mlsanityData to 14 Oct 2026

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

arXiv 2401.13947 ↗First posted 25 Jan 2024eess.SY cs.LG cs.MAcited by 1

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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.

Key facts. Cited by 1 paper 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

15 of 37 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  4. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
  5. 2019Scalable Reinforcement Learning for Multi-Agent Networked Systemscited by 24
  6. 2021Adversarial attacks in consensus-based multi-agent reinforcement learningcited by 17
  7. 2021PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systemscited by 16
  8. 2020Online Proximal-ADMM For Time-varying Constrained Convex Optimizationcited by 10
  9. 2020Peer-to-Peer Trading in Electricity Networks: An Overviewcited by 6
  10. 2018Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approachescited by 4
  11. 2018Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Networkcited by 4
  12. 2020Peer-to-Peer Energy Systems for Connected Communities: A Review of Recent Advances and Emerging Challengescited by 3
3 more
  1. 2019Solvability of Power Flow Equations Through Existence and Uniqueness of Complex Fixed Pointcited by 1
  2. 2022Optimal Retail Tariff Design with Prosumers: Pursuing Equity at the Expenses of Economic Efficiencies?cited by 1
  3. 2022Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Networkcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Tradingcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026A Hierarchical MARL-Based Approach for Coordinated Retail P2P Trading and Wholesale Market Participation of DERs
  2. 2025Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
  3. 2026Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities
  4. 2024Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms
  5. 2024Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
  6. 2025Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning
  7. 2025An Explainable Equity-Aware P2P Energy Trading Framework for Socio-Economically Diverse Microgrid
  8. 2025Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market