Multi-Agent Energy Market Simulations With Machine Learning Integration: A Systematic Literature Review

The transition to green energy requires advanced models to analyze complex energy systems. Among these models, agent-based modeling (ABM) or multi-agent systems represents the most pivotal simulation techniques. Machine learning (ML) methods are integrated into ABM to accurately simulate real-world scenarios and the behavior of emerging energy market agents, such as renewable power plants, incentive schemes, electric vehicles, and battery energy storage systems. Initially, ML methods were primarily used to improve agent learning using a reinforcement learning (RL) method. However, the need to estimate external data has motivated the broader adoption of various ML methods. This paper reviews the integration of ML within ABM frameworks, by examining articles from 2014 to 2024. The review methodology utilized self-organizing map (SOM) based clustering using the market data types, simulation periods, and ML contribution types as the clustering attributes. The study provides an overview of the current state of the field and identifies potential research gaps. The findings indicate that agent learning and traditional RL methods dominate; however, significant gaps are evident in terms of the inclusion of long-term and data prediction studies, and future studies should integrate more observations from other energy markets along with electricity.