Distributed model predictive control of building energy systems coupled to geothermal fields

Abstract

In building energy systems (BES), operation strategies are a key factor to exploit the full potential of renewable energy sources and thus to reduce the CO2 emission and energy costs. However, advanced operation strategies like model predictive control require high engineering effort, which limits the applicability. To ease the implementation of model predictive control in BES, we present a flexible and robust distributed control framework. The framework supports the use of different model types and timescales for the various subsystems, allowing the user to tailor models from white to black modelling according to the available system knowledge. In a case study, we apply this framework to control a real-life industrial hall. We further simulate a case in which the hall is supplied by a geothermal field to investigate the sustainable long-term control of the field. We vary the temperature setpoint of the hall and adjust the thermostats in the office rooms. Since the set temperature of the hall and offices differs, the model predictive control framework finds an economic trade-off between the requirements of those two zones. Judging from these results, the approach and our implementation could be an important contribution to energy efficient operation of buildings with renewable energy sources.