The DOI matches its Crossref record.
Maximising penetration of wind generation in existing distribution networks
Abstract
Connections of embedded wind generation (EWG) in the rural distribution system are susceptible to voltage rise. Current operating policy, based on the passive operation of the distribution network, limits the capacity of connected generation based on extreme conditions of minimum load and maximum generation. It is demonstrated that an active distribution network will allow considerably greater penetration of EWG. Three alternative control strategies are evaluated to increase the penetration level of EWG: EWG generation curtailment during low demand; reactive power management using a reactive compensator; and area-based OLTC co-ordinated voltage control. The application of these schemes is illustrated on a rurally connected 11 kV wind farm in a realistic 265-node generic distribution system model. Advanced optimal power flow is used to quantify the benefits of alternative control schemes for various EWG penetration levels. The impact of these schemes on network losses is also assessed.
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
Cited by
Assessment of Energy Distribution Losses for Increasing Penetration of Distributed Generation
MendezQuezada, V. H., RivierAbbad, J., & GomezSanRoman, T. (2006) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.873115 · Cited by 616
Keane, A., Ochoa, L. F., Borges, C. L. T., Ault, G. W., Alarcon-Rodriguez, A. D., Currie, R. A. F., Pilo, F., Dent, C., & Harrison, G. P. (2013) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2012.2214406 · Cited by 429
Metadata
- Authors
- Liew, S. N., & Strbac, G.
- Venue
- IEE Proceedings - Generation, Transmission and Distribution
- Published
- 2002
- Volume
- 149
- Issue
- 3
- Pages
- 256
- ISSN
- 1350-2360 (ISSN-L)
- OpenAlex
- W2064055853
- Cited by
- 277
- References
- 0
- FWCI
- 5.26
- 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: Maximising penetration of wind generation in existing distribution networks. https://app.cochat.ai/papers/10.1049/ip-gtd:20020218
- The paper
- Liew, S. N., & Strbac, G. (2002). Maximising penetration of wind generation in existing distribution networks. IEE Proceedings - Generation, Transmission and Distribution. https://doi.org/10.1049/ip-gtd%3A20020218
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.