Evaluation of Optimal Chiller Plant Control Algorithms in Model-Based Design Platform with Hardware-in-the-Loop

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

Model Predictive Control and Fault Detection and Diagnostics of a Building Heating, Ventilation, and Air Conditioning System

The paper presents Model Predictive Control (MPC) and Fault Detection and Diagnostics (FDD) technologies, their on-line implementation, and results from several demonstrations conducted for a large-size HVAC system. The two technologies are executed at the supervisory level in a hierarchical control architecture as extensions of a baseline Building Management System (BMS). The MPC algorithm generates optimal set points for the HVAC actuator loops which minimize energy consumption while meeting equipment operational constraints and occupant comfort constraints. The MPC algorithm is implemented using a new tool, the Berkeley Library for Optimization Modeling (BLOM), which generates automatically an efficient optimization formulation directly from a simulation model. The FDD algorithm detects and classifies in real-time potential faults of the HVAC actuators based on data from multiple sensors. The performance and limitations of FDD and MPC algorithms are illustrated and discussed based on measurement data recorded from multiple tests

Information in coordinated system control

In this thesis, two subjects are considered: new techniques to improve stabilizing performance and feasibility in model predictive control and disturbance rejection control in coordinated systems. Model predictive control is powerful when a system has constraints. However, by nature, feasibility and stabilizing property of model predictive control can be lost without proper treatments. A new idea is studied for the case that a system is not well stabilized by classical model predictive control since the origin is not reachable from initial states in a limited horizon. We handle this matter by using a time- varying contractive terminal state equality constraint in model predictive control. The core condition to execute our idea is a structural property of the system such as contractibility or a known control lyapunov function. In addition, algorithmic approaches to guarantee feasible model predictive control are developed with several state constraint structures. Assuming that the model predictive control problem at current time is feasible, we want to know the set of terminal states or new references such that the problem at the next time instant is still feasible. Solutions are given for the linear system case using reachability analysis. The rest of the talk considers disturbance rejection control in coordinated systems. We employ a fixed vehicle formation problem as a working problem. The aim is to design a controller to maintain the formation and avoid collisions in the presence of disturbance, measurement, and communication noises. Each vehicle has its own local controller that uses the state and input information from neighbors via communication. We formulate local model predictive control and estimators for one vehicle to estimate the states of the neighboring vehicles. Since coordinated systems interact via the exchange of information through communication, as the network of coordinated systems increases in the number of subsystems, natural limits on the available bandwidth of communication need to be imposed. With the gaussian assumptions on the noises and disturbance, the estimators are designed by linear matrix inequality methods, which link control objective, estimation performance, and communication limits. Even when bounds on the uncertainties are known instead of the gaussian assumptions, controllers and estimators can be formulated. Case studies are provided to demonstrate the main ideas and discuss interesting design issue

Information in coordinated system control

In this thesis, two subjects are considered: new techniques to improve stabilizing performance and feasibility in model predictive control and disturbance rejection control in coordinated systems. Model predictive control is powerful when a system has constraints. However, by nature, feasibility and stabilizing property of model predictive control can be lost without proper treatments. A new idea is studied for the case that a system is not well stabilized by classical model predictive control since the origin is not reachable from initial states in a limited horizon. We handle this matter by using a time- varying contractive terminal state equality constraint in model predictive control. The core condition to execute our idea is a structural property of the system such as contractibility or a known control lyapunov function. In addition, algorithmic approaches to guarantee feasible model predictive control are developed with several state constraint structures. Assuming that the model predictive control problem at current time is feasible, we want to know the set of terminal states or new references such that the problem at the next time instant is still feasible. Solutions are given for the linear system case using reachability analysis. The rest of the talk considers disturbance rejection control in coordinated systems. We employ a fixed vehicle formation problem as a working problem. The aim is to design a controller to maintain the formation and avoid collisions in the presence of disturbance, measurement, and communication noises. Each vehicle has its own local controller that uses the state and input information from neighbors via communication. We formulate local model predictive control and estimators for one vehicle to estimate the states of the neighboring vehicles. Since coordinated systems interact via the exchange of information through communication, as the network of coordinated systems increases in the number of subsystems, natural limits on the available bandwidth of communication need to be imposed. With the gaussian assumptions on the noises and disturbance, the estimators are designed by linear matrix inequality methods, which link control objective, estimation performance, and communication limits. Even when bounds on the uncertainties are known instead of the gaussian assumptions, controllers and estimators can be formulated. Case studies are provided to demonstrate the main ideas and discuss interesting design issue