Optimal Control Approaches for Analysis of Energy Use Minimization of Hybrid Ground-Coupled Heat Pump Systems

摘要

In this paper, we present a prediction-based dynamic programming control approach, a nonlinear model predictivecontrol approach and a linear optimal control approach to analyze the minimization of the total energy use of a hybrid ground coupled heat pump system (incorporating a ground-coupled heat pump, a gas boiler, a passive cooler and an active chiller) under operational constraints. A large-scale emulator model (based on finite-volume method and the equivalent diameter approach) is used for the borehole system and for assessment of different control algorithms. A nonlinear autoregressive exogenous model(NARX) is identified from the input-output data generated by the emulator model to be used in a dynamic programming based controller. Since dynamic programming is a global optimal control method, it was used as a reference for performance assessment. Next, a state-space, reduced-order, control-oriented model with a larger sampling time is obtained from the emulator model using the so-called Proper Orthogonal Decomposition (POD)model reduction technique. This model is used in a nonlinear model predictive control (NMPC) algorithm to see how much NMPC is suboptimal with respect to the dynamic programming in terms of annual energy-use minimization. Finally, a series of linear optimal controllers based on constant heat pump coefficients of performance are tested to see how much thesystem performance deteriorates. The control algorithms areused for satisfaction of heating-cooling demands of three types of buildings: heating dominated, cooling-dominated and thermally balanced. The effects of constraining thermal build-up/depletion of ground, variable electricity prices and marginal violation of thermal comfort on the performance of the different controllers applied are also separately analyzed.