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

arXiv: https://arxiv.org/abs/2604.20586 · HTML: https://mlsanity.com/paper/2604.20586/

## 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.

## Builds on (most cited first, 3 of 3)

- [A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity](https://mlsanity.com/paper/1707.09183/) (2017)
- [Incentivizing Energy Trading for Interconnected Microgrids](https://mlsanity.com/paper/1609.07576/) (2016)
- [A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids](https://mlsanity.com/paper/2009.10905/) (2020)

## 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.