# A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

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## Abstract

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## 1. Introduction

## 2. Modelling of PEV Charging Demand

- System-wide PEV charging demand estimation, which is used for the charging profile of all PEVs in the system—Utilities use this estimation method in evaluating impacts of PEV integration on the power system [4], to upgrade the distribution system (including transformers, cables, and protection devices). Moreover, this estimation can be used when utilities need to establish new generation sources [13], to dispatch of current generation sources, and to apply demand side management (DSM) programs [14];
- Single charging facility demand estimation, which is used to estimate the charging profile of a certain charging station or parking lot. Utilities use this type of estimation to assess the impact of certain charging facility—Service providers also use this type of estimation in determination of charging station capacity, such as the numbers of chargers and waiting positions of a new charging facility based on the targeted blocking probability of new customers [15];
- Network of charging station demand estimation, which is used to analyze the interactions among multiple charging stations [16]—utilities and service providers use this estimation to allocate power among charging facilities based on the expected charging profile [17]. Moreover, this estimation is used to allocate (route) customers among charging stations either by direct control or by incentives such as charging price [17]. The joint allocation of power and customers for the stations in such a network is to maximize the operator’s profit and ensure the highest quality of service for customers.

#### 2.1. System-Wide PEV Charging Demand Modelling

#### 2.1.1. Travel Pattern Model

#### 2.1.2. Energy Consumption Model

- The energy consumed by PEV per unit travel distance—This variable depends on vehicle characteristic (e.g., vehicle weight, and aerodynamics drag coefficient), driving habits, geographical location, road conditions, and so on. The variable is random and is assumed to have a normal distribution with the mean depending on the vehicle class [18];
- The daily travel distance of PEV—This variable is dependent on vehicle travel pattern, as discussed in the preceding subsection. Therefore, the PEV energy consumption model utilizes the outcomes of a travel pattern model of PEVs [18]. A linear relationship between the SOC at the beginning of charging and the traveled distance since last charging can be applied [23,25];
- Other variables such as the energy required to keep up a comfortable cabin temperature for the vehicle driver and passengers are taken into consideration in [19].

#### 2.1.3. Power Consumption Model

- Number of simultaneously charging PEVs in the system—The statistics of this variable can be extracted from a travel pattern model which provides the spatial and temporal distribution of the number of PEVs in charging state [3];
- Charging scenarios—It includes both controlled and uncontrolled charging [28]. Charging scenarios determine the charging start time and charging rate. In uncontrolled charging, a PEV starts charging immediately after plugged into the power grid, and the charging rate is fixed. Controlled charging, on the other hand, can be price based control or direct control (smart charging). It adds some coordination over charging process of multiple PEVs, in order to shape the aggregated PEV charging profile. In price based control, utilities use time of use (TOU) electricity price to shift the charging load of PEVs to off-peak times. In smart charging, utilities directly adjust the charging start time and charging rate. Controlled charging can optimize the scheduling of PEVs charging process, in order to maximize the benefits of utilities and users. Utilities can use controlled charging in load flattening, frequency regulation and voltage regulation. Additionally, users can reduce the PEV charging cost. A survey on various smart charging algorithms is given in [9].
- Charging level—The standard charging levels for PEVs include the alternating current (AC) level 1, AC level 2, and direct current (DC) fast charging [29], as summarized in Table 1. Slow charging facilities contain level 1 and level 2 chargers and take 3–17 hours (h) to charge a PEV battery. Usually, slow charging facilities are deployed in homes, parking lots in workplaces, and shopping malls. Fast charging stations contain DC fast chargers, which can charge a PEV battery in approximately 20 minutes (min);
- Charging duration—There are different types of PEV batteries, and each battery type has its unique characteristics. Charging duration can be calculated based on information about charging level, charging rate, and PEV battery capacity, in addition to the available SOC at the beginning of charging process, which can be extracted from the energy consumption model [28].

#### 2.2. Demand Modelling of Single PEV Charging Facility

- The charging (service) process is independent among chargers and exponentially distributed [31]. PEV battery charging behavior should be taken into account when modeling the service time [4]. The relation between the charging time and PEV battery SOC is nonlinear, which may be approximated by a piece-wise linear relationship.

