Grid-connected Electric Vehicles Research Articles

Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model

Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model

Empowering Dynamic Active and Reactive Power Control: A Deep Reinforcement Learning Controller for Three-Phase Grid-Connected Electric Vehicles

Empowering Dynamic Active and Reactive Power Control: A Deep Reinforcement Learning Controller for Three-Phase Grid-Connected Electric Vehicles

Battery Protective Electric Vehicle Charging Management in Renewable Energy System

The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This article develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a U.K. microgrid with actual energy generation and consumption data. This article can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.

Frequency Regulation of Electric Vehicle Aggregator Considering User Requirements with Limited Data Collection

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.

Online Battery Protective Energy Management for Energy-Transportation Nexus

Grid-connected electric vehicles (GEVs) and energy-transportation nexus bring a bright prospect to improve the penetration of renewable energy and the economy of microgrids (MGs). However, it is challenging to determine optimal vehicle-to-grid (V2G) strategies due to the complex battery aging mechanism and volatile MG states. This article develops a novel online battery anti-aging energy management method for energy-transportation nexus by using a novel deep reinforcement learning (DRL) framework. Based on battery aging characteristic analysis and rain-flow cycle counting technology, the quantification of aging cost in V2G strategies is realized by modeling the impact of number of cycles, depth of discharge, and charge and discharge rate. The established life loss model is used to evaluate battery anti-aging effectiveness of agent actions. The coordination of GEVs charging is modeled as multiobjective learning by using a DRL algorithm. The training objective is to maximize renewable penetration while reducing MG power fluctuations and vehicle battery aging costs. The developed energy-transportation nexus energy management method is verified to be effective in optimal power balancing and battery anti-aging control on a MG in the U.K. This article provides an efficient and economical tool for MG power balancing by optimally coordinating GEVs charging and renewable energy, thus helping promote a low-cost decarbonization transition.

A Soft-Switched Power-Factor-Corrected Single-Phase Bidirectional AC–DC Wireless Power Transfer Converter With an Integrated Power Stage

Bidirectional ac–dc wireless power transfer (WPT) converters are widely used in grid-connected electric vehicle systems for grid-to-vehicle and vehicle-to-grid applications. They usually apply a two-stage topology with a bidirectional ac–dc power factor correction converter and a bidirectional dc–dc WPT converter. Such a two-stage topology cannot achieve high efficiency and low cost. Recently, single-stage matrix bidirectional ac–dc WPT converters have been proposed. However, the matrix-converter-based topologies do not utilize remarkably lower count of power switches and generate large double-line frequency ripple in the dc-side voltage or current. This article proposes a new single-phase bidirectional ac–dc WPT topology with an integrated power stage to overcome the drawbacks of the matrix-converter-based topologies and retain the advantages of single-stage topologies. Description of the proposed topology with the modulation and control methods, theoretical analysis, and reference design procedure are presented in detail. Finally, a scaled-down laboratory prototype with 500-W output power is implemented with experimental results presented to validate the principle, analysis, and design.

Flexible Operation of Grid-Connected Electric Vehicle Powertrain Converters: Power Conditioning and Consumed Energy Management in Household Networks

Flexible Operation of Grid-Connected Electric Vehicle Powertrain Converters: Power Conditioning and Consumed Energy Management in Household Networks

Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components.

Vehicle-to-grid management for multi-time scale grid power balancing

The mitigation of peak-valley difference and transient power fluctuation are both of great significance to the economy and stability of the power grid. This paper proposes a novel vehicle-to-grid behavior management method that can provide peak-shaving and fast power balancing service to the grid simultaneously. Firstly, a multi-time scale vehicle-to-grid behavior management framework is designed to enable large-scale optimization and real-time control at the same time in vehicle-to-grid scheduling. Then, the grid peak-shaving requirement is modeled as an optimization problem in a centralized V2G state coordinator, where the charging behavior of all grid-connected electric vehicles can be synergistically scheduled. The optimization variable is designed as a group of vehicle-to-grid state control signals that can respond to grid peak-shaving requirements. Further, a V2G power controller is designed to manage the vehicle charging power in real time based on the sampled grid frequency state and discrete state control signals. In the developed scheduling method, the charging power of grid-connected electric vehicles is scheduled by the cooperation between the V2G state coordinator and the power controller. The effectiveness of the proposed methodologies is verified on a microgrid system, and results indicate that the V2G scheduling can achieve multi-time scale grid power balancing. This work can bring dual benefits, enabling system operators to use cheap solutions to manage energy networks and allowing vehicle owners to gain profits from providing V2G services to the grid.

