# Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network

arXiv: https://arxiv.org/abs/2311.15594 · HTML: https://mlsanity.com/paper/2311.15594/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Learning values across many orders of magnitude](https://mlsanity.com/paper/1602.07714/) (2016)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Multi-Agent Constrained Policy Optimisation](https://mlsanity.com/paper/2110.02793/) (2021)
- [Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks](https://mlsanity.com/paper/2007.02991/) (2020)
- [Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks](https://mlsanity.com/paper/2005.11142/) (2020)
- [Dynamic Power Distribution System Management With a Locally Connected Communication Network](https://mlsanity.com/paper/1803.10804/) (2018)
- [Low-complexity decentralized algorithm for aggregate load control of thermostatic loads](https://mlsanity.com/paper/2010.12443/) (2020)
- [Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach](https://mlsanity.com/paper/2305.13538/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications](https://mlsanity.com/paper/2409.16256/) (2024)
- [Traceable Knowledge Graph Reasoning Enables LLM-Assisted Decision Support for Industrial VOCs in the Steel Industry](https://mlsanity.com/paper/2605.27071/) (2026)

## Abstract (arXiv)

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.