Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area

Qian, Xinwu; Xue, Jiawei; Sobolevsky, Stanislav; Yang, Chen; Ukkusuri, Satish V.

doi:10.1109/itsc.2019.8917359

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Therefore, the operational behavior model of electric plug-in taxis (PET) is presented based on environmental uncertainty [85]. In [86], regarding the movements of electric taxis as random walks, the Markov process is used to simulate the distribution of charging demand in static space, while in [87], the random forest (RF) method based on a regression tree is used to predict the driving characteristics of each EV, so as to obtain the travel mode data set of the EV. From the measured charging information and big data mining technology, the EV charging behavior model is presented based on RF and principal component analysis (PCA), which captures the EV with different charging characteristics based on a data-driven model [88].…”

Section: Radial Basis Function [45]mentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

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The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

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Section: Radial Basis Function [45]mentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

View full text Add to dashboard Cite

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“…Another line of research focuses on predicting aggregated power consumption of EV charging stations at the power grid level. The existing literature discusses a variety of models including analytical model [11], linear regression [12], ARIMA models [13], [14], machine learning models [14], [15], as well as deep learning models [16], [17]. While these approaches focus on the overall power consumption by EV charging stations, this paper addresses the problem of occupation forecasting for individual charging stations.…”

Section: Related Workmentioning

confidence: 99%

Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting

Sao¹,

Tempelmeier²,

Demidova³

2021

Preprint

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With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effectively forecast the charging station occupation. We exploit static information, such as the mean occupation concerning the time of day, to learn the specific charging station patterns. We supplement such static data with dynamic information reflecting the preceding charging station occupation and temporal information such as daytime and weekday. Our model efficiently fuses dynamic and static information to facilitate accurate forecasting. We evaluate the proposed model on a real-world dataset containing 593 charging stations in Germany, covering August 2020 to December 2020. Our experiments demonstrate that DFDS outperforms the baselines by 3.45 percent points in F1-score on average.

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“…Unlike private EVs where charging locations can be easily estimated, the location of charging needs of LDEVs depending on the stochastic trip and relocation activities. To address this issue, we introduce a stochastic model which considers moving activities in the MaaS system as a discrete-time Markov chain, following the preliminary analyses from the authors' prior work [35]. With the zonal representation, the Markov chain regards each zone as a state and the movement between zone i and zone j as the transition probability P ij .…”

Section: Generation Of Stationary Charging Demandmentioning

confidence: 99%

“…where a detailed derivation can be found in [35]. Let K be the average number of trips that an LDEV may complete per unit time, N LDEV be the LDEV fleet size and N w be the number of LDEVs that are awaiting recharging of the battery, we can eventually express the arrival rate of charging demand per unit time in a given zone i as:…”

Section: Generation Of Stationary Charging Demandmentioning

confidence: 99%

Charging-as-a-Service: On-demand battery delivery for light-duty electric vehicles for mobility service

Guo,

Qian,

Liu

2020

Preprint

Self Cite

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This study presents an innovative solution for powering electric vehicles, named Charging-asa-Service (CaaS), that concerns the potential large-scale adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service (MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to dispatch service vehicles (SVs) that carry modular battery units (MBUs) to provide LDEVs for mobility service with on-demand battery delivery. The CaaS system is expected to tackle major bottlenecks of a large-scale LDEV adoption in the MaaS industry due to the lack of charging infrastructure and excess waiting and charging time. A hybrid agent-based simulation model (HABM) is developed to model the dynamics of the CaaS system with SV agents, and a trip-based stationary charging probability distribution is introduced to simulate the generation of charging demand for LDEVs. Two dispatching algorithms are further developed to support the optimal operation of the CaaS. The model is validated by assuming electrifying all 13,000 yellow taxis in New York City (NYC) that follow the same daily trip patterns. Multiple scenarios are analyzed under various SV fleet sizes and dispatching strategies. The results suggest that an optimal CaaS system with 250 SVs may serve the LDEV fleet in NYC with an average waiting time of 5 minutes, save the travel distance to visit charging station at over 50 miles per minute, and gain considerable profits of up to $50 per minute. This study offers significant insights into the feasibility, service efficiency, and financial sustainability for deploying city-wide CaaS systems to power the electric MaaS industry.

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Stationary Spatial Charging Demand Distribution for Commercial Electric Vehicles in Urban Area

Cited by 10 publications

References 17 publications

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Deep Information Fusion for Electric Vehicle Charging Station Occupancy Forecasting

Charging-as-a-Service: On-demand battery delivery for light-duty electric vehicles for mobility service

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