Optimising Battery Energy Storage System Trading via Energy Market Operator Price Forecast
Aymeric Fabre
TL;DR
This study evaluates whether publicly available AEMO price forecasts can be systematically exploited to boost BESS arbitrage in the Australian NEM. It builds a data-driven framework that first assesses forecast accuracy across regions and horizons, then develops a forecast-informed MILP trading algorithm, and finally enhances forecasts with a Random Forest model to drive a superior ML+MILP strategy. The results show substantial financial gains from forecast-informed approaches: baseline profitability is limited, MILP with AEMO forecasts yields meaningful returns, and ML-enhanced forecasting delivers the strongest economics with an NPV of AU$3.71M and IRR of 11%, under reasonable cost assumptions. The work demonstrates the practical value of integrating forecast reliability into dispatch decisions, and it outlines concrete steps for industry deployment, including handling regulatory compliance, data latency, and potential expansion to ancillary services to further improve market efficiency and system resilience.
Abstract
In electricity markets around the world, the ability to anticipate price movements with precision can be the difference between profit and loss, especially for fast-acting assets like battery energy storage systems (BESS). As grid volatility increases due to renewables and market decentralisation, operators and forecasters alike face growing pressure to transform prediction into strategy. Yet while forecast data is abundant, especially in advanced markets like Australia's National Electricity Market (NEM), its practical value in driving real-world BESS trading decisions remains largely unexplored. This thesis dives into that gap. This work addresses a key research question: Can the accuracy of the Australian Energy Market Operator (AEMO) energy price forecasts be systematically leveraged to develop a reliable and profitable battery energy storage system trading algorithm? Despite the availability of AEMO price forecasts, no existing framework evaluates their reliability or incorporates them into practical BESS trading strategies. By analysing patterns in forecast accuracy based on time of day, forecast horizon, and regional variations, this project creates a novel, forecast-informed BESS trading model to optimise arbitrage financial returns. The performance of this forecast-driven algorithm is benchmarked against a basic trading algorithm with no knowledge of forecast data. The study further explores the potential of machine learning techniques to predict future energy prices by enhancing AEMO forecasts to govern a more advanced trading strategy. The research outcomes will inform future improvements in energy market trading models and promote more efficient BESS integration into market operations.