Behavioral Generative Agents for Energy Operations

Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This paper introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. We demonstrate that these agents behave more optimally and rationally in simpler market scenarios, while their performance becomes more variable and suboptimal as task complexity rises. Furthermore, the agents exhibit heterogeneous customer preferences, consistently maintaining distinct, persona-driven reasoning patterns. Our findings highlight the potential value of integrating generative agents into energy management simulations to improve the design and effectiveness of energy policies and incentive programs.