Geographical Peer Matching for P2P Energy Sharing

Abstract

Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater costsavings. 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. Hence dedicated algorithms are required to be able to perform a cost-efficient matching of thousands of peers in a computationalefficient fashion. We define and analyse in this work a precise mathematical modelling of the geographical peer matching problem. We then propose and analyse dedicated algorithms. Our experimental study, based on real-world energy data, demonstrates that our solutions are both efficient in terms of cost-savings achieved by the peers and in terms of computing requirements. Previous results have already shown that small communities were efficient but only small-enough datasets were used allowing for brute-force solutions. Our scalable algorithms thus provide one core building block on the way to build fully practical and data-efficient peer-to-peer energy sharing communities for large-scale systems.