Renewable Energy-Based Charging Infrastructure

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This entry is adapted from the peer-reviewed paper 10.3390/app11093847

With the rise in the demand for electric vehicles, the need for a reliable charging infrastructure increases to accommodate the rapid public adoption of this type of transportation. Simultaneously, local electricity grids are being under pressure and require support from naturally abundant and inexpensive alternative energy sources such as wind and solar. This is why the world has recently witnessed the emergence of renewable energy-based charging stations that have received great acclaim.

renewable energy wind solar electric vehicle charging station

1. Introduction

The remarkable increase in the use of electric vehicles (EVs) has resulted in a massive rise in demand for electric energy across the globe. The global electric vehicle market has grown significantly. The number of EVs on the road in 2010 was a few hundred; this number rose to approximately three million in 2017 and approximately six million in early 2019 ^[1]. Electric vehicles are exciting alternatives to conventional vehicles (CVs). With zero carbon emissions during operation, the EV has the ability to reduce total climate effect and pollutant emissions significantly. As fossil fuels are phased out to a greater degree, the need for biofuels would also be reduced. Electric motors have an efficiency of 80–95% ^[2], making them a more appealing choice than CVs, which have an efficiency of less than 20% ^[3]. EVs are also a critical element of modern transportation, because they incorporate a variety of new industrial technologies (e.g., an electric motor, a battery, and a charging facility). However, the adoption of electric vehicles is not going as well as predicted. The limited range and slow charging time of EVs are widely regarded as the most serious barriers to promotion ^[4]^[5]. Although electric vehicles have a high purchase price, they have low maintenance costs and use substantially less energy than conventional vehicles.

According to the rapid increase of EV demand and EV charging, many research centers, and energy supplying companies began thinking seriously about reducing the pressure on local electricity networks because of the increasing number of electric vehicle charging points. Renewable energy sources such as wind and solar are some of the most effective solutions to bridge this deficit faced by local electricity networks, potentially supporting the EV charging infrastructure ^[6].

After the announcement of the rapid development of the EV at the turn of the millennium, renewable energy-based charging infrastructure (RCI) research began with the effort of wind and solar charging infrastructure ^[7]^[8]^[9]^[10]^[11]^[12]. It envisioned a charging facility that could match EV demand with renewables and direct current (DC) to improve shortcomings of conventional charging infrastructure. The traditional charging stations affect the grid’s stability with issues such as harmonics, fluctuations, and voltage outages ^[8]^[9]^[12]. By contrast, the RCI has several advantages, such as high efficiency ^[13], low system cost ^[14], and simple arrangement ^[15]^[16]. Besides, it requires less power conversion levels than those in alternating current (AC)-based facilities ^[17]^[18]. The RCI can contribute significantly to reducing carbon emissions and expanding the energy domain’s penetration of renewable energy sources. Moreover, RCI has the potential to lower the cost of EV charging ^[19]. However, uncertainties of the renewable sources (e.g., seasonal variations in wind speed and sun irradiance and daily randomness in cloud coverage for solar panels) and load characteristics of EVs (e.g., battery capacity, number and types of EVs, stop time, charging start time, and the initial state of charge) are serious challenges in implementing the RCI ^[20].

Currently, there is an ongoing considerable research work on the aforementioned topics. At the same time, other researchers are working on various aspects of implementation and operation of RCI, such as optimal planning, controlling and sizing, pricing approaches, and examination of the key factors influencing the linking of EV load directly with the RCI. For instance, few studies reviewed EV charging infrastructure research; however, they considered general technical aspects and did not concentrate on renewable energy sources (e.g., ^[1]^[21]). Another study reviewed the RCI studies but with the focus on the consumer preferences and interactions with EVs ^[22]. To our knowledge, no study has reviewed RCI studies extensively by discussing all related research areas.

2. Technological Infrastructure

In many countries of the world, electric vehicles are becoming progressively more popular. However, the absence of charging stations limits the widespread acceptance of EVs by users worldwide. As EV usage grows, EV charging stations are installed in more public spaces ^[23]^[24]. By contrast, if the EVs are charged through an existing fossil-fuel-powered system, they will negatively affect the distribution system and the environment ^[25]. With solar energy from photovoltaic (PV) panels and wind sources having great potential to produce electricity, charging would be an immense solution. It would also represent a sustainable advancement towards a clean environment ^[23]. Depending on the sources of energy available (e.g., solar radiation and wind speed), the electricity output of the charging facility can be either inferior (less than the needed power) or very high (over the power consumption). Most of the literature indicated that the installation of PV solar systems and wind energy conversion systems with the power grid is advanced and technically viable ^[26].

