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

2025-01-13

AI Summary

This study quantifies the energy storage value of battery energy storage systems (BESS) in the European continuous intraday (CID) market and proposes a rolling-window optimization trading strategy to maximize revenue under price volatility. Methodologically, we develop a rolling optimization framework integrating short-term electricity price forecasting, BESS physical constraints (e.g., power limits, round-trip efficiency, state-of-charge dynamics), and cross-market benchmarks, validated empirically on 2023 German CID market data. Our contribution lies in the first systematic comparison of multiple forecasting strategies against a perfect-foresight benchmark, establishing ID1 as a rigorous lower bound for achievable revenue and revealing CID’s superior revenue potential relative to other wholesale markets. Results show that a 1 MW/1 MWh BESS yields €146,000 annual revenue—only 11% below the perfect-foresight optimum—and outperforms the ID1 and ID3 benchmarks by 4% and 32%, respectively.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Battery Energy Storage Systems
• Continuous Intraday Market
• Optimization Strategy

Innovation

• Battery Energy Storage Systems
• Continuous Intraday Market
• Rolling Window Prediction Model