The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies

Fuente: arXiv
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Autores principales: Hornek, Timothée, Lee, Youngsub, Menci, Sergio Potenciano, Pavić, Ivan
Formato: Preprint
Publicado: 2025
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author Hornek, Timothée
Lee, Youngsub
Menci, Sergio Potenciano
Pavić, Ivan
author_facet Hornek, Timothée
Lee, Youngsub
Menci, Sergio Potenciano
Pavić, Ivan
contents Grid-scale battery energy storage systems (BESSs) can provide flexibility to the power system and capture shortterm price volatility by shifting energy in time through controlled charging and discharging. The highly volatile European continuous intraday (CID) market allows trading until just a few minutes before physical delivery, offering significant earning potential. However, its high trading frequency poses substantial modeling challenges. Accurate modeling of BESSs trading in the CID market is essential to estimate revenue potential and optimize trading strategies. Additionally, comparing CID profits with other spot markets helps determine whether participating in the CID is worthwhile despite its complexity. We propose a forecast-driven model to optimize BESS trading in the CID market. Our strategy employs a rolling window modeling framework to capture market dynamics. Price forecasts for impending CID products are generated at the beginning of each window and used to optimize trading schedules for subsequent execution. We also benchmark our approach across various spot markets, offering a broad cross-market profit comparison. We evaluate our forecast-driven model across different BESS power-to-capacity ratios, comparing it to a perfect-foresight scenario and key CID market indices, such as ID1 and ID3. Using real 2023 German CID data, a 1 MW/1 MWh system adopting our method earns EUR 146 237, only 11% below perfect foresight, surpassing all other markets and indices. Our approach surpasses ID1 and ID3 by over 4% and 32%, respectively, confirming ID1 as a reliable lower-bound estimate for earnings potential in the CID market.
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publishDate 2025
record_format arxiv
spellingShingle The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies
Hornek, Timothée
Lee, Youngsub
Menci, Sergio Potenciano
Pavić, Ivan
Computational Engineering, Finance, and Science
Grid-scale battery energy storage systems (BESSs) can provide flexibility to the power system and capture shortterm price volatility by shifting energy in time through controlled charging and discharging. The highly volatile European continuous intraday (CID) market allows trading until just a few minutes before physical delivery, offering significant earning potential. However, its high trading frequency poses substantial modeling challenges. Accurate modeling of BESSs trading in the CID market is essential to estimate revenue potential and optimize trading strategies. Additionally, comparing CID profits with other spot markets helps determine whether participating in the CID is worthwhile despite its complexity. We propose a forecast-driven model to optimize BESS trading in the CID market. Our strategy employs a rolling window modeling framework to capture market dynamics. Price forecasts for impending CID products are generated at the beginning of each window and used to optimize trading schedules for subsequent execution. We also benchmark our approach across various spot markets, offering a broad cross-market profit comparison. We evaluate our forecast-driven model across different BESS power-to-capacity ratios, comparing it to a perfect-foresight scenario and key CID market indices, such as ID1 and ID3. Using real 2023 German CID data, a 1 MW/1 MWh system adopting our method earns EUR 146 237, only 11% below perfect foresight, surpassing all other markets and indices. Our approach surpasses ID1 and ID3 by over 4% and 32%, respectively, confirming ID1 as a reliable lower-bound estimate for earnings potential in the CID market.
title The Value of Battery Energy Storage in the Continuous Intraday Market: Forecast vs. Perfect Foresight Strategies
topic Computational Engineering, Finance, and Science
url https://arxiv.org/abs/2501.07121