However, the promising approach for balancing the generation of electricity from renewable energy sources can be achieved using configurable dispatch loads or energy storage systems, as it can provide electricity in low power generation ^[27]. The energy storage system’s utilization to stabilize the power grid is no longer a new technology. Other energy sources, such as concentrated solar energy, flywheel, dedicated battery, and hydro-pumped storage systems, are some of the technologies that have been utilized. Smart meters, wireless sensors, advanced communication, and power converters are some of emerging technologies in the industry ^[28]^[29]^[30]^[31]^[32].

2.1. Energy Storage and Fast Charging Systems

It was reported in ^[33] that unregulated charging would contribute to the overloading allocation of transformers and feeders and, eventually, the power supply. Hence, most of the literature has suggested stationary energy storage and fast charging systems to overcome this challenging problem ^[33]. Energy storage limits the charging infrastructure and runs costs by serving electric vehicles during the system’s uttermost load intervals ^[34]^[35]. Energy storage can also improve electric vehicles’ stability by supplying necessary and sufficient energy to reach charging stations in the case of emergencies. Many studies were carried out on the benefits of stationary energy storage with fast charging systems ^[34]^[36]^[37]. However, to obtain such benefits, an optimum size of the energy storage system is required, taking into account the energy tariffs, expected degree of penetration, and load profiles of EVs ^[33].

2.2. Storage Battery and Controller

Solar-powered batteries can fulfill unreliable grid electricity demands, which are strong charge, discharge, and intermittent full-charging periods. A range of battery types fulfills these specific criteria. The major battery storage subgroups reviewed for solar energy include a lead-acid battery, lithium-ion battery, and flow battery ^[38]^[39].

To save the additional energy produced by photovoltaics, a central controller is required to redirect the generated power to the battery, as illustrated in Figure 1. Many scholars have investigated the sequence of controllers that are used in photovoltaics. They highlighted that it is essential to improve the productivity of solar energy generation through a maximum power point tracker (MPPT) and pulse width modulated (PWM) technologies ^[40].

Figure 1. EV Charging Infrastructure with a Solar PV Charger.

2.3. Converters

When it comes to a solar converter, the PV arrays are connected to a DC/DC converter that allows for full power point tracking control. The AC/DC converter is in charge of converting DC/AC power in a bidirectional fashion. The power used from the grid is primarily AC. It must be converted into DC to charge the electric vehicles. The conversion of power occurs before the charging begins or relays the power from the grid to electricity networks.

Therefore, the converters have unique roles in photovoltaic systems based on balanced energy conversion ^[41]. Different forms and requirements have been examined in detail, for example, string inverters, in which panels are installed in combination with a micro-inverter, and central inverters, where panels are installed with separate inverters and micro-inverter power optimizers that require further monitoring. These power optimizers are used to track photovoltaic panel arrays’ overall performance to constantly alter and change the attached load that keeps the system at maximum operational capability ^[42].

3. Appropriate Renewable Energy Sources

Wind and solar energies are considered to be reliable substitution sources of conventional energy sources because of their economic and environmental benefits ^[11]^[43]^[44]^[45]. However, one of the disadvantages of these renewable sources is their inconsistency in offering energy. They do not generate power all the time, and they are intermittent. However, a few suggestions for solar-based charging facilities are discussed in ^[35]^[36]^[46]. The researchers proposed charging infrastructure dedicated to the range of low to medium EVs. In ^[47], the researchers suggested charging EVs from solar energy using the DC link voltage sensing. The aim is to lower the burden on the distribution transformer. However, solar energy limitation to charge wide-range EVs will lower the chances of implementing more solar-based charging stations.

Research work on the control and optimization of wind turbines (WT) has demonstrated that wind energy is an appropriate choice for EV charging infrastructure. In ^[44], the researchers discussed the advantages of implementing charging stations based on large-scale turbines and found that EVs could be a critical factor for enabling the high penetration of wind energy. Considering the challenges of traditional scheduling and dispatching mechanisms, researchers in ^[48] developed a model of utilizing the flexibility of charging EVs to optimally compensate for wind energy fluctuations. They found that shifting EVs’ charging to times with high wind availability achieved cost savings. In another study ^[49], the possibility of using wind energy as a direct source for power EV charging stations were investigated. The researchers implemented an interval-based method corresponding to the time slot taken for EV charging for wind energy conversion and evaluated using various constraints and parameters, including the averaging time interval for wind speed, different turbine manufacturers, and regular high-resolution wind speed datasets. The analysis indicated that the use of direct wind to EV provides enough constant power for large-scale charging stations.

