Large-scale Electric Vehicles Research Articles

Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market

Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.

Energy Management and Optimization of Large-Scale Electric Vehicle Charging on the Grid

The sustainability of a clean energy transition for electric vehicle transportation is clearly affected by increased energy consumption cost, which is associated with large-scale electric vehicles (EVs) charging on a fossil-fuel dependent electricity grid. This places a potential threat on the safe operations and stability of the grid and increases the emissions of greenhouse gases (GHGs) from the power stations that generate the electricity. Furthermore, the uncontrolled large-scale integration of EVs charging on the grid will increase exponentially in the coming years. Because of this, new peaks on the grid will be generated due to the EV charging load variance, and a significant impact on the transformer limit and substation capacity violation will occur. To mitigate the significant impact of the high cost of energy consumption by large-scale EVs charging on the grid, and to reduce the emissions of GHGs, there is a need to provide a multi-level optimization approach that is robust and dynamic to solve the uncontrolled charging problem of large-scale integration of EVs to the grid. This paper investigates the grid energy consumption by EVs and reviews recent applications of EV charging controls and optimization approaches used for the energy management of large-scale EVs charging on the grid. Energy management in this context is not trivial. It implies that the objectives such as load shifting, peak shaving, and minimizing the high cost of electricity consumption with a stable grid operation can be achieved. In the context of this study, EVs charging on the grid includes both battery electric vehicles (BEVs), which have larger battery banks with a longer charging duration and higher energy consumption capacity, and plug-in hybrid electric vehicles (PHEVs) which have smaller battery capacities.

Distribution Network Expansion Planning Approach for Large Scale Electric Vehicles Accommodation

The increased power demand incurred by electric vehicles (EVs) has imposed new challenges on the distribution network. One significant responsibility of the distribution network operator (DNO) is to design and expand the distribution network periodically to accommodate the ever-increasing demand. The previous researches mainly use relaxation techniques to enhance the problem tractability, which would involve additional binary variables and incur computation burden to the optimization problem, or even worse, may lead to an intractable problem. This paper develops a mixed-integer non-linear programming (MINLP) model considering various network constraints for the distribution network expansion planning under a high penetration level of EVs. The stochastic characteristics of demands are addressed through set of scenarios. A distributed biased min-consensus algorithm based approach is proposed to solve the MINLP model. Finally, comparison tests are conducted on three different scales of distribution networks to validate the effectiveness of the proposed approach. Simulation results demonstrate that the computation time of the proposed approach is reduced by 42.46% on average for different systems compared with that of the traditional shortest path algorithm based approach.

Research and Application on High Quality Power Supply Technology of New Distribution System

Under the dual-carbon target, the intermittent grid-connection of high-permeability distributed power generation and random charging of large-scale electric vehicles increase the volatility and randomness of the distribution network. Meanwhile, the coupling correlation between the power network, information network and social network will be significantly enhanced. In view of the highly uncertain operation of the new distribution system, under the existing distribution network operation control and operation and maintenance management system, This paper puts forward the realization mode of high quality power supply for the new distribution system from three aspects: the adaptability of the grid, the overall perception ability and the improvement of the operation control ability.

Multi Agent Interaction Mechanism of Intelligent Network Connection for Interaction Between EV and Power Grid

As a new type of flexible load, the charging behavior of electric vehicles is random and intermittent in space and time, which puts forward higher requirements for the safe, stable grid operation and power quality. When a large-scale electric vehicle charging load is connected to the power grid, its disordered charging behavior will also bring adverse effects to the power system. From the perspective of charging behavior, the gradual increase of devices such as electric vehicles and charging piles have formed the Internet of Electric Vehicles, power distribution network, and charging equipment Internet of Things. At the same time, more and more charging loads are connected to the grid, and the charging behavior also contains a lot of load information on the grid side. These data are fused through the server, which can provide an economical and reasonable charging strategy for the charging behavior of electric vehicles on the regional grid side.

