MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations

Huang, Xingshuai; Wu, Di; Boulet, Benoît

doi:10.1109/tits.2023.3276947

Cited by 18 publications

(3 citation statements)

References 32 publications

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“…Currently, data-driven learning and mathematical model are the two main ways to solve this problem. In [5][6][7] and other literatures, load forecasting is performed by building a statistical learning model based on users' historical data. Among them, the DCC-2D neural network proposed in [5] not only has lower prediction errors than traditional neural networks but can also dynamically predict the charging load distribution of EVs in space and time.…”

Section: Introductionmentioning

confidence: 99%

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Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Shi,

Liu,

Wang

et al. 2024

Preprint

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The access of electric vehicle charging stations (EVCS) brings challenges to the stable operation of the distribution network.At present, there is a lack of indicator to quantify the economic losses caused by the decrease in power quality of the distribution network due to the access of EVCSs.In the paper, the travel trajectories of electric vehicle users are constructed through trip chain and state transition matrices,thereby obtaining the spatiotemporal distribution of charging loads.In addition, the voltage deviation and line loss caused by charging loads are unified into economic indicator to quantify.The simulations are conducted in a road network coupled to the IEEE 33-node distribution network.The result shows that the charging load of electric vehicle charging stations have a significant impact on the power quality of the distribution network.At the same time, optimizing the location of charging stations and guiding electric vehicle users’ charging behaviorcan effectively improve the power quality and economic efficiency of distribution networks.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Through this model, charging station operators can obtain the number of dispatchable EVs in the area and objectively alleviate traffic congestion. Aiming at the problem that newly established charging stations lack historical data as data sets, a forecasting framework MetaProbformer is proposed for load forecasting in [7].…”

Section: Introductionmentioning

confidence: 99%

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Shi,

Liu,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Electric vehicles (EVs), renowned for their diminished greenhouse gas emissions compared to conventional fuel-powered vehicles, are attracting significant global attention [2]. Projections indicate that EVs will account for approximately two-thirds of the worldwide sales of light-duty vehicles by 2035 [3]. Nevertheless, the escalating ownership of EVs has led to a concomitant rise in energy supply demands, posing significant challenges to the stability of power grid operations.…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Bian,

Ren,

Guo

et al. 2024

Energies

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A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method.

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Multi-Decoder Transformer for Charging Load Prediction of Electric Vehicle Charging Stations

Wang,

Peng,

Zhu

et al. 2024

Lecture Notes in Electrical Engineering

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MetaProbformer for Charging Load Probabilistic Forecasting of Electric Vehicle Charging Stations

Cited by 18 publications

References 32 publications

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations

Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation

Multi-Decoder Transformer for Charging Load Prediction of Electric Vehicle Charging Stations

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