Coordinated Trading Decision-making for Aggregators in Wholesale and Retail Electricity Markets Based on Large Language Models

GAO Junlu; WANG Yiheng; ZHU Yiting; HUANG Gang; YANG Qiang

doi:10.7500/AEPS20250909004

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Coordinated Trading Decision-making for Aggregators in Wholesale and Retail Electricity Markets Based on Large Language Models

Basic Research | 更新时间：2026-11-04

- Coordinated Trading Decision-making for Aggregators in Wholesale and Retail Electricity Markets Based on Large Language Models
- Automation of Electric Power Systems Vol. 50, Issue 21, Pages: 139-150(2026)
- 作者机构：
  
  1.浙江大学工程师学院，浙江省杭州市 310015
  2.浙江大学电气工程学院，浙江省杭州市 310027
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52177119)
- DOI：10.7500/AEPS20250909004
  CLC：
- Received：09 September 2025，
  
  Revised：2026-04-23，
  
  Online First：13 July 2026，
  
  Published：10 November 2026
- 稿件说明：
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高君璐,王艺衡,祝怡婷等.基于大语言模型的聚合商批发-零售市场协同交易决策[J].电力系统自动化,2026,50(21):139-150.

GAO Junlu,WANG Yiheng,ZHU Yiting,et al.Coordinated Trading Decision-making for Aggregators in Wholesale and Retail Electricity Markets Based on Large Language Models[J].Automation of Electric Power Systems,2026,50(21):139-150.
高君璐,王艺衡,祝怡婷等.基于大语言模型的聚合商批发-零售市场协同交易决策[J].电力系统自动化,2026,50(21):139-150. DOI： 10.7500/AEPS20250909004.

GAO Junlu,WANG Yiheng,ZHU Yiting,et al.Coordinated Trading Decision-making for Aggregators in Wholesale and Retail Electricity Markets Based on Large Language Models[J].Automation of Electric Power Systems,2026,50(21):139-150. DOI： 10.7500/AEPS20250909004.

摘要

在新型电力系统和电力市场加速发展背景下，资源聚合商已成为参与电力市场的重要主体。聚合商如何协调批发市场购电与零售市场售电环节中的交易决策是其核心问题之一。然而，传统数学规划方法高度依赖精确模型，且易出现维度灾难导致计算复杂度高。同时，数据驱动的机器学习模型存在可解释性差和对训练数据依赖度高的问题，导致现有方法难以有效兼顾决策透明性与环境适应性。为此，文中提出一种基于大语言模型（LLM）的聚合商批发-零售市场协同交易决策方法。该方法通过基于思维链和心智理论建模的推理机制，实现了结构化市场数据与非结构化外部信息的融合利用，提升了交易决策模型在动态市场交易环境下的适用性。仿真算例验证结果表明，所提方法在多场景下较保守型和激进型交易策略在电力市场适应性和聚合商交易收益方面具有优势。

Abstract

In the background of the rapid development of new power systems and electricity markets

resource aggregators are playing an important role as electricity market participants. One of the key challenges for aggregators lies in making coordinated trading decisions across wholesale electricity procurement and retail electricity sales. However

conventional mathematical programming methods depend heavily on accurate models and are prone to the curse of dimensionality

resulting in high computational complexity. Meanwhile

the data-driven machine learning models suffer from poor interpretability and heavy dependence on training data

which renders existing methods unable to balance decision transparency and environmental adaptability effectively. Therefore

this paper proposes a coordinated trading decision-making method for aggregators in the wholesale and retail electricity markets based on large language models (LLMs). The proposed method leverages chain-of-thought (CoT) and theory-of-mind (ToM) modeling based reasoning mechanisms to realize the integrated utilization of structured market data and unstructured external information

thereby improving the applicability of the trading decision-making model in the dynamic market trading environments. Verification results of simulation cases show that the proposed method outperforms both conservative and aggressive trading strategies in terms of electricity market adaptability and aggregator trading revenues in multiple scenarios.

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