#### 2.3. Demand Modeling of PEV Charging Station Network

## 3. Impact Assessment of PEVs on Power System

## 4. Charging Infrastructure Planning

#### 4.1. Sizing and Sitting Problems: Demand Coverage Models

#### 4.1.1. Nodal PEV Density-Based Model

#### 4.1.2. Traffic Flow-Based Model

#### 4.1.3. Real Data-Based Model

#### 4.1.4. Stochastic Planning Model

#### 4.2. Sizing and Sitting Problem: Planning Objectives

#### 4.2.1. Objective Functions Associated with Power System

#### 4.2.2. Objective Functions Associated with Transportation System

#### 4.2.3. Multi-Objective Planning Associated with Transportation and Power Systems

## 5. Design of PEV Charging Facilities with Integrated DER

## 6. Summary and Future Research Directions

- PEV behavior modelling: Existing models of PEV charging demand are based on the assumption that PEV users have the same behaviors as conventional vehicle users. This assumption is not accurate as PEVs have a limited driving range, limited battery capacities, and a relatively longer charging time than gas refueling. In addition, PEV users are expected to have a relatively higher income level, due to high cost of PEVs. These differences will affect the PEV usage patterns. Specialized studies, travel surveys, and field measurements are needed in order to extract the exact PEV usage patterns and travel behaviors. One such study is presented on [13], based on 76 vehicles with installed GPS devices over a one year period. The harvested data from this study includes the actual SOC of the battery, parking durations, parking locations, and vehicle powertrain characteristics. Studies on larger PEV fleets in various countries in the world will be necessary to accurately model the usage patterns and driving behaviors of PEVs. Such data can be used in modelling of PEV charging demand, which enables optimizing of charging infrastructure planning and alleviating the negative impacts of PEVs on power and transportation systems;
- Co-planning of DG and PEV charging infrastructure: As discussed, integration of DG units into the distribution system can mitigate the negative impacts of PEV loads on the distribution system [19]. However, how to simultaneously locate DG units and PEV charging infrastructure in the distribution system, while considering the uncertainty in both DG outputs and PEV load, needs further investigation. The co-planning problem should address challenges such as voltage control, harmonic distortions, fault current, and flickers. Integration of new operational approaches for the distribution system to manage co-existence of these technologies is a non-trivial task. A stochastic modelling of this system is necessary to deal with many random phenomena in the system. Reliability analysis and system protection are essential;
- Wireless charging of PEVs: Wireless power transfer technology is a relatively new charging technology, which is introduced to enable PEV charging without cables. Wireless charging can be classified to either stationary or dynamic charging [79,80]. Stationary charging enables the PEVs to be charged when they are parked. It is successfully introduced in electrical transient buses, which are frequently stopped during loading and unloading passengers. When stationary wireless chargers are deployed in bus stations, bus battery sizes can be reduced by two thirds. SAE international approves the standard TIR J2954 for stationary wireless charging [81]. On the other hand, dynamic charging enables PEVs to be charged when moving on-roads. The on-board battery back can be significantly reduced; hence PEVs become lighter in weight and may be less expensive. Moreover, range anxiety and concerns about charging time will be eliminated if a large scale charging infrastructure based on dynamic wireless charging is deployed. However, wireless charging suffers from low efficiency, high power loss, and lack of standards for dynamic charging [80]. Also, power flow management of simultaneously charging PEVs on the same dynamic charging lane and the impacts on the distribution system are still open issues. Intensive research is needed to enable such technology and features such as V2G via wireless charging;
