  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 cost savings. Energy Peer-to-Peer (P2P)
systems have thus been shown to be beneficial for prosumers and consumers
through reductions in energy cost while also being attractive to grid or
service providers. However, many practical challenges have to be overcome
before all players could gain in having efficient and automated local energy
communities; such challenges include the inherent complexity of matching
together geographically distributed peers and the significant computations
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 computational-efficient fashion. We define and analyze
in this work a precise mathematical modelling of the geographical peer matching
problem and several heuristics solving it. Our experimental study, based on
real-world energy data, demonstrates that our solutions are efficient both in
terms of cost savings achieved by the peers and in terms of communication and
computing requirements. Our scalable algorithms thus provide one core building
block for practical and data-efficient peer-to-peer energy sharing communities
within large-scale optimization systems.
