Geographical Peer Matching for P2P Energy Sharing

TL;DR

This paper presents a mathematical model and heuristics for geographically matching energy peers in P2P energy sharing systems, demonstrating cost-effective and computationally efficient solutions based on real-world data.

Contribution

It introduces a novel mathematical model and scalable heuristics for geographically peer matching in P2P energy sharing, addressing computational challenges.

Findings

Efficient cost savings achieved in real-world energy data simulations.

Algorithms reduce communication and computing requirements.

Scalable solutions enable practical large-scale energy communities.

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 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.