Vehicle Grid Integration Research Articles

Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system

The expansion of electric vehicles (EVs) and renewable energy (RE) are the two major strategies countries are adopting to achieve energy transition. However, the discrepancy between intermittent RE generation and EV charging load may impose burden on the RE-dominant power grid of the future. For the issue, Time-of-Use (ToU) pricing for EV charging has been widely discussed or adopted as its potential is well acknowledged by previous studies. While ToU mechanism has been primarily based on drivers' price-responsive behavior, this research highlights that EV charging decisions are influenced by various factors beyond price, such as time of a day, charger accessibility and waiting time. Here, we conducted the discrete choice experiment to measure drivers' preferences for EV charging, and developed an EV charging behavior model which incorporated drivers' situational-responsiveness as well as price-responsiveness. Also, the model was used to design optimal ToU tariffs to minimize net-load variation. The results showed that strategic ToU tariffs can shift EV charging load, but achieving desirable load shifts requires a significant price gap. Additionally, combining ToU pricing with strategic deployment of charging infrastructure can effectively shift EV charging load, reducing RE curtailments by 22.14%, and LNG generation and carbon emissions by 10.12%, compared to the Business-as-Usual (BaU) scenario with current tariff rates. Thus, this study highlights the importance of flexible EV charging pricing and the importance of considering charging infrastructure deployment when designing EV ToU pricing.

Quantification of CO<sub>2</sub> Emission Reduction and Battery Degradation Using Vehicle Grid Integration

Quantification of CO<sub>2</sub> Emission Reduction and Battery Degradation Using Vehicle Grid Integration

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

Assessing the Nationwide Benefits of Vehicle–Grid Integration during Distribution Network Planning and Power System Dispatching

The diffusion of electric vehicles is fundamental for transport sector decarbonization. However, a major concern about electric vehicles is their compatibility with power grids. Adopting a whole-power-system approach, this work presents a comprehensive analysis of the impacts and benefits of electric vehicles’ diffusion on a national power system, i.e., Italy. Demand and flexibility profiles are estimated with a detailed review of the literature on the topic, allowing us to put forward reliable charging profiles and the resulting flexibility, compatible with the Italian regulatory framework. Distribution network planning and power system dispatching are handled with dedicated models, while the uncertainty associated with EV charging behavior is managed with a Monte Carlo approach. The novelty of this study is considering a nationwide context, considering both transmission and distribution systems, and proposing a set of policies suitable for enabling flexibility provision. The results show that the power and energy demand created by the spread of EVs will have localized impacts on power and voltage limits of the distribution network, while the consequences for transmission grids and dispatching will be negligible. In 2030 scenarios, smart charging reduces grid elements’ violations (−23%, −100%), dispatching costs (−43%), and RES curtailment (−50%).

Grid Integration for Electric Vehicles: A Realistic Strategy for Environmentally Friendly Mobility and Renewable Power

The promotion of electric vehicles (EVs) as sustainable energy sources for transportation is advocated due to global considerations such as energy consumption and environmental challenges. The recent incorporation of renewable energy sources into virtual power plants has greatly enhanced the influence of electric vehicles in the transportation industry. Vehicle grid integration offers a practical and economical method to improve energy sustainability, addressing the requirements of consumers on the user side. The effective utilisation of electric vehicles in stationary applications is highlighted by technological breakthroughs in the energy sector. The continuous advancement in science and industry is confirming the growing efficiency of electric vehicles (EVs) as virtual power plants. Nonetheless, a thorough inquiry is imperative to elucidate the principles, integration, and advancement of virtual power plants in conjunction with electric automobiles, specifically targeting academics and researchers in this field. The examination specifically emphasises the energy generation and storage components used in electric vehicles. In addition, it explores several vehicle–grid integration (VGI) configurations, such as single-stage, two-stage, and hybrid-multi-stage systems. This study also considers the various types of grid connections and the factors related to them. This detailed investigation seeks to offer insights into the various facets of incorporating electric vehicles into virtual power plants. It takes into account technology improvements, energy sustainability, and the practical ramifications for users.