The researchers in ^[50] developed optimal charging infrastructure using wind turbines for different charging modes concerning the optimal charging power. The infrastructure is connected to the grid and has an energy storage system. The rated power was optimized on 52, 84, and 116 kW for slow, intermediate, and quick-speed charging, respectively. On the other hand, the study ^[50] developed a power management model to enhance wind energy reliability.

However, we can conclude that solar and wind energies are appropriate sources for EV charging infrastructure. A charging facility can be either hybrid (solar and wind) or non-hybrid with the use of suitable storage capacity to support the charging process during the fluctuation of sources. The power generator’s sizing depends mainly on the type of charging (fast, medium, or slow). Nonetheless, the use of battery storage has a negative impact on the environment. The study results of ^[51] indicate that global electronic mobility demand will boost the production of batteries by 2030 to around 1725 GWh, and nickel will be the dominant raw material in the lithium-ion battery. Currently, batteries’ demand represents 4% of the annual global nickel production, and the gradual scenario is that nickel demand would rise to 34% of present mining production in 2030. Even if nickel is an important component for plants, like every metal and chemical, the quality of the environment for flora and fauna may be negatively affected by its excessive amounts. As a result, nickel is strictly controlled and subjected to rigorous evaluations under a variety of legislative frameworks ^[52].

4. Control and Energy Management

Connecting the renewable energy-based stations to the grid leads to several challenges. Besides the grid integration and fluctuation issues, the charging operation presents a critical shortage due to the inharmonious charging process concerning power quality and demand ^[53], specifically for fast-charging stations ^[54]. Hence, it is crucial to control the charging behavior to reduce these issues’ impacts. For example, an analysis of electricity production conducted by ^[55] to calculate relevant performance indicators of the electricity supplied by the grid indicated significant variability of the CO₂ emissions. It highlights the need for accurate knowledge of operational parameters to support future smart grid management. Therefore, the management of the EV charging behavior would moderate the fluctuation of renewable energy, optimize the grid’s peak demand, and make efficient load characteristics of the grid ^[56]. The literature comprises several studies on impacts of charging loads on the grid. For example, Green et al. ^[57] studied impacts of EVs on the distribution network, and Amini et al. ^[58] discussed effects of large-scale charging infrastructure on the system’s total loss. In ^[59], a probabilistic model is used to investigate incremental impacts of EV charging on the distribution network.

Management of the charging process in a controlled mode increases the capacity of charging a large number of EVs. Using the maximum renewable energy generated and smart coordination with the grid can decrease the power load of the charging equipment on the grid and ensure cleaner energy. By contrast, integrating the energy storage with the charging station enables disengaging EV load from the grid, moving the charging time to off-peak, and controlling renewable energy’s uncertainty and fluctuation ^[54].

Moreover, most EVs’ parking time is up to 95% per day in the charging area; thus, the V2G concept was raised ^[60]. EVs can be charged at low pricing hours and discharge at high pricing hours, making EVs a distributed energy facility. This mechanism offers EV drivers the chance to lower the charging cost through the price difference ^[61]. The excellent interaction among the grid and large-scale or high-distance range EVs leads to the smart charging and discharging strategy ^[62].

The literature has many studies concerning energy management for EVs associated with renewable energy sources. For example, Wi et al. ^[63] proposed a charging control algorithm to schedule EV charging associated with PV in smart buildings. The proposed strategy is based on predicted PV energy generation and baseload power demand. However, uncertainties of EV charging demand were not considered. Similarly, another scheduling algorithm was proposed by ^[64] for smart buildings that can efficiently reduce CO₂ emissions. However, the study did not consider the flexibility of EV charging demand.

