Small-Scale Communities Are Sufficient for Cost- and Data-Efficient Peer-to-Peer Energy Sharing

Abstract

Due to ever lower cost, investments in renewable electricity generation and storage have become more attractive to electricity consumers in recent years. At the same time, electricity generation and storage have become something to share or trade locally in energy communities or microgrid systems. In this context, peerto-peer (P2P) sharing has gained attention, since it offers a way to optimize the cost-benefits from distributed resources, making them financially more attractive. However, it is not yet clear in which situations consumers do have interests to team up and how much cost is saved through cooperation in practical instances. While introducing realistic continuous decisions, through detailed analysis based on large-scale measured household data, we show that the financial benefit of cooperation does not require an accurate forecasting. Furthermore, we provide strong evidence, based on analysis of the same data, that even P2P networks with only 2-5 participants can reach a high fraction (96% in our study) of the potential gain, i.e., of the ideal offline (i.e., non-continuous) achievable gain. Maintaining such small communities results in much lower associated costs and better privacy, as each participant only needs to share its data with 1-4 other peers. These findings shed new light and motivate requirements for distributed, continuous and dynamic P2P matching algorithms for energy trading and sharing.

Geographical Peer Matching for P2P Energy Sharing

“…Often also associated with geographical closeness, the long-term communities present many advantages from an infrastructure point of view, being able to better regulate local loadbalancing of distributed energy resources, providing higher local self-sufficiency and/or reducing the local peak demand. Research on optimizing the gain outcome from the peering process using constrained optimization has focused on different aspects: how communications are handled [22], reaching stable partitions [5,4], finding optimal resources based on community sizes [18], privacy aspects and amount of data transmitted over the network [20,10,11], etc. Small neighborhoods have been shown to provide a high share of possible gain while being favorable in terms of data exchange [11] and different matching were previously studied in [5,4], based upon stable partitions (i.e., nodes with self-interest) and on a cost-sharing mechanism known beforehand.…”

“…Zhou et al [39,4] expand the partitioning to communities of size k > 2 and evaluate partition-forming algorithms for groups of size 2 or 3 over a set of 30 households. Duvignau et al [10,11] advocate small-scale communities made of a few peers only (2 to 5) claiming that smaller groups are both efficient in terms of data and cost. No mathematical analysis is done concerning the cost of computing the different matchings as the work is essentially focused on energy cost-optimization and data-efficiency.…”