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| 基于多智能体图强化学习的虚拟电厂分层功率调控策略 |
| Hierarchical Power Regulation Strategy for Virtual Power Plants Based on Multi-Agent Graph Reinforcement Learning |
| 投稿时间：2025-12-24 修订日期：2026-05-08 |
| DOI： |
| 中文关键词: 虚拟电厂 智能楼宇 分层功率调控 多智能体 图强化学习 |
| 英文关键词: Virtual Power Plant Intelligent Building Hierarchical Power Regulation Multi - Agent Graph Reinforcement Learning |
| 基金项目:国家自然科学基金项目智能电网联合基金项目＂基于制度有效性理论的综合能源系统运营机制及关键技术研究＂（U1966204）；国家自然科学基金面上项目＂基于信息驱动的综合能效电厂弹性能量管理与运营策略研究＂（51977032）。 |
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| 中文摘要: |
| 虚拟电厂功率调控优化对提升电网灵活性与经济性意义重大。传统优化调控方法依赖强假设、适应性不足。本文提出一种基于楼宇多智能体聚合的虚拟电厂功率调控优化策略。构建分层多智能体图强化学习框架，将虚拟电厂分为局部调节层与全局状态评估层；基于该框架建立虚拟电厂-智能楼宇分层功率调控优化模型，结合图注意力网络和多智能体深度确定性策略梯度算法，提出改进多智能体图强化学习算法。算例分析表明，该算法在收敛速度、稳定性上优于传统强化学习方法,可快速响应功率调节需求并保持较低功率偏差,能有效解决楼宇间超限功率分配问题,为虚拟电厂功率调控提供了有效解决方案。 |
| 英文摘要: |
| The optimization of virtual power plant power regulation is of great significance for enhancing the flexibility and economy of the power grid. Traditional optimization control methods rely on strong assumptions and have insufficient adaptability. This paper proposes a virtual power plant power regulation optimization strategy based on the aggregation of multi-agent in buildings. A hierarchical multi-agent graph reinforcement learning framework is constructed, dividing the virtual power plant into local regulation layer and global state evaluation layer; based on this framework, a hierarchical power regulation optimization model for virtual power plant and intelligent buildings is established; combined with graph attention network and multi-agent deterministic strategy gradient algorithm, an improved multi-agent graph reinforcement learning algorithm is proposed. The case study analysis shows that this algorithm outperforms traditional reinforcement learning methods in terms of convergence speed and stability, can quickly respond to power regulation requirements and maintain a low power deviation, and can effectively solve the problem of excessive power allocation among buildings, providing an effective solution for virtual power plant power regulation. |
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