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Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load
K Liu, Y Liu - Applied Energy, 2023 - Elsevier
As the number of electric vehicles (EVs) connected to the grid increases, the EV electricity
demand rises dramatically, affecting the grid's planning and operation and deepening the …
demand rises dramatically, affecting the grid's planning and operation and deepening the …
Deep stochastic reinforcement learning-based energy management strategy for fuel cell hybrid electric vehicles
Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment
friendly transportation, but they necessitate efficient energy management strategies (EMSs) …
friendly transportation, but they necessitate efficient energy management strategies (EMSs) …
[HTML][HTML] A data-aided robust approach for bottleneck identification in power transmission grids for achieving transportation electrification ambition: a case study in New …
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 …
transportation electrification becomes a prominent topic in research and policy discussions …
Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation
H Bian, Q Ren, Z Guo, C Zhou, Z Zhang, X Wang - Energies, 2024 - mdpi.com
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 …
proposed in this paper, considering multimodal travel behavior and microscopic traffic …
MPC-driven optimal scheduling of grid-connected microgrid: Cost and degradation minimization with PEVs integration
The lifespan and degradation of energy storage systems are important factors in ensuring
efficient energy management and reducing operational costs in microgrids. This paper …
efficient energy management and reducing operational costs in microgrids. This paper …
A distributed week-ahead scheduling method for the charging/discharging of plug-in electric vehicles integrated into the grid
Y Cui, Z Hu, Y Wan, J Li, C Shao… - CSEE Journal of …, 2024 - ieeexplore.ieee.org
With the proliferation of electric vehicles (EVs) around the world, the large-scale grid-vehicle
interaction (GVI) has become more and more promising recently. However, the existing …
interaction (GVI) has become more and more promising recently. However, the existing …
Performance Analysis of EV Battery Based on Trip Chain Model
R Poojitha, S Lekshmi - 2024 10th International Conference on …, 2024 - ieeexplore.ieee.org
Electric Vehicle (EV) batteries are crucial for sustainable way of transportation and
understanding their performance. In this paper, a Novel method called Trip Chain Model …
understanding their performance. In this paper, a Novel method called Trip Chain Model …
Economic Indicator-Based Power Quality Assessment of Distribution Network Incorporating Electric Vehicle Stations
S Shi, Y Liu, Q Wang, B Cen - 2024 14th International …, 2024 - ieeexplore.ieee.org
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 …
operation of the distribution network. At present, there is a lack of indicator to quantify the …
Forecasting the Flexibility Potential of Electric Vehicles Limited by Individual Charging Targets
NL Fischer, K Rudion - 2023 IEEE PES Innovative Smart Grid …, 2023 - ieeexplore.ieee.org
The future massive integration of electric vehicles into the grid represents not only an
additional load, but also a decentralized flexible resource that can be used in aggregated …
additional load, but also a decentralized flexible resource that can be used in aggregated …
Predictive analysis of load at EVCS with deep learning algorithms
This paper considers Electric vehicle charging station (EVCS) as load and predicts the total
charging power demand of an EVCS with deep learning algorithms. For pragmatic approach …
charging power demand of an EVCS with deep learning algorithms. For pragmatic approach …