Identification of natural disaster impacted electricity load profiles with k means clustering algorithm

Simon Hedegård Jessen*, Zheng Grace Ma, Francisco Danang Wijaya, Juan Vasquez, Josep Guerrero, Bo Nørregaard Jørgensen

*Kontaktforfatter

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Abstract

Natural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system’s recovering process and progress after natural disasters’ hit which strongly influence the system operators’ planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes’ impact on the electricity system and conduct better long-term energy management strategies.
OriginalsprogEngelsk
Artikelnummer59
TidsskriftEnergy Informatics
Vol/bind5
Udgave nummerSuppl. 4
Antal sider29
ISSN2520-8942
DOI
StatusUdgivet - 2022
BegivenhedEnergy Informatics.Academy Conference 2022 - Dandy Business Park, Vejle, Danmark
Varighed: 24. aug. 202225. aug. 2022
https://www.energyinformatics.academy/eia-2022-conference

Konference

KonferenceEnergy Informatics.Academy Conference 2022
LokationDandy Business Park
Land/OmrådeDanmark
ByVejle
Periode24/08/202225/08/2022
Internetadresse

Bibliografisk note

Funding Information:
This paper is a part of the TECH-IN project (Project title: Microgrid Technologies for Remote Indonesian Islands, 2021–2024) funded by Danida Fellowship Centre.

Publisher Copyright:
© 2022, The Author(s).

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