# Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2401.13947 · HTML: https://mlsanity.com/paper/2401.13947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Scalable Reinforcement Learning for Multi-Agent Networked Systems](https://mlsanity.com/paper/1912.02906/) (2019)
- [Adversarial attacks in consensus-based multi-agent reinforcement learning](https://mlsanity.com/paper/2103.06967/) (2021)
- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [Online Proximal-ADMM For Time-varying Constrained Convex Optimization](https://mlsanity.com/paper/2005.03267/) (2020)
- [Peer-to-Peer Trading in Electricity Networks: An Overview](https://mlsanity.com/paper/2001.06882/) (2020)
- [Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches](https://mlsanity.com/paper/1804.00962/) (2018)
- [Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network](https://mlsanity.com/paper/1809.06976/) (2018)
- [Peer-to-Peer Energy Systems for Connected Communities: A Review of Recent Advances and Emerging Challenges](https://mlsanity.com/paper/2011.11118/) (2020)
- [Solvability of Power Flow Equations Through Existence and Uniqueness of Complex Fixed Point](https://mlsanity.com/paper/1904.08855/) (2019)
- [Optimal Retail Tariff Design with Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?](https://mlsanity.com/paper/2209.14505/) (2022)
- [Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network](https://mlsanity.com/paper/2212.14156/) (2022)

## Built on by (most cited first, 1 of 1)

- [LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading](https://mlsanity.com/paper/2507.14995/) (2025)

## Abstract (arXiv)

Utilizing distributed renewable and energy storage resources in local distribution networks via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy systems' resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have the expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose multi-agent reinforcement learning (MARL) frameworks to help automate consumers' bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL frameworks can integrate physical network constraints to realize voltage control, hence ensuring physical feasibility of the P2P energy trading and paving way for real-world implementations.