This paper presents a comprehensive review and assessment of the latest research and advancement of electric vehicles (EVs) interaction with smart grid portraying the future electric power system model. The concept goal of the smart grid along with the future deployment of the EVs puts forward various challenges in terms of electric grid infrastructure, communication and control. Following an intensive review on advanced smart metering and communication infrastructures, the strategy for integrating the EVs into the electric grid is presented. Various EV smart charging technologies are also extensively examined with the perspective of their potential, impacts and limitations under the vehicle-to-grid (V2G) phenomenon. Moreover, the high penetration of renewable energy sources (wind and photovoltaic solar) is soaring up into the power system. However, their intermittent power output poses different challenges on the planning, operation and control of the power system networks. On the other hand, the deployment of EVs in the energy market can compensate for the fluctuations of the electric grid. In this context, a literature review on the integration of the renewable energy and the latest feasible solution using EVs with the insight of the promising research gap to be covered up are investigated. Furthermore, the feasibility of the smart V2G system is thoroughly discussed. In this paper, the EVs interactions with the smart grid as the future energy system model are extensively discussed and research gap is revealed for the possible solutions.

Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration

Electrifying transportation is a promising approach to alleviate the climate change issue. The adoption of electric vehicle into market has introduced significant impacts on various fields, especially the power grid. Various policies have been implemented to foster the electric vehicle deployment and the increasing trend of electric vehicle adoption in the recent years has been satisfying. The continual development of electric vehicle power train, battery and charger technologies have further improved the electric vehicle technologies for wider uptake. Despite the environmental and economical benefits, electric vehicles charging introduce negative impacts on the existing network operation. Appropriate charging management strategies can be implemented to cater for this issue. Furthermore, electric vehicle integration in the smart grid can bring many potential opportunities, especially from the perspective of vehicle-to-grid technology and as the solution for the renewable energy intermittency issue. This paper reviews the latest development in electric vehicle technologies, impacts of electric vehicle roll out and opportunities brought by electric vehicle deployment.

A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects

With the number of electric vehicles (EVs) on the rise, there is a need for an adequate charging infrastructure to serve these vehicles. The emerging extreme fast-charging (XFC) technology has the potential to provide a refueling experience similar to that of gasoline vehicles. In this article, we review the state-of-the-art EV charging infrastructure and focus on the XFC technology, which will be necessary to support the current and future EV refueling needs. We present the design considerations of the XFC stations and review the typical power electronics converter topologies suitable to deliver XFC. We consider the benefits of using the solid-state transformers (SSTs) in the XFC stations to replace the conventional line-frequency transformers and further provide a comprehensive review of the medium-voltage SST designs for the XFC application.

Extreme Fast Charging of Electric Vehicles: A Technology Overview

Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

https://www.preprints.org/manuscript/201705.0090/v1/download

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

The integration of a massive number of small-scale wind turbines and plug-in electric vehicles (PEVs) brought about urgent technical challenge to power distribution network operators (DNOs) in terms of secure power supply and energy dispatching optimization. In this paper, we exploited three coordinated wind-PEV energy dispatching approaches in the vehicle-to-grid (V2G) context, i.e., valley searching, interruptible and variable-rate energy dispatching, aiming to promote the user demand response through optimizing the utilization efficiency of wind power generation as well as meeting the dynamic power demands. This issue is addressed in a stochastic framework considering the uncertainties of wind power generation as well as the statistical PEV driving patterns. The performances of the proposed solutions are assessed through a comparative study through numerical simulation experiments covering sufficient system scenarios by the use of scenario generation and reduction techniques. The result demonstrates that the energy dispatch based on the latter two approaches can achieve better matching between power generation and demands as well as PEV user satisfaction. In addition, the suggested approaches can be adopted by DNOs in practice with minimal deployment hurdles to promote the energy supplies within microgrid with wind power sources and PEVs.

Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles

Deep learning based automatic defect identification of photovoltaic module using electroluminescence images

The efficient condition monitoring and accurate module defect detection in large-scale photovoltaic (PV) farms demand for novel inspection method and analysis tools. This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles. The convolutional neural network is used in the machine learning process to classify various forms of module defects. Such a supervised learning process can extract a range of deep features of operating PV modules. It significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions. The proposed algorithmic solution is extensively evaluated from different aspects, and the numerical result clearly demonstrates its effectiveness for efficient defect detection of PV modules.

Deep Learning Based Module Defect Analysis for Large-Scale Photovoltaic Farms

The asset assessment and condition monitoring of large-scale photovoltaic (PV) systems spanning over a large geographical area has imposed urgent challenges and demands for novel and efficient inspection paradigm In this study, an automatic UAV-based inspection system is presented and implemented for asset assessment and defect detection for large-scale PV systems Two typical visible defects of PV modules, snail trails and dust shading, are characterised and the defect detection through image processing algorithms based on first order derivative of Gaussian function and feature matching is carried out for the aerial PV module images captured by visible light cameras The functionality of the developed unmanned aerial vehicle (UAV)-based inspection system can be easily extended with more advanced fault detection algorithms and different forms of sensing devices (eg infrared thermal camera) for specialised inspection tasks Such UAV-based imaging can carry out a variety of inspection and condition monitoring tasks in PV systems spanning over a large geographical area in an autonomous or supervised fashion with significantly promoted efficiency in comparison with conventional methods

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2017.0001

Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems

The integration of a massive number of plug-in electric vehicles (PEVs) into current power distribution networks brings direct challenges to network planning, control, and operation. To increase the PEV penetration level with minimal negative impact, the dynamical PEV travel behaviors and charging demand need to be better understood. This paper presents a Markov-based analytical approach for modeling PEV travel behaviors and charging demand. The travel behaviors of individual PEVs are expressed mathematically through Monte Carlo simulation considering two essential factors: temporal travel purposes and state of charge (SoC). Markov model and hidden Markov model (HMM) are adopted to explicitly formulate the probabilistic correlation between multiple PEV states and SoC ranges. This modeling approach provides an efficient and generic tool for analyzing PEV travel behaviors and charging demand based on available PEV statistics. The analytical model is further adopted in the impact assessment of two PEV normal charging scheduling strategies for a range of PEV penetration levels in an IEEE 53-bus test network with field data (network parameters and realistic PEV statistics). The results demonstrate the benefit of the proposed modeling approach in network analysis considering PEV integration.

Wenjun Yan

Papers

Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles

Deep learning based automatic defect identification of photovoltaic module using electroluminescence images

Deep Learning Based Module Defect Analysis for Large-Scale Photovoltaic Farms

Visible defects detection based on UAV-based inspection in large-scale photovoltaic systems

A Novel Markov-Based Temporal-SoC Analysis for Characterizing PEV Charging Demand