Geographical Peer Matching for P2P Energy Sharing

TL;DR

This paper tackles the scalability challenge of forming long-term P2P energy-sharing communities by introducing Geographical Peer Matching (GPM), a maximum-weight bipartite hypergraph matching problem with spatial and size constraints $(k,\Delta)$. It formalizes the problem, proves hardness results, and derives approximation avenues that connect GPM to one-to-many and assignment problems, enabling practical heuristics. The authors propose three efficient matching algorithms (Round Robin, Single Pass, Classic Greedy) and four weight-instrumentation schemes (memoryless/memoryful variants), plus sampling to reduce weight evaluations, all validated on real consumption and generation data from 2221 households. Experimental results show substantial cost savings (up to around €150k in a large pool) with near-optimal performance when using small, geographically tight communities ($k=5$, $\Delta$), while keeping weight computations manageable. The work demonstrates scalable, data-efficient building blocks for large-scale P2P energy-sharing communities and suggests directions for online, privacy-preserving, and dynamic extensions.

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.

Figures (6)

• Figure 1: Overview of P2P energy sharing showing grouping and interactions between prosumers (equipped with PV panels on roof top and optionally a battery system) and traditional consumers (without any energy resources).
• Figure 2: Illustration of the matching procedures with $P = \{p_1, p_2, p_3\}$, $C = \{c_1, c_2, c_3\}$ and $k = 3$: (a) authorized edge set $E_\Delta$ for a given search radius $\Delta$, (b) all hyperedges based on $E_\Delta$, (c) pairwise weights, and (d) Round Robin, (e) Single Pass, and (f) Classic Greedy matchings.
• Figure 3: Dataset used in our evaluation: (a) Position of the users with an example of grid infrastructure relying them; (b) Size of the neighborhoods.
• Figure 4: Comparison of hyperedge weights with the sum of pairwise weights for memoryless cost-saving weights.
• Figure 5: Average number of calculated weights for the different matching algorithms.

Theorems & Definitions (17)

• Definition 1
• Definition 2
• Proposition 1
• proof
• Proposition 2
• proof
• Proposition 3
• proof
• Corollary 1
• Remark 1