Source-Load Separation Method Based on Multi-Agent Reinforcement Learning

HUA Xingyuan; DUAN Sijing; CUI Wenpeng; CHEN Yuzhe; YANG Qingchen; QIAO Nan; REN Ju

doi:10.12263/DZXB.20260338

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Source-Load Separation Method Based on Multi-Agent Reinforcement Learning

PAPERS | 更新时间：2026-10-16

- Source-Load Separation Method Based on Multi-Agent Reinforcement Learning
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2763-2775(2026)
- 作者机构：
  
  1.清华大学计算机科学与技术系，北京 100084
  2.北京智芯微电子科技有限公司，北京 102200
  3.中南大学计算机学院，湖南长沙 410083
- 作者简介：
- 基金信息：
  
  Smartchip Research Project of the Chinese Institute of Electronics(2024-02);The National Natural Science Foundation of China(62432004;62402279);The Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China(JYB2025XDXM122);The Postdoctoral Fellowship Program(BX20250375);The China Postdoctoral Science Foundation(2025M771503)
- DOI：10.12263/DZXB.20260338
  CLC： TP39;TM715
- Received：28 April 2026，
  
  Accepted：21 May 2026，
  
  Published：25 June 2026
- 稿件说明：
移动端阅览
华星源, 段思婧, 崔文朋, 等. 基于多智能体强化学习的源荷分离方法[J]. 电子学报, 2026, 54(06): 2763-2775.

HUA Xingyuan, DUAN Sijing, CUI Wenpeng, et al. Source-Load Separation Method Based on Multi-Agent Reinforcement Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2763-2775.
华星源, 段思婧, 崔文朋, 等. 基于多智能体强化学习的源荷分离方法[J]. 电子学报, 2026, 54(06): 2763-2775. DOI：10.12263/DZXB.20260338

HUA Xingyuan, DUAN Sijing, CUI Wenpeng, et al. Source-Load Separation Method Based on Multi-Agent Reinforcement Learning[J]. Acta Electronica Sinica, 2026, 54(06): 2763-2775. DOI：10.12263/DZXB.20260338

摘要

近年来，全球可持续能源发展的需求日益增长，推动了光伏发电系统在居民、商业及工业场景中的应用。为了保障电网运行安全和提升能源调度效率，如何准确预测用户侧的电力负荷变化尤为关键。然而现有方法忽略了实际用户侧接入电表所采集的数据中往往同时包含光伏发电（源）和用电负荷（荷）信息，二者未被有效分离，导致预测结果存在偏差。本文提出基于多智能体强化学习的源荷分离方法SoLED，解决了用户之间复杂时空依赖关系建模能力不足，以及负荷与光伏发电混合数据未分离导致的预测偏差的问题。具体地，本文首先设计了一种特征提取模块，能够捕捉用户负荷短期与长期尺度上的变化规律，从而提升对电网时序特征的表达能力。然后，基于物理世界的电网结构构建仿真电网环境，通过在仿真环境中模拟增强时序特征对空间关系的敏感性。最后，提出了基于多智能体强化学习的源荷分离方法，利用仿真环境生成反馈来训练解耦模型，解决了数据分离困难导致的解耦不准确问题。通过仿真实验和实际系统验证，本文提出方法的准确率在两个城镇电网真实数据集中的不同天气条件下相较现有方法分别提升5.7%~24.6%和6.6%~24.2%。该实验结果表明，提出的方法能够准确解耦功率，有效地解决了用户之间复杂时空依赖关系建模能力不足，以及负荷与光伏发电混合数据未分离导致的预测偏差的问题。

Abstract

In recent years

the growing demand for global sustainable energy development has accelerated the deployment of photovoltaic (PV) systems across residential

commercial

and industrial sectors. To ensure the safe operation of power grids and improve energy dispatch efficiency

accurately forecasting user-side power load variations has become increasingly critical. However

existing methods often overlook that the data collected from user-side smart meters typically contain both PV generation (source) and power consumption (load) components

which are not effectively separated

leading to biased prediction results. To address this issue

this paper proposes a multi-agent reinforcement learning-based source-load decoupling method

namely SoLED

that mitigates prediction bias caused by the unseparated mixed data of load and PV generation

as well as insufficient modeling of complex spatiotemporal dependencies among users. Specifically

we first design a feature extraction module to capture both short-term and long-term variations in user load

thereby enhancing the representation of spatiotemporal characteristics within the power grid. Then

based on the physical topology of the grid

we construct a simulation environment that models voltage responses under different load and PV power conditions. Finally

leveraging the feedback generated from this simulation

we train a decoupling model within a multi-agent reinforcement learning framework to achieve accurate source-load separation. Experimental results demonstrate that the proposed method improves prediction accuracy by 5.7%~24.6% and 6.6%~24.2% under different weather conditions on two real-world urban power grid datasets. These results confirm the effectiveness of the proposed approach in accurately decoupling user-side mixed power data and enhancing the modeling capability of complex spatiotemporal dependencies

thereby reducing the prediction bias in user-side load forecasting.

关键词

Keywords

references

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