Enhancing Bipolar DC Microgrid Resilience through Coordinated Control of Vehicle-Grid Integration and Renewable Energy Sources

Enhancing Bipolar DC Microgrid Resilience through Coordinated Control of Vehicle-Grid Integration and Renewable Energy Sources

Technology and economics of electric vehicle power transfer: insights for the automotive industry

Battery-based electric vehicles (BEVs) in the United States (U.S.) set a new sales record in 2022, driven by technology, policy, environmental, and economic objectives. However, the rapid deployment of BEVs and charging infrastructure without a careful review of their integration with the electric grid can have negative economic impacts on reliable and resilient electricity supply. Bi-directional power transfer (Bi-Di) vehicle-grid integration technologies and services such as vehicle-to-home or building (V2H/B) and vehicle-to-grid (V2G) can potentially lower local and system peak demand, improve economics for grid operators, and benefit BEV customers. Original equipment manufacturers (OEMs) in the automotive industry are exploring technologies and economics (techno-economics) for Bi-Di services. The study conducted a literature review of eleven case studies in the U.S. and Europe that featured Bi-Di demonstrations from 2005 to 2022 to highlight insights and techno-economic opportunities and challenges for OEMs. The findings should motivate the OEMs to prioritize technology innovation and business models to increase BEV sales and gain continuous revenue from Bi-Di services, which can potentially transition "car makers" to "technology solution" companies.

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.

Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid

Electric vehicle (EV) technology has revolutionized the transportation sector in the last few decades. The adoption of EVs, along with the advancement of smart grid technologies and Renewable Energy Sources (RES), has introduced new concepts in the automobile and power industries. Vehicle-Grid Integration (VGI) or Vehicle-to-Grid (V2G) is a technology revolutionizing both the transport and electric power sectors. From a V2G perspective, these sectors are complementary and mutually beneficial. For the power sector, mitigation of voltage and frequency excursions and the prospect of grid stabilization on the brink of uncertainties owing to the dynamics in the grid scenario are very important. This article focuses on various aspects of EV-power grid integration. The tremendous benefits of this technology, as presented in the literature, are reviewed. Furthermore, the concerns and the implementation challenges are reviewed in detail in this work.

Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview

The increasing electric vehicle (EV) adoption in the context of transportation electrification and carbon neutrality pursuit brings both new challenges and opportunities for all the stakeholders in EV-grid integration. To fully unleash the potentials of EVs as flexible distributed energy storage to facilitate efficient EV-grid interactions, it is imperative to predicting spatio-temporal distributions of EV charging demand, optimizing charging infrastructure layout and implementing smart charging scheduling schemes. Appropriate EV behavior modeling plays a fundamental role to realize these targets. This paper aims to provide a comprehensive review on EV behavior modeling and its applications in EV-grid integration algorithm development. Various models have been developed to describe EV usage patterns, charging decision making process and response to smart charging schemes. In particular, the existing usage pattern models including temporal, spatial and energy sub-models are expounded, and different sub-models of charging choice and response to smart charging are also presented. An EV behavior modeling paradigm is proposed to provide guidance for EV behavior model selection in different application scenarios by developing different portfolios of temporal, spatial, energy usage, charging choice and response models. Accordingly, enabling EV behavior modeling for EV charging demands prediction and smart charging scheduling is covered in details. This study provides in-depth behavioural insights and viable approaches to developing efficient EV behavior models for advancing EV-grid integration and provides perspectives towards future research directions.

Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm

The area of a Microgrid (μG) is a very fast-growing and promising system for overcoming power barriers. This paper examines the impacts of a microgrid system considering Electric Vehicle Grid Integration (EVGI) based on stochastic metaheuristic methods. One of the biggest challenges to slowing down global climate change is the transition to sustainable mobility. Renewable Energy Sources (RESs) integrated with Evs are considered a solution for the power and environmental issues needed to achieve Sustainable Development Goal Seven (SDG7) and Climate Action Goal 13 (CAG13). The aforementioned goals can be achieved by coupling Evs with the utility grid and other RESs using Vehicle-to-Grid (V2G) technology to form a hybrid system. Overloading is a challenge due to the unknown number of loads (unknown number of Evs). Thus, this study helps to establish the system impact of the uncertainties (arrival and departure Evs) by proposing Stochastic Monte Carlo Method (SMCM) to be addressed. The main objective of this research is to size the system configurations using a metaheuristic algorithm and analyze the impact of an uncertain number of Evs on the distribution of residential power in Tripoli-Libya to gain a cost-effective, reliable, and renewable system. The Improved Antlion Optimization (IALO) algorithm is an optimization technique used for determining the optimal number of configurations of the hybrid system considering multiple sources, while the Rule-Based Energy Management Strategy (RB-EMS) controlling algorithm is used to control the flow of power in the electric power system. The sensitivity analysis of the effect parameters has been taken into account to assess the expected impact in the future. The results obtained from the sizing, controlling, and sensitivity analyses are discussed.

Strategic Integration Of Electric Vehicles: An Australian Analysis

This paper presents the challenges and opportunities of strategic electric vehicle (EV) grid integration with a particular focus on its status and outlook in Australia. EVs are a critical part of many countries net zero commitments because electrification is a key piece in the puzzle to decarbonise the transport sector. Approaches are needed to ensure EVs and their charging practices can be incorporated within the electricity grid cost-effectively, while increasing system reliability and stability, supporting transport decarbonisation, and ensuring other societal benefits. Australia is a large country with a relatively small population which poses challenges for both EV charging and electricity distribution infrastructure provision. However, other market characteristics (such as high solar PV adoption among households) and the rapidly decarbonising grid make Australia an useful case study. Especially for how challenges over range, grid integration, and lack of domestic EV production is leading to new innovations that could inform steps towards more rapid EV adoption in other jurisdictions. This paper will discuss the challenges and opportunities of electric vehicle (EV) uptake in Australia based on a national consultation and workshop series involving stakeholders from the energy and transport sectors.

Assessing potential of optimized Time-of-Use pricing for Electric Vehicle charging in Deep Vehicle-Grid integration system

Assessing potential of optimized Time-of-Use pricing for Electric Vehicle charging in Deep Vehicle-Grid integration system

Utilization of Electric Vehicle Grid Integration System for Power Grid Ancillary Services

Electric vehicle grid integration (EVGI) is one of the most important parts of transportation electrification. However, large-scale EV charging/discharging can have an adverse effect on the distribution grid, due to a large amount of load being drawn from or fed back to the power grid. Additionally, the power electronics used in the grid interaction may impose additional complications, such as voltage and frequency deviation, harmonic distortion, etc. With proper control scheme designs for the grid-connected inverters, such complications can be mitigated, and several grid ancillary services, such as voltage and frequency support, reactive power support, and harmonic mitigation, can be facilitated from large-scale EVGI. In this study, a large-scale EVGI system is developed where the vector control implementation of a grid-connected inverter in the d-q reference frame is presented for providing different grid ancillary services using the EVGI system. The EVGI system is operated in different control modes to ensure multiple ancillary services of the power grid. The study is supported by the electromagnetic transient simulation performed in Matlab/Simulink of a large-scale EVGI system. The simulation shows that with the proper control mechanism of grid-connected inverters, EVGI can be used to provide several useful grid ancillary services.

Smart Charging and Consumer Behavior in the United States

To ensure that charging electric vehicles (EVs) supports, rather than strains, the nation’s electric grids, we will need consumers to adopt smart charging, also known as vehicle-grid integration (VGI). This paper presents the findings of a consumer survey on VGI of prospective and current Honda EV owners in the United States.