A comprehensive data-driven assessment scheme for power battery of large-scale electric vehicles in cloud platform

In cloud platform with power battery data from large-scale electric vehicles (EVs), cloud battery management system needs to achieve various monitoring tasks, including safety, degradation, and variation analysis. The unstable data quality and deployment conditions limit the full usage of existing methods, such as model-based estimation method and machine learning algorithm. This paper presents a comprehensive data-driven assessment scheme, named as ComDAS, to improve the multi-tasks realization for cloud platform. The proposed ComDAS can be fast deployed for lithium-ion batteries of EVs, and integrates three mainly concerned aspects: capacity estimation, anomaly detection, and variation evaluation. Neural network is applied for calibration of capacity estimation to achieve higher accuracy. A risk scoring method is proposed to achieve long-scale statistics filtering in time dimension, and weeks-early risk pre-warning for anomaly detection. Considering weak labels or even no labels of cloud EV data, correlation analysis among capacity, anomaly, and variation are also calculated to conduct cross validations and reveal the coupling evolution mechanism among the three aspects. Based on the designed monitoring process for large-scale EVs, ComDAS has been deployed to a realistic cloud big-data platform in Sichuan, China. The working ComDAS can hold the capacity error within 4.5 %, obtain the risk pre-warning more than one week earlier, and maintain the variation score of all EVs into 0–100 for further detection. Hence, it proves that ComDAS can maintain satisfying real-time performance for both large-scale EV statistics and specific detection of risky EV.

Research on electric vehicle charging load based on the monte Carlo

With the increasing number of electric cars, they may become the most important load in the grid of the future. Due to the randomness of electric vehicles access to the grid, when large-scale electric vehicles are linked to the grid, they will fluctuate on the grid, resulting in the failure of the power supply mode and operation control means of the traditional distribution network to attain the optimal result. In view of this phenomenon, based on the Monte Carlo calculation and simulation, the charging load of electric vehicles is predicted, which can achieve comprehensive prediction under different kinds of electric cars, different battery capacities, different charging methods, etc, and provide theoretical and technical support for the effect analysis of electric vehicles access to power grid.

Coordinated Scheduling for Multimicrogrid Systems Considering Mobile Energy Storage Characteristics of Electric Vehicles

Because of the rapid development of electric vehicles (EVs), the energy management of multimicrogrid (MMG) systems has attracted considerable research attention. The objective of this study is to coordinate scheduling performance for MMG systems under large-scale EV operations. To address the problem that the calculation time increases exponentially with the scale of EVs, a clustering algorithm was proposed to speed up the solving efficiency of the coordinated scheduling of MMG systems. To address the problem of the departure and arrival times of clustering EV crossover among various MGs, the proposed clustering algorithm set the boundary of the parking period of clustering EVs by maximizing their controllable time in various MGs. The coordinated scheduling strategy of MMG systems was executed considering the charging cost of EVs, the optimization of transmission power curves, and the absorption of renewable energy. The simulation results revealed that by fully utilizing the mobile energy storage characteristics of EVs, the performance of MMG systems can be maximized. Meanwhile, the computing efficiency of coordinated scheduling can be considerably improved in the case of large-scale EVs integrated into MMG systems by using the proposed clustering algorithm.

An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging

This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.

Interaction Strategy Between the Vehicle-station-grid Considering Privacy Protection

The number of electric vehicles (EVs) is increasing daily, and the charging demand for large-scale EVs has a greater impact on the distribution network, so it is urgent to study the cooperative interaction optimization strategy of the “EV-charging station-distribution grid.” In this paper, we take the charging load of EVs in the regional distribution network as the research object. We establish a multi-intelligence reinforcement learning-based optimization strategy for the interaction between EVs and the grid to obtain the optimal charging decision to minimize the peak-to-valley difference between the user-side economy and the grid-side load. The results show that the proposed EV-grid interaction optimization strategy can effectively reduce the peak-to-valley difference in charging station load and realize the efficient cooperative interaction of the “vehicle-station-grid.”

Hierarchically coordinated optimization of power distribution systems with soft open points and electric vehicles

The integration of large-scale electric vehicles (EVs) into power distribution systems (PDSs) causes low voltage and line congestion problems. EV charger converters can improve the voltage profile by compensating the reactive power, whereas soft open points (SOPs) can transfer active power between branches and compensate reactive power, which can help balance the branch power. Therefore, this study proposes a coordinated day-ahead (DA) and real-time (RT) optimization model for PDSs with SOPs and high penetration of EVs. In addition, directly controlling large-scale EVs is difficult for the distribution systems operator. Thus, hierarchical management of EVs is performed by using an EV aggregator (EVA) model. First, on the basis of the probability distribution of the state of EVs of each EVA, a DA optimization model with the minimum operation cost is established. A dynamic SOC boundary model is proposed to optimize active and reactive power of EVAs and EVs in RT stage. The original nonlinear problems of the DA and RT models are linearized by convex relaxation and linearization, which significantly improves the solving efficiency. Finally, the case studies demonstrate that SOPs can promote the integration of EVs, and the hierarchical coordinated optimization strategy not only can reduce the operation cost of the PDS, but also control active and reactive power of EVs in real time.

