Large-scale Electric Vehicle Charging Research Articles

Integration of smart charging of large-scale electric vehicles into generation and storage expansion planning: a case study in south china

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

The impact of massive EV charging on distribution lines

The large-scale charging of electric vehicles at the same time is bound to have a large impact on the distribution network and then reduce the stability of the grid, especially during peak hours when it is easy to overload the lines. In this paper, we combine battery capacity and charging power data of electric vehicles and user usage habits to model the charging load changes of electric vehicles at different scales. The impact of charging by EV owners on network load was also analyzed through case studies. Our results have a certain practical significance for the planning of power grid lines.

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.

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.

Optimization and control strategy of electric vehicles access to power grid

PurposeThis paper aims to reduce the impact of disordered charging of large-scale electric vehicles (EVs) on the grid. EV is great significance for environmental protection, energy conservation and emission reduction to replace fuel vehicles with EVs. However, as a kind of random mobile load, large-scale integration into the power grid may lead to power quality problems such as line overload, line loss increase and voltage reduction. This paper realizes the orderly charging of electric vehicles and the safe operation of the distribution network by optimizing the dispatching scheme.Design/methodology/approachThis paper takes the typical IEEE-33 node distribution system as the research object, adopts the improved particle swarm optimization algorithm and takes the minimum operation cost, the minimum environmental pollution, the minimum standard deviation of daily load, the minimum peak valley difference of load, the minimum node voltage offset rate and the minimum system grid loss rate as the optimization objectives.FindingsControlling the disordered charging of large-scale electric vehicles by optimizing the dispatching algorithm can realize the full consumption of renewable energy and the safe operation of the power grid.Originality/valueResults show that the proposed scheme can realize the transfer of charging load in time and space, so as to stabilize the load fluctuation of distribution grid, improve the operation quality of power grid, reduce the charging cost of users and achieve the expected research objectives.

Scheduling and Routing of Mobile Charging Stations With Stochastic Travel Times to Service Heterogeneous Spatiotemporal Electric Vehicle Charging Requests With Time Windows

This article presents an alternative service of mobile charging stations for the large-scale charging of electric vehicles, which consider the spatiotemporal heterogeneity of charging requests. As charging infrastructure is the key determinant for the large-scale adoption of electric vehicles, state-of-the-art scheduling and control strategies need to be explored. The charging of electric vehicles in a conventional charging station even with the fast dc–dc chargers takes around 30 min, which results in congestion and large waiting queues at public charging stations. To account for this issue, a novel strategy of routing and scheduling mobile charging stations to charge electric vehicles without the constraints of time and space is discussed in detail. Furthermore, the traveling times of mobile charging stations in reality are stochastic in nature. We formulate the optimization problem to minimize the cost of charging and show that the problem formulated is a combination of a bin packing problem and a multicity traveling salesman problem; hence, it is NP-hard and cannot be solved in reasonable CPU time, unless P = NP. We, thus, present modified saving’s heuristic and modified genetic algorithm metaheuristic to solve the optimization problem. Furthermore, numerical simulations show that the proposed scheduling and routing algorithm requires less number of mobile charging stations and can appreciably reduce the cost of charging.

Cooperative optimization strategy for large-scale electric vehicle charging and discharging

In order to match the basic load of the power grid and the charging demand of electric vehicles, this paper fully considers the high pollution and non-renewability of coal-fired power generation, the clean and renewable nature of wind power, and the characteristics of intermittent and fluctuation. In this paper, a high-confidence wind power scenario is used to establish a multi-objective optimal scheduling model that considers the V2G characteristics of electric vehicles, generator operating costs, abandoned air volume, environmental pollution, and charging costs for electric vehicle users, the optimal multi-objective scheduling model adopts CPLEX solver tool, by setting the simulation comparison of three scenarios: non-electric vehicle charging, electric vehicle charging, and electric vehicle charging and discharging, the calculation results show that the proposed optimal scheduling strategy realizes the collaborative optimization of thermal power units, wind power and electric vehicles. This paper provides a solution for the optimal scheduling of large-scale electric vehicles connected to the grid.

Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

Optimal scheduling of electric vehicles charging in battery swapping station considering wind- photovoltaic accommodation

The disorderly charging of large-scale Electric Vehicles increases the peak-to-valley difference of the grid and new energy absorption is facing difficulties. Considering these problems and difficulties, an aggregate management strategy for orderly scheduling of Electric Vehicles in the battery swapping station is proposed, which considers promoting the wind and photovoltaic power consumption and shaving peak of the grid. A multi-objective optimization model is established with the goal of the minimum variance of the grid load, the least amount of abandoned wind and solar energy, and the maximum revenues of the LAs, which is solved by Genetic Algorithm. The results of the example show that the optimization model can effectively realize the orderly charging of Electric Vehicles, release the peak shaving capacity, absorb the excess wind and photovoltaic power, and reduce costs of LAs and users.

