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

被引:5
|
作者
Finck, Christian [1 ]
Li, Rongling [2 ]
Zeiler, Wim [1 ]
机构
[1] Eindhoven Univ Technol, Dept Built Environm, de Rondom 70, NL-5612 AP Eindhoven, Netherlands
[2] Tech Univ Denmark, Dept Civil Engn, Bldg 118, DK-2800 Lyngby, Denmark
关键词
Optimal control; Model predictive control; ANN; Black box modelling; Grey box modelling; ARTIFICIAL NEURAL-NETWORK; RESIDENTIAL HVAC SYSTEM; PREDICTIVE CONTROL; PERFORMANCE PREDICTION; DEMAND FLEXIBILITY; OPTIMIZATION; MPC; IMPACT;
D O I
10.1016/j.mex.2020.100866
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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. (C) 2020 The Author(s). Published by Elsevier B.V.
引用
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页数:12
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