Biggins, F.A.V., Homan, S., Ejeh, J.O. et al. (1 more author) (2022) To trade or not to trade: Simultaneously optimising battery storage for arbitrage and ancillary services. Journal of Energy Storage, 50. 104234. ISSN 2352-152X
Abstract
This work presents a novel methodology for determining the value a battery storage system provides while participating in a competitive frequency response market, considering uncertainties. Battery storage systems are an attractive choice for power services in low-carbon electricity grids and their optimal operation are a commonly studied matter. However, the non-deterministic nature of competitive electricity markets is often overlooked. Here, we consider these market uncertainties for a storage device providing Great Britain’s Firm Frequency Response (FFR) and arbitrage services. We use a machine learning classifier to determine the set of all possible FFR market outcomes and their associated probabilities. These are then propagated through a linear optimisation model to generate a set of possible scenarios, from which the most likely can be ascertained. Several different classifiers and bidding strategies are compared, the most suitable classifier and bidding strategies which maximise revenue whilst minimising the probability of the worse-case scenario are identified. It is found that the mean expected income is overestimated by ~28% when uncertainties in FFR market outcomes are not considered. Providing arbitrage over a tight band can still provide significant income and does not impede on the storage’s ability to provide FFR services in real time.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author produced version of a paper subsequently published in Journal of Energy Storage. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Battery Storage; Ancillary Services; Classifier; Arbitrage; Auction Modelling; Machine Learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/L016818/1 The Department For Business, Energy & Industrial Strategy n/a |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Feb 2022 10:29 |
Last Modified: | 04 Mar 2023 01:13 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.est.2022.104234 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183597 |
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Licence: CC-BY-NC-ND 4.0