Research on charging strategy of electric vehicle considering user and load curve

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

Research on Ordered Charging Strategy of Electric Vehicle with Double Layer Optimization Method

Based on the adverse impact of disorderly charging of large-scale ELECTRIC vehicles on the power grid, the feasibility of orderly charging control is firstly analyzed, the objective function and constraint condition model of orderly charging control are established, and then the charging time of electric vehicles is controlled by reasonable distribution. Then a two level optimization solution strategy based on dynamic programming was proposed to solve the problem. In order to control the order of electric vehicles, the outer layer utilized the dynamic programming method to solve the problem. The inner layer selected various charging schemes for each electric vehicle to find the best charging mode. Finally, the rationality of the two-layer optimization solution strategy was verified by the case analysis.

Research on Demand Response of Electric Vehicle Agents Based on Multi-Layer Machine Learning Algorithm

Charging of large-scale electric vehicles (EVs) will have a serious impact on the power grid. In the environment of power market, the orderly scheduling of EVs through agents is an effective way to solve this problem. Due to the uncertainty of EV travels, how to participate and profit in demand response has become urgent. In this paper, a set of optimal bidding method for agents participating in the market is proposed. Firstly, because the EV service provider cannot collect the historical travel data of all the owners in the whole region, a multi-layer machine learning algorithm is used to simulate the travel behavior of EV in this paper. Secondly, based on the demand response rules of the current electricity market, the optimal bidding model for EV service providers is proposed. In addition, considering the offset between the predicted and the actual electricity price, the model is designed based on interval optimization. Finally, the behavior model and the bidding method are applied to IEEE30 bus system and a provincial practical system. It is shown that the proposed method can bring greater benefits to EV service providers.

Charging scheduling strategy for different electric vehicles with optimization for convenience of drivers, performance of transport system and distribution network

With the popularization of electric vehicles, large-scale electric vehicle charging may negatively impact drivers, the power grid, and traffic conditions. Currently, research conducted on the charging and battery swap of electric vehicles is insufficient. The objective for optimization and the type of electric vehicle proposed by other papers are limited in scope. In order to achieve an overall optimization of the whole system, the driver demands, the road traffic speed, the number of vehicles in the charging station and the charging network load are considered in the development of the charging scheduling strategy for electric vehicles. Such a strategy can further enhance driver convenience in terms of making decisions for charging and battery swap of electric vehicles. Moreover, different types of electric vehicles are taken into account for a more practical proposed scheduling strategy. Utilizing MATLAB and MATPOWER, a simulation platform is established to validate the strategy. Simulation results demonstrate that the proposed scheduling strategy can relieve local traffic jams, smooth network load curve, increase safety and economy of the power network, and decrease the number of charging electric vehicles in station. Ultimately, this plan can simultaneously reduce waiting time of charging and increase the operational efficiency of charging stations.

Optimal charging of large-scale electric vehicles over extended time scales

The multi-day spaced charging, hardly considered in current strides aiming at optimal electric vehicle charging, is the concern of this study, and thus, an extended time scale covering several days should be dealt with. This study firstly analyzed the routines and implementations of charging behaviors over the extended time scale and then quantified the range of charging start time of electric vehicles precisely, which turned out to be discontinuous, huge and complicated. Finally, the mathematical formulation of the optimal charging strategy over the extended time scale as well as its constraints was established. The proposed problem can be solved by MATLAB simulations, which was addressed in details. Moreover, numerous simulations were conducted and demonstrated that the proposed strategy can reach a better system load characteristics than the ordered charging on a daily basis with a good maneuverability.

A Summary of Research on Wide-area Distributed Electric Vehicle and Grid Interaction Technology

Electric vehicle charging behavior and grid interaction technology, including the impact assessment of electric vehicles for distribution networks and the planning and operation optimization of electric vehicles connected to the grid.Specifically, the main contents include: researching the agglomeration characteristics of large-scale electric vehicle charging behavior based on statistical data and charging history data, and evaluating the regulatory potential of large-scale electric vehicles; wide-area coordination strategy and station-level and regional-level orderly charging.The interaction mode and architecture, the hierarchical control strategy of large-scale electric vehicle charging behavior and grid interaction.

