Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems. (5th March 2017)

• Record Type: Journal Article
• Authors: Zong, Yi; Böning, Georg Martin; Santos, Rui Mirra; You, Shi; Hu, Junjie; Han, Xue
• Is Part Of: Applied thermal engineering. Volume 114(2017:Mar.)
• Page Start: 1476
• Page End: 1486
• Publication Date: 2017-03-05
• Subjects: Active smart building -- Data availability -- Economic model predictive control -- Modelling -- Optimization -- State estimation; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.11.141
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: EMPC for buildings integrated into smart energy systems were summarized in detail. The challenges on how to implement EMPC in practice were investigated. Implementing a stationary Kalman filter to overcome the uncertainty. Adding soft constraints to ensure feasible solutions for optimization problems. EMPC outperformed the traditional PID schemes for energy saving and load shifting. Abstract: When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system's reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals.