Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
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Abstract (arXiv)
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Mar 2018.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2022Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricingcited by 1
- 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approachcited by 1
- 2025FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Marketscited by 0
- 2026SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Marketscited by 0
- 2026SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets9 Jul 2026
- 2025FairMarket-RL: LLM-Guided Fairness Shaping for Multi-Agent Reinforcement Learning in Peer-to-Peer Markets28 Jun 2025
- 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach25 Jan 2024
- 2022Prospect Theory-inspired Automated P2P Energy Trading with Q-learning-based Dynamic Pricing26 Aug 2022