# MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids

arXiv: https://arxiv.org/abs/2303.08447 · HTML: https://mlsanity.com/paper/2303.08447/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Consensus Learning for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2206.02583/) (2022)

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Generalized Policy Learning for Smart Grids: FL TRPO Approach](https://mlsanity.com/paper/2403.18439/) (2024)
- [Generalising Battery Control in Net-Zero Buildings via Personalised Federated RL](https://mlsanity.com/paper/2412.20946/) (2024)

## Abstract (arXiv)

Integrating variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply.