A novel adaptive penalty mechanism for Peer-to-Peer energy trading

Zhang, Bidan, Du, Yang, Chen, Xiaoyang, Lim, Eng Gee ORCID: 0000-0003-0199-7386, Jiang, Lin ORCID: 0000-0001-6531-2791 and Yan, Ke (2022) A novel adaptive penalty mechanism for Peer-to-Peer energy trading. Applied Energy, 327. p. 120125.

Text
applied energy peer peer energy trade.pdf - Author Accepted Manuscript
Download (2MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.apenergy.2022.120125

Abstract

With the rapid development of Distributed Energy Sources (DERs), Peer-to-Peer (P2P) energy trading is regarded as an effective scheme to improve local energy utilization. Nevertheless, unlike wholesale electricity markets of the current grid size, small-scale prosumers and highly unpredictable intermittent DERs account for a significant proportion of P2P markets, leading to an escalation of market uncertainties. To facilitate effective market functionality, penalty mechanisms for unqualified participants are essential, as is typically the case in the wholesale electricity market. However, there has been little discussion of the use of penalty mechanisms in P2P markets. In this context, we propose a novel adaptive penalty mechanism (APM) to drive the defaulting prosumers to fulfill orders. Unlike the traditional two-dimensional (price, quantity) penalty price, APM uses a three-dimensional penalty and introduces deviation percentage factors to reduce the risk of excessive penalty rates. Penalty prices are determined by utilizing the distributed default clearing algorithm to adapt to market conditions, thereby preventing deviations in clearing prices. Case studies are conducted to demonstrate the feasibility and efficiency of the proposed APM in the P2P market. The results indicate that the APM strike an appropriate between cost-effectiveness and regulation, requiring about 20% less reserve capacity than the severe penalty.

Item Type:	Article
Uncontrolled Keywords:	Peer-to-Peer energy trading, Penalty mechanism, Double auction, Energy management system, Uncertainty
Depositing User:	Symplectic Admin
Date Deposited:	16 Nov 2022 11:04
Last Modified:	19 Oct 2023 01:30
DOI:	10.1016/j.apenergy.2022.120125
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3166227