# Behavioral Generative Agents for Energy Operations

arXiv: https://arxiv.org/abs/2506.12664 · HTML: https://mlsanity.com/paper/2506.12664/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 57 references that are in the map.
- First posted on arXiv 14 Jun 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 13 of 13)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia](https://mlsanity.com/paper/2312.03664/) (2023)
- [LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory](https://mlsanity.com/paper/2408.02784/) (2024)
- [Large Language Models: An Applied Econometric Framework](https://mlsanity.com/paper/2412.07031/) (2024)
- [Learning to Cooperate with Humans using Generative Agents](https://mlsanity.com/paper/2411.13934/) (2024)
- [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://mlsanity.com/paper/2306.01475/) (2023)
- [Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making](https://mlsanity.com/paper/2403.15640/) (2024)
- [Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage using Approximate Dynamic Programming](https://mlsanity.com/paper/1402.3575/) (2014)
- [Optimal Mechanisms for Demand Response: An Indifference Set Approach](https://mlsanity.com/paper/2409.07655/) (2024)
- [Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints](https://mlsanity.com/paper/2005.06042/) (2020)

## Built on by (most cited first, 2 of 2)

- [Competition and Cooperation of LLM Agents in Games](https://mlsanity.com/paper/2604.00487/) (2026)
- [BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research](https://mlsanity.com/paper/2609.02729/) (2026)

## Abstract (arXiv)

Problem definition: Accurately modeling consumer behavior in energy operations is challenging due to uncertainty, behavioral heterogeneity, and limited empirical data-particularly in low-frequency, high-impact events. While generative AI trained on large-scale human data offers new opportunities to study decision behavior, its role in operational applications remains unclear. We examine how generative agents can support customer behavior discovery in energy operations, complementing rather than replacing human-based experiments. Methodology/results: We introduce a novel approach leveraging generative agents-artificial agents powered by large language models-to simulate sequential customer decisions under dynamic electricity prices and outage risks. We find 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 in both operational decisions and textual reasoning. Comparisons with dynamic programming and greedy policy benchmarks show alignment between specific personas and distinct heuristic decision policies. In low-frequency, high-impact events such as blackouts, agents prioritize energy reliability over cost or profit, demonstrating their ability to uncover behavioral patterns beyond the rigidity of traditional mathematical models. Managerial Implications: Our findings suggest that behavioral generative agents can serve as scalable and flexible tools for studying consumer behavior in energy operations. By enabling controlled experiments across heterogeneous customer types and rare events, these agents can enhance the design of energy management systems and support more informed analysis of energy policies and incentive programs.