Improving Algorithmic Trading for BESS on Intraday Electricity Markets through Distributional Price Forecasts

Paper: arXiv 2611.17170

Authors: Simon Hirsch, David Schaurecker, David Wozabal

Abstract

Grid-scale battery energy storage systems (BESS) are becoming increasingly important in short-term energy markets, where they help balance fluctuations in variable renewable generation. In many countries, the continuous intraday market is the most important wholesale market for BESS to monetize their flexibility. In this paper, we propose an extension to the myopic rolling intrinsic policy that incorporates intraday price forecasts tailored specifically to the BESS trading problem. While a large literature addresses intraday price forecasting, most existing work focuses on volume-weighted prices during the last hours of trading and consequently fails to capture the features most relevant to BESS trading. Our approach addresses this gap by introducing a novel target variable derived from the limit order book and its dynamics. The resulting forecasts are designed to inform the optimization about future profit opportunities while preserving computational efficiency and avoiding the burden of full stochastic optimization. In a realistic high-frequency simulation setting covering 2023 and 2024, our method improves profits by up to 42% over the naive rolling intrinsic formulation. We further analyze the relationship between forecast accuracy, stability, and financial performance, establishing a clear inverse relationship between accuracy and stability. Finally, we find evidence that BESS size and duration jointly influence both overall profitability and the marginal benefit of incorporating forecasts.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a novel approach to intraday electricity price forecasting for BESS, leveraging limit order book dynamics. It demonstrates strong empirical rigor through high-frequency simulations and detailed performance analysis. The mathematical underpinnings of the forecasting and optimization are solid, contributing to a ‘Holy Grail’ classification.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve BESS Profitability on Intraday Markets] --> B{Key Problem: Existing Price Forecasts Fail BESS Needs};
    B --> C[Methodology: Novel Distributional Price Forecast from Limit Order Book];
    C --> D{Data/Inputs: Intraday Electricity Market Data (2023-2024), Limit Order Book Dynamics};
    D --> E[Computational Process: High-Frequency Simulation, Optimization (Rolling Intrinsic Policy)];
    E --> F{Key Finding 1: Up to 42% Profit Improvement over Naive Policy};
    F --> G{Key Finding 2: Inverse Relationship between Forecast Accuracy & Stability};
    G --> H[Key Finding 3: BESS Size & Duration Influence Profitability & Forecast Benefit];

Cited References (78)

1. Frequent Auctions for Intraday Electricity Markets (2024) — link
2. Loss functions in regression models: Impact on profits and risk in day-ahead electricity trading (2025)
3. Statistical and economic evaluation of forecasts in electricity markets: Beyond RMSE and MAE — → 2511.13616
4. Intraday power trading: toward an arms race in weather forecasting? (2023)
5. A literature review of intraday electricity markets and prices (2019)
6. Directional Price Forecasting in the Continuous Intraday Market under Consideration of Neighboring Products and Limit Order Books — → 2509.04452
7. Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets — → 2510.12685
8. Analysing trading trends in continuous intraday electricity markets (2021)

11 of 78 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2570 linked papers.