Solving security constrained optimal power flow problems : a hybrid evolutionary approach
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34760624
- Type
- D - Journal article
- DOI
-
10.1007/s10489-018-1167-5
- Title of journal
- Applied Intelligence
- Article number
- -
- First page
- 3672
- Volume
- 48
- Issue
- 10
- ISSN
- 0924-669X
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
6
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The proposed algorithm (previous version and new developments) won IEEE competitions: planning of sustainable transmission and distribution, smart grid operation, renewable energy sources interaction and uncertain environments (https://site.ieee.org/psace-mho/2019-expansion-planning-and-flexibility-optimization-in-sustainable-electrical-power-systems-competition-panel/). The algorithm is not only better than state of the art with some IEEE bus system problems but also it has been taken up by other researchers. It formed the basis of a successful GET-MSCA- Cofound Fellowship (under H2020 - Grant Agreement 754382).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
