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Stabilizing Peer-to-Peer Energy Trading in Prosumer Coalition Through Computational Efficient Pricing

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Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated.


Jing Li    
Imperial College London
United Kingdom

Yujian Ye    
Fetch.AI, Imperial College London
United Kingdom

Goran Strbac    
Imperial College London
United Kingdom


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