Improved Q-learning for Energy Management in a Grid-tied PV Microgrid

This paper proposes an improved Q-learning method to obtain near-optimal schedules for grid and battery power in a grid-connected electric vehicle charging station for a 24-hour horizon. The charging station is supplied by a solar PV generator with a backup from the utility grid. The grid tariff model is dynamic in line with the smart grid paradigm. First, the mathematical formulation of the problem is developed highlighting each of the cost components considered including battery degradation cost and the real-time tariff for grid power purchase cost. The problem is then formulated as a Markov Decision Process (MDP), i.e., defining each of the parts of a reinforcement learning environment for the charging station’s operation. The MDP is solved using the improved Q-learning algorithm proposed in this paper and the results are compared with the conventional Q-learning method. Specifically, the paper proposes to modify the action-space of a Q-learning algorithm so that each state has just the list of actions that meet a power balance constraint. The Q-table updates are done asynchronously, i.e., the agent does not sweep through the entire state-space in each episode. Simulation results show that the improved Q-learning algorithm returns a 14% lower global cost and achieves higher total rewards than the conventional Q-learning method. Furthermore, it is shown that the improved Q-learning method is more stable in terms of the sensitivity to the learning rate than the conventional Q-learning.

Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit

Increased worldwide uptake of Electric Vehicles (EVs) accentuates the need for developing coordinated EV charging and discharging methods that mitigate detrimental and sustained under-voltage and over-voltage conditions in distribution networks. In this paper, a centrally coordinated EV charge-discharge scheduling method is proposed, referred to as Network-aware EV Charging (and Discharging) N-EVC(D), that takes into account both EV customer economics and distribution grid constraints. Specifically, N-EVC(D) is designed to maintain quasi-steady-state feeder voltages within statutory power quality limits, while minimizing EV customer operational costs associated with: (1) purchasing (or otherwise being compensated for delivering) electricity on a time-of-use tariff; and (2) battery degradation due to frequent charging and discharging. The optimization problem for N-EVC(D) is formulated as a quadratic program, with voltage constraints to limit voltage variability across a radial distribution feeder, and individual EV constraints to satisfy heterogeneous EV charge requirements. In N-EVC(D), each grid-connected EV follows an operator-specified battery schedule that is obtained by solving the proposed quadratic program. A receding horizon implementation is also proposed to support near-real-time N-EVC(D) operations while accommodating non-deterministic EV arrivals and departures. The benefits of N-EVC(D) are assessed by means of numerical simulations carried out on an IEEE test feeder populated with a real-world dataset of residential load collected from households within an Australian distribution network. The simulation results confirm that N-EVC(D) mitigates non-compliant voltage deviations that would otherwise occur when voltage constraints are not enforced. Compared to uncoordinated EV charging, N-EVC(D) offers a 92% – 111% reduction in the operational costs incurred by EV customers.

Comparison of common DC and AC bus architectures for EV fast charging stations and impact on power quality

Abstract This work covers the comparative analysis of common DC and AC bus architectures for grid-connected Electric Vehicle Fast Charging Stations (EVFCS) and addresses the relevant power quality issues. In this manuscript, Unipolar Common DC Bus and Common AC bus topologies are proposed for FCS. Two-level Voltage Source Converter (VSC) is used to connect the EVFCS to the grid through DC and AC buses in both the methods. Converters are connected to the grid through a Distribution Transformer (DT), and the output side is connected to the EV battery chargers. Grid-connected FCS are examined in terms of harmonic generation, and their impact on the host electric grid is evaluated. Voltage and current harmonics of both the topologies with different transformer configurations are compared, and an optimum transformer configuration is proposed. The Unit Template-based Control (UTC) algorithm is also proposed in this work, and the Constant Current-Constant Voltage (CC-CV) strategy is used for the charging control of the batteries. The work is focused on feasibility and comparison of common DC and AC bus FCS and impacts of harmonics generated due to EV charging on the grid power quality. The proposed architectures are then evaluated as per IEC and IEEE Power Quality standards. Power quality impacts are studied by varying the number of EV charging bays. The charging profile of both the architectures is examined by performing steady-state and dynamic analysis. Furthermore, a comparative analysis is also performed by considering different transformer configurations.

System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review

The optimal planning of electric vehicle (EV) charging stations (ECSs) with advanced control algorithms is very important to accelerate the development of EVs, which is a promising solution to reduce carbon emissions of conventional internal combustion engine vehicles (ICEVs). The large and fluctuant load currents of ECSs can bring negative impacts to both EV-related power converters and power distribution systems if the energy flow is not regulated properly. Recent review papers related to EVs found in open literature have mainly focused on the design of power converter-based chargers and power interfaces, analyses of power quality (PQ) issues, the development of wireless charging techniques, etc. There is currently no review paper that focuses on key technologies in various system configurations, optimal energy management and advanced control issues in practical applications. To compensate for this insufficiency and provide timely research directions, this paper reviews 143 previously published papers related to the aforementioned topics in recent literature including 17 EV-related review papers found in Institute of Electrical and Electronics Engineers (IEEE)/Institution of Engineering and Technology (IET) (IEEE/IET) Electronic Library (IEL) and ScienceDirect OnSite (SDOS) databases. In this paper, existing system configurations, related design methods, algorithms and key technologies for ECSs are systematically reviewed. Based on discussions given in the reviewed papers, the most popular ECS configuration is a hybrid system design that integrates renewable energy (RE)-based power generation (REBPG), various energy storage systems (ESSs), and utility grids. It is noteworthy that the addition of an ESS with properly designed control algorithms can simultaneously buffer the fast, fluctuant power demand during charging, smooth the intermittent power generation of REBPG, and increase the overall efficiency and operating flexibility of ECSs. In addition, verifying the significance of the flexibility and possible profits that portable ESSs provide in ECS networks is a potential research theme in ECS fields, in which the potential applications of portable ESSs in the grid-tied ECSs are numerous and could cover a full technical spectrum.

