Decentralized P2P Energy Trading under Network Constraints in a Low-Voltage Network
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Abstract (arXiv)
The increasing uptake of distributed energy resources (DERs) in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is that of ensuring that network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical UK low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 0; 2026: 2.
4 papers.
- 2021False Data Injection Threats in Active Distribution Systems: A Comprehensive Surveycited by 5
- 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approachcited by 1
- 2026Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Marketscited by 0
- 2026MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Marketscited by 0
- 2026MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets17 Feb 2026
- 2026Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets17 Feb 2026
- 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach25 Jan 2024
- 2021False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey28 Nov 2021