Air handling unit and rooftop unit with predictive control
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Solution Overview
Problem
HVAC systems, such as air handling units (AHUs) and rooftop units (RTUs), face challenges in minimizing power consumption, leading to high energy costs due to the operation of power-consuming components like fans and compressors, without effective strategies to optimize energy usage based on time-varying energy prices and demand charges.
Innovation Solution
Incorporating a predictive controller in AHUs and RTUs that optimizes energy usage by determining the optimal amount of energy to purchase from the grid and store in batteries, using energy pricing data and subplant curves to minimize costs, and adjusting power setpoints to reduce demand charges, while also considering the costs of heating and cooling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AHUs and RTUs operate power-consuming components (fans, compressors) continuously to maintain building HVAC functionality, then reliable heating and cooling is provided, but energy costs increase significantly
Solution Approach 1:
The system dynamically adjusts the operation of power-consuming components based on real-time energy pricing signals and predictive cost functions. Fans and compressors are modulated to operate at optimal power levels that balance reliability with energy cost minimization, rather than running continuously at fixed capacity
Solution Approach 2:
The predictive controller uses forecasted energy prices and demand charge information to pre-adjust component operation before peak pricing periods occur. Thermal energy storage systems are charged in advance during low-cost periods, and components are scheduled to operate at reduced capacity during anticipated high-cost periods while maintaining minimum reliability thresholds
2Productivity
If the AHU purchases electric energy from the energy grid at all times to power components, then continuous operation is maintained, but energy costs increase during high-price periods
Solution Approach 1:
The system continuously monitors real-time energy pricing data, demand charge levels, and battery state of charge, feeding this information back to the predictive controller. The controller adjusts purchasing decisions based on this feedback loop, buying energy when prices are low and using stored energy or reduced operation when prices are high, while maintaining continuous productive operation
Solution Approach 2:
The system changes operational parameters dynamically based on energy price conditions. During low-price periods, the AHU operates at full capacity and charges the battery. During high-price periods, it shifts to using battery power and reduces grid purchasing, changing the mix of energy sources without interrupting continuous operation
3Use of energy by moving object
If the AHU stores electric energy in the battery during low-cost periods and discharges during high-cost periods, then energy costs are reduced, but system complexity increases
Solution Approach 1:
The battery serves as an intermediary energy storage device between the grid and the AHU components. The predictive controller acts as another intermediary, mediating between energy pricing signals and component operation decisions. This layered intermediary structure manages complexity by isolating the control logic from the physical components
4Use of energy by moving object
If the predictive controller optimizes power setpoints to minimize demand charges, then operational expenses are reduced, but control complexity increases
Solution Approach 1:
The controller segments the optimization problem into distinct functional modules: an economic controller that optimizes power setpoints based on pricing signals, a tracking controller that translates power setpoints to temperature setpoints, and equipment controllers that execute specific component adjustments. This segmentation reduces control complexity by dividing the overall optimization task into manageable, specialized subsystems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces energy costs by optimizing energy purchasing and storage, shifting peak demand, and minimizing demand charges, thereby lowering the overall operational expenses of HVAC systems.
Implementation Method 1
The battery is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered AHU components
Data Source
AI summary
An air handling unit (AHU) or rooftop unit (RTU) in a building HVAC system includes one or more powered components, a battery, and a predictive controller. The powered components include a fan configured to generate a supply airstream provided to one or more building zones. The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.