Plug-in Electric Vehicles Users Research Articles

Impact of Communication System Characteristics on Electric Vehicle Grid Integration: A Large-Scale Practical Assessment of the UK’s Cellular Network for the Internet of Energy

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of electric mobility can be successful. EVGI is facilitated through charging stations and empowers PEV users to manage their charging demand by using smart charging solutions. This makes PEV grids assets that provide flexibility to the power grid. The Internet of Things (IoT) feature can make smooth EVGI possible through a supporting communication infrastructure. In this regard, the selection of an appropriate communication protocol is essential for the successful implementation of EVGI. This study assesses the efficacy of the UK’s 4G network with TCP and 4G UDP protocols for potential EVGI operations. For this, an EVGI emulation test bed is developed, featuring three charging parking lots with the capacity to accommodate up to 64 PEVs. The network’s performance is assessed in terms of data packet loss (e.g., the data-exchange capability between EVGI entities) and latency metrics. The findings reveal that while 4G TCP often outperforms 4G UDP, both achieve latencies of less than 1 s with confidence intervals of 90% or greater for single PEV cases. However, it is observed that the high penetration of PEVs introduces a pronounced latency due to queuing delays in the network including routers and the base station servers, highlighting the challenges associated with maintaining efficient EVGI coordination, which in turn affects the efficient use of grid assets.

Placement and Capacity of EV Charging Stations by Considering Uncertainties With Energy Management Strategies

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

Optimized Deep Reinforcement Learning for Smart Charging Scheduling of Plug-in Electric Vehicles

In recent decades, fossil fuels have been the major source of energy but concerns about price fluctuations, security, and environmental issues. Plug-in electric vehicles (PEVs) should be connected to the power grid for battery charging. With a wide range of cars, the power system performance in the distribution network. The uncontrolled charging of the battery can cause issues such as overvoltage, overload, unbalanced load, instability, harmonics, and increased loss. Hence, efficient charging methodologies need to be developed for the power grid. The objective of this article is to maximize the total revenue of the charging stations and schedule the charging stations for PEV charging with reduced cost. First, the system model with its objectives such as demand, constraints, and fitness values are computed. Next, efficient charging path planning is proposed using the deep Q network, which is a reinforcement learning model. This can assist the PEV user in selecting the charging station and planning its path. Lastly, the particle sailfish optimization from our previous research work has been used to solve the charging station optimal solution. The simulation results analysis shows the efficiency of the proposed model in reducing the charging cost and time and improves the charging scheduling of PEVs.

A scenario-based stochastic programming approach for the public charging station location problem

This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a public charging station location-allocation model using a scenario-based stochastic programming (SP) approach. In order to capture the dynamic charging behaviour of PEV users, a chi-squared automatic interaction detector (CHAID)-based mixed effects decision tree is induced from multi-day activity diaries. Moreover, because the stochastic error of the micro-simulation approach brings about uncertainty, we adopted a two-stage stochastic mixed-integer programming (TSMIP) model, which measures uncertainty by means of a finite set of scenarios obtained from the derived decision rules underlying PEV charging. The proposed approach is demonstrated for the City of Eindhoven, The Netherlands, and benefits of the stochastic solution are discussed.

Cyber-physical co-modeling and optimal energy dispatching within internet of smart charging points for vehicle-to-grid operation

Vehicle-to-grid (V2G) technology plays an important part in achieving carbon neutrality. Hence, reducing the execution time under the real-time application becomes an urgent issue. In this paper, we develop a cyber-physical co-modeling to fulfill the fundamental insights into the internet of smart charging points (ISCP), wherein the local controllers are designed near the plug-in electric vehicles (PEVs), and are coordinated with each other. In perspective of energy dispatching, a hierarchical V2G scheduling is implemented in a distributed way to decompose the optimization problem into several sub-problems. Besides, the parallel computing is applied in the V2G problem to accelerate the speed of obtaining results. Moreover, the voltage regulation is applied near the energy coordinator with high-performance computer rather than by the local controller. In perspective of network communication, the small-world network is applied to ensure the communication efficiency and decrease the wiring costs. Besides, the privacy-preserving of both the energy coordinator and the PEV users is guaranteed by processing and storing the sensitive information of the two participants nearby. Finally, the cyber-physical co-modeling is performed in Matlab and Network Simulator 2. Results show load flatting, self-consumption of photovoltaic output, voltage regulation, and up/down regulation are achieved. Moreover, the delay of small-world network is 90.94 times faster than that of lattice network, and the cost of small-world network is nearly 500 times less than that of full mesh network. Particularly, the execution time for V2G operation at one-time interval is less than 1 s.

