Efficient and scalable geographical peer matching for P2P energy sharing communities

Paper i proceeding, 2022

Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Energy Peer-to-Peer (P2P) systems have been shown to be beneficial for prosumers and consumers through reductions in energy cost while being attractive to grid or service provider. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities, including the inherent complexity of matching together geographically distributed peers and the significant computation required to calculate the local matching preferences. This makes it challenging to perform a cost-efficient matching of thousands of peers in a computationally-efficient fashion. We define and analyse in this work a precise mathematical modelling of the geographical peer matching problem, then propose and analyse dedicated algorithms. Our experimental study demonstrates that our solutions are both efficient in terms of cost-savings achieved by the peers and in terms of computational requirements. Our scalable algorithms, out-performing previous brute-force solutions, thus provide one core building block on the way to build fully practical and data-efficient P2P energy sharing communities for large-scale systems.