Fan
Impact of Smart Metering Data Aggregation on Distribution System State Estimation
Fan
Authors
Abstract
Pseudo MV/LV (Medium/Low Voltage) transformer loads are usually used as partial inputs to the Distribution System
State Estimation (DSSE) in MV systems. Such pseudo load can be represented by the aggregation of Smart Metering (SM) data. This follows the government restriction that Distribution Network Operators (DNOs) can only use aggregated SM data. Therefore, we assess the subsequent performance of DSSE, which shows the impact of this restriction - it affects the voltage angle estimation significantly. The possibilities for improving the DSSE accuracy under this restriction are further studied. First, two strategies that can potentially relax this restriction’s impact are studied: the correlations among pseudo loads’ errors are taken into account in the DSSE process; a power loss estimation method is proposed. Second, the investments (i.e., either adding measurement devices or increasing the original devices’ accuracy) for the satisfactory DSSE results are assessed. All these are for addressing DNOs’ concerns on this restriction.
Citation
Fan. (2016). Impact of Smart Metering Data Aggregation on Distribution System State Estimation. IEEE Transactions on Industrial Informatics, 1426 - 1437. https://doi.org/10.1109/TII.2016.2573272
Acceptance Date | May 26, 2016 |
---|---|
Publication Date | May 26, 2016 |
Journal | IEEE Transactions On Industrial Informatics |
Print ISSN | 1551-3203 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1426 - 1437 |
DOI | https://doi.org/10.1109/TII.2016.2573272 |
Keywords | Distribution system state estimation (DSSE), medium voltage power system, smart meter |
Publisher URL | https://ieeexplore.ieee.org/document/7479535/ |
Files
ALL_TII-16-0136.pdf
(3.1 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
You might also like
A Review of Privacy-preserving Federated Learning for the Internet-of-Things
(2021)
Book Chapter
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search