The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles
Abstract
High penetration of electric vehicles (EVs) as moving loads in power system have drawn increasing concerns about their negative impacts. Due to the spatial-temporal random dynamics of EVs, it is a challenge for identification and positioning of the space and time varying impacts. Most previous studies investigated system-wide EV charging demand based on data analysis with deterministic charging location and time. In this circumstance, this paper proposes a probabilistic model for nodal charging demand based on the spatial-temporal dynamics of moving EVs. Following the introduction to the integrated system with graph theory, a spatial-temporal model of moving EV loads is established based on random trip chain and Markov decision process (MDP). The nodal EV charging demands are derived from the charging probabilities of single and multiple EVs. The system studies show that this model is capable to assess the nodal charging demand due to the spatial-temporal distribution of moving EVs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Modeling of Load Demand Due to EV Battery Charging in Distribution Systems
Qian, K., Zhou, C., Allan, M., & Yuan, Y. (2011) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2010.2057456 · Cited by 1,268
Rahman, S., & Shrestha, G. B. (1993) · IEEE Transactions on Power Delivery · doi:10.1109/61.216865 · Cited by 259
Assessment of electric vehicle charging scenarios based on demographical data
Steen, D., Tuan, L. A., Carlson, O., & Bertling, L. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2195687 · Cited by 145
Cited by
Al-Ogaili, A. S., Tengku Hashim, T. J., Rahmat, N. A., Ramasamy, A. K., Marsadek, M. B., Faisal, M., & Hannan, M. A. (2019) · IEEE Access · doi:10.1109/access.2019.2939595 · Cited by 303
Daily electric vehicle charging load profiles considering demographics of vehicle users
Zhang, J., Yan, J., Liu, Y., Zhang, H., & Lv, G. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115063 · Cited by 259
Coincidence Factors for Domestic EV Charging From Driving and Plug-In Behavior
Bollerslev, J., Andersen, P. B., Jensen, T. V., Marinelli, M., Thingvad, A., Calearo, L., & Weckesser, T. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3088275 · Cited by 78
Metadata
- Authors
- Tang, D., & Wang, P.
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2015
- Pages
- 1-1
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2290359803
- Cited by
- 209
- References
- 3
- FWCI
- 4.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles. https://app.cochat.ai/papers/10.1109/tsg.2015.2437415
- The paper
- Tang, D., & Wang, P. (2015). Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2015.2437415
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