Decentralized charging control strategy of the electric vehicle aggregator based on augmented Lagrangian method

Uncoordinated charging of large-scale electric vehicles can affect the safe and economic operation of distribution power system. As an intermediary between power grid and individual electric vehicle, electric vehicle aggregator can play an important role in the coordination and management of electric vehicle charging. In this paper, we develop a decentralized EV charging control strategy of the electric vehicle aggregator for scheduling the flexible charging demand of plug-in electric vehicles in residential distribution networks. First, the centralized charging control model of the electric vehicle aggregator aiming at the maximization of its revenue under the background of time of use price mechanism is established, meanwhile meeting individual charging requirements and satisfying distribution network constraints. Then a decentralized charging control strategy of the electric vehicle aggregator based on the augmented Lagrangian method and the alternating direction multiplier method is proposed. The electric vehicles can decide their charging plan locally with the decentralized strategy, which can eliminate the problem of high communication cost, low computing efficiency and privacy protection caused by the centralized charging control model in practical application. The effectiveness of the proposed strategy is evaluated with a representative distribution network model.

Coordinated dispatch of the wind-thermal power system by optimizing electric vehicle charging

The rapid development of renewable energy poses new challenges to grid operation. Owing to deficiencies in peak regulation capability, wind power cannot be fully connected to the grid. As a controllable load, electric vehicle (EV) charging enables the coordinated operation of EV charging, and the wind/thermal power generation system. In order to determine the coordinated operation mechanism of a power system with large-scale EV charging, a new dynamic multi-objective dispatch (DMOD) model of the power system that includes the economy, pollutant discharge, and abandonment volume was established to optimize the output of thermal power units. Here, we report the two-phase optimization strategy we developed to solve this model. In the first phase of the strategy, EV charging load is determined by optimizing user charging, combined with the time-sharing price of electricity, and the charging protocol. In its second phase, an improved multi-objective evolutionary algorithm (IMOEA) based on a modified particle swarm optimization (PSO) method was proposed to determine the coordinated operation of EV charging, and wind/thermal power generation system. A power system with 10 conventional units, and a grid-connected wind farm was simulated, and the analysis verifies the feasibility of the dispatching model and the effectiveness of the proposed optimization algorithm as a solution.

High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles

With the rapid development of electric vehicles (EVs), the impacts on power grids arising from EV charging have drawn increasing attention worldwide. Uncoordinated charging of large-scale EVs will inevitably elevate load peaks at rush hours, therefore, poses serious challenges to the stability and security of power grid. Coordinated charging is expected to alleviate these negative impacts by utilizing the surplus power in the lower-demand hours with the help of the so-called valley filling algorithm. Nevertheless, when the amount of EVs involved reaches 1 million or above, the complexity of the scheduling method becomes a critical issue. In this paper, a very high efficient valley-filling strategy is proposed. Two key indexes, viz., capacity margin index and charging priority index are defined, the former one is used to select the target time slots on which the power grid has abundant surplus power for EV charging, and the latter one is used to determine the charging priority of EVs on each time slot. The simulation results demonstrate that the coordinated charging scheme with the proposed valley-filling strategy significantly outperforms the uncoordinated charging in the aspect of suppressing the elevated peak loads of power grid. Moreover, the complexity analysis shows that the proposed algorithm is much high-efficient than its existing counterparts.

Dynamic stochastic optimal power flow of wind power and the electric vehicle integrated power system considering temporal-spatial characteristics

The intermittent volatility of wind power integrated into the grid poses a great threat to the stable operation of power systems on the supply side. Conversely, large-scale charging of electric vehicles (EVs) also brings new challenges to dispatch on the demand side. In response, the randomness and temporal-spatial correlations of stochastic wind power generation are considered in this paper. Additionally, the EV charging infrastructure is studied. A dynamic stochastic optimal power flow (DSOPF) for wind farms and EVs integrated power system based on the chance-constrained programming model is proposed. An optimal dispatch scheme is obtained by solving the dynamic optimal power flow. After that, dynamic probabilistic power flow based on cumulants is performed under the scheme to obtain the probability distribution of state variables. The upper and lower bounds of chance constraints are adjusted according to the probability distribution function until they are all satisfied. Illustrative examples demonstrate the effectiveness of DSOPF for firming the variable wind energy, and EV charging is performed on the Institute of Electrical and Electronics Engineers systems. On this basis, different EV charging modes and the temporal-spatial correlations are specifically discussed.

Optimal Scheduling of Electric Vehicles Charging in low-Voltage Distribution Systems

Uncoordinated charging of large-scale electric vehicles (EVs) will have a negative impact on the secure and economic operation of the power system, especially at the distribution level. Given that the charging load of EVs can be controlled to some extent, research on the optimal charging control of EVs has been extensively carried out. In this paper, two possible smart charging scenarios in China are studied: centralized optimal charging operated by an aggregator and decentralized optimal charging managed by individual users. Under the assumption that the aggregators and individual users only concern the economic benefits, new load peaks will arise under time of use (TOU) pricing which is extensively employed in China. To solve this problem, a simple incentive mechanism is proposed for centralized optimal charging while a rolling-update pricing scheme is devised for decentralized optimal charging. The original optimal charging models are modified to account for the developed schemes. Simulated tests corroborate the efficacy of optimal scheduling for charging EVs in various scenarios.