Multi-Level HVAC Predictive Control for Large-Zone Energy Cost
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Solution Overview
Problem
The deployment of model predictive control (MPC) in HVAC systems is hindered by the complexity of managing large numbers of building zones, leading to impractical single optimization problems that are too large to solve in real time, and existing energy optimization techniques often neglect airside costs, which have become significant due to advancements in waterside equipment efficiency.
Innovation Solution
A distributed MPC system is implemented, comprising a high-level MPC and multiple low-level MPCs for both airside and waterside systems, which generates optimal load profiles and setpoints to minimize total energy cost by considering both airside and waterside power consumption, decomposing the optimization problem to manage large-scale applications effectively and incorporating airside power consumption models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a single combined control system is used for campus-wide HVAC optimization, then comprehensive energy cost optimization is 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 building-level subsystems, each with its own MPC controller. Each controller optimizes only its local building's energy costs in real-time, while a separate long-term optimizer handles strategic decisions. This segmentation transforms one intractable large-scale optimization problem into multiple smaller, computationally feasible problems that can be solved independently and simultaneously.
2Use of energy by moving object
If traditional energy optimization techniques focus only on waterside equipment, then chiller and pump efficiency is optimized, but airside equipment costs are neglected
Solution Approach 1:
The patent creates a unified MPC framework that simultaneously optimizes both waterside equipment (chillers, pumps) and airside equipment (fans, air handlers). The cost function integrates energy consumption from all major HVAC components, allowing the system to make coordinated control decisions that minimize total energy costs rather than optimizing individual subsystems in isolation.
Data Source
AI summary
A heating, ventilation, or air conditioning (HVAC) system for a building includes HVAC equipment configured to provide heating or cooling to one or more building spaces and one or more controllers. The one or more controllers include one or more processing circuits configured to generate energy targets for the one or more building spaces using a thermal capacitance of the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, generate setpoints for the HVAC equipment using the energy targets for the one or more building spaces to which the heating or cooling is provided by the HVAC equipment, and operate the HVAC equipment using the setpoints to provide the heating or cooling to the one or more building spaces.