Electric Vehicle Charging Demand Research Articles

Maximizing the number of satisfied charging demands of electric vehicles on identical chargers

This paper addresses the electric vehicle charging scheduling problem in a charging station with a limited overall power capacity and a limited number of chargers. Electric vehicle drivers submit their charging demands. Given the limited resources, these charging demands are either accepted or rejected and accepted demands must be satisfied. The objective of the scheduler is to maximize the number of satisfied demands. The paper provides theoretical results on the scheduling problem and proposes different linear programming models and heuristic methods to provide good-quality solutions in a shorter computational time.

Short-Term Charging Load Prediction of Electric Vehicles with Dynamic Traffic Information Based on a Support Vector Machine

This study proposes a charging demand forecasting model for electric vehicles (EVs) that takes into consideration the characteristics of EVs with transportation and mobile load. The model utilizes traffic information to evaluate the influence of traffic systems on driving and charging behavior, specifically focusing on the characteristics of EVs with transportation and mobile load. Additionally, it evaluates the effect of widespread charging on the distribution network. An urban traffic network model is constructed based on the multi-intersection features, and a traffic network–distribution network interaction model is determined according to the size of the urban road network. Type classification simplifies the charging and discharging characteristics of EVs, enabling efficient aggregation of EVs. The authors have built a singular EV transportation model and an EV charging queue model is established. The EV charging demand is forecasted and then used as an input in the support vector machine (SVM) model. The final projection value for EV charging load is determined by taking into account many influencing elements. Compared to the real load, the proposed method’s feasibility and effectiveness are confirmed.

Demand Time Series Prediction of Stacked Long Short-Term Memory Electric Vehicle Charging Stations Based on Fused Attention Mechanism

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window.

Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson’s correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks.

Impact of electric vehicle charging demand on power distribution grid congestion

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

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.

Maximum flow‐based formulation for the optimal location of electric vehicle charging stations

AbstractWith the increasing effects of climate change, the urgency to step away from fossil fuels is greater than ever before. Electric vehicles (EVs) are one way to diminish these effects, but their widespread adoption is often limited by the insufficient availability of charging stations. In this work, our goal is to expand the infrastructure of EV charging stations, in order to provide a better quality of service in terms of user satisfaction (and availability of charging stations). Specifically, our focus is directed towards urban areas. We first propose a model for the assignment of EV charging demand to stations, framing it as a maximum flow problem. This model is the basis for the evaluation of user satisfaction with a given charging infrastructure. Secondly, we incorporate the maximum flow model into a mixed‐integer linear program, where decisions on the opening of new stations and on the expansion of their capacity through additional outlets is accounted for. We showcase our methodology for the city of Montreal, demonstrating the scalability of our approach to handle real‐world scenarios. We conclude that considering both spacial and temporal variations in charging demand is meaningful when solving realistic instances.

A physics-informed graph learning approach for citywide electric vehicle charging demand prediction and pricing

A growing number of electric vehicles (EVs) is putting pressure on smart charging services. As a foundation of informing drivers of vacant charging facilities and rationalizing pricing, an effective approach for predicting citywide spatio-temporal EV charging demand that incorporates pricing information is required. Although many deep learning models have been carried out with the orientation of improving prediction accuracy and achieved good results, there is still a lack of prediction models that are economically interpretable and can consider the spillover effect of price adjustments on EV charging demand. To fill the research gaps, we propose a learning approach for accurate EV charging demand prediction and reasonable pricing, named PIAST, which enables the integration of convolutional feature engineering, spatio-temporal dual attention mechanism and physics-informed neural network training. On a dataset containing 18,061 EV charging piles, we demonstrate the state-of-the-art performance of the proposed model. The results empirically showed that the proposed approach achieved an average improvement of 14.78% in accuracy, compared to other representative models. Moreover, the model can uncover the price elasticity of demand during training, making the model interpretable. Finally, price adjustment experiments are conducted to demonstrate the self-influence and spillover effects of price.

Quantifying the impact of electricity pricing on electric vehicle user behavior: a V2G perspective for smart grid development

ABSTRACT This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm.

Locating and sizing charging station in multi-period to promote electric vehicles adoption in urban areas

The layout of public fast charging stations (CSs) often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multi-period locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multi-period change formula is proposed to reflect the interaction between public fast CSs and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast CSs to improve the charging service quality and promote the adoption of EVs.

Utilizing electric bus depots for public Charging: Operation strategies and benefit analysis

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs’ charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running.

Research on charging demands of commercial electric vehicles based on Voronoi diagram and spatial econometrics model: An empirical study in Chongqing China

In order to address the mismatch between the current charging demand of electric vehicles and the layout of charging stations, this paper collects multi-source heterogeneous data that may affect charging demand, and explores their spatiotemporal distributions in depth. Then, this paper proposes a Voronoi polygon spatial partitioning method based on charging demand clustering and two spatial econometric models, spatial lag model (SLM) and spatial error model (SEM), to quantitatively analyze the influences of various elements on charging demands. Empirical research in Chongqing, China reveals that the spatiotemporal distribution of charging demand is uneven, with peak areas and periods for charging, and the division of Voronoi polygon can better reflect the spatial heterogeneity of charging demands than regular grid division. The positive factors include the low SOC, parking lot density, population density, and road network density, especially the low SOC and parking lot density, and the negative items contain consumption related Point Of Interest (POI), tourism related POI, and transportation hub related POI. Among them, population density and road network density, as static data, have similar influences on charging demands at different intervals, while the impact of other factors on charging demand varies with periods, with obvious peak-valley characteristics. There is spatial dependence and lag effect between charging demands within polygons, causing SLM outperforms SEM. The research results are beneficial for the rational siting and optimization of charging stations.

