Deep reinforcement learning-enabled methods for large-scale active distribution network planning with forbidden zones
- DOI
- 10.3389/fenrg.2026.1776639
- Published
- 2026-05-08
- Container
- Frontiers in Energy Research
- Publisher
- Frontiers Media SA
- 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:10.3389/fenrg.2026.1776639,
title = {Deep reinforcement learning-enabled methods for large-scale active distribution network planning with forbidden zones},
author = {Zhenlei Zhang and Liang Ma and Bin Bai and Xuejian Zheng and Shigong Jiang and Yushuai Wang and Yuan Gao},
year = {2026},
journal = {Frontiers in Energy Research},
doi = {10.3389/fenrg.2026.1776639},
url = {https://doi.org/10.3389/fenrg.2026.1776639}
}RIS
TY - JOUR TI - Deep reinforcement learning-enabled methods for large-scale active distribution network planning with forbidden zones AU - Zhenlei Zhang AU - Liang Ma AU - Bin Bai AU - Xuejian Zheng AU - Shigong Jiang AU - Yushuai Wang AU - Yuan Gao PY - 2026 JO - Frontiers in Energy Research DO - 10.3389/fenrg.2026.1776639 UR - https://doi.org/10.3389/fenrg.2026.1776639 ER -
APA
Zhang, Z., Ma, L., Bai, B., Zheng, X., Jiang, S., Wang, Y., & Gao, Y. (2026). Deep reinforcement learning-enabled methods for large-scale active distribution network planning with forbidden zones. Frontiers in Energy Research. https://doi.org/10.3389/fenrg.2026.1776639
Source records
- crossref · retrieved 2026-09-25T20:26:34.490Z