dc.creator | Song, Shaojian | es |
dc.creator | Wei, Huangjiao | es |
dc.creator | Lin, Yuzhang | es |
dc.creator | Wang, Cheng | es |
dc.creator | Gómez Expósito, Antonio | es |
dc.date.accessioned | 2023-06-19T16:55:32Z | |
dc.date.available | 2023-06-19T16:55:32Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Song, S., Wei, H., Lin, Y., Wang, C. y Gómez Expósito, A. (2022). A holistic state estimation framework for active distribution network with battery energy storage system. Journal of Modern Power Systems and Clean Energy, 10 (3), 627-636. https://doi.org/10.35833/MPCE.2020.000613. | |
dc.identifier.issn | 2196-5625 | es |
dc.identifier.uri | https://hdl.handle.net/11441/147339 | |
dc.description | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). | es |
dc.description.abstract | Battery energy storage systems (BESSs) are expect‐
ed to play a crucial role in the operation and control of active
distribution networks (ADNs). In this paper, a holistic state esti‐
mation framework is developed for ADNs with BESSs integrat‐
ed. A dynamic equivalent model of BESS is developed, and the
state transition and measurement equations are derived. Based
on the equivalence between the correction stage of the iterated
extended Kalman filter (IEKF) and the weighted least squares
(WLS) regression, a holistic state estimation framework is pro‐
posed to capture the static state variables of ADNs and the dy‐
namic state variables of BESSs, especially the state of charge
(SOC). A bad data processing method is also presented. The
simulation results show that the proposed holistic state estima‐
tion framework improves the accuracy of state estimation as
well as the capability of bad data detection for both ADNs and
BESSs, providing comprehensive situational awareness for the
whole system. | es |
dc.format | application/pdf | es |
dc.format.extent | 10 p. | es |
dc.language.iso | eng | es |
dc.publisher | IEEE | es |
dc.relation.ispartof | Journal of Modern Power Systems and Clean Energy, 10 (3), 627-636. | |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Active distribution network (ADN) | es |
dc.subject | Anomaly detection | es |
dc.subject | Battery energy storage system (BESS) | es |
dc.subject | Kalman filtering | es |
dc.subject | Situational awareness | es |
dc.subject | State estimation | es |
dc.subject | State of charge (SOC) | es |
dc.title | A holistic state estimation framework for active distribution network with battery energy storage system | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería Eléctrica | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9426542 | es |
dc.identifier.doi | 10.35833/MPCE.2020.000613 | es |
dc.contributor.group | Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica | es |
dc.journaltitle | Journal of Modern Power Systems and Clean Energy | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 627 | es |
dc.publication.endPage | 636 | es |