Aggregated operation of heterogeneous small-capacity distributed energy resources in peer-to-peer energy trading. (October 2022)

• Record Type: Journal Article
• Authors: Zhao, Xuesong; Li, Long; Tao, Yuechuan; Lai, Shuying; Zhou, Xingchao; Qiu, Jing
• Is Part Of: International journal of electrical power & energy systems. Volume 141(2022)
• Publication Date: 2022-10
• Subjects: Heterogeneous distributed energy resources -- Supervised-learning assisted aggregation model -- Peer-to-peer trading -- Fast alternating direction method of multipliers; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108162
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: An aggregation method of heterogeneous distributed energy resources is proposed. The P2P energy trading between aggregators is considered. The privacy of the aggregators is preserved. The distributed optimization method is applied. The computation efficiency of distributed optimization is enhanced. Abstract: Distributed energy resources (DERs) can provide flexibility and promote controllability for distribution networks. However, there are still some obstacles in the management of the small-capacity DERs. In this paper, an aggregated operation model of various heterogeneous DERs in peer-to-peer (P2P) energy trading is proposed. First, a supervised-learning assisted aggregation model of DERs is presented, which aims at estimating the aggregated parameters of heterogeneous DERs. Second, a P2P trading model is proposed for the aggregated DERs. The aggregators can trade with each other in a distributed manner so that privacy can be protected. Third, the fast alternating direction method of multipliers (F-ADMM) is utilized to accelerate the iteration process between the aggregators. Therefore, a quick decision can be made, and less information exchange is required between aggregators, which can reduce the communication burden. The effectiveness of the proposed methodology is verified in case studies.