- Charging station selection: PEV users may select a charging facility based on their preference, for example, charging facility that is closest to the driver or that the driver is familiar with. This preference may not conform to power system constraints, which can cause power loss or stress on the distribution system. Therefore, further studies are needed to firstly define PEVs users’ personal preferences, then identify a charging spot which is close to customer choice and is confined with power system constraints. Vehicular ad hoc networks (VANETs) can be used as a method of exchanging such information between PEVs user and LDC [82]. In such network, road side units (RSUs) deployed along the roads will facilitate information exchange with vehicles. This communication network can help to obtain a real-time information of the SOC of PEVs, trip route and destination, in addition to user preferences and acceptable charging price. LDC can also collect information about the occupancy status of the charging stations, and loading on the distribution system. LDC can then advice PEV users to use certain charging stations, to manage the integration of a large fleet of PEVs in the system with minimal negatively impact the power system and to ensure the high QoS for PEV users.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- International Energy Agency. Global EV Outlook 2016: Beyond One Million Electric Cars; International Energy Agency: Paris, France, 2016. [Google Scholar]
- Rautiainen, A.; Repo, S.; Jarventausta, P.; Mutanen, A.; Vuorilehto, K.; Jalkanen, K. Statistical charging load modeling of PHEVs in electricity distribution networks using national travel survey data. IEEE Trans. Smart Grid
**2012**, 3, 1650–1659. [Google Scholar] [CrossRef] - Tang, D.; Wang, P. Probabilistic modeling of nodal charging demand based on spatial-temporal dynamics of moving electric vehicles. IEEE Trans. Smart Grid
**2016**, 7, 627–636. [Google Scholar] [CrossRef] - Hafez, O.; Bhattacharya, K. Queuing analysis based PEV load modeling considering battery charging behavior and their impact on distribution system operation. IEEE Trans. Smart Grid
**2016**. [Google Scholar] [CrossRef] - Alharbi, W.; Bhattacharya, K. Electric vehicle charging facility as a smart energy microhub. IEEE Trans. Sustain. Energy
**2017**, 8, 616–628. [Google Scholar] [CrossRef] - Sbordone, D.; Bertini, I.; Di Pietra, B.; Falvo, M.C.; 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. [Google Scholar] [CrossRef] - Jin, C.; Sheng, X.; Ghosh, P. Optimized electric vehicle charging with intermittent renewable energy sources. IEEE J. Sel. Top. Signal Process.
**2014**, 8, 1063–1072. [Google Scholar] [CrossRef] - Yong, J.Y.; Ramachandaramurthy, V.K.; Tan, K.M.; Mithulananthan, N. A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew. Sustain. Energy Rev.
**2015**, 49, 365–385. [Google Scholar] [CrossRef] - Wang, Q.; Liu, X.; Du, J.; Kong, F. Smart charging for electric vehicles: A survey from the algorithmic perspective. IEEE Commun. Surv. Tutor.
**2016**, 18, 1500–1517. [Google Scholar] [CrossRef] - Hu, J.; Morais, H.; Sousa, T.; Lind, M. Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects. Renew. Sustain. Energy Rev.
**2016**, 56, 1207–1226. [Google Scholar] [CrossRef] [Green Version] - Lam, A.; Leung, Y.