Large-scale Electric Vehicles Research Articles

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles

Accurately predicting the power demand of large-scale electric vehicles (EVs) is one of the key tasks of power grid operation optimization. However, this task is difficult to complete due to insufficient data and high randomness of power demand. To address this issue, this paper proposes a transfer learning based hybrid method for power demand prediction of large-scale EVs. Firstly, the linear trend of power demand is extracted by Multiple Linear Regression (MLR), and the nonlinear residual error is obtained by removing the trend from the original data. Secondly, the residual error is predicted via the bidirectional long short-term memory network (BiLSTM). Meanwhile, transfer learning is employed to improve the prediction accuracy of BiLSTM. The BiLSTM is pre-trained using the residual error data from building energy consumption and fine-tuned by the residual error data from EV power demand. Finally, the extracted trend of large-scale EVs’ power demand and the predicted residual errors are combined to obtain the final predicted results. To validate the proposed method, the power demand of a real EV charging station is predicted using the proposed method, and fifteen models are taken for comparison (including the most popular data-driven models). The experimental result indicates that the proposed method can improve the predictive performance by at least 14.23% compared with the comparative models in terms of symmetric mean absolute percentage error, effectively avoids negative transfer results, and significantly enhance the confidence level.

A Novel Automatic Generation Control Method Based on the Large-Scale Electric Vehicles and Wind Power Integration Into the Grid.

In order to solve the problem of frequency instability of power system due to strong random disturbance caused by large-scale electric vehicles and wind power grid connection, an improved reinforcement learning algorithm, namely, optimistic initialized double Q, is proposed in this article from the perspective of automatic generation control. The proposed algorithm uses the optimistic initialization principle to expand the agent action exploration space, so as to prevent Q-learning from falling into local optimum by greedy strategy; meanwhile, it integrates double Q-learning to solve the problem of overestimation of action value in traditional reinforcement learning based on Q-learning. In the algorithm, the hyperparameter ατ is introduced to improve the learning efficiency, and the reward bτ based on exploration times is introduced to increase the Q value estimation to drive the exploration of the algorithm, so as to obtain the optimal solution. By simulating the two-area load frequency control model integrated with large-scale electric vehicles and the four-area interconnected power grid model integrated with large-scale wind power generation, it is verified that the proposed algorithm can obtain the global optimal solution, thus effectively solvinng the frequency instability caused by strong random disturbance in the grid-connected mode of large-scale wind power generation, and compared with many reinforcement learning algorithms, the proposed algorithm has better control performance.

A novel controllable bias reinforcement learning method for distributed automatic generation control integrated with large-scale electric vehicles

Due to the introduction of large-scale renewable energy resources and flexible load access, strong random disturbances will bring to the current integrated energy system. The traditional control methods fall short in adequately addressing the frequency regulation needs of distributed systems. From the perspective of distributed automatic generation control (AGC), an optimal coordinated control method is proposed based on reinforcement learning. The proposed method is developed to mitigate the estimation bias of the action-values via the minimum action-value from multiple action-value estimates. Further, the bias of action-value estimates can be adjusted from positive to negative to enhance the control accuracy. Then this method uses behaviour utility trace to achieve adaptive adjustment of the exploration rate, preventing repetitive and unbalanced exploration. Simulation experiments have been performed on the two-area LFC model integrated with electric vehicles and the multi-area integrated energy system model to verify the effectiveness of the proposed control method.

Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

Optimization Strategies for Real-time Energy Management of Electric Vehicles Based on LSTM Network Learning

The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy.

A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New York state

As the enthusiasm for electric vehicles passes the range anxiety and other tests, large-scale transportation electrification becomes a prominent topic in research and policy discussions. In consequence, the public attention has shifted upstream and holistically towards the integration of large-scale transportation electrification to power systems. This paper proposes a method to identify bottlenecks in power transmission systems to accommodate large-scale and stochastic electric vehicles charging demands. First, a distributionally robust chance-constrained direct current optimal power flow model is developed to quantify the impacts of stochastic electric vehicles charging demands. Subsequently, an agent-based model with the trip-chain method is applied to obtain the spatiotemporal distributions of electric vehicles charging demands for both light-duty electric vehicles and medium and heavy-duty electric vehicles. The first two moments of those distributions are extracted to build an ambiguity set of electric vehicles charging demands. Finally, a 121-bus synthetic transmission network for New York State power systems is used to validate the future transportation electrification in New York State from 2025 to 2035. Results show that the large-scale transportation electrification in New York State will account for approximately 13.33 % to 16.79 % of the total load demand by 2035. The transmission capacity is the major bottleneck in supporting New York State to achieve its transportation electrification. To resolve the bottlenecks, we explore some possible solutions, such as transmission capacity expansion and distributed energy resources investment. Wind power shows an advantage over solar energy in terms of total operational costs due to better peak alignment between wind power and electric vehicles charging demand.

