicles while working in Qatar at Hamad Bin Khalifa University, and have published many articles and conference papers about EVs and renewable energies in general. We included one of his articles in our literature review which was talking about the impact of EV charging on power generation in Qatar [4]. However, we couldn't get Dr. Islam’s response before the deadline. This interview will help us gain more specific information from a person who has experience and worked in this field. His knowledge will help us in our project in regards to working with EVs particularly in Qatar, since Dr. Islam have done research about the effects of Qatar’s weather on PV panels. He might also open our eyes to things that we did not think about that would improve the project and some possible challenges that we might face during our project. The kind of questions that we asked Dr. Islam were about the adoption of EV in Qatar and its challenges in general. We also asked him questions related to his articles that he published in regards to charging and the integration of Solar PV panels to the stations. Lastly, we asked him questions related to the charging algorithms and what kind of things should we consider while making our own region-specific algorithm.

Section V: Budget For our project, the inputs and outputs are mainly simulations and graphs. The overall project is software based rather than hardware. A MATLAB software is required to run the simulation model. We will be using the version provided by the University. Although it is freely given to TAMUQ students. The full version costs 2350$ [9] as shown in Table 3. Table 3: Software Price

Software Price Justification

MATLAB 2350$ Simulation Purposes

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Section VI: Timeline The following is our project timeline, that has the list of tasks/assignments in a chronological order. It will help us in tracking the deliverables and keeping the project progress on track.

Figure 4: Project Timeline

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Section VII: Conclusion

The use of renewable energies in the near future will be the main focus of many researches and businesses. Since the world and in particular Qatar is searching for alternative energy sources and ways of implementing them, our project contributes positively in helping Qatar reach its goal of having 10% of operating cars be EVs. Our project will be looking at TAMUQ building’s power network and how adding EVCSs would affect the network. After that, we will be developing an adaptive algorithm that would manage the building’s power load. This algorithm will help in avoiding straining the grid beyond its capacity, power blackouts, and requiring additional generation capacity. The goal of this project is to study and provide enough data for EVCSs to be implemented in Qatar, our benchmark is Texas A&M at Qatar. But this project could be implemented to any other building in Qatar. We are aiming to improve on the sustainable solutions for future EV owners, building managements, improving the existing technologies, and reducing the load on the utility grid by studying the different options available.

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References

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[14] edX, Electric Cars: Electric Vehicle charging process and smart charging , Jan. 23, 2019. Accessed on: Sep.11, 2020. [Video file]. Available: https://www.youtube.com/watch?v=rI0Ak7xihAQ&t=309s