用分层强化学习让分布式能源参与电力交易，提升市场效率。
A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
- 分层多智能体强化学习协调个体与集体交易行为
- 通过斯塔克尔伯格博弈优化整体市场表现
- 适合关注分布式能源聚合与市场机制设计的研究者
电力系统正向去中心化转型，终端用电电气化和分布式能源（DERs）的普及推动其主动参与电力市场以支持电网运行。随着能量与通信双向流动成为常态，具备智能性、易部署和低资源消耗特征的需求侧参与，对保障电网灵活性和市场效率至关重要。本文提出一种市场参与框架，利用分层多智能体深度强化学习（MARL），使单个产消者参与点对点零售拍卖，并进一步聚合这些智能产消者，实现分布式能源在批发市场的有效参与。最终，引入斯塔克尔伯格博弈协调该分层MARL框架，以提升市场整体性能。
原文摘要 · Abstract (English)
The ongoing shift towards decentralization of the electric energy sector, driven by the growing electrification across end-use sectors, and widespread adoption of distributed energy resources (DERs), necessitates their active participation in the electricity markets to support grid operations. Furthermore, with bi-directional energy and communication flows becoming standard, intelligent, easy-to-deploy, resource-conservative demand-side participation is expected to play a critical role in securing power grid operational flexibility and market efficiency. This work proposes a market engagement framework that leverages a hierarchical multi-agent deep reinforcement learning (MARL) approach to enable individual prosumers to participate in peer-to-peer retail auctions and further aggregate these intelligent prosumers to facilitate effective DER participation in wholesale markets. Ultimately, a Stackelberg game is proposed to coordinate this hierarchical MARL-based DER market participation framework toward enhanced market performance.
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