-W.; Chu, X. Electric vehicle charging station placement: Formulation, complexity, and solutions. IEEE Trans. Smart Grid
**2014**, 5, 2846–2856. [Google Scholar] [CrossRef] - Wang, G.; Xu, Z.; Wen, F.; Wong, K.P. Traffic-constrained multiobjective planning of electric-vehicle charging stations. IEEE Trans. Power Deliv.
**2013**, 28, 2363–2372. [Google Scholar] [CrossRef] - Ashtari, A.; Bibeau, E.; Shahidinejad, S.; Molinski, T. PEV charging profile prediction and analysis based on vehicle usage data. IEEE Trans. Smart Grid
**2012**, 3, 341–350. [Google Scholar] [CrossRef] - Alizadeh, M.; Scaglione, A.; Davies, J.; Kurani, K.S. A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles. IEEE Trans. Smart Grid
**2014**, 5, 848–860. [Google Scholar] [CrossRef] - Ismail, M.; Bayram, I.S.; Abdallah, M.; Serpedin, E.; Qaraqe, K. Optimal Planning of Fast PEV Charging Facilities. In Proceedings of the First Workshop on Smart Grid and Renewable Energy (SGRE), Doha, Qatar, 22–23 March 2015; pp. 1–6. [Google Scholar]
- Liang, H.; Sharma, I.; Zhuang, W.; Bhattacharya, K. Plug-in Electric Vehicle Charging Demand Estimation based on Queueing Network Analysis. In Proceedings of the IEEE PES General Meeting, National Harbor, MD, USA, 27–31 July 2014; pp. 1–5. [Google Scholar]
- Bayram, I.S.; Michailidis, G.; Devetsikiotis, M.; Granelli, F. Electric power allocation in a network of fast charging stations. IEEE J. Sel. Areas Commun.
**2013**, 31, 1235–1246. [Google Scholar] [CrossRef] - Wu, D.; Aliprantis, D.C.; Gkritza, K. Electric energy and power consumption by light-duty plug-in electric vehicles. IEEE Trans. Power Syst.
**2011**, 26, 738–746. [Google Scholar] [CrossRef] - Shaaban, M.F.; Atwa, Y.M.; El-Saadany, E.F. PEVs modeling and impacts mitigation in distribution networks. IEEE Trans. Power Syst.
**2013**, 28, 1122–1131. [Google Scholar] [CrossRef] - 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.
**2011**, 15, 544–553. [Google Scholar] [CrossRef] - Shetty, S.; Bhattacharya, K. A Stochastic Distribution Operations Framework to Study the Impact of PEV Charging Loads. In Proceedings of the North American Power Symposium (NAPS), Charlotte, NC, USA, 4–6 October 2015; pp. 1–6. [Google Scholar]
- Tehrani, N.H.; Wang, P. Probabilistic estimation of plug-in electric vehicles charging load profile. Electr. Power Syst. Res.
**2015**, 124, 133–143. [Google Scholar] [CrossRef] - Shafiee, S.; Fotuhi-Firuzabad, M.; Rastegar, M. Investigating the impacts of plug-in hybrid electric vehicles on power distribution systems. IEEE Trans. Smart Grid
**2013**, 4, 1351–1360. [Google Scholar] [CrossRef] - Shaaban, M.F.; Ismail, M.; El-Saadany, E.F.; Zhuang, W. Real-time PEV charging/discharging coordination in smart distribution systems. IEEE Trans. Smart Grid
**2014**, 5, 1797–1807. [Google Scholar] [CrossRef] - Lojowska, A.; Kurowicka, D.; Papaefthymiou, G.; van der Sluis, L. Stochastic modeling of power demand due to EVs using copula. IEEE Trans. Power Syst.
**2012**, 27, 1960–1968. [Google Scholar] [CrossRef] - Tang, D.; Wang, P.; Wu, Q. Probabilistic Modeling of Nodal Electric Vehicle Load due to Fast Charging Stations. In Proceedings of the IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China, 16–20 October 2016; pp. 1–7. [Google Scholar]
- Xiong, J.; Zhang, K.; Guo, Y.; Su, W. Investigate the impacts of PEV charging facilities on integrated electric distribution system and electrified transportation system. IEEE Trans. Transp. Electrific.