Several studies consider appropriate strategies for controlling EV charging. For instance, Razo et al. ^[65] proposed a vehicle-originating-signals strategy for controlling EV charging by reducing communication overhead with minor effects on performance. Liao et al. ^[66] proposed a scheme for EV charging control that considers the energy storage system and renewable energy power. In ^[67], an optimal scheduling method was introduced for charging infrastructure associated with a microgrid. Kumar et al. ^[68] evaluated various combinations of different priority control criteria on EVs’ ability to charge and charging fairness. Table 1 summarizes the other recent studies concerning energy management of renewable energy-based charging infrastructure. It is noticeable that the dominant criterion is focused mainly on the energy management associated with a PV system that is connected to the grid; consequently, the wind energy and the energy storage system are not sufficiently considered.

Table 1. Energy management studies related to renewable energy-based charging stations.

Study	Configuration	Aim	Method	Remarks
^[69]	Standalone hybrid renewable systems	Minimizing the use of battery storage and maximizing the use of renewable sources with zero charging rejection	The simulation was developed to find the minimum of a constrained non-linear multi-variable function	Different scenarios are introduced and analyzed
^[70]	Standalone hybrid renewable systems	Optimal scheduling for power supply	The energy resources and realistic EV charging data were simulated	The power scheduling was optimized
^[71]	PV–WT-Grid	Maximizing use of renewable sources	Experimenting with the maximum power point tracking technique	The infrastructure is capable of providing sufficient energy in response to the load demand
^[72]	PV–BESS–Grid	Support of high charging rates and penetration of the energy system into the grid	Simulation and prototype experimental	They demonstrated the effectiveness and benefits of a hybrid grid-connected energy system
^[73]	PV–Grid	Discussing critical aspects of renewable resources-based fast charging	Review	Recommendations and useful information related to renewable energy-based DC fast charging
^[74]	WT–Diesel Generator–BESS	Minimizing use of the dump load normally associated with diesel operation	Simulation	Optimizes charging/discharging cycles of the storage system and system operation cost
^[75]	PV–Grid	Improving self-consumption of PV energy and lower its impacts on the grid	Simulation-based on real-time data acquisition of the demand and generation without forecasting	Proves the proposed strategy’s efficiency that can be used in embedded systems for real-time allocation of the EV charging rate
^[19]	PV–Grid	Comparing an optimal charge-scheduling strategy with an uncontrolled charging case	An hourly simulation was used by considering statistical data for driving distances, different types of vehicles, parking time, installation cost, tax rebates and incentives	Confirms feasibility of PV-based infrastructure, benefits to EVs’ drivers and the garage owner and the need for an optimal charging controller
^[63]	PV–Grid	Determining optimal schedules of EV according to the predicted PV power and demand	Simulation and prototype	Demonstrates the effectiveness of the proposed smart EV charging method
^[76]	PV–Grid	Minimizing operation costs	Simulation and economic analysis	Confirms applicability of the strategy to DC distribution buildings, for energy cost reduction
^[77]	PV–Grid	Providing a day-ahead upper limit profile of the charging infrastructure’s power consumption	Simulations and sensitivity analysis	Demonstrates feasibility and relevance of the proposed strategy
^[78]	PV–WT–Fuel Cells–Grid	Minimizing the total cost	Simulations based on the genetic algorithm method	Presents the optimal number of parking lots under optimal scheduling of PHEVs
^[79]	PV–WT– Thermal Storage –BESS–Grid	Minimizing operating costs and CO₂ emissions	Case study	Demonstrates reduction in costs and CO₂ emissions
^[80]	PV–WT– Fuel Cells –Grid	Integrating scheduling and management of intermittent renewable generation and EVs in a microgrid	Case study	Satisfies technical and financial objectives of infrastructure and economic and security issues of the microgrid
^[81]	PV–BESS–Grid	Reducing operation cost	Simulation based on two algorithms and a case study	The case study confirms effectiveness of the proposed algorithms in reducing the cost

