Consensus-Based Decentralized Energy Trading for Distributed Energy Resources

TL;DR

This paper presents a decentralized energy trading system for prosumers using a consensus algorithm, enhancing privacy and reducing costs in smart grids with IoT-enabled DERs.

Contribution

It introduces a novel fully decentralized transactive energy management approach utilizing a consensus algorithm and IoT support, ensuring privacy and cost efficiency.

Findings

The proposed system reduces overall system costs.

The consensus algorithm enables privacy-preserving energy scheduling.

Simulations validate the feasibility of the approach.

Abstract

In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy. Residential households and commercial buildings with DERs have become prosumers in the local grids, since they can sell surplus power to others. Researches have been initiated to integrate and utilize DERs through better control and communication strategies. With the advances in the Internet of Things (IoT) technology, unprecedented coordination among DERs can be achieved to facilitate energy trading and transactive energy management. However, preventing leakage of users' information during the optimization process keeps challenging researchers, which drives them to develop privacy-preserving energy management systems. In this paper, we develop a fully decentralized transactive energy management using the consensus-based algorithm. To be specific, we design a virtual pool for prosumers to trade energy and exchange information with IoT technologies' support. The consensus-based algorithm enables prosumers to obtain the optimal energy schedule independently in a coordinated manner without revealing any personal data. We use real-world data to perform simulations and validate our developed algorithm. The results show that our consensus-based decentralized transactive energy management strategy is feasible and can significantly reduce the overall system cost.