Distributed integrated energy management systems in residential buildings. (5th March 2017)

• Record Type: Journal Article
• Authors: Honold, Johannes; Kandler, Christian; Wimmer, Patrick; Schropp, Bernhard; Reichle, Roland; Gröne, Matthias; Bünemann, Mathias; Klein, Jens; Kufner, Maximilian
• Is Part Of: Applied thermal engineering. Volume 114(2017:Mar.)
• Page Start: 1468
• Page End: 1475
• Publication Date: 2017-03-05
• Subjects: Decentralized energy management -- Distributed energy management -- Demand-side-management -- Home energy management -- Multi-agent system; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.10.158
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: A home energy management system with multiple decision-making units (energy managers) is proposed. Each energy manager represents a household device. The decision making procedure is based on a discovery-and-negotiation-mechanism. Load shifting schedules within user specified periods is the outcome of the system. The system is able to save operational energy costs of a household. Abstract: This paper explains the concept of a distributed integrated energy management (diEM) system for residential buildings. The overall goal of the system is to minimize operational energy costs of the household. This is obtained by load shifting in order to enhance the self-consumption rate of the on-site renewable electricity production. The crucial difference to centralized energy management systems (where data from household devices must be gathered and evaluated centrally) is the presence of multiple smart energy managers that negotiate with each other on the switch-on times of their dedicated electric devices. The major benefit is that devices of various manufacturers can be incorporated in the same home energy management system with open standards and open protocols without any additional decision-making unit. The basic procedure of the diEM is divided into a discovering phase and a negotiation phase. Best practice parameter settings are deduced from realistic scenarios with constant and variable electricity tariffs, and a run-time analysis indicates that up to seven devices can run simultaneously with a one minute renegotiation frequency. The monetary evaluation shows that the diEM can reduce the operational energy costs at a rate dependent on user behavior and tariff structures.