Raceways, rebates, and retrofits: an exploration of several American cities’ policies to facilitate electric vehicle purchase and usage

ABSTRACT Plug-in Electric Vehicles (PEVs) form a critical part of the infrastructure needed for sustainable transportation. Local governments are expected to play a key role in ensuring widespread adoption of PEVs by their residents. This study specifically investigates how large American cities have used the policy process to prepare for broader PEV usage. Based on a questionnaire sent to policy leaders in major American cities, this study investigates issues of building codes, city-utility relations, tax incentives for PEV users, and social equity. The study identifies and analyzes common and influential policies perceived as being most effective at advancing PEV adoption. Findings suggest that cities are either not preparing for PEVs at all, or are preparing in a very substantive and tech-savvy manner. Policymakers also highlight key areas of needed focus, and lay out ways in which regulators and electricity providers can aid in PEV adoption.

Dynamic Pricing for Differentiated PEV Charging Services Using Deep Reinforcement Learning

With the increasing popularity of plug-in electric vehicles (PEV), charging infrastructure becomes widely available and offers multiple services to PEV users. Each charging service has a distinct quality of service (QoS) level that matches user expectations. The charging service demand is interdependent, i.e., the demand for one service is often affected by the prices of others. Dynamic pricing of charging services is a coordination mechanism for QoS satisfaction of service classes. In this article, we propose a differentiated pricing mechanism for a multiservice PEV charging infrastructure (EVCI). The proposed framework motivates PEV users to avoid over-utilization of particular service classes. Currently, most of dynamic pricing schemes require full knowledge of the customer-side information; however, such information is stochastic, non-stationary, and expensive to collect at scale. Our proposed pricing mechanism utilizes model-free deep reinforcement learning (RL) to learn and improve automatically without an explicit model of the environment. We formulate our framework to adopt the twin delayed deep deterministic policy gradient (TD3) algorithm. The simulation results demonstrate that the proposed RL-based differentiated pricing scheme can adaptively adjust service pricing for a multiservice EVCI to maximize charging facility utilization while ensuring service quality satisfaction.

A centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

In order to accommodate additional plug-in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle-to-grid (V2G) technology has been regarded as a cost-effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub-problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak-shaving and valley-filling scheduling algorithm is built up. The proposed algorithm can be conducted in real-time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak-shaving and valley-filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak-shaving and valley-filling strategy is near-linear, which verifies the proposed scheme can be carried out very efficiently.

Optimal Public Parking Lot Allocation and Management for Efficient PEV Accommodation in Distribution Systems

Progressive popularization of plug-in electric vehicles (PEVs) calls for synergistic deployment of public charging infrastructures to serve the recharging needs in future urban systems. For this end, a new planning framework to facilitate efficient accommodation of PEVs via gridable parking lots (PLs) is proposed in this study. Different from most previous works, the presented method explicitly considers the potential uncertainties in the users’ behaviors in the PL planning, which will allow decision-makers to better assess the effect of human factors on the efficiency of their investment. Moreover, the concerned PL allocation problem has been investigated in conjunction with the optimal contract offer for PEV users to obtain the final planning scheme. The overall problem is formulated as a two-stage stochastic programming model, where the first-stage mainly deals with the planning decisions associated with PLs, including siting and sizing of PLs and contractual arrangements; while the second stage evaluates the operational performance of the suggested PL system under different scenario realizations of PEV behaviors. A combinatorial optimization algorithm is used to solve the problem. The proposed framework is demonstrated on an illustrative testing system and numerical results verify the effectiveness of our method.

QoS-Aware Capacity Planning of Networked PEV Charging Infrastructure

Plug-in electric vehicle (PEV) charging infrastructure is necessary to accommodate the rapid increase in PEV penetration rate. Capacity planning of PEV charging infrastructure (EVCI) must ensure not only a satisfactory charging service for PEV users but also a reliable operation of the power grid. In this paper, we propose a quality-of-service (QoS) aware capacity planning of EVCI. In particular, the proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems (ESSs) and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. We model the EVCI as a queuing network with finite capacity, and utilize the non-stationary queuing models to study the temporal variability of the PEV charging demand. A network of charging facilities is used to demonstrate the effectiveness of the proposed framework.

A Framework for Determining a Prediction-Of-Use Tariff Aimed at Coordinating Aggregators of Plug-In Electric Vehicles

The objective of this article is to propose a framework for defining a day-ahead prediction-of-use tariff (POU) that promotes aggregators of the plug-in electric vehicles (PEVs) to operate as closely as possible to an optimal charging curve previously calculated by the distribution system operator (DSO). The DSO calculates this optimal charging curve to flatten the load curve of the distribution transformers as much as possible by coordinating the daily recharging of PEVs. The objective is to establish the optimal power profile of the PEV aggregators needed to flatten the power curve supplied by the transformer, so that PEV customers’ needs can be met throughout the day. The proposed framework is applied in a case study accounting for uncertainties associated with charging through Monte Carlo simulation, in order to find the POU tariff. The results demonstrate that applying the POU tariff determines the transformer’s minimum power limit necessary to meet all PEV users’ needs. Additionally, the day-ahead POU tariff does not generate new demand peaks, since it does not concentrate the energy supply of flexible loads in pre-established time bands. Finally, simulation reflects the significant effect of the PEV charging on the distribution system in terms of enhancing the voltage profile, maximizing the transformer life, and reducing the power/energy losses.

