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Simplistic Revenue Based BESS Sizing Tool Developed in Python Using Historical Grid Data
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems. (RCAM)ORCID iD: 0000-0002-6095-7811
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2022 (English)In: IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

The introduction of transmission operators enabling small-scale energy storage to participate in the frequency containment market through augmented bidding requires estimating the potential revenue gain of such instalments. Due to this, the overall goal of this study has been to develop and implement a simplistic model within Python for consumers looking into investing in such systems, capable of analyzing the potential of generating profit for a specific battery configuration through the use of historical electricity prices and grid frequency data. This paper proposes a model utilizing sets of linear and nonlinear constraints in order to represent the process of bidding on the Nordic frequency containment reserve market. Furthermore, the potential cost savings made by peak shaving and a method of estimating the cost of the degradation due to cycling the battery have been included within the model. The final result shows a software capable of identifying appropriate system sizes based on the instalment’s cost and the potential revenue.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 1-6
Keywords [en]
Electric potential, Costs, Data models, Software, Frequency estimation, Batteries, Smart grids
National Category
Energy Systems
Research subject
Energy Technology
Identifiers
URN: urn:nbn:se:kth:diva-324973DOI: 10.1109/ISGT-Europe54678.2022.9960580ISI: 001466627500121Scopus ID: 2-s2.0-85143759953OAI: oai:DiVA.org:kth-324973DiVA, id: diva2:1745232
Conference
IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)
Note

QC 20230405

Available from: 2023-03-22 Created: 2023-03-22 Last updated: 2025-12-08Bibliographically approved

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Shafique, HamzaBertling, Lina

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