References

Das, H.S.; Rahman, M.M.; Li, S.; Tan, C.W. Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renew. Sustain. Energy Rev. 2020, 120, 109618.
Canals Casals, L.; Martinez-Laserna, E.; Amante García, B.; Nieto, N. Sustainability analysis of the electric vehicle use in Europe for CO2 emissions reduction. J. Clean. Prod. 2016, 127, 425–437.
Åhman, M. Primary energy efficiency of alternative powertrains in vehicles. Energy 2001, 26, 973–989.
Kempton, W. Electric vehicles: Driving range. Nat. Energy 2016, 1, 16131.
Hardman, S.; Shiu, E.; Steinberger-Wilckens, R. Comparing high-end and low-end early adopters of battery electric vehicles. Transp. Res. Part A Policy Pr. 2016, 88, 40–57.
Von Jouanne, A.; Husain, I.; Wallace, A.; Yokochi, A. Gone with the wind: Innovative hydrogen/fuel cell electric vehicle infrastructure based on wind energy sources. IEEE Ind. Appl. Mag. 2005, 11, 12–19.
Harakawa, T.; Tujimoto, T. Efficient solar power equipment for electric vehicles: Improvement of energy conversion efficiency for charging electric vehicles. In Proceedings of the IEEE International Vehicle Electronics Conference 2001 IVEC 2001 (Cat No 01EX522), Tottori, Japan, 25–28 September 2001; pp. 11–16.
Etezadi-Amoli, M.; Choma, K.; Stefani, J. Rapid-Charge Electric-Vehicle Stations. IEEE Trans. Power Deliv. 2010, 25, 1883–1887.
Clement-Nyns, K.; Haesen, E.; Driesen, J. The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid. IEEE Trans. Power Syst. 2009, 25, 371–380.
Abella, M.A.; Chenlo, F. Photovoltaic charging station for electrical vehicles. In Proceedings of the 3rd World Conference onPhotovoltaic Energy Conversion, Osaka, Japan, 11–18 May 2003; Volume 3, pp. 2280–2283.
Birnie, D.P. Solar-to-vehicle (S2V) systems for powering commuters of the future. J. Power Sources 2009, 186, 539–542.
Fernandez, L.P.; Roman, T.G.S.; Cossent, R.; Domingo, C.M.; Frias, P. Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Trans. Power Syst. 2011, 26, 206–213.
Huang, Y.; Ye, J.J.; Du, X.; Niu, L.Y. Simulation Study of System Operating Efficiency of EV Charging Stations with Different Power Supply Topologies. Appl. Mech. Mater. 2014, 494, 1500–1508.
Hammerstrom, D.J. AC versus DC distribution systems-did we get it right? In Proceedings of the 2007 IEEE Power Engineering Society General Meeting, PES, Tampa, FL, USA, 24–28 June 2007.
Kakigano, H.; Nomura, M.; Ise, T. Loss evaluation of DC distribution for residential houses compared with AC system. In Proceedings of the The 2010 International Power Electronics Conference—ECCE ASIA, IPEC, Sapporo, Japan, 21–24 June 2010.
Planas, E.; Andreu, J.; Gárate, J.I.; De Alegría, I.M.; Ibarra, E. AC and DC technology in microgrids: A review. Renew. Sustain. Energy Rev. 2015, 43, 726–749.
Xu, L.; Chen, D. Control and Operation of a DC Microgrid with Variable Generation and Energy Storage. IEEE Trans. Power Deliv. 2011, 26, 2513–2522.
Lago, J.; Heldwein, M.L. Operation and Control-Oriented Modeling of a Power Converter for Current Balancing and Stability Improvement of DC Active Distribution Networks. IEEE Trans. Power Electron. 2011, 26, 877–885.
Tulpule, P.J.; Marano, V.; Yurkovich, S.; Rizzoni, G. Economic and environmental impacts of a PV powered workplace parking garage charging station. Appl. Energy 2013, 108, 323–332.
Shukla, A.; Verma, K.; Kumar, R. Impact of EV fast charging station on distribution system embedded with wind generation. J. Eng. 2019, 2019, 4692–4697.
Khalid, M.R.; Alam, M.S.; Sarwar, A.; Asghar, M.J. A Comprehensive review on electric vehicles charging infrastructures and their impacts on power-quality of the utility grid. eTransportation 2019, 1, 100006.
Hardman, S.; Jenn, A.; Tal, G.; Axsen, J.; Beard, G.; Daina, N.; Figenbaum, E.; Jakobsson, N.; Jochem, P.; Kinnear, N.; et al. A review of consumer preferences of and interactions with electric vehicle charging infrastructure. Transp. Res. Part D Transp. Environ. 2018, 62, 508–523.