Analysis of public acceptance of electric vehicle charging scheduling based on the technology acceptance model

As a form of vehicle-grid integration technology, electric vehicle charging scheduling (EVCS) brings benefits to the operation of power grids and the consumption of renewable energies. Exploring users’ acceptance of EVCS helps to understand the formation mechanisms of acceptance and improve user adoption. Based on 426 questionnaires from Hangzhou, China, technology acceptance model and mean difference test were applied in this paper to explore the impact factors of willingness to accept and the differences in EVCS acceptance among groups with different social attributes. Results showed that 81.2% of the respondents were willing to participate in EVCS and were more inclined to accept charging time scheduling compared to charging location scheduling. Perceived risk had a significantly direct and negative effect on user acceptance, while initial trust, social value, and social influence had a significantly indirect and positive effect. There were no statistically significant differences in the acceptance of EVCS between males and females, or among groups with different education and income levels, with the married group and the 31–40 age group being more willing to accept than the unmarried group and the 18–30 age group respectively. Finally, the results were discussed and recommendations were presented accordingly.

Learning to Operate an Electric Vehicle Charging Station Considering Vehicle-Grid Integration

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 process, EVs are allocated to either the waiting or charging spots. In the decentralized execution process, each charger makes its own charging/discharging decision while learning the action-value functions from a shared replay memory. This CADE framework significantly improves the scalability and sample efficiency of the RL algorithm. Numerical results show that the proposed CADE framework is both computationally efficient and scalable, and significantly outperforms the baseline model predictive control (MPC). We also provide an in-depth analysis of the learned action-value function to explain the inner working of the reinforcement learning agent.

A comprehensive review on electric vehicles: charging and control techniques, electric vehicle-grid integration

Abstract Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of electric vehicles (EVs) to the smart grid. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Smart grid technologies are summarized in Section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

Tariff‐based regulatory sandboxes for EV smart charging: Impacts on the tariff and the power system in a national framework

Electrification of private transport is a fundamental step for decarbonizing mobility. Electric vehicles (EV) can be a burden for the power system if vehicle-grid integration is not implemented by design. Market-based smart charging projects are effective, but their massive diffusion is limited. A fundamental instrument toward a large adoption of smart charging is the inclusion of smart charging-oriented measures in regulatory sandboxes, conveniently acting on electricity tariff. This paper presents a set of possible toolboxes for smart charging to show the potential that regulatory measures can have on steering the infrastructure deployment and the charging activity. Each proposed toolbox addresses a specific charging mode, including domestic, workplace, and public access charging. Proposed measures are target-oriented and evaluated based on their environmental, technical, and economic impacts. These include the carbon footprint of the electricity used for EV charging, the impact in terms of peak power withdrawal from the public grid and the charging cost born by EV users. Additionally, the assessment about the impact of prospected measures on the electricity tariffs' income is provided. Results show the possibility of reducing the evening EV-related peak load by 30% to 50% via home smart charging. Also, a 10% decrease in carbon footprint is achieved by valley-filling with work charging. Charging at the destination can reduce the system cost for the new distribution infrastructure, dropping the number of new dedicated connection points for public charging. The cost of incentives is partially repayable considering the additional EV penetration fostered by the reduced charging costs.

A Cloud-Based Simulation and Testing Framework for Large-Scale EV Charging Energy Management and Charging Control

<div class="section abstract"><div class="htmlview paragraph">The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems. The subsystems can communicate with each other via message queuing telemetry transport communication (MQTT) protocol. The OPAL-RT is used to perform grid simulation and optimal EV charging energy management at the distribution grid level. It communicates with node level EVCSS and the SEP to collect real-time charging data and send charging power commands. The OPAL-RT can also communicate with transmission level controllers to provide grid services, such as frequency regulation. The EVCSS manages regional EV charging to limit the effects of clustered EV charging on the distribution grid. It uses standardized communication protocols: Open Charge Point Protocol 2.0 for charging station networks and ISO 15118 between EVs and charging stations. The modular open systems design approach of the platform allows the integration of EV charging control algorithms and hardware charging systems for performance evaluation and interoperability testing. The experimental test results show that the communication links of the platform work properly, and the EV charging control algorithms can respond to transmission level grid service request with minimal impact on local operations.</div></div>