Chiller Plant Control, Hardware-in-the-Loop, Model-Based Design, Modelica
Chiller systems account for 31% of the total cooling electricity consumption of medium-sized commercial buildings within 25k-200k square feet. In the last decade, advanced controls such as model predictive control (MPC) has demonstrated energy savings that typically range from 5% to 15%. However, the installation and commissioning efforts to deploy MPC into existing building automation system (BAS) are often cost prohibitive and therefore undermine the energy saving benefit it brings into the game. This paper presents a framework and results of using model-based design (MBD) to evaluate the benefit and trade-offs of different chiller plant control algorithms for medium-sized commercial buildings including an optimization-based algorithm that can be deployed rapidly with little installation and commission effort. A high-fidelity dynamic simulation model for selected building types and climate zones were developed and implemented in the hardware-in-the-loop (HiL) platform. Baseline and optimization-based control algorithms were deployed in Automated Logic Corporation (ALC) controller hardware with their performance monitored through WebCtrl in real-time. The first contribution of this paper is the development and successful integration of Modelica-based high-fidelity dynamic models of chiller plants, air-handling units, and building envelope and zones. The building types of medium office and large hotel were selected and modeled in details. In particular, the building envelope and zone models were developed based on a direct translation of the selected DOE EnergyPlus reference building models, which are widely accepted in the building modeling community. The chiller plant was modeled with physics-based components such as chillers, pumps, valves, and pipes that include typical dynamics in a real chiller plant. Both primary-secondary and primary-only configurations were modeled and considered in the controls evaluation. The air handling unit was modeled based on the component models from Modelica Buildings Library developed by LBNL and includes a finite-volume based cooling coil model capable of calculating latent heat transfer. The second contribution of this paper is the demonstration of utilizing HiL platform to benchmark baseline and optimal control algorithms based on detailed whole-building level dynamic models. In the HiL setup, a real-world hardware controller is coupled to the high-fidelity simulation model and operates in real-time. The HiL setup provides the same interface for installation of overlay software as it would be a demonstration site BAS, eliminates the risk associated with seasonal operation and availability in demonstration sites, enables precise evaluation of energy savings potential for various internal and external building load scenarios.