HVAC Model Predictive Control with Distributed Airside Optimization
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Solution Overview
Problem
Commercial HVAC systems face challenges in deploying model predictive control (MPC) due to the complexity of managing large numbers of building zones and the increasing energy costs associated with airside equipment, which are often neglected in existing optimization techniques.
Innovation Solution
A distributed HVAC system incorporating a high-level MPC and low-level airside MPCs to optimize both airside and waterside power consumption, generating optimal load profiles and temperature setpoints to minimize total energy cost, while considering airside and waterside system interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a single combined control system is used for campus-wide HVAC optimization, then comprehensive energy cost optimization can be achieved, but the optimization problem becomes too large to solve in real time
Solution Approach 1:
The patent divides the campus-wide HVAC system into multiple independent building control systems, each optimized separately. This segmentation allows each building's optimization problem to be solved in real-time while collectively achieving campus-wide energy cost optimization, resolving the contradiction between comprehensive optimization and computational feasibility.
2Loss of energy
If optimization focuses only on waterside equipment power consumption, then chiller and pump efficiency can be improved, but airside equipment energy costs are neglected
Solution Approach 1:
The patent merges the optimization of waterside equipment (chillers and pumps) with airside equipment (fans and air handlers) into a unified cost function. This combination ensures that both waterside and airside energy consumptions are simultaneously optimized, achieving comprehensive energy management while maintaining chiller and pump efficiency.
3Device complexity
If simple on/off or PID controllers are used, then system simplicity is maintained, but total energy consumption cannot be minimized
Solution Approach 1:
The patent implements dynamic model predictive control that adapts to varying conditions such as time-of-use pricing, weather forecasts, and building occupancy. This dynamic approach allows the system to minimize total energy consumption by adjusting control strategies in real-time while maintaining manageable complexity through modular architecture and standardized control algorithms.
Data Source
AI summary
A building HVAC system includes an airside system having a plurality of airside subsystems, a high-level controller, and a plurality of low-level airside controllers. Each airside subsystem includes airside HVAC equipment configured to provide heating or cooling to one or more building spaces. The high-level controller is configured to generate a plurality of airside subsystem energy targets, each airside subsystem energy target corresponding to one of the plurality of airside subsystems and generated based on a thermal capacitance of the one or more building spaces to which heating or cooling is provided by the corresponding airside subsystem. Each low-level airside controller corresponds to one of the airside subsystems and is configured to control the airside HVAC equipment of the corresponding airside subsystem in accordance with the airside subsystem energy target for the corresponding airside subsystem.