Optimized charging strategy for large-scale electric vehicles

Abstract Electric vehicles (EVs) have gained support from many governments, including China, due to their low-carbon, oil-independent and environmentally friendly characteristics. However, many EVs connected to the grid for charging will significantly burden the grid. In this paper, considering the actual situation, we propose a control strategy centered on EV users and supplemented by the power grid. Under the premise of guaranteeing users’ demand, we establish mathematical models to reduce users’ expenses and the peak-to-valley difference of the power grid and improve the particle swarm optimization algorithm to obtain better performance. We have verified the feasibility of the proposed strategy with examples, prioritizing the state of charge (SOC) of EVs even if the electricity price is expensive because EV users are more concerned about the EV SOC. The proposed strategy has a positive significance in reducing the cost and the grid’s peak-to-valley difference. The strategy has an excellent prospect for application because it takes into account the actual situation of large-scale charging of EVs.

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

Research on a two-stage power management system for electric vehicles

The large-scale application of electric vehicles that can be connected to the grid in the future will pose new challenges to the existing theories and methods of power system planning and operation. The disorderly charging behaviour of a large number of electric vehicles may bring significant negative effects on the safety and economic operation of the power system. Free charging of a large number of electric vehicles will increase the load of the power system, and even cause the phenomenon of line congestion, so that the active distribution network cannot operate normally. Based on the two-level power management system, this paper proposes a price elasticity matrix to reflect car owners' response to charging prices of charging stations at different locations, and designs an optimal real-time charging service fee pricing scheme for multiple charging stations to guide car owners' charging behaviour. Simulation results show that the proposed blocking scheduling strategy can effectively guide car owners to make reasonable charging plans while taking into account the benefits of charging facility operators, owners' charging costs and owners' satisfaction with the way they use electricity, solve the possible distribution network congestion caused by large-scale electric vehicles' centralized charging in the network and improve the safety and economy of power grid operation.

Mean Field Game based Energy Flow Management for Grid Integration of Large-scale Electric Vehicles*

To integrate the distributed energy of electric vehicles (EVs) into power grid, handling the collective energy demand of the EVs is a key issue due to the uncertainties in mobility behavior of the individual vehicle. This paper addresses this problem by using mean-field limit approach to challenge this issue. The system targeted in this paper consists of an electric energy supplier and a parking lot that hosts a electric vehicle (EV) fleet with sufficiently large population. The energy integration between the system and the power grid is formulated as two problems: a day-ahead electric trading planning problem of the supplier, where the benefit maximization is considered under the constraint of charging/discharging demand of the parking lot, and a decentralized charging control problem is solved for the individual EV with consideration of the collective behavior of the whole EV fleet. To decouple the two problems, the mean-field limit approach is introduced to predict the charging/discharging demand of the EV fleet, and this also enables one to design the decentralized control strategy by solving a mean-field game problem.

A novel stability index-based virtual synchronous machine droop scheme for performance improvement of hybrid AC/DC microgrid under volatile loading conditions

The large-scale electric vehicle penetration into the grid and the volatile nature of the electric vehicle loading adversely affect grid stability. This paper presents a novel index-based droop scheme for improving the performance of the hybrid AC/DC microgrid under volatile loading conditions. The novelty of the proposed control scheme is that the adaptiveness in selecting active droop gain based on the volatile stability index generates an accurate power reference rather than a constant power reference as in conventional droop gain methods. The state–space modelling of the proposed controller and index-based approach is used to determine the theoretical stability margin. The simulation experiment results agree with the theoretical stability margin limit. The efficacy of the control scheme is validated using real-time experiments on a hardware-in-the-loop real-time simulator, OPAL-RT. The results show that the proposed control scheme improves the performance of the hybrid AC/DC microgrid by maintaining the frequency/voltage fluctuations in the AC and DC sub-grids within the acceptable operating limits of ± 2% and ± 10% as per IEEE 1547 standards. As a result, the percentage improvement in voltage profile is 2.68% at the AC sub-grid and 5.55% at the DC sub-grid. • A novel stability index to reflect volatile loading conditions of HMG. • HMG stability enhancement using HVI-based VSM-V droop controller. • Mathematical modelling and analysis of VSM-V droop scheme. • Real-time experimental verification using OPAL-RT.

