HVAC Load Distribution Using Uneven Set Point Control
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Solution Overview
Problem
Existing HVAC systems face inefficiencies in distributing thermal energy loads, leading to suboptimal power consumption, as they often evenly distribute loads among devices without considering more power-efficient combinations of operating set points.
Innovation Solution
A processing circuit is used to predict and compare power consumption based on different combinations of set points for HVAC devices, selecting the combination that results in lower power consumption, allowing for uneven load distribution to optimize energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If thermal energy load is distributed evenly among HVAC devices, then load distribution is simple and balanced, but power consumption efficiency deteriorates
Solution Approach 1:
The system changes the operating parameters (set points) of HVAC devices dynamically based on predicted power consumption. Instead of fixed even distribution, the system adjusts set points to achieve uneven load distribution that minimizes total power consumption while meeting thermal energy demands.
Solution Approach 2:
The load distribution transitions from static even distribution to dynamic uneven distribution. The system continuously predicts power consumption for different load combinations and adjusts the distribution in real-time based on current operating conditions, device characteristics, and thermal energy requirements.
2Use of energy by moving object
If thermal energy load is distributed unevenly among HVAC devices, then power consumption efficiency improves, but control complexity increases
Solution Approach 1:
The system uses the inherent characteristics of each HVAC device (their own performance curves, efficiency maps, and operational constraints) to determine the optimal load distribution. Each device's unique properties are leveraged to identify the most efficient operating point, reducing the need for external complex control mechanisms.
Solution Approach 2:
The system implements a feedback loop where power consumption is predicted based on different load distribution scenarios, compared against target consumption levels, and used to adjust set points iteratively. This closed-loop control automatically converges on the optimal uneven load distribution without requiring complex manual intervention.
3Use of energy by moving object
If multiple combinations of set points are evaluated, then power consumption optimization improves, but computational load increases
Solution Approach 1:
The system pre-establishes performance characteristics, efficiency curves, and operational constraints for each HVAC device during commissioning or initial setup. This preliminary data preparation enables rapid evaluation of multiple set point combinations during operation without requiring complex real-time calculations, reducing computational burden while maintaining optimization capability.
Data Source
AI summary
Systems and methods for distributing load of an energy plant are disclosed herein. A target load of at least a first device and a second device of the energy plant is obtained. A first power consumption of the first device and the second device predicted to result from operating the first device and the second device according to a first combination of set points to satisfy the target load is predicted. A second power consumption of the first device and the second device predicted to result from operating the first device and the second device according to a second combination of set points to satisfy the target load is predicted. The first power consumption and the second power consumption are compared, and a combination of set points rendering lower power consumption is selected to operate the energy plant.