This paper describes a method based on mixed-integer linear programming to cost-optimally locate and size chargers for electric vehicles (EVs) in distribution grids as a function of the driving demand. The problem accounts for the notion of single-port chargers (SPCs), where a charger can interface one EV maximum, and multi-port chargers (MPCs), where the same charger can interface multiple EVs. The advantage of MPCs is twofold. First, multiple ports allow arbitraging the charging among multiple vehicles without requiring the drivers to plug and unplug EVs. Second, the charger's power electronics is not sized for the total number of charging ports, enabling cost savings when the grid constraints are bottleneck of the problem. The proposed method can account for different charger typologies, such as slow and fast chargers, and model the drivers' flexibility of plugging and unplugging their EVs. Simulation results from a synthetic case study show that implementing MPCs is beneficial over both SPCs and drivers' flexibility in terms of total investments required for the charging infrastructure.
Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice mode…
Electric vehicles are booming and with them the required public charging stations. Knowing how charging stations are used is crucial for operators of the charging stations themselves, navigation systems, electricity grids, and many more. Given that there are now 2.5 as many vehicles per charging station compared to 2017, the system needs to allocate charging points intelligently and efficiently. This paper presents representative data on energy consumption, arrival times, occupation, and profitability of charging stations in Germany by combining usage data of 27,800 installations. Charging hap…
As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charg-ing/discharging scheduling algorithm. In thi…
Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks
The large adoption of EVs brings practical interest to the operation optimization of the charging station. The joint scheduling of pricing and charging control will achieve a win-win situation both for the charging station and EV drivers, thus enhancing the operational capability of the station. We consider this important problem in this paper and make the following contributions. First, a joint scheduling model of pricing and charging control is developed to maximize the expected social welfare of the charging station considering the Quality of Service and the price fluctuation sensitivity of…
As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the loc…
Global warming leads the world to think of a different way of transportation: avoiding internal combustion engines and electrifying the transportation sector. With a high penetration of electric vehicle (EV) charging stations on an existing power distribution network, the impact may be consistent. The loads of the fast-charging stations would potentially result in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. The degrading performance of the power system due to the negative impact of the EV charging stations can even lead to penalties to be…
This paper proposes a novel optimal Energy Management System (EMS) algorithm for Electric Vehicle (EV) charging in smart electric railway stations with renewable generation. As opposed to previous railway EMS methods, the proposed EMS coordinates the combined Regenerative Braking Energy (RBE), renewable generation, electric railway demand and EV charging demand at the EV parking lot of the railway station. Numerical results using a scenario-based approach on an actual railway station in Chur, Switzerland demonstrate that the proposed algorithm can effectively minimize the expected daily operat…
Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute, The Chinese University of Hong Kong（香港中文大学深圳研究院）；College of Information Engineering, Shenzhen University（深圳大学信息工程学院）
本文提出JoAP机制，通过联合优化电动汽车接入控制、定价和充电调度，提升充电站利润。通过 tandem 队列网络模型分析，设计最优算法并在模拟中验证其在不同等待时间惩罚率下利润提升达330%-531%。
Electric vehicle (EV) charging stations have experienced rapid growth, whose impacts on the power grid have become non-negligible. Though charging stations can install energy storage to reduce their impacts on the grid, the conventional "one charging station, one energy storage" method may be uneconomical due to the high upfront cost of energy storage. Shared energy storage can be a potential solution. However, effective management of charging stations with shared energy storage in a distribution network is challenging due to the complex coupling, competing interests, and information asymmetry…
This paper presents a consolidated grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provide corresponding solutions, we select four representative distribution systems - including both single-feeder cases and a multi-feeder case - and design thorough test metrics for the impact analysis. The charging load profiles used in the an…
This paper presents a framework for simultaneous bidding and pricing strategy for wholesale market participation of electric vehicle (EV) charging stations aggregator. The proposed framework incorporates the EV charging stations' technical constraints as well as EV owners' preferences. A bi-level optimization is adopted to model the problem. In the upper level, the total profit of the EV charging station aggregator is maximized. In the lower-level problem, the EV owner's utility function is maximized. The EV owners' preferences are modeled using the quadratic utility function. The bi-level opt…
Range and charge anxiety remain essential barriers to a faster electric vehicle market diffusion. To this end, quickly and reliably finding suitable charging stations may foster an electric vehicle uptake by mitigating drivers' anxieties. Here, existing commercial services help drivers to find available stations based on real-time availability data but struggle with data inaccuracy, e.g., due to conventional vehicles blocking the access to public charging stations. In this context, recent works have studied stochastic search methods to account for availability uncertainty in order to minimize …
In this paper we propose a stochastic decentralized algorithm to recommend the most convenient Charging Station (CS) to Plug-in Electric Vehicles (PEVs) that need charging. In particular, we use different cost functions to describe the possibly different priorities of PEV drivers, such as the preference to minimize charging costs, charging times, or the distance between them and the CS. For this purpose, we leverage on an IoT architecture based on a permissioned Distributed Ledger Technology (DLT) to enforce compliance of drivers and reduces the occurrence of detrimental misbehaviours of drive…
Safe and secure electric vehicle charging stations (EVCSs) are important in smart transportation infrastructure. The prevalence of EVCSs has rapidly increased over time in response to the rising demand for EV charging. However, developments in information and communication technologies (ICT) have made the cyber-physical system (CPS) of EVCSs susceptible to cyber-attacks, which might destabilize the infrastructure of the electric grid as well as the environment for charging. This study suggests a 5Ws \& 1H-based investigation approach to deal with cyber-attack-related incidents due to the incap…