A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework

The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs) intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results.

Inverse sinusoidal pulse width modulation switched electric vehicles’ battery charger

<p>This paper documents an efficient, cost-effective and sustainable grid-connected electric vehicles (EVs) battery charger based on a buck converter to reduce the harmonics injected into the mains power line. To utilize the switching converter as an effective power factor controller (PFC), inverse sinusoidal pulse width modulation (ISPWM) signals have been applied. A mathematical relationship between the sending-end power factor and the duty ratio of the switching converter has been presented. To ensure the sustenance of the proposed method, a simulation model of the proposed battery charging system has been tested on PSIM simulation platform. The simulation results yield to a lossless charging system with a sending-end power factor close to unity. An experimental testbed comprising a 60 V battery bank of 100 A-h capacity with a charging current of 7 A has been generated. The laboratory assessments present an 88.1% efficient charging prototype with a resultant sending-end power factor of 0.89. The laboratory framework concerns with the comparative analysis of the power efficiency, sending-end power factor and lines current total harmonic distortion (THD) values obtained for different charging methods and the evaluations corroborate the reliability of the proposed work.</p>

Design of hybrid forward boost converter for renewable energy powered electric vehicle charging applications

The grid-connected electric vehicle charging stations are being integrated into renewable energy for preserving the conventional energy resources as well as to ensure sustainable development of the society. In addition to the effortless charging, the sudden demand variations in the grid can be met by the renewable energy storage system in the charging stations. The main challenge in this concept is the unstable nature of renewable energy. As the energy storage system of charging station is directly connected to the renewable energy source like a solar panel, according to the variable and deficient input power condition, the converter should supply a regulated output at the required magnitude. If a conventional converter is used for this purpose, the charging efficiency will be poor for higher gain conditions. This article presents a hybrid forward-boost converter to overcome this challenge. By maintaining the power transfer efficiency higher, the gain can be varied to supply a regulated voltage, even for very low input conditions in this proposed topology. The maximum overall efficiency of 95% is achieved for a variable input condition of 30-50 V to provide an output voltage of 170 V. The concept is validated by implementing a 150 W hybrid converter.

Charge and Discharge Control Method for Grid-Connected Electric Vehicle

A inverter with LCL filter was adopted as a main interface between electric vehicle and power grid. The mathematical model of the LCL filter was built, and then a charge and discharge control method for grid-connected electric vehicle was proposed. This control method employed an outer grid-side current loop and an inner inverter-side current loop. And this control method can protect power switches effectively, but also ensure the system stability and high in-grid power factor. Based on the Hurwitz stability criterion and Lienard Chipart stability criterion, the design method of controller parameters in inner and outer loops is derived. Simulations and experiments of electric vehicle in grid-connected mode have been conducted. Simulation and experiment results validate the correctness and feasibility of the proposed method.

Perspectives on decarbonizing the transport sector in the EU-28

The transport sector is of great importance at a global level in order to become a low-carbon economy by 2050. In the European Union the transport sector accounts for 20% of anthropogenic greenhouse gas emissions. Electric propulsion systems might be a feasible solution for greenhouse gas mitigation in the transport sector. Based on our cost assumptions, grid-connected electric vehicles play no major role in the analyzed scenarios until 2030 but reach high market shares (over 90%) under stringent greenhouse gas mitigation targets until 2050. Renewable electricity plays a crucial role in providing the additional power needed in the transport sector. Financial incentives seem to be effective in order to reach the cost optimal car mix in France and Germany.

Smart Charging for Grid-Connected Electric Vehicles to Provide Regulation Service

In the power system, grid-connected electric vehicles (EVs) have been considered as attractive resources that provide demand response (DR) and regulation service by controlling the charging power. However, due to the minimum resource size requirements in the electricity market, individual EVs are required to be aggregated by EV charging service providers (CSPs). The CSPs can minimize the charging costs of individual EVs by scheduling EV charging power considering electricity rates. At the same time, the CSPs benefits from the electricity market by providing the DR and regulation capacity. This paper proposes a systematic scheduling method for smart charging to maximize the benefits of CSP from the ancillary service (AS) market and demand response (DR) market as well as to minimize its charging cost. Numerical examples are also presented in order to demonstrate the effectiveness of the proposed scheduling model.

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles’ state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.