Multi-Objective Optimization Scheduling of Active Distribution Network Considering Large-Scale Electric Vehicles Based on NSGAII-NDAX Algorithm

Multi-Objective Optimization Scheduling of Active Distribution Network Considering Large-Scale Electric Vehicles Based on NSGAII-NDAX Algorithm

Metal Fluoride Cathode Materials for Lithium Rechargeable Batteries: Focus on Iron Fluorides

Exploring prospective rechargeable batteries with high energy densities is urgently needed on a worldwide scale to address the needs of the large-scale electric vehicle market. Conversion-type metal fluorides (MFs) are attractive cathodes for next-generation rechargeable batteries because of their high theoretical potential and capacities and provide new perspectives for developing novel battery systems that satisfy energy density requirements. However, some critical issues, such as high voltage hysteresis and poor cycling stability must be solved to further enhance MF cathode materials. In this review, the recent advances in mechanisms focused on FeF3 cathodes under lithiation/delithiation processes are discussed in detail. Then, the classifications and advantages of various synthesis methods to prepare MF-based materials are first minutely discussed. Moreover, the performance attenuation mechanisms of MFs and the effort in the development of mitigation strategies are comprehensively reviewed. Finally, prospects for the current obstacles and possible research directions, with the aim to provide some inspiration for the development of MF cathode-based batteries are presented.

Study of an Optimized Micro-Grid’s Operation with Electrical Vehicle-Based Hybridized Sustainable Algorithm

Recently, the expansion of energy communities has been aided by the lowering cost of storage technologies and the appearance of mechanisms for exchanging energy that is driven by economics. An amalgamation of different renewable energy sources, including solar, wind, geothermal, tidal, etc., is necessary to offer sustainable energy for smart cities. Furthermore, considering the induction of large-scale electric vehicles connected to the regional micro-grid, and causes of increase in the randomness and uncertainty of the load in a certain area, a solution that meets the community demands for electricity, heating, cooling, and transportation while using renewable energy is needed. This paper aims to define the impact of large-scale electric vehicles on the operation and management of the microgrid using a hybridized algorithm. First, with the use of the natural attributes of electric vehicles such as flexible loads, a large-scale electric vehicle response dispatch model is constructed. Second, three factors of micro-grid operation, management, and environmental pollution control costs with load fluctuation variance are discussed. Third, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) approach is proposed to confirm the method’s authenticity and reliability. The constructed large-scale electric vehicle response dispatch model significantly improves the load smoothness of the micro-grid after the large-scale electric vehicles are connected and reduces the impact of the entire grid. The proposed hybridized optimization method was solved within 296.7 s, the time taken for electric vehicle users to charge from and discharge to the regional micro-grid, which improves the economy of the micro-grid, and realizes the effective management of the regional load. The weight coefficients λ1 and λ2 were found at 0.589 and 0.421, respectively. This study provides key findings and suggestions that can be useful to scholars and decisionmakers.

Bidirectional AC–DC Modular Multilevel Converter With Electric Spring Functions for Stabilizing Renewable AC Power Grid at the Distribution Voltage Level

Bidirectional ac–dc power converters are essential in emerging smart grids with increasing renewable energy penetration. This article presents a bidirectional ac–dc power converter system comprising modular multilevel converters (MMCs) and dual active bridges (DABs) with medium-frequency transformer isolation designed for linking an ac distribution voltage of 6.6 kV to a dc grid of 800 V for future electric vehicle (EV) charging infrastructure. The novel contributions include: 1) a modular method to power EV charging infrastructures in multistorey carparks without mains-frequency transformers and 2) the incorporation of a front-end control with electric spring (ES) functions that enable the dc power grid with battery energy storage to interact dynamically with the ac power grid at the distribution voltage level to achieve instantaneous power balance and hence system stability. The long-term aim is to use large EV charging infrastructures to stabilize increasing intermittent renewable energy via the proposed ac–dc converter, consequently accelerating the adoption of large-scale renewable energy and EV as a complementary solution to combat climate change. This article focuses on the bidirectional ac–dc converter of this overall idea based on the MMC and DAB technologies as an example. Results on the power converter operation level and ac microgrid level are included.