Refrigeration cycle optimization
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Solution Overview
Problem
Existing refrigeration cycle control methods fail to optimize compressor start/stop operations efficiently, leading to increased costs and machinery degradation, while not adequately considering temperature requirements for comfort conditions.
Innovation Solution
A novel refrigeration cycle system with a controller that uses mixed-integer linear programming (MILP) optimization to predict and minimize energy costs and demand charges by cooperatively controlling multiple compressors, incorporating temperature predictions and comfort constraints to ensure optimal air-conditioning performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frequent start/stop control of compressors is implemented to match low load conditions, then compressor capacity utilization is improved, but compressor reliability deteriorates due to increased wear and inrush current damage
Solution Approach 1:
The controller predicts future air-conditioning requirements and pre-determines optimal compressor start/stop schedules before actual load changes occur. This allows compressors to be kept running during predicted high-demand periods, avoiding frequent starts while still matching load conditions through advance planning.
Solution Approach 2:
The system dynamically adjusts compressor operation schedules based on predicted future load patterns rather than reacting to immediate load changes. This dynamic optimization balances capacity utilization with reliability by smoothing out operation patterns while adapting to changing conditions over time.
2Loss of energy
If MILP optimization is used to control compressor operation, then cost optimization is improved, but computational complexity increases
Solution Approach 1:
The MILP optimization is performed in advance to determine compressor schedules for future time periods. By pre-calculating optimal operation patterns based on predicted requirements, the system achieves cost optimization without requiring complex real-time computation during actual operation.
Solution Approach 2:
The control problem is segmented into discrete time periods and compressor units, allowing the MILP optimization to handle complexity through structured decomposition. This segmentation enables systematic optimization of multiple compressors across multiple time periods while maintaining computational tractability.
3Reliability
If compressor start/stop frequency is reduced to improve reliability, then compressor wear is reduced, but air-conditioning temperature requirements may not be met
Solution Approach 1:
The system predicts future air-conditioning temperature requirements and incorporates these predictions as constraints in the MILP optimization. This ensures that compressor schedules are predetermined to meet temperature requirements while minimizing start/stop frequency, balancing reliability with comfort.
Solution Approach 2:
The optimization changes operational parameters such as compressor run schedules and capacity settings to meet temperature requirements with fewer start/stop cycles. By adjusting these parameters through predictive optimization, the system maintains temperature comfort while reducing mechanical stress on compressors.
Data Source
AI summary
A refrigeration cycle including at least one outdoor unit including a plurality of compressors and indoor units each placed in indoor spaces comprises a plurality of compressors for supplying refrigerant to indoor units; and a controller for controlling cooperatively a plurality of the compressors in the outdoor unit to provide a capacity for air-conditioning in the indoor spaces through the indoor units, wherein the controller controls operation of the compressors so as to minimize a cost including start/stop of each compressor by prediction of an air-conditioning requirement in a next time chunk.