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A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs

arXiv 2604.20586 ↗First posted 22 Apr 2026cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 39 references that are in the map. First posted on arXiv 22 Apr 2026.

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3 of 39 references.

References · 3

  1. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 94
  2. 2016Incentivizing Energy Trading for Interconnected Microgridscited by 7
  3. 2020A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Gridscited by 4

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