PEV Charging Infrastructure Siting Based on Spatial–Temporal Traffic Flow Distribution

Plug-in electric vehicles (PEVs) offer a solution to reduce greenhouse gas emissions and decrease fossil fuel consumption. PEV charging infrastructure siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. In this paper, we propose a spatial–temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial–temporal distribution of traffic flows. We utilize the dynamic traffic assignment model to estimate the time-varying traffic flows on the road transportation network. Then, we cluster the traffic flow dataset into distinct categories using the Gaussian mixture model and site each type of charging facilities to capture a specific traffic pattern. We formulate our siting model as an mixed integer linear programming optimization problem. The model is evaluated based on two benchmark transportation networks, and the simulation results demonstrate the effectiveness of the proposed model.

What do consumers think of smart charging? Perceptions among actual and potential plug-in electric vehicle adopters in the United Kingdom

Widespread adoption of Plug-in Electric Vehicles (PEVs) would add substantially to overall electricity demand, while increasing deployment of renewable supply sources will make supply-demand balancing more difficult. Early PEV market experience suggests that users preferentially charge their vehicles in the early evening, when other demands are high. Managed (or Smart) charging aims to shift PEV charging load to times when other demands are low, to facilitate better integration into the wider energy system, operation within network constraints, and minimisation of future network reinforcement costs. However, material effectiveness would require mass engagement with managed charging. To gain initial insight into the potential for managed charging, 60 semi-structured interviews were carried out with actual or potential UK PEV users. Interviews explored present charging behaviour and responses to two types of managed charging schemes, user-managed charging (UMC) based on time-of-use tariffs, and supplier-managed charging (SMC). Participants’ willingness to engage with either was conditional on large reductions in charging costs. UMC was preferred over SMC, because of perceived personal control and lower perceived risk that a vehicle might not be fully charged at the required time. Preference for SMC was based mainly on perceived advantages to society as a whole.

Frequency emergency control method considering the plug-in electric vehicles aggregator load shedding

The Plug-In Electric Vehicle (PEV) has fast response characteristics. In this paper, it is used for emergency control of power system frequency in Vehicle to Grid (V2G) mode. The power system frequency dynamics assessment method is given based on PEV aggregator and single machine equivalent model. The PEV aggregator-based system frequency emergency control framework is established, and the main components and scope of the framework are described. A calculation method based on dispatchable PEV load shedding is proposed. The method injects power into the system through V2G mode to balance the Power shortages on the basis of guaranteeing the quality of power system services and the charging demand of PEV users. The simulation results show that the proposed method can cut the load without cutting or under load on the premise of ensuring the target frequency.

Influence of the controllability of electric vehicles on generation and storage capacity expansion decisions

This paper proposes a capacity investment model to analyze the influence of the controllability of the charge of plug-in electric vehicles (PEVs) in generation and storage expansion decisions. The proposed model provides the financial incentives that should be offered to PEV users in order to implement the optimal expansion decisions. Considering that the decision-making process faced by the power system planner must simultaneously consider long- and short-term uncertainties, a three-stage stochastic programming problem is formulated. In this model, capacity investments and financial incentives for PEV users are decided in the first stage, whereas operating decisions regarding the day-ahead and real-time markets are made in the second and third stages, respectively. Numerical results are provided from a realistic case study based on the isolated power system comprising Lanzarote and Fuerteventura islands in Spain.

Locating residential on-street electric vehicle charging infrastructure: A practical methodology

Depending on the method of electricity generation, mass-market penetration of electric vehicles has the potential to reduce emissions of greenhouse gases and air pollutants considerably, and to reduce dependency on fossil fuels. This paper presents a novel methodology for Local Government Authorities (LGAs) to identify suitable locations for the initial provision of residential on-street Plug-in Electric Vehicle (PEV) charging infrastructure in urban areas to help remove barriers to PEV uptake. The methodology is practical for use by LGAs with limited financial resources as it is based on simple Geographic Information System (GIS) analysis of routinely available census and parking data to identify the spatial overlaps between areas where residents are most likely to be PEV users and areas where there is a high reliance on residential on-street parking. The methodology has been implemented in practice to determine a charging infrastructure installation strategy for Southampton, UK, where 128 streets (out of 1924 in total) were recommended as suitable locations. These streets were reviewed by a group of experts during a workshop and confirmed as suitable locations for the initial installation of residential on-street charge points in the city.