Modelling the determinants of electrical vehicles adoption in Riyadh, Saudi Arabia

This study investigates consumer acceptance of electric vehicles (EVs) in Riyadh, focusing on their response to adoption incentives. Utilising a stated preference survey, a mixed logit model was used in conjunction with survey insights to derive the final findings. Those include finding a higher sensitivity to changes in the monetary values of EVs than conventional vehicles. Also, the presence of accessible charging infrastructure and non-financial incentives (e.g., designated public parking spaces) were found to significantly increase the likelihood of EV adoption. While Saudi participants were less likely than expats to purchase EVs, females and consumers in their 40s showed a likelihood of purchasing an EV. Furthermore, shorter trip durations and personal acquaintance with EV owners in Riyadh positively influenced adoption rates. It is worth noting that the study shows that almost 60% of the targeted population indicated an interest in purchasing a vehicle in the upcoming three years, where the great majority stated that a new vehicle would replace an existing one. This creates a window of opportunity to implement optimal incentivising policies, as once a conventional vehicle is purchased, investment becomes ‘locked’ for years. The study then concludes with policy insights that aim to contribute to the policies of large-scale EV adoption in Riyadh.

Energy optimal scheduling strategy considering V2G characteristics of electric vehicle

in recent years, large-scale electric vehicles connected to the power grid has a significant impact on demand-side resources. In addition, the vehicle-to-grid technology of electric vehicles introduces more uncertainties, which brings great challenges to the optimal scheduling of integrated energy systems. To solve this problem, this paper fully considers the uncertainty and fluctuation of wind power. Firstly, the objective function of joint configuration is constructed to maximize the utilization of wind power and minimize the planned generation cost of the system. Secondly, the linearized power flow equations are used to represent the relations among the state variables, and the second-order cone relaxation method is applied to deal with the branch power flow constraints, which is transformed into a solvable mixed-integer second-order cone programming model. Finally, the validity of the model is verified by an example. The system achieves obvious peak-shaving and valley-filling effect and improves the economic benefit of the system.

Robust Operation of Flexible Distribution Network With Large-Scale EV Charging Loads

The increasing integration of electric vehicles (EVs) significantly challenges the efficient operation of distribution networks. With the rapid development of power electronic devices represented by soft open point (SOP), flexible distribution networks (FDNs) provide a promising opportunity for integrating large-scale EVs. In this paper, a robust operation method is proposed to improve the economy of EVs and ensure the flexible operation of FDN. First, an EVs-aggregator (EVs-AGG) model is formulated to incorporate diverse charging behaviors. The EVs-AGG is efficiently integrated into FDN through the DC link of multi-terminal SOP. Then, optimal operation strategies are obtained by coordinating EVs charging/discharging power with the regulation of multi-terminal SOP. Furthermore, considering the uncertainties of EVs and photovoltaics, a robust operation framework is established to deal with uncertainties. Finally, case studies based on a practical distribution network indicate that through the interactive power regulation between multi-terminal SOP and EVs-AGG, the proposed method alleviates the negative impacts caused by large-scale EVs integration and improves the operational flexibility of FDN.

Switchable Charge Storage Mechanism via in Situ Activation of MXene Enables High Capacitance and Stability in Aqueous Electrolytes.

The need for reliable renewable energy storage devices has become increasingly important. However, the performance of current electrochemical energy storage devices is limited by either low energy or power densities and short lifespans. Herein, we report the synthesis and characterization of multilayer Ti4N3Tx MXene in various aqueous electrolytes. We demonstrate that Ti4N3Tx can be electrochemically activated through continuous cation intercalation over a 10 day period using cyclic voltammetry. A wide operating window of 2 V is maintained throughout activation. After activation, capacitance at 2 mV s-1 increases by 300%, 140%, and 500% in 1 M H2SO4, 1 M MgSO4, and 1 M KOH, respectively, while maintaining ∼600 F g-1 at 2 mV s-1 after 50000 cycles in 1 M H2SO4. This activation process is possibly attributed to the unique morphology of the multilayered material, allowing cation intercalation to increase access to redox-active sites between layers. This work adds to the growing repository of electrochemically stable MXenes reported for aqueous energy storage applications. These findings offer a reliable option for reliable energy storage devices with potential applications in large-scale grid storage and electric vehicles.

