# A Novel Multiagent Flexibility Aggregation Framework

arXiv: https://arxiv.org/abs/2307.08401 · HTML: https://mlsanity.com/paper/2307.08401/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 17 Jul 2023.

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

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [A Fast Distributed Algorithm for Large-Scale Demand Response Aggregation](https://mlsanity.com/paper/1603.00149/) (2016)
- [Day-ahead Trading of Aggregated Energy Flexibility - Full Version](https://mlsanity.com/paper/1805.02301/) (2018)

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [Graph Neural Networks, Deep Reinforcement Learning and Probabilistic Topic Modeling for Strategic Multiagent Settings](https://mlsanity.com/paper/2511.10501/) (2025)

## Abstract (arXiv)

The increasing number of Distributed Energy Resources (DERs) in the emerging Smart Grid, has created an imminent need for intelligent multiagent frameworks able to utilize these assets efficiently. In this paper, we propose a novel DER aggregation framework, encompassing a multiagent architecture and various types of mechanisms for the effective management and efficient integration of DERs in the Grid. One critical component of our architecture is the Local Flexibility Estimators (LFEs) agents, which are key for offloading the Aggregator from serious or resource-intensive responsibilities -- such as addressing privacy concerns and predicting the accuracy of DER statements regarding their offered demand response services. The proposed framework allows the formation of efficient LFE cooperatives. To this end, we developed and deployed a variety of cooperative member selection mechanisms, including (a) scoring rules, and (b) (deep) reinforcement learning. We use data from the well-known PowerTAC simulator to systematically evaluate our framework. Our experiments verify its effectiveness for incorporating heterogeneous DERs into the Grid in an efficient manner. In particular, when using the well-known probabilistic prediction accuracy-incentivizing CRPS scoring rule as a selection mechanism, our framework results in increased average payments for participants, when compared with traditional commercial aggregators.