**2015**, 1, 178–187. [Google Scholar] [CrossRef] - Jiang, C.; Torquato, R.; Salles, D.; Xu, W. Method to assess the power-quality impact of plug-in electric vehicles. IEEE Trans. Power Deliv.
**2014**, 29, 958–965. [Google Scholar] [CrossRef] - 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**, 43, 501–516. [Google Scholar] [CrossRef] - Li, G.; Zhang, X.-P. Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations. IEEE Trans. Smart Grid
**2012**, 3, 492–499. [Google Scholar] [CrossRef] - Bae, S.; Kwasinski, A. Spatial and temporal model of electric vehicle charging demand. IEEE Trans. Smart Grid
**2012**, 3, 394–403. [Google Scholar] [CrossRef] - Bayram, I.S.; Tajer, A.; Abdallah, M.; Qaraqe, K. Capacity planning frameworks for electric vehicle charging stations with multiclass customers. IEEE Trans. Smart Grid
**2015**, 6, 1934–1943. [Google Scholar] [CrossRef] - Verzijlbergh, R.A.; Grond, M.O.; Lukszo, Z.; Slootweg, J.G.; Ilic, M.D. Network impacts and cost savings of controlled EV charging. IEEE Trans. Smart Grid
**2012**, 3, 1203–1212. [Google Scholar] [CrossRef] - Chen, P.; Chen, Z.; Bak-Jensen, B. Probabilistic Load Flow: A Review. In Proceedings of the 3rd International Conference Electric Utility Deregulation Restructuring Power Technologies, Nanjing, China, 6–9 April 2008; pp. 1586–1591. [Google Scholar]
- Leou, R.; Su, C.; Lu, C. Stochastic analyses of electric vehicle charging impacts on distribution network. IEEE Trans. Power Syst.
**2014**, 29, 1055–1063. [Google Scholar] [CrossRef] - Navarro-Espinosa, A.; Ochoa, L.F. Probabilistic impact assessment of low carbon technologies in LV distribution systems. IEEE Trans. Power Syst.
**2016**, 31, 2192–2203. [Google Scholar] [CrossRef] - Tang, D.; Wang, P. Nodal impact assessment and alleviation of moving electric vehicle loads: From traffic flow to power flow. IEEE Trans. Power Syst.
**2016**, 31, 4231–4242. [Google Scholar] [CrossRef] - Khodayar, M.E.; Wu, L.; Shahidehpour, M. Hourly coordination of electric vehicle operation and volatile wind power generation in SCUC. IEEE Trans. Smart Grid
**2012**, 3, 1271–1279. [Google Scholar] [CrossRef] - Ahn, Y.; Yeo, H. An analytical planning model to estimate the optimal density of charging stations for electric vehicles. PLoS ONE
**2015**, 10, e0141307. [Google Scholar] [CrossRef] [PubMed] - Liu, Z.; Wen, F.; Ledwich, G. Optimal planning of electric-vehicle charging stations in distribution systems. IEEE Trans. Power Deliv.
**2013**, 28, 102–110. [Google Scholar] [CrossRef] - Trivedi, A.; Menon, B.; Srinivasan, D.; Sharma, A.; Nicholas, W.F.D. Multi-Objective Siting and Sizing of E Charging Stations in the Distribution System. In Proceedings of the IEEE Smart Grid Technologies-Asia (ISGT ASIA), Bangkok, Thailand, 3–6 November 2015; pp. 1–6. [Google Scholar]
- Lin, X.; Sun, J.; Ai, S.; Xiong, X.; Wan, Y.; Yang, D. Distribution network planning integrating charging stations of electric vehicle with V2G. Int. J. Electr. Power
**2014**, 63, 507–512. [Google Scholar] [CrossRef] - Yao, W.; Zhao, J.; Wen, F.; Dong, Z.; Xue, Y.; Xu, Y.; Meng, K. A multi-objective collaborative planning strategy for integrated power distribution and electric vehicle charging systems. IEEE Trans. Power Syst.
**2014**, 29, 1811–1821. [Google Scholar] [CrossRef] - Xiang, Y.; Liu, J.; Li, R.; Li, F.; Gu, C.; Tang, S. Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates. Appl. Energy
**2016**, 178, 647–659. [Google Scholar] [CrossRef] - Chung, S.H.; Kwon, C. Multi-period planning for electric car charging station locations: A case of Korean expressways. Eur. J. Oper. Res.
**2015**, 242, 677–687. [Google Scholar] [CrossRef] - He, F.; Wu, D.; Yin, Y.; Guan, Y. Optimal deployment of public charging stations for plug-in hybrid electric vehicles. Trans. Res. Part B Methodol.
**2013**, 47, 87–101. [Google Scholar] [CrossRef] - Huang, Y.; Li, S.; Qian, Z.S. Optimal deployment of alternative fueling stations on transportation networks considering deviation paths. Netw. Spat. Econ.
**2015**, 15, 183–204. [Google Scholar] [CrossRef] - Tu, W.; Li, Q.; Fang, Z.; Shaw, S.-L.; Zhou, B.; Chang, X. Optimizing the locations of electric taxi charging stations: A spatial—Temporal demand coverage approach. Transp. Res. C-Emerg.
**2016**, 65, 172–189. [Google Scholar] [CrossRef] - Shahraki, N.; Cai, H.; Turkay, M.; Xu, M. Optimal locations of electric public charging stations using real world vehicle travel pattern. Transp. Res. D-Transp. Environ.
**2015**, 41, 165–176. [Google Scholar] [CrossRef] - Neyestani, N.; Damavandi, M.Y.; Shafie-Khah, M.; Contreras, J.; Catalão, J.P. Allocation of plug-In vehicles’ parking lots in distribution systems considering network-constrained objectives. IEEE Trans. Power Syst.
**2015**, 30, 2643–2656. [Google Scholar] [CrossRef] - Kazemi, M.A.; Sedighizadeh, M.; Mirzaei, M.J.; Homaee, O. Optimal siting and sizing of distribution system operator owned EV parking lots. Appl. Energy
**2016**, 179, 1176–1184. [Google Scholar] [CrossRef] - Mirzaei, M.J.; Kazemi, A.; Homaee, O. A Probabilistic approach to determine optimal capacity and location of electric vehicles parking lots in distribution networks. IEEE Trans. Ind. Inf.
**2016**, 12, 1963–1972. [Google Scholar] [CrossRef] - Baouche, F.; Billot, R.; Trigui, R.; El Faouzi, N.-E. Efficient allocation of electric vehicles charging stations: Optimization model and application to a dense urban network. IEEE Intell. Transp. Syst. Mag.
**2014**, 6, 33–43. [Google Scholar] [CrossRef] - Yang, S.; Wu, M.; Yao, X.; Jiang, J. Load modeling and identification based on ant colony algorithms for ev charging stations. IEEE Trans. Power Syst.
**2015**, 30, 1997–2003. [Google Scholar] [CrossRef] - Sadeghi-Barzani, P.; Rajabi-Ghahnavieh, A.; Kazemi-Karegar, H. Optimal fast charging station placing and sizing. Appl. Energy
**2014**, 125, 289–299. [Google Scholar] [CrossRef] - Hodgson, M.J. A flow-capturing location-allocation model. Geogr. Anal.
**1990**, 22, 270–279. [Google Scholar] [CrossRef] - Kuby, M.; Lim, S. The flow-refueling location problem for alternative-fuel vehicles. Socio-Econ. Plan. Sci.
**2005**, 39, 125–145. [Google Scholar] [CrossRef] - Kong, C.; Jovanovic, R.; Bayram, I.S.; Devetsikiotis, M. A hierarchical optimization model for a network of electric vehicle charging stations. Energies
**2017**, 10, 675. [Google Scholar] [CrossRef] - Ding, H.; Hu, Z.; Song, Y. Value of the energy storage system in an electric bus fast charging station. Appl. Energy
**2015**, 178, 630–639. [Google Scholar] [CrossRef] - Negarestani, S.; Fotuhi-Firuzabad, M.; Rastegar, M.; Rajabi-Ghahnavieh, A. Optimal sizing of storage system in a fast charging station for plug-in hybrid electric vehicles. IEEE Trans. Transp. Electrific.
**2016**, 2, 443–453. [Google Scholar] [CrossRef] - Bayram, I.S.; Michailidis, G.; Devetsikiotis, M.; Bhattacharya, S.; Chakrabortty, A.; Granelli, F. Local Energy Storage Sizing in Plug-in Hybrid Electric Vehicle Charging Stations under Blocking Probability Constraints. In Proceedings of the International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 17–20 October 2011; pp. 78–83. [Google Scholar]
- Chaudhari, K.; Ukil, A. TOU Pricing based Energy Management of Public EV Charging Stations Using Energy Storage System. In Proceedings of the IEEE International Conference on Industrial Technology (ICIT), Taipei, Taiwan, 14–17 March 2016; pp. 460–465. [Google Scholar]
- Liu, L.; Kong, F.; Liu, X.; Peng, Y.; Wang, Q. A review on electric vehicles interacting with renewable energy in smart grid. Renew. Sustain. Energy Rev.
**2015**, 51, 648–661. [Google Scholar] [CrossRef] - Hafez, O.; Bhattacharya, K. Optimal design of electric vehicle charging stations considering various energy resources. Renew. Energy
**2017**, 107, 576–589. [Google Scholar] [CrossRef] - Haq, A.U.; Chadhry, M.J.; Saleemi, F. A Smart Charging Station for EVs with Evaluation of Different Energy Storage Technologies. In Proceedings of the IEEE Conference on Clean Energy and Technology (CEAT), Lankgkawi, Malaysia, 18–20 November 2013; pp. 248–253. [Google Scholar]
- Alhamali, A.; Farrag, M.E.; Bevan, G.; Hepburn, D.M. Review of Energy Storage Systems in Electric Grid and their Potential in Distribution Networks. In Proceedings of the 18th International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, 27–29 December 2016; pp. 546–551. [Google Scholar]
