A novel communication efficient peer-to-peer energy trading scheme for enhanced privacy in microgrids. (15th August 2021)

• Record Type: Journal Article
• Authors: Umer, Khalid; Huang, Qi; Khorasany, Mohsen; Afzal, Muhammad; Amin, Waqas
• Is Part Of: Applied energy. Volume 296(2021)
• Publication Date: 2021-08-15
• Subjects: ADMM -- Decentralized -- Distributed optimization -- Energy trading -- Microgrids -- Peer-to-peer -- Privacy preserving; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117075
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Peer-to-peer energy trading is an emerging concept for implementing power markets with a large number of distributed energy resources. The success of such trading depends on the participation of lots of prosumers. Therefore, the trading scheme should motivate peers to participate in the market through fair pricing while guaranteeing their privacy. A novel communication efficient algorithm, called Energy Trading Distributed Alternating Direction Method of Multipliers (ETD-ADMM), is proposed to solve the social welfare maximization problem in a decentralized manner without requiring any central authority. The algorithm is formulated using the concept of node coloring, where each node only needs to negotiate with its neighbors to reach the global optimal solution. The decrease in communications makes the trading scheme more privacy-preserving and scalable than other contemporary approaches. The proposed algorithm is tested on a wide range of scenarios that prove its optimality and fast convergence. The comparison of the algorithm with other state-of-the-art distributed approaches shows that it requires much fewer communications and iterations to get similar optimal outcomes. Highlights: A decentralized peer-to-peer energy trading scheme is proposed for the direct interaction of prosumers. A novel communication efficient algorithm is designed to solve the social welfare maximization problem of the whole community. The comparison of the proposed algorithm with other state-of-the-art approaches shows that it is more privacy-preserving and scalable. The trading scheme is tested on four different scenarios to show its feasibility.