# Safe Bottom-Up Flexibility Provision from Distributed Energy Resources

arXiv: https://arxiv.org/abs/2504.20529 · HTML: https://mlsanity.com/paper/2504.20529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2025.

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

- [Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks](https://mlsanity.com/paper/2512.12803/) (2025)
- [Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16062/) (2026)

## Abstract (arXiv)

Modern renewables-based power systems need to tap on the flexibility of Distributed Energy Resources (DERs) connected to distribution networks. It is important, however, that DER owners/users remain in control of their assets, decisions, and objectives. At the same time, the dynamic landscape of DER-penetrated distribution networks calls for agile, data-driven flexibility management frameworks. In the face of these developments, the Multi-Agent Reinforcement Learning (MARL) paradigm is gaining significant attention, as a distributed and data-driven decision-making policy. This paper addresses the need for bottom-up DER management decisions to account for the distribution network's safety-related constraints. While the related literature on safe MARL typically assumes that network characteristics are available and incorporated into the policy's safety layer, which implies active DSO engagement, this paper ensures that self-organized DER communities are enabled to provide distribution-network-safe flexibility services without relying on the aspirational and problematic requirement of bringing the DSO in the decision-making loop.