- Brenna, M.; Dolara, A.; Foiadelli, F.; Leva, S.; Longo, M. Urban scale photovoltaic charging stations for electric vehicles. IEEE Trans. Sustain. Energy
**2014**, 5, 1234–1241. [Google Scholar] [CrossRef] - Cairo, J.; Sumper, A. Requirements for EV Charge Stations with Photovoltaic Generation and Storage. In Proceedings of the 3rd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies (ISGT Europe), Berlin, Germany, 14–17 October 2012; pp. 1–6. [Google Scholar]
- Islam, M.S.; Mithulananthan, N.; Bhumkittipich, K.; Sode-Yome, A. EV Charging Station Design with PV and Energy Storage Using Energy Balance Analysis. In Proceedings of the IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), Bangkok, Thailand, 3–6 November 2015; pp. 1–5. [Google Scholar]
- Mouli, G.C.; Bauer, P.; Zeman, M. System design for a solar powered electric vehicle charging station for workplaces. Appl. Energy
**2016**, 168, 434–443. [Google Scholar] [CrossRef] - Tabatabaee, S.; Mortazavi, S.S.; Niknam, T. Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources. Energy
**2017**, 121, 480–490. [Google Scholar] [CrossRef] - Di Giorgio, A.; Liberati, F.; Germana, R.; Presciuttini, M.; Celsi, L.R.; Priscoli, F.D. On the Control of Energy Storage Systems for Electric Vehicles Fast Charging in Service Areas. In Proceedings of the 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece, 21–24 June 2016; pp. 955–960. [Google Scholar]
- Mendes, P.R.; Isorna, L.V.; Bordons, C.; Normey-Rico, J.E. Energy management of an experimental microgrid coupled to a V2G system. J. Power Sources
**2016**, 327, 702–713. [Google Scholar] [CrossRef] - Jiang, X.; Wang, J.; Han, Y.; Zhao, Q. Coordination Dispatch of Electric Vehicles Charging/Discharging and Renewable Energy Resources Power in Microgrid. Procedia Comput. Sci.
**2017**, 107, 157–163. [Google Scholar] [CrossRef] - Marra, F.; Yang, G.Y.; Træholt, C.; Larsen, E.; Ostergaard, J.; Blažič, B.; Deprez, W. EV charging facilities and their application in LV feeders with photovoltaics. IEEE Trans. Smart Grid
**2013**, 4, 1533–1540. [Google Scholar] [CrossRef] - Fazelpour, F.; Vafaeipour, M.; Rahbari, O.; Rosen, M.A. Intelligent optimization to integrate a plug-in hybrid electric vehicle smart parking lot with renewable energy resources and enhance grid characteristics. Energy Convers. Manag.
**2014**, 77, 250–261. [Google Scholar] [CrossRef] - Karfopoulos, E.L.; Panourgias, K.A.; Hatziargyriou, N.D. Distributed coordination of electric vehicles providing V2G regulation services. IEEE Trans. Power Syst.
**2016**, 31, 2834–2846. [Google Scholar] [CrossRef] - Buja, G.; Bertoluzzo, M.; Fontana, C. Reactive power compensation capabilities of V2G-enabled electric vehicles. IEEE Trans. Power Electron.
**2017**, 32, 9447–9459. [Google Scholar] [CrossRef] - Jeong, S.; Jang, Y.J.; Kum, D. Economic analysis of the dynamic charging electric vehicle. IEEE Trans. Power Electron.
**2015**, 30, 6368–6377. [Google Scholar] [CrossRef] - Ou, C.-H.; Liang, H.; Zhuang, W. Investigating wireless charging and mobility of electric vehicles on electricity market. IEEE Trans. Ind. Electron.
**2015**, 62, 3123–3133. [Google Scholar] [CrossRef] - Bi, Z.; Kan, T.; Mi, C.C.; Zhang, Y.; Zhao, Z.; Keoleian, G.A. A review of wireless power transfer for electric vehicles: Prospects to enhance sustainable mobility. Appl. Energy
**2016**, 179, 413–425. [Google Scholar] [CrossRef] - Wang, M.; Shen, X.S.; Zhang, R. Mobile Electric Vehicles; Springer: Berlin, Germany, 2016. [Google Scholar]

