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Real-time Operation Model for Energy Management System of Battery Energy Storage System - Case Study: The School of Sinntorp
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-6095-7811
CheckWatt AB, Stockholm, Sweden..
Svenska Kraftnät, Swedish Natl Grid, Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0003-4763-9429
2022 (English)In: 2022 17th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022Conference paper, Published paper (Refereed)
Abstract [en]

This paper shows the real-time operation model of Energy Management System (EMS) for control of Battery Energy Storage System (BESS). These models are needed to manage uncertain electricity loads, delivery of peak shaving and frequency regulation services and the BESS financial feasibility. The delivery of these services result in economic return for the resource owner and increased grid flexibility. The Energy Management System implemented in this work uses the electricity load and grid frequency data as inputs to generate BESS control commands that deliver the said services. The validation simulation performed on the School of Sinntorp in Sweden returns an annual return on investment of 22% for the BESS and inverter. Implementation of storage systems was shown to expedite the adoption of renewable energy while also solving the challenges that this adoption on scale poses. This active adoption is pivotal in achieving the UN sustainable developmental goals (SDGs) for energy.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022.
Series
International Conference on Probabilistic Methods Applied to Power Systems, ISSN 2642-6730
Keywords [en]
EMS, BESS, Peak Shaving, Electricity Grid, Renewable Integration
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-320421DOI: 10.1109/PMAPS53380.2022.9810577ISI: 000853744900020Scopus ID: 2-s2.0-85135065048OAI: oai:DiVA.org:kth-320421DiVA, id: diva2:1709157
Conference
17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), JUN 12-15, 2022, Manchester, England
Note

QC 20221108

Part of proceedings: ISBN 978-1-6654-1211-7

Available from: 2022-11-08 Created: 2022-11-08 Last updated: 2022-11-08Bibliographically approved

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Shafique, HamzaBertling Tjernberg, Lina

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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
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Output format
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  • asciidoc
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