Model reduction and model predictive control of energy-efficient buildings for electrical heating load shifting

Abstract

In France, buildings account for a significant portion of the electricity consumption (around 68%), due to an important use of electrical heating systems. This results in high peak load in winter and causes tensions on the production-consumption balance. In view of reducing such fluctuations, advanced control systems (including the Model Predictive Control framework) have been developed to shift heating load while maintaining indoor comfort and taking advantage of the building thermal mass. In this paper, a framework for developing optimisation-based control strategies to shift the heating load in buildings is introduced. The balanced truncation method and a time-continuous optimisation method were used to develop a real-time control of the heating power. These two methods are well suited for control problems and yield precise results. The novelty of the approach is to use reduced models derived from advanced building simulation software. A simulation case study demonstrates the controller performance in the synthesis of a predictive model-based optimal energy management strategy for a single-zone test building of the “INCAS” platform built in Le Bourget-du-Lac, France, by the National Solar Energy Institute (INES). The controller exhibits excellent performance, reaching between 6 and 13% cost reduction, and can easily be applied in real-time.