Chiller Plant Control Using Binary and Nonlinear Optimization
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Solution Overview
Problem
Chiller plants face challenges in efficiently controlling energy consumption due to varying building environments and equipment performance, leading to inefficient operation and high energy costs.
Innovation Solution
A computerized method using binary optimization to determine optimal combinations of chiller plant equipment and nonlinear optimization to minimize power consumption, which identifies the most energy-efficient on/off statuses and operating setpoints for the equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional control methods are used for chiller plant equipment, then the system can operate with simple control logic, but energy consumption is high and efficiency is low
Solution Approach 1:
The system performs preliminary actions by using binary optimization to determine potential equipment combinations before actual operation, and uses nonlinear optimization to calculate power consumption estimates in advance. This allows the control system to pre-determine the most energy-efficient equipment configuration before the chiller plant operates, thereby reducing actual energy consumption without requiring complex real-time control during operation.
Solution Approach 2:
The patent replaces traditional mechanical control systems with computerized optimization algorithms. Specifically, binary optimization algorithms determine which equipment combinations are feasible, while nonlinear optimization algorithms calculate the power consumption for each combination. This substitution of mechanical control with computational optimization enables energy-efficient operation without proportionally increasing control system complexity.
2Productivity
If multiple equipment combinations are evaluated to find optimal configuration, then energy efficiency improves, but calculation time and processing complexity increase
Solution Approach 1:
The optimization process is segmented into two distinct stages: first, binary optimization is used to identify potential equipment combinations that meet cooling demands; second, nonlinear optimization is applied to calculate power consumption for each identified combination. This segmentation allows the system to evaluate multiple equipment configurations systematically without requiring exhaustive search of all possible combinations, thereby improving energy efficiency while limiting calculation time.
Solution Approach 2:
The system performs partial optimization by evaluating only the most promising equipment combinations identified through binary optimization, rather than exhaustively analyzing every possible configuration. This partial action approach identifies sufficiently optimal solutions without the excessive time investment required for complete enumeration of all equipment combinations, achieving practical energy efficiency with acceptable calculation time.
Data Source
AI summary
A computerized method for controlling a chiller plant for cooling a building is provided. The chiller plant has a chiller plant load. The method includes estimating an optimal combination of chiller plant equipment for meeting the chiller plant load. Estimating the optimal combination of chiller plant equipment includes using binary optimization to determine at least two potential combinations of chiller plant equipment. Estimating the optimal combination of chiller plant equipment also includes using nonlinear optimization to determine a potential power consumption minimum for each of the at least two potential combinations. The method also includes controlling the chiller plant according to the estimated optimal combination of chiller plant equipment.