Hierarchical Control of Power Distribution in the Hybrid Energy Storage System of an Ultrafast Charging Station for Electric Vehicles

This paper presents a two-level hierarchical control method for the power distribution between the hybrid energy storage system (HESS) and the main dc bus of a microgrid for ultrafast charging of electric vehicles (EVs). The HESS is composed of a supercapacitor and a battery and is an essential part to fulfill the charging demand of EVs in a microgrid made up of a 220 VRMS ac bus, two dc buses of 600 V and 1500 V, respectively, and four charging points. A state machine defines the four operating modes of the HESS and establishes the conditions for the corresponding transitions among them, namely, charging the battery and the supercapacitor from the bus, injecting the current from the HESS into the 1500 V dc bus to ensure the power balance in the microgrid, regulating the bus voltage, and establishing the disconnection mode. The primary level of the control system regulates the current and voltage of the battery, supercapacitor, and dc bus, while the secondary level establishes the operating mode of the HESS and provides the appropriate references to the primary level. In the primary level, sliding mode control (SMC) is used in both the battery and supercapacitor in the inner loop of a cascade control that implements the standard constant current–constant voltage (CC-CV) charging protocol. In the same level, linear control is applied in the CV phase of the protocol and for bus voltage regulation or the current injection into the bus. PSIM simulations of the operating modes and their corresponding transitions verify the theoretical predictions.

A Review of Capacity Allocation and Control Strategies for Electric Vehicle Charging Stations with Integrated Photovoltaic and Energy Storage Systems

Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.

Orderly charging strategy for electric vehicles based on multi-level adjustability

The development of electric vehicles (EVs) is one of the essential ways to reduce environmental pollution. With the rapid growth in EVs, an orderly charging strategy based on multi-level adjustable charging power is proposed to address the problem of increasing peak-to-valley difference due to disorderly charging in different scenarios. Based on the information of multi-level adjustable charging power, information about staying in the residential area, and charging demands of EVs, this research designs a centralized charging mode with complete information under the centralized scenario and a decentralized charging mode with incomplete information under the decentralized scenario. This research takes the minimization of peak-to-valley difference in the residential area as the objective function and considers that the charging pile can have the function of multi-level adjustable charging power to support these two scenarios. Two charging modes of the charging pile are designed, and orderly charging model of EVs in the residential area is constructed. EVs can select charging time and charging power by using Bluetooth or code scanning in the charging pile. This research aims to design two orderly charging modes to effectively implement peak shaving and valley filling while ensuring the charging demand of EVs. This research uses the CPLEX solver in MATLAB to solve the objective. The simulation results show that EVs can reasonably select the multi-level adjustable charging power under different scenarios and provide a reference for engineering related to orderly charging. Strategy 4, proposed in this research, has the lowest peak-to-valley difference of the four strategies. The peak-to-valley difference is only 87 kW under the centralized scenario, and the peak-to-valley difference is 282 kW under the decentralized scenario.

Probabilistic forecast of electric vehicle charging demand: analysis of different aggregation levels and energy procurement

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Research on energy storage charging piles based on improved genetic algorithm

Aiming at the charging demand of electric vehicles, an improved genetic algorithm is proposed to optimize the energy storage charging piles optimization scheme. Firstly, the characteristics of electric load are analyzed, the model of energy storage charging piles is established, the charging volume, power and charging/discharging timing constraints in the charging process are considered, and the optimization is carried out by the improved genetic algorithm, and the profit of charging piles and user charging electricity charges are calculated, so as to obtain the possible optimal parameters, as well as the impact of the energy storage system on the stability of the power grid. Secondly, the analysis of the results shows that the energy storage charging piles can not only improve the profit to reduce the user’s electricity cost, but also reduce the impact of electric vehicle charging on the power grid load.

A Frequency Control Strategy for EV Stations Based on MPC-VSG in Islanded Microgrids

The inertia level of microgrids decreases with the access of inverter-based distributed generations. Uncertainty of source loads in islanded microgrids can lead to frequency instability. Considering electric vehicles (EVs) as a typical regulable load, a charging station (CS) virtual synchronous generator (VSG) frequency control (FC) strategy that considers the user charging and grid FC demand is proposed. First, the control architecture of CS based on the VSG is designed. Second, considering the impact of VSG FC on EV user charging demand, the charging power response model considering the charging power FC cost is designed. Furthermore, multi-objective model predictive control (MPC) algorithm is designed to improve the VSG control strategy, and the FC strategy is obtained. Simulation results show that the proposed method can provide inertial support and frequency regulation capability by the CS with VSG control strategy while satisfying EV charging demand. In addition, the FC cost can be decreased.

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.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.