**Figure 1.**Evaluation of the global electrical car stock, 2010–2015 [1].

**Figure 2.**Comparison between different energy storage technologies [6].

Charging Level | Voltage | Current | Rated Power | Charging Time |
---|---|---|---|---|

AC level 1 | 120 V AC, single phase | 12–16 A | 1.4–1.9 kW | 7–17 h |

AC level 2 | 240 V AC, single or three phase | Up to 80 A | 19.2 kW | 3–7 h |

DC fast charging | 200–450 V DC | Up to 80 A | Up to 40 kW | 1.2 h–20 min |

Function | Tool | Main Feature |
---|---|---|

Uncontrolled Operation | Power flow analysis [33,37] | Steady state analysis |

Probabilistic power flow [30] Monte-Carlo simulation [35,36] | Capture of the uncertainty of the load demand | |

Optimal Operation | Optimal power flow [4] | Optimization of state variables |

Stochastic OPF [4,21] | Capturing the uncertainty of the demand and optimizing the state variables |

Demand Coverage Model | Main Feature |
---|---|

Nodal PEV density model [39,40,41,42] | Estimation of the stationary demand density at system nodes |

Traffic flow model [12,43,44,45,46,47] | Estimation of the spatial demand and mobility of PEVs |

Real data-based model [48,49] | Estimation of the spatial-temporal PEV demand |

Stochastic model [50,51,52] | Considering the uncertainty of PEVs |

Objectives Category | Planning Objective |
---|---|

Power system objectives | Minimizing the grid loss and PEV loss [55] Minimizing total energy loss and total voltage deviation [41] Minimizing total charging infrastructure cost [40,42,52] |

Transportation system objectives | Flow-capturing location model (FCLM) [56] Flow-refueling location model (FRLM) [57] Multipath-refueling location model (MPRLM) [47] |

Multi-objectives planning | Minimizing the overall annual investment cost and energy loss, as well as FCLM [43] Minimizing total energy loss and total voltage deviation, as well as FCLM [12] Minimizing the operational and investment cost, as well as minimizing the traveling cost [44] Maximizing utilization of the facilities and system profit, as well as FCLM [58] Maximizing the social welfare, and combined distribution and assignment (CDA) model [46] |

Integration Scheme | Research Work |
---|---|

Within the charging facility | Benefits of using PV systems in charging facilities [67] Charging facility design based on Markov chains [61,68] Sizing of system components to minimize operation cost [60,69] On-roof PV system design considering seasonal variation [70] Sizing the ESS to benefit from energy arbitrage [59] |

Within a microgrid | Design of charging facility microgrids [64] Scheduling of distributed generation (DG) units in a charging facility microgrid [71] Control of the ESS in a charging facility microgrid [72] Expremental implementation of a charging facility microgrid [6] Control of the microgrid interaction with V2G technology [73] Schedulling PEVs charging/discharging to minimize the load variance [74] |

Within the distribution system | Design of charging facilities to regulate voltage in distribution system with high PV penetration [75] Design of a smart energy microhub [5] Sizing and sitting of DG units in the distribution system with integrated charging facilities [19,76] PEVs coordination to acheive valley filling concept via V2G technology [77] Investigating the cababilty of V2G enabled PEVs in reactive power compansation [78] |

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## Share and Cite

**MDPI and ACS Style**

Abdalrahman, A.; Zhuang, W.
A Survey on PEV Charging Infrastructure: Impact Assessment and Planning. *Energies* **2017**, *10*, 1650.
https://doi.org/10.3390/en10101650

**AMA Style**

Abdalrahman A, Zhuang W.
A Survey on PEV Charging Infrastructure: Impact Assessment and Planning. *Energies*. 2017; 10(10):1650.
https://doi.org/10.3390/en10101650

**Chicago/Turabian Style**

Abdalrahman, Ahmed, and Weihua Zhuang.
2017. "A Survey on PEV Charging Infrastructure: Impact Assessment and Planning" *Energies* 10, no. 10: 1650.
https://doi.org/10.3390/en10101650