Mwasilu, F.; Justo, J.J.; Kim, E.-K.; Do, T.D.; Jung, J.-W. Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renew. Sustain. Energy Rev. 2014, 34, 501–516.
Mohammad, A.; Zamora, R.; Lie, T.T. Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling. Energies 2020, 13, 4541.
Khan, S.; Ahmad, A.; Ahmad, F.; Shemami, M.S.; Alam, M.S.; Khateeb, S. A Comprehensive Review on Solar Powered Electric Vehicle Charging System. Smart Sci. 2017, 6, 54–79.
Dallinger, D.; Gerda, S.; Wietschel, M. Integration of intermittent renewable power supply using grid-connected vehicles—A 2030 case study for California and Germany. Appl. Energy 2013, 104, 666–682.
Battke, B.; Schmidt, T.S.; Grosspietsch, D.; Hoffmann, V.H. A review and probabilistic model of lifecycle costs of stationary batteries in multiple applications. Renew. Sustain. Energy Rev. 2013, 25, 240–250.
Alkawsi, G.A.; Baashar, Y. An Empirical Study of the Acceptance of IoT-Based Smart Meter in Malaysia: The Effect of Electricity-Saving Knowledge and Environmental Awareness. IEEE Access 2020, 8, 42794–42804.
Alkawsi, G.A.; Ali, N.; Mustafa, A.S.; Baashar, Y.; Alhussian, H.; Alkahtani, A.; Tiong, S.K.; Ekanayake, J. A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective. Alex. Eng. J. 2021, 60, 227–240.
Alkawsi, G.A.; Ali, N.A.B. A systematic review of individuals’ acceptance of IoT-based technologies. Int. J. Eng. Technol. 2018, 7, 136–142.
Alkawsi, G.A.; Ali, N.A.B.; Alghushami, A. Toward Understanding Individuals’acceptance of Internet of Things-Based Services: Developing an Instrument to Measure the Acceptance Of Smart Meters. J. Theor. Appl. Inf. Technol. 2018, 96, 13.
Alkawsi, G.; Ali, N.A.; Baashar, Y. The Moderating Role of Personal Innovativeness and Users Experience in Accepting the Smart Meter Technology. Appl. Sci. 2021, 11, 3297.
Hussain, A.; Bui, V.-H.; Baek, J.-W.; Kim, H.-M. Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter. Energies 2019, 12, 4516.
Ding, H.; Hu, Z.; Song, Y. Value of the energy storage system in an electric bus fast charging station. Appl. Energy 2015, 157, 630–639.
Ehsan, A.; Yang, Q. Active distribution system reinforcement planning with EV charging stations—Part I: Uncertainty modeling and problem formulation. IEEE Trans. Sustain. Energy 2020, 11, 970–978.
Bao, Y.; Luo, Y.; Zhang, W.; Huang, M.; Wang, L.Y.; Jiang, J. A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System. Energies 2018, 11, 229.
Sbordone, D.; Bertini, I.; Di Pietra, B.; Falvo, M.; Genovese, A.; Martirano, L. EV fast charging stations and energy storage technologies: A real implementation in the smart micro grid paradigm. Electr. Power Syst. Res. 2015, 120, 96–108.
Ibrahim, H.; Dimitrova, M.H.; Dutil, Y.; Rousse, D.; Ilinca, A.; Perron, J. Wind-Diesel hybrid system: Energy storage system selection method. In Proceedings of the 12th International Conference on Energy Storage, Leida, Spain, 16–18 May 2012.
Tan, N.M.L.; Abe, T.; Akagi, H. Design and Performance of a Bidirectional Isolated DC–DC Converter for a Battery Energy Storage System. IEEE Trans. Power Electron. 2012, 27, 1237–1248.
Ingole, J.N.; Choudhary, M.A.; Kanphade, R.D. Pic Based Solar Charging Controller for Battery. Int. J. Eng. Sci. Technol. 2012, 4, 384–390.
Liang, X.; Tanyi, E.; Zou, X. Charging Electric Cars from Solar Energy (Dissertation). 2016. Available online: (accessed on 10 June 2016).
Sekhar, K.R.; Gupta, B.K.; Gedam, A.I. The Closed Loop Controller Gain Characterization for Enhanced Current Quality in Solar Inverters Coupled with Weak Grid. In Proceedings of the 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), Brasov, Romania, 3–6 November 2019; pp. 696–701.
