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Optimal Day Ahead Planning and Bidding Strategy of Battery Storage Unit Participating in Nordic Frequency Markets
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.ORCID iD: 0000-0003-4791-8380
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-8189-2420
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 76870-76883Article in journal (Refereed) Published
Abstract [en]

The current energy transition needed to meet the world climate objectives is causing stability challenges in all the power systems. As a consequence, finding solutions that allow dealing with those frequency stability issues is critical to achieving the sustainability objectives. In this context, batteries can find new revenue opportunities by being part of the solution to this problem. This paper develops a mathematical model that provides the optimal bidding strategy and the most convenient operational planning for batteries participating in balancing markets. More specifically, this project is focused on the Swedish frequency markets whose minimum bid size is smaller than 5 MW. The proposed optimization problem is a two-stage stochastic mixed-integer and linear optimization from the battery operator's point of view that includes a novel step-by-step process for properly selecting the bid prices by the quantification of the risks, control costs and amortization of the battery. The originality of the work resides in the aforementioned process that allows risk assessment together with the linear modelling of paid-as-bid markets, commonly solved through non-linear problems. As a result, not only the profitability of this application for batteries is demonstrated, but it is also possible to observe the seasonal differences when it comes to revenue and power requirements. The model is tested with data from Sweden, but it is designed to be adjustable to other balancing markets.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 10, p. 76870-76883
Keywords [en]
Batteries, Economics, Risk management, Optimization, Costs, Planning, Power system stability, Frequency control, balancing markets, stochastic optimisation, battery energy storage
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Energy Engineering
Identifiers
URN: urn:nbn:se:kth:diva-316239DOI: 10.1109/ACCESS.2022.3192131ISI: 000832941100001Scopus ID: 2-s2.0-85135242599OAI: oai:DiVA.org:kth-316239DiVA, id: diva2:1688455
Note

QC 20220818

Available from: 2022-08-18 Created: 2022-08-18 Last updated: 2022-08-18Bibliographically approved

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Khodadadi, AbolfazlSöder, Lennart

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Manzanares Casla, IreneKhodadadi, AbolfazlSöder, Lennart
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