Design of a bidirectional EV charger using unit template-based current-controlled synchronous reference frame hysteresis

Abstract Existing distribution networks were not designed with large-scale electric vehicle (EV) charging infrastructure in mind. Integrating EV charging stations with the distribution grid might lead to power quality (PQ) issues at the point of common coupling (PCC). This work proposes a two-mode, unit template-based synchronous reference frame hysteresis current-controlled (SRF-HCC) three-phase Level 2 EV charger. Mode one focuses on charging the EV battery from the grid (G2V) and utilizes current and voltage control techniques to enhance battery life and performance. Whereas mode two enables the EV’s stored energy to be discharged to the grid (V2G) by the EV user, allowing the sale of power to support the transient effect of the grid voltage and frequency and enhancing the grid’s PQ. The HCC generates switching pulses for both the AC-DC and buck-boost converters. The SRF-based unit template-based control (SRF-UTC) method ensures system stability, voltage, and frequency regulation for power exchange with the grid by combining its efficiency with that of the HCC. The EV charger proposal comprises three primary components: a 3-phase bidirectional AC-DC converter, a bidirectional buck-boost converter, and a filter circuit. The proposed system was modeled using MATLAB/Simulink and evaluated in two case studies to assess its performance.

Inorganic Sodium Solid Electrolytes: Structure Design, Interface Engineering and Application.

All-solid-state sodium batteries (ASSSBs) are particularly attractive for large-scale energy storage and electric vehicles due to their exceptional safety, abundant resource availability, and cost-effectiveness. The growing demand for ASSSBs underscores the significance of sodium solid electrolytes; However, the existed challenges of sodium solid electrolytes hinder their practical application despite continuous research efforts. Herein, recent advancements and the challenges for sodium solid electrolytes from material to battery level are reviewed. The in-depth understanding of their fundamental properties, synthesis techniques, crystal structures and recent breakthroughs is presented. Moreover, critical challenges on inorganic sodium solid electrolytes are emphasized, including the imperative need to enhance ionic conductivity, fortifying interfacial compatibility with anode/cathode materials, and addressing dendrite formation issues. Finally, potential applications of these inorganic sodium solid electrolytes are explored in ASSSBs and emerging battery systems, offering insights into future research directions.

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

Evaluating and forecasting methods for assessing the health status of cables under the load of large-scale electric vehicle charging

The assessment of the health status and prediction of the lifespan of cable equipment are critical for ensuring the stability and efficiency of the power grid. This paper develops a temperature-current-capacity-life calculation model for cables, considering the fast and slow charging demands of electric vehicles (EVs). Analyses under scenarios of rapid and slow charging demands are conducted, introducing a cable health index and establishing a health status assessment framework based on this index. The framework accounts for various factors leading to cable faults, offering a comprehensive evaluation of the health status of cables with different fault rates. Building upon this, a prediction method using the Fire Hawk Optimization (FHO) Algorithm and Convolutional Neural Network (CNN) is proposed. This method enhances performance by optimizing the hyperparameters of Bidirectional Gated Recurrent Unit (BiGRU) through FHO, effectively searching and determining the optimal hyperparameter configuration. The impact of different scenarios and varying EV penetration rates on cable temperature is analyzed through case studies, facilitating the assessment and prediction of health status.

Health benefits of vehicle electrification through air pollution in Shanghai, China

