Leveraging Large Language Model Based Agent for Automated Electricity Market Modelling and Simulation

An electricity market is a complex, dynamically operated network encompassing multiple participants under defined rules, thereby ensuring real-time supply-demand balance and system reliability. However, the inherent complexity and dynamism of the electricity market pose significant challenges to conventional modelling approaches, which often rely on expert knowledge and manual processes informed by market regulations. This reliance frequently leads to inefficiencies and elevated risks of error. To address these limitations, this paper proposes a framework for automated electricity market modelling and simulation centered on a large language model based agent, termed the modelling and simulation system agent (MSS-Agent) framework. The proposed MSS-Agent framework employs the hierarchical chain-of-thought (HCoT) method to more accurately extract essential information from relevant documents, thereby enhancing modelling fidelity. Moreover, it integrates tool usage and reflexive debugging to optimize the code generation process, ensuring reliability in automated electricity market modelling and simulation. Experimental results demonstrate that the proposed MSS-Agent framework significantly improves both mathematical model extraction accuracy and code execution reliability. Consequently, the proposed MSS-Agent framework not only increases simulation efficiency but also provides more precise and dependable tools for informed decision-making in electricity markets.