Identification of a dynamic system model for a building and heating system including heat pump and thermal energy storage

• Published in: MethodsX Vol. 7; p. 100866
• Main Authors: Finck, Christian, Li, Rongling, Zeiler, Wim
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2020; Elsevier
• Subjects: ANN; Black box modelling; Energy; Grey box modelling; Model predictive control; Optimal control; Grey box modelling; Black box modelling; Model predictive control; ANN; Optimal control
• ISSN: 2215-0161; 2215-0161
• DOI: 10.1016/j.mex.2020.100866

Controllers employing optimal control strategies will path the way to enable flexible operations in future power grids. As buildings will increasingly act as prosumers in future power grids, optimal control of buildings’ energy consumption will play a major role in providing flexible operations. Optimal controllers such as model predictive controller are able to manage buildings’ operations and to optimise their energy consumption. For online optimisation, model predictive controller requires a model of the energy system. The more accurate the system model represents the system dynamics, the more accurate the model predictive controller predicts the future states of the energy system while optimising its energy consumption. In this article, we present a system model that can be used in online MPC, including dynamic programming as optimisation strategy. The system model is validated using a building and heating system, including heat pump and thermal energy storage. The following bullet points summarise the main requirements for the configuration of the system model:•The system model performs fast with low computational effort in less than 1 s;•The system model can be implemented in online MPC;•The system model accurately represents the dynamic behaviour. [Display omitted]