Vehicle electrification has been recognized for its potential to reduce emissions of air pollutants and greenhouse gases in China. Several studies have estimated how national-level policies of electric vehicle (EV) adoption might bring very large environmental and public health benefits from improved air quality to China. However, large-scale adoption is very costly, some regions derive more benefits from large-scale EV adoption than others, and the benefits of replacing internal combustion engines in specific cities are less known. Therefore, it is important for policymakers to design incentives based on regional characteristics – especially for megacities like Shanghai – which typically suffer from worse air quality and where a larger population is exposed to emissions from vehicles. Over the past five years, Shanghai has offered substantial personal subsidies for passenger EVs to accelerate its electrification efforts. Still, it remains uncertain whether EV benefits justify the strength of incentives. The purpose of our study is to evaluate the health and climate benefits of replacing light-duty gasoline vehicles (ICEVs) with battery EVs in the city of Shanghai. We assess health impacts due to ICEV emissions of primary fine particulate matter, NOx, and volatile organic compounds, and to powerplant emissions of NOx and SO2 due to EV charging. We incorporate climate benefits from reduced greenhouse gas emissions based on existing research. We find that the benefit of replacing the average ICEV with an EV in Shanghai is US$6400 (2400-14,700), with health impacts of EVs about 20 times lower than the average ICEV. Larger benefits ensue if older ICEVs are replaced, but replacing newer China ICEVs also achieves positive health benefits. As Shanghai plans to stop providing personal subsidies for EV purchases in 2024, our results show that EVs achieve public health and climate benefits and can help inform policymaking strategies in Shanghai and other megacities.

An Analytical Polytope Approximation Aggregation of Electric Vehicles Considering Uncertainty for the Day-Ahead Distribution Network Dispatching

The integration of large-scale heterogeneous electric vehicles (EVs) into the distribution network increases the system management complexity significantly. Due to the flexible charging/discharging operation and shiftable energy consumption during the parking time, EVs possess a great dispatching potential for the distribution network. This paper develops an analytical polytope approximation (APA) aggregation model to efficiently depict the dispatchable regions of large-scale EVs with uncertainties and proposes a bilevel cooperative optimization approach for EV aggregators to participate in the distribution network day-ahead optimal scheduling. Uncertainties of EVs are modeled by chance constraints with different confidence levels to accommodate various regulatory requirements. The proposed approach can obtain large-scale EVs dispatchable capabilities with minimum flexibility loss and realize the regulation command disaggregation without violations. Numerical case studies are conducted on IEEE 33-bus and 141-bus distribution networks based on real data sets, which show that the proposed approach outperforms other state-of-art methods in terms of low operation flexibility conservatism, high system economics, and computational efficiency.

A Fast Polytope-Based Approach for Aggregating Large-Scale Electric Vehicles in the Joint Market Under Uncertainty

The aggregation of electric vehicles (EVs) has been advocated as an effective means to manage and control large-scale plug-in EVs. However, the efficient aggregation is challenging due to the heterogeneity and uncertainty of individual EVs. To cope with this challenge, this paper puts forth a fast polytope-based aggregation approach, which could deal with the time-coupled, uncertain, and heterogeneous individual EV models. First, the feasible region of each EVs power profiles is expressed as a polytope under H-representation considering various operational constraints. To improve the aggregation efficiency, H-representation reconstruction and polytopic projection processes are designed to address the heterogeneity of EVs. The uncertainties of EVs are modeled by the distributionally robust joint chance-constrained programming. The original individual and aggregate feasible region of EVs are subsequently inner approximated by the homothets of a basic prototype. Last, a day-ahead optimal bidding model for charging service provider in a joint market, including energy, peak-shaving and reserve markets, is developed. Numerical simulation results based on real-world dataset demonstrate that the proposed aggregation method can improve both the computation efficiency and accuracy of approximate feasible region.

A Two-Point Estimate Method for Optimal Dispatching of Large-Scale Electric vehicles into Distribution Networks under Uncertainty

Electric vehicle (EV) charging irregularities have a massive effect on the distribution system. In recent years, research has focused on how to address the issue of large-scale charging and discharging of EVs while ensuring the grid's security. Distribution network operators should choose places where EV charging is practicable in terms of environmental and financial benefits. To establish how to best charge and discharge EVs in the distribution network, first comprehend and then determine the optimal position of EV parking lots in the distribution network. In this article, to solve this optimization problem, first the mathematical model of the problem is built, and then - according to the nonlinear features of the proposed optimization model - the second-order cone method is used to find the optimal solution with high accuracy and speed. The objective functions can accomplish more realistic objectives like lowering losses and minimizing voltage deviation changes with the charging and discharging time control technique, in addition to optimizing the technical requirements of the distribution network. The two-point estimation method of Hang is used to describe the uncertainty in the loads and capacities of solar sources in the random state, and optimization is applied to the IEEE 33-bus distribution system. The obtained results reveal that the proposed method not only meets the charging and discharging requirements of large-scale EVs, but also ensures the stability of the power grid, demonstrating the effectiveness of the proposed method in the optimal planning of EVs.