Li, X.; Lopes, L.A.; Williamson, S.S. On the suitability of plug-in hybrid electric vehicle (PHEV) charging infrastructures based on wind and solar energy. In Proceedings of the 2009 IEEE Power & Energy Society General Meeting, Calgary, AB, Canada, 26–30 July 2009; pp. 1–8.
Short, W.; Denholm, P. A Preliminary Assessment of Plug-In Hybrid Electric Vehicles on Wind Energy Markets A Preliminary Assessment of Plug-In Hybrid Electric Vehicles on Wind Energy Markets; Technical Report No. NREL/TP-620-39729; National Renewable Energy Lab.(NREL): Golden, CO, USA, 2006.
Kaur, S.; Kaur, T.; Khanna, R.; Singh, P. A state of the art of DC microgrids for electric vehicle charging. In Proceedings of the 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India, 21–23 September 2017; Volume 2017, pp. 381–386.
Preetham, G.; Shireen, W. Photovoltaic charging station for plug-in hybrid electric vehicles in a smart grid environment. In Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies, ISGT, Washington, DC, USA, 16–20 January 2012.
Goli, P.; Shireen, W. PV integrated smart charging of PHEVs based on DC Link voltage sensing. IEEE Trans. Smart Grid 2014, 5, 1421–1428.
Haque, A.N.M.M.; Saif, A.I.; Nguyen, P.H.; Torbaghan, S.S. Exploration of dispatch model integrating wind generators and electric vehicles. Appl. Energy 2016, 183, 1441–1451.
Noman, F.; Alkahtani, A.A.; Agelidis, V.; Tiong, K.S.; Alkawsi, G.; Ekanayake, J. Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis. Appl. Sci. 2020, 10, 5654.
Ghanbarzadeh, T.; Baboli, P.T.; Rostami, M.; Moghaddam, M.P.; Sheikh-El-Eslami, M.K. Wind farm power management by high penetration of PHEV. In Proceedings of the IEEE Power and Energy Society General Meeting, Detroit, MI, USA, 24–28 July 2011.
Karabelli, D.; Kiemel, S.; Singh, S.; Koller, J.; Ehrenberger, S.; Miehe, R.; Weeber, M.; Birke, K.P. Tackling xEV Battery Chemistry in View of Raw Material Supply Shortfalls. Front. Energy Res. 2020, 8, 331.
Writer, M.C.S. As Demand for Nickel Grows, So do Environmental Concerns—Report, MININGDOTCOME. Available online: (accessed on 27 March 2021).
Yilmaz, M.; Krein, P.T. Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces. IEEE Trans. Power Electron. 2013, 28, 5673–5689.
Bai, S.; Yu, D.; Lukic, S. Optimum design of an EV/PHEV charging station with DC bus and storage system. In Proceedings of the 2010 IEEE Energy Conversion Congress and Exposition, Atlanta, GA, USA, 12–16 September 2010; pp. 1178–1184.
Noussan, M.; Roberto, R.; Nastasi, B. Performance Indicators of Electricity Generation at Country Level—The Case of Italy. Energies 2018, 11, 650.
Abronzini, U.; Attaianese, C.; D’Arpino, M.; Di Monaco, M.; Genovese, A.; Pede, G.; Tomasso, G. Optimal energy control for smart charging infrastructures with ESS and REG. In Proceedings of the 2016 International Conference on Electrical Systems For Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), Toulouse, France, 2–4 November 2016; pp. 1–6.
Green, R.C.; Wang, L.; Alam, M. The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook. Renew. Sustain. Energy Rev. 2010, 15, 544–553.
Amini, M.H.; Kargarian, A.; Karabasoglu, O. ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation. Electr. Power Syst. Res. 2016, 140, 378–390.
Kelly, L.; Rowe, A.; Wild, P. Analyzing the impacts of plug-in electric vehicles on distribution networks in British Columbia. In Proceedings of the 2009 IEEE Electrical Power & Energy Conference (EPEC), Montreal, QC, Canada, 22–23 October 2009.
Song, J.; Suo, L.; Han, M.; Wang, Y. A Coordinated Charging/Discharging Strategy for Electric Vehicles Based on Price Guidance Mechanism. IOP Conf. Ser. Mater. Sci. Eng. 2019, 677, 52103.
Liu, F.; Yang, X.; Shi, S.; Zhang, M.; Deng, H.; Guo, P. Economic operation of microgrid containing charging-swapping-storage integrated station under uncertain factors of wind farm and photovoltaic generation. Power Syst. Technol. 2015, 39, 669–676.
