Variable refrigerant flow system with multi-level model predictive control
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Solution Overview
Problem
Large-scale HVAC systems face challenges in implementing model predictive control (MPC) due to the complexity of managing numerous building zones, leading to impractical and computationally intensive optimization problems that are difficult to solve in real time, especially in campus-wide implementations with hundreds of zones.
Innovation Solution
A distributed model predictive control system is implemented, featuring a high-level MPC that generates optimal indoor subsystem load profiles and low-level indoor MPCs that optimize setpoints for indoor VRF units, decomposing the overall problem into manageable sub-problems to reduce computational complexity and enable real-time solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single, combined control system is used for campus-wide HVAC implementations, then centralized optimization can be achieved, but the resulting 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. This segmentation allows each controller to solve a smaller optimization problem independently, enabling real-time solutions while maintaining coordinated optimization across the entire campus through inter-building thermal coupling considerations.
2Loss of energy
If MPC is implemented in large-scale HVAC systems with hundreds of zones, then energy cost optimization can be achieved, but the computational complexity becomes impractical
Solution Approach 1:
The system segments the large-scale HVAC control into hierarchical levels: campus-level coordination and building-level optimization. This reduces computational complexity by allowing each building to solve its own MPC problem independently while the campus level handles coordination, making energy cost optimization tractable for large systems.
Solution Approach 2:
Each building is treated as a separate decision-making unit with its own MPC controller that optimizes based on local conditions (weather, occupancy, utility rates). This local optimization approach reduces overall computational complexity while achieving energy cost savings, as each controller only needs to process local data and constraints.
3Ease of operation
If simple on/off or PID controllers are used, then system operation is simple, but energy cost minimization cannot be achieved
Solution Approach 1:
The patent transitions from simple on/off or PID control to MPC by changing the control parameters from reactive temperature adjustments to predictive energy cost optimization. The MPC controller uses forecasts of utility rates, weather, and occupancy to proactively optimize setpoints, achieving energy cost minimization while maintaining manageable complexity through standardized control algorithms.
Data Source
AI summary
A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.