Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning
- DOI
- 10.3390/en15072323
- Published
- 2022-03-23
- Container
- Energies
- Publisher
- MDPI AG
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.3390/en15072323,
title = {Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning},
author = {Athanasios Paraskevas and Dimitrios Aletras and Antonios Chrysopoulos and Antonios Marinopoulos and Dimitrios I. Doukas},
year = {2022},
journal = {Energies},
doi = {10.3390/en15072323},
url = {https://doi.org/10.3390/en15072323}
}RIS
TY - JOUR TI - Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning AU - Athanasios Paraskevas AU - Dimitrios Aletras AU - Antonios Chrysopoulos AU - Antonios Marinopoulos AU - Dimitrios I. Doukas PY - 2022 JO - Energies DO - 10.3390/en15072323 UR - https://doi.org/10.3390/en15072323 ER -
APA
Paraskevas, A., Aletras, D., Chrysopoulos, A., Marinopoulos, A., & Doukas, D. I. (2022). Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning. Energies. https://doi.org/10.3390/en15072323
Source records
- crossref · retrieved 2026-09-26T01:14:24.241Z