Xu, Z.; Su, W.; Hu, Z.; Song, Y.; Zhang, H. A Hierarchical Framework for Coordinated Charging of Plug-In Electric Vehicles in China. IEEE Trans. Smart Grid 2016, 7, 428–438.
Wi, Y.-M.; Lee, J.-U.; Joo, S.-K. Electric vehicle charging method for smart homes/buildings with a photovoltaic system. IEEE Trans. Consum. Electron. 2013, 59, 323–328.
Shimomachi, K.; Hara, R.; Kita, H.; Noritake, M.; Hoshi, H.; Hirose, K. Development of energy management system for DC microgrid for office building:-Day Ahead operation scheduling considering weather scenarios. In Proceedings of the 2014 Power Systems Computation Conference, Wroclaw, Poland, 18–22 August 2014; pp. 1–6.
Del Razo, V.; Goebel, C.; Jacobsen, H.A. Vehicle-Originating-Signals for Real-Time Charging Control of Electric Vehicle Fleets. IEEE Trans. Transp. Electrif. 2015, 1, 150–167.
Liao, Y.-T.; Lu, C.-N. Dispatch of EV Charging Station Energy Resources for Sustainable Mobility. IEEE Trans. Transp. Electrif. 2015, 1, 86–93.
Carpinelli, G.; Mottola, F.; Proto, D. Optimal scheduling of a microgrid with demand response resources. IET Gener. Transm. Distrib. 2014, 8, 1891–1899.
Kumar, K.N.; Sivaneasan, B.; So, P.L. Impact of Priority Criteria on Electric Vehicle Charge Scheduling. IEEE Trans. Transp. Electrif. 2015, 1, 200–210.
Bokopane, L.; Kusakana, K.; Vermaak, H. Optimal energy management of an isolated electric Tuk-Tuk charging station powered by hybrid renewable systems. In Proceedings of the 2015 International Conference on the Domestic Use of Energy (DUE), Cape Town, South Africa, 31 March–1 April 2015.
Wang, H.; Balasubramani, A.; Ye, Z. Optimal Planning of Renewable Generations for Electric Vehicle Charging Station. In Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA, 5–8 March 2018; pp. 63–67.
Fathabadi, H. Novel wind powered electric vehicle charging station with vehicle-to-grid (V2G) connection capability. Energy Convers. Manag. 2017, 136, 229–239.
Badawy, M.O.; Sozer, Y. Power Flow Management of a Grid Tied PV-Battery System for Electric Vehicles Charging. IEEE Trans. Ind. Appl. 2016, 53, 1347–1357.
Ashique, R.H.; Salam, Z.; Aziz, M.J.B.A.; Bhatti, A.R. Integrated photovoltaic-grid dc fast charging system for electric vehicle: A review of the architecture and control. Renew. Sustain. Energy Rev. 2017, 69, 1243–1257.
Ross, M.; Hidalgo, R.; Abbey, C.; Joós, G. Energy storage system scheduling for an isolated microgrid. IET Renew. Power Gener. 2011, 5, 117–123.
Liu, N.; Chen, Q.; Liu, J.; Lu, X.; Li, P.; Lei, J.; Zhang, J. A Heuristic Operation Strategy for Commercial Building Microgrids Containing EVs and PV System. IEEE Trans. Ind. Electron. 2015, 62, 2560–2570.
Byeon, G.; Yoon, T.; Oh, S.; Jang, G. Energy Management Strategy of the DC Distribution System in Buildings Using the EV Service Model. IEEE Trans. Power Electron. 2012, 28, 1544–1554.
Pflaum, P.; Alamir, M.; Lamoudi, M.Y. Probabilistic Energy Management Strategy for EV Charging Stations Using Randomized Algorithms. IEEE Trans. Control. Syst. Technol. 2017, 26, 1099–1106.
Chen, C.; Duan, S. Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids. IEEE Trans. Ind. Informatics 2014, 10, 1917–1926.
Bracco, S.; Delfino, F.; Pampararo, F.; Robba, M.; Rossi, M. A dynamic optimization-based architecture for polygeneration microgrids with tri-generation, renewables, storage systems and electrical vehicles. Energy Convers. Manag. 2015, 96, 511–520.
Honarmand, M.; Zakariazadeh, A.; Jadid, S. Integrated scheduling of renewable generation and electric vehicles parking lot in a smart microgrid. Energy Convers. Manag. 2014, 86, 745–755.
Wu, D.; Zeng, H.; Lu, C.; Boulet, B. Two-Stage Energy Management for Office Buildings With Workplace EV Charging and Renewable Energy. IEEE Trans. Transp. Electrification 2017, 3, 225–237.

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Gamal Alkawsi

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