The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence
Open accessAbstract
This tutorial summarizes recent advances in the convex relaxation of the optimal power flow (OPF) problem, focusing on structural properties rather than algorithms. Part I presents two power flow models, formulates OPF and their relaxations in each model, and proves equivalence relationships among them. Part II presents sufficient conditions under which the convex relaxations are exact.
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
Inverter VAR control for distribution systems with renewables
Farivar, M., Clarke, C. R., Low, S. H., & Chandy, K. M. (2011) · 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgridcomm.2011.6102366 · Cited by 331
Self-citationGeometry of injection regions of power networks
Zhang, B., & Tse, D. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2208205 · Cited by 118
Cited by
Antoniadou-Plytaria, K. E., Kouveliotis-Lysikatos, I. N., Georgilakis, P. S., & Hatziargyriou, N. D. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2679238 · Cited by 545
Estimating the Active and Reactive Power Flexibility Area at the TSO-DSO Interface
Silva, J., Sumaili, J., Bessa, R. J., Seca, L., Matos, M. A., Miranda, V., Caujolle, M., Goncer, B., & Sebastian-Viana, M. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2018.2805765 · Cited by 299
A Survey on Applications of Machine Learning for Optimal Power Flow
Hasan, F., Kargarian, A., & Mohammadi, A. (2020) · 2020 IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec48276.2020.9042547 · Cited by 85
Sun, X., & Qiu, J. (2022) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2021.3138204 · Cited by 27
Metadata
- Authors
- Low, S. H.
- Venue
- IEEE Transactions on Control of Network Systems
- Published
- 2014-03
- Volume
- 1
- Issue
- 1
- Pages
- 15-27
- ISSN
- 2325-5870 (ISSN-L)
- OpenAlex
- W2093909370
- Cited by
- 929
- References
- 2
- FWCI
- 33.96
- Type
- journal article
- Language
- en
- Open access
- green
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: Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence. https://app.cochat.ai/papers/10.1109/tcns.2014.2309732
- The paper
- Low, S. H. (2014). Convex Relaxation of Optimal Power Flow—Part I: Formulations and Equivalence. IEEE Transactions on Control of Network Systems. https://doi.org/10.1109/tcns.2014.2309732
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.
