Many existing trading algorithms for battery storage systems operate as “black boxes”, limiting insight into their decision logic and interaction with electricity markets. To address this, we present a modular, rule-based trading framework for battery energy storage systems (BESS) in the intraday continuous market, coupled with a simulated order book matching mechanism. The focus is not on outperforming state-of-the-art trading algorithms in terms of profit, but on providing a transparent environment for analyzing trading behavior and market effects.

The framework structures trading logic into three rule types: opening rules create new positions based on market conditions, day-ahead price profiles, and external information such as weather data; cancelling rules remove unmatched orders according to uptime or price-deviation criteria; and closing rules realize profits while ensuring the technical feasibility of the resulting BESS operation. By combining these rules, complex trading strategies and behavior patterns can be generated and systematically studied.

To realistically represent market interactions, we implement a simulated order book matching algorithm based on the procedure published by the Nominated Electricity Market Operators. The original order book, containing around 3–5 million events per day, is compressed into an aggregated order book through a three-step process: filtering, time bucketing with a resolution of 6 seconds, and price aggregation into 10 €/MWh buckets per product and time interval. This reduces the data volume by a factor of about 45 while preserving the essential price–volume structure. The resulting simulated matching reproduces actual transaction data with a mean absolute error of 2.16 €/MWh, demonstrating a high degree of fidelity.

First simulations with a baseline rule set, relying on simple market-following behavior using current transaction prices, yield only a small profit of 3,587.95 €/MW/a. Nevertheless, the transparent setup reveals clear behavioral patterns in trader decisions, especially in response to price spreads and recent trades, and clarifies how market interactions shape trader outcomes. The developed environment thus provides a basis for backtesting existing trading strategies and for systematically investigating the impact of forecast deviations on both trading performance and market results.