Central Plant Load Prediction Using Thermal Mass Storage Model
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Solution Overview
Problem
Existing HVAC systems face inefficiencies in power consumption due to the practice of cycling chillers based on fixed temperature rules, which can lead to brief periods of chiller operation at low loads, resulting in increased energy costs and reduced efficiency.
Innovation Solution
A controller system that predicts thermal energy loads and generates models to optimize the operation of HVAC devices, including chillers, by analyzing temperature data and thermal mass within the system, allowing for dynamic control of temperature bounds and deferred load management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a chiller is cycled on and off based on fixed temperature rules, then the chiller can meet cooling loads during low load periods, but power efficiency deteriorates due to brief operation periods at low loads
Solution Approach 1:
The system performs preliminary action by storing thermal energy in the thermal storage tank during periods when the chiller is running, so that cooling can be provided later without immediate chiller operation. This allows the chiller to run continuously at optimal load rather than cycling on and off, improving power efficiency while still meeting cooling demands.
Solution Approach 2:
A thermal storage tank is introduced as an intermediary between the chiller and the cooling load. The tank stores thermal energy when the chiller produces it, and releases it when cooling is needed, decoupling the chiller operation from immediate cooling demands and allowing continuous optimal operation.
2Use of energy by moving object
If a chiller operates continuously to avoid cycling losses, then power efficiency improves, but thermal energy storage capability is underutilized
Solution Approach 1:
The thermal storage tank serves multiple functions: it stores thermal energy for later use, buffers temperature fluctuations in the loop, and enables the chiller to operate continuously at optimal load. This multi-functionality maximizes the utility of the storage system while maintaining power efficiency.
Solution Approach 2:
The system dynamically adjusts the temperature bounds of the thermal storage based on real-time conditions such as chiller capacity, loop temperature, and cooling demand. This dynamic adjustment allows the system to fully utilize storage capacity while maintaining optimal chiller operation across varying load conditions.
3Ease of operation
If fixed temperature bounds are used for thermal storage, then control simplicity is maintained, but system adaptability to varying load conditions deteriorates
Solution Approach 1:
The temperature bounds for thermal storage are made dynamic rather than fixed. The system continuously adjusts these bounds based on chiller capacity, loop temperature, and cooling demand conditions. This allows the system to adapt to varying load conditions while maintaining a relatively simple control structure that builds upon the basic fixed-bound approach.
Solution Approach 2:
The control system uses feedback from temperature sensors and load measurements to dynamically adjust thermal storage bounds. This feedback mechanism enables the system to adapt to changing conditions automatically, improving versatility while keeping the control logic manageable through rule-based adjustments.
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 consumption by optimizing chiller operation and utilizing thermal energy storage to maintain temperature within set bounds, mimicking chiller cycling while minimizing demand charges and improving overall system efficiency.
Implementation Method 1
obtain a temperature of gas or liquid in the loop during the time period... generating a model indicating a relationship between (i) the temperature of the gas or the liquid in the loop and (ii) a difference between the induced thermal energy load and the produced thermal energy load
Implementation Method 2
predict an induced thermal energy load at a consuming device... determine a thermal mass of the gas or the liquid in the loop based on the predicted induced load
Data Source
AI summary
Disclosed herein are related to a system, a method, and a non-transitory computer readable medium for operating an energy plant. In one aspect, the system generates a regression model of a produced thermal energy load produced by a supply device of the plurality of devices. The system predicts the produced thermal energy load produced by the supply device for a first time period based on the regression model. The system determines a heat capacity of gas or liquid in the loop based on the predicted produced thermal energy load. The system generates a model of mass storage based on the heat capacity. The system predicts an induced thermal energy load during a second time period at a consuming device of the plurality of devices based on the model of the mass storage. The system operates the energy plant according to the predicted induced thermal energy load.