The increasing trend of transportation electrification presents investors the opportunity to provide charging services to Electric Vehicle (EV) owners via the energy purchased from the wholesale electricity market. This will benefit EV owners with the availability of competitive rates compared to the regulated utility time-of-use (TOU) rates. The fundamental questions addressed in this paper are 1) will EV owners benefit from the additional choice of Electric Vehicle Charging Stations (EVCSs) compared to home charging? 2) is there any profitable market opportunity for charging stations while t…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Colorado, Boulder（科罗拉多大学博尔德分校）
The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in New York, the expected rise in the EV population requires additional charging stations to maintain a sufficient level of efficiency across the charging infrastructure. A novel application of Reinforcement Learning (RL) is able to find optimal locations for new charging stations given the predicted charging demand and current charging locations. The most important factors that influence charging demand prediction includ…
The growing deployment of electric mobility calls for power system analyses to investigate to what extent the simultaneous charging of electric vehicles leads to degraded network operation and to validate the efficiency of countermeasures. To reduce complexity and CPU time, a common approach while performing these analyses consists in replacing electric vehicles and their charging stations with constant PQ loads. However, this approach is inaccurate, as the power absorbed by these elements actually depends not only on voltage but also on the state of charge, charging method, cathode chemistry …
Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using cont…
We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the…
Considering large scale implementation of electric vehicles (EVs), public EV charging stations are served as fuel tanks for EVs to meet the need of longer travelling distance and overcome the shortage of private charging piles. The allocation of local battery energy storage (BES) can enhance the flexibility of the EV charging station. This paper proposes an optimal decision making model of the BES-assisted EV charging station considering the incentive demand response. Firstly, the detailed models of the BES-assisted EV charging station are presented. Secondly, as a representative incentive dem…
The first of this two-paper series proposes and elaborates a concept of electric vehicle (EV)-based e-mobility system. To this end, models designed to reap the benefits of EVs' flexibility in the literature almost exclusively consider charging stations as active players exploiting the EVs' flexibility. Such stations are seen as static loads able to provide flexibility only when EVs are connected to them. However, this standpoint suffers from two major issues. First, the charging stations need to anticipate some important parameters of the incoming EVs, e.g. time of arrival and departure, state…
Ultra-Fast Charging (UFC) is a rising technology that can shorten the time of charging an Electric Vehicle (EV) from hours to minutes. However, the power consumption characteristics of UFC bring new challenges to the existing power system, and its pros and cons are yet to be studied. This project aims to set up a framework for studying the different aspects of substituting the normal non-residential EV chargers within the San Francisco Bay Area with Ultra-Fast Charging (UFC) stations. Three objectives were defined for three stakeholders involved in this simulation, namely: the EV user, the sta…
The accelerated electrification of transport systems with EVs has brought new challenges for charging scheduling, fleet management, and charging infrastructure location and configuration planning. In this review, we have provided a systematic review of the recent development in strategic, tactical, and operational decisions for demand responsive transport system planning using electric vehicles (EV-DRT). We have summarized recent developments in mathematical modeling approaches and identified future research directions. A list of existing open-access datasets, numerical test instances, and sof…
Electric vehicles (EVs) and unmanned aerial vehicles (UAVs) show great potential in modern transportation and communication networks, respectively. However, with growing demands for such technologies, the limited energy infrastructure becomes the bottleneck for their future growth. It might be of high cost and low energy efficiency for all the operators to each have their own dedicated energy infrastructure, such as charging stations. In this paper, we analyze a wireless charging infrastructure sharing strategy in UAV and EV-involved networks. We consider a scenario where UAVs can charge in EV…
As the plug-in electric vehicle (PEV) market expands worldwide, PEV penetration has out-paced public PEV charging accessibility. In addition to charging infrastructure deployment, charging station operation is another key factor for improving charging service accessibility. In this paper, we propose a mathematical framework to optimally operate a PEV charging station, whose service capability is constrained by the number of available chargers. This mathematical framework specifically exploits human behavioral modeling to alleviate the "overstaying" issue that occurs when a vehicle is fully cha…
The rapid adoption of electric vehicles (EVs) calls for the widespread installation of EV charging stations. To maximize the profitability of charging stations, intelligent controllers that provide both charging and electric grid services are in great need. However, it is challenging to determine the optimal charging schedule due to the uncertain arrival time and charging demands of EVs. In this paper, we propose a novel centralized allocation and decentralized execution (CADE) reinforcement learning (RL) framework to maximize the charging station's profit. In the centralized allocation proces…
Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV char…
While Electric vehicles (EVs) adoption is accelerating in an unprecedented way, lacking EV charging infrastructure hinders the development of the EV market. To compensate for these shortcomings, Mobile Charging Stations (MCS) could play a prominent role to accelerate EV penetration by providing charging services with no restrictions on the location and time of the charging process. This paper disseminates information on other papers and technical reports on MCS in the literature. It also discusses the benefits of MCS, its challenges, and finally introduces the research gaps in this area.
Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved…