Optimal Scheduling of Plug-in Electric Vehicle Charging Including Time-of-Use Tariff to Minimize Cost and System Stress

Technological advancement, environmental concerns, and social factors have made plug-in electric vehicles (PEVs) popular and attractive vehicles. Such a trend has caused major impacts to electrical distribution systems in terms of efficiency, stability, and reliability. Moreover, excessive power loss, severe voltage deviation, transformer overload, and system blackouts will happen if PEV charging activities are not coordinated well. This paper presents an optimal charging coordination method for a random arrival of PEVs in a residential distribution network with minimum power loss and voltage deviation. The method also incorporates capacitor switching and on-load tap changer adjustment for further improvement of the voltage profile. The meta-heuristic methods, binary particle swarm optimization (BPSO) and binary grey wolf optimization (BGWO), are employed in this paper. The proposed method considers a time-of-use (ToU) electricity tariff such that PEV users will get more benefits. The random PEV arrival is considered based on the driving pattern of four different regions. To demonstrate the effectiveness of the proposed method, comprehensive analysis is conducted using a modified of IEEE 31 bus system with three different PEV penetrations. The results indicate a promising outcome in terms of cost and the distribution system stress minimization.

Distributed control for large-scale plug-in electric vehicle charging with a consensus algorithm

As the development of plug-in-electric vehicles (PEVs) accelerates, the effective integration of large-scale PEVs with power grid becomes an urgent problem for utilities and PEV users. Mainly considering grid side preference, this paper proposes a distributed control method with a consensus algorithm to deal with large-scale PEV charging coordination. The objectives of the proposed coordination strategy are: minimizing the charging power loss and maximizing the available PEV power for vehicle-to-grid (V2G) services. The incremental cost functions are formulated for these objectives. The graph theory is applied to specify the communication network for PEVs. An interaction rule, the consensus algorithm is applied to realize the local information updating and external information exchanging among neighboring PEVs. A variable-size stochastic matrix is defined to specify the communication topology among PEVs, as well as guarantee the convergence of the consensus targets. The simulation results show that, with limited communication, the large-scale PEV charging/discharging can be efficiently coordinated to reduce total charging power loss or provide V2G service with maximum available PEV power. Meanwhile the PEV charging requirements specified by drivers can be satisfied. In addition, the convergence of the proposed distributed consensus algorithm can always be guaranteed upon random PEV departures.

A model predictive control approach for matching uncertain wind generation with PEV charging demand in a microgrid

The matching between uncertain wind power supply and plug-in electric vehicles (PEVs) charging demand has the potential to reduce the greenhouse gas emission and the fossil fuel pollution. In view of this, we propose a hierarchical model predictive control approach to coordinate the wind generation and PEV charging in the context of microgrid. The proposed control approach consists of two control layers. In the top layer, a stochastic model predictive controller computes the optimal power references for the wind generator and the PEV fleet. These references are fed to the bottom layer, and are further executed by the wind generator controller and PEV fleet controller, respectively. A salient feature of this approach is that it comprehensively incorporates the uncertainties in both sides of supply and demand, i.e., the uncertainties associated with the maximum available wind generation, and the uncertainties associated with the PEVs charging demand. Using the Chebyshev inequality and the chance constraints, the corresponding stochastic optimization problem is approximated as a quadratic programming problem. By doing so, the proposed approach not only keeps the microgrid power balance, but also ensures the PEV users’ quality of experience. Furthermore, it can bring the power flow between microgrid and utility power system to a predefined trajectory. Simulation results based on real-world wind and PEV data validate the effectiveness of the proposed approach.

A Novel Optimization of Plug-In Electric Vehicles Charging and Discharging Behaviors in Electrical Distribution Grid

In most countries, the problems of energy and environment are becoming worse. To deal with the environmental impacts and the dependence on fossil energy, many solutions were proposed. Plug-in electric vehicles (PEVs) is one of the best technique among these solutions. However, the large number of PEVs connected to the power grid simultaneously might increase power fluctuation or even cause the electricity shortage and thus affecting the typical use of the basic load. To cope with this issue and inspire PEV users coordinating with scheduling results, an algorithm was proposed to ensure the power transmission safety of branches and maximize the economic benefits. Considering the cost of both PEV owners and the power grid, a two-phase model of optimizing PEVs charging and discharging behaviors was built. According to the traveling purpose of PEV owners and the current electricity price, in the first phase, a novel model which defines each PEV’s charging or discharging status was established. The number of PEVs’ charging and discharging in each charging station can be obtained. Considering the constraints on the power transportation of branch, in the second phase, we built a mathematical model to maximize the benefit of both power grid and PEV owners. The genetic algorithm was used to optimize the charging and discharging power of PEVs. Simulation results show that the optimization method proposed in this paper has a better performance on the daily power curve compared with the uncoordinated PEVs charging.