Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard. (July 2020)

• Record Type: Journal Article
• Authors: Fattahi, Javad; Samadi, Mikhak; Erol-Kantarci, Melike; Schriemer, Henry
• Is Part Of: Engineering. Volume 6:Number 7(2020)
• Page Start: 801
• Page End: 811
• Publication Date: 2020-07
• Subjects: Transactive demand response -- IEEE 2030.5 -- Smart grid -- Multi-agent system -- Neighborhood coordination; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2020.06.005
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: This paper presents a transactive demand response (TDR) scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture. A complete laboratory-based implementation provides the first (to our knowledge) realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers (IEEE) 2030.5 standard, which addresses interoperability within a cybersecure smart energy profile (SEP) context. Verification is provided by a full system integration with commercial hardware using Internet Protocol (IP)-based (local area network (LAN) and Wi-Fi) communication protocols and transport layer security (TLS) 1.2 cryptographic protocol, and validation is provided by emulation using extensive residential smart meter data. The demand response (DR) scheme is designed to accommodate privacy concerns, allows customers to select their DR compliance level, and provides incentives to maximize their participation. The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent (TA) and home energy management system (HEMS) agents. Customer response is handled by a multi-input multi-output (MIMO) fuzzy controller that manages negotiation between the customer agent and the TA. We take a multi-agent system approach to neighborhood coordination, with the TA servicing multiple residences on a common transformer, and use a reward mechanism to maximize customer engagement during the event-based optimization. Based on a set of smart meter data acquired over an extended time period, we engage in multiple TDR scenarios, and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22% under realistic conditions.