Neural Network Chiller System Control for Dynamic Energy Optimization
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Solution Overview
Problem
Existing chiller systems for data centers and controlled environments face challenges in efficiently maintaining the required cooling capacity while minimizing energy consumption, especially as cooling demands fluctuate with ambient temperature changes.
Innovation Solution
A supervisory control system that dynamically manages a network of chiller devices by adjusting outlet temperature and flow rate setpoints, activating or deactivating chiller devices, and using neural networks to model and optimize chiller system configurations for reduced energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the chiller system increases cooling capacity by ramping up compressors or activating additional chillers, then the required cooling load is maintained, but total energy consumption increases
Solution Approach 1:
The system dynamically adjusts chiller configurations based on real-time cooling demands and environmental conditions. The supervisory control device continuously monitors plant temperature, ambient conditions, and chiller performance, then optimizes the number of active chillers and their operating parameters (flow rates, temperatures) to maintain cooling capacity while minimizing energy consumption. This dynamic optimization resolves the contradiction by adapting the system state rather than operating at fixed high-capacity settings.
Solution Approach 2:
The system changes operational parameters such as chiller inlet/outlet temperatures, flow rates, and compressor speeds to optimize energy efficiency. By adjusting these parameters within acceptable ranges, the system can maintain the required cooling load while operating at more efficient points on the performance curves, thereby reducing total energy consumption without sacrificing cooling capacity.
2Productivity
If the chiller system operates multiple devices at high capacity, then cooling demand is met during peak loads, but energy efficiency decreases
Solution Approach 1:
The chiller system is divided into multiple independent chiller devices that can be individually controlled and optimized. Rather than operating one large chiller at high capacity, the system can segment the cooling load across multiple smaller chillers operating at more efficient capacity levels. The supervisory control device determines the optimal combination of active chillers and their individual setpoints to satisfy the total cooling load while maximizing overall energy efficiency.
3Use of energy by moving object
If the system frequently adjusts chiller configurations, then energy optimization is achieved, but system stability and uninterrupted cooling may be compromised
Solution Approach 1:
The supervisory control device evaluates multiple potential optimization solutions before implementing configuration changes. It predicts the impact of each potential adjustment on both energy consumption and cooling delivery, selecting only those changes that will maintain uninterrupted cooling while reducing energy use. This preliminary evaluation prevents destabilizing adjustments and ensures reliability is preserved during optimization transitions.
Solution Approach 2:
The system continuously monitors plant temperature, cooling load, and chiller performance after configuration changes are implemented. This feedback mechanism allows the supervisory control device to verify that uninterrupted cooling is maintained and to make further adjustments if needed. The closed-loop control ensures that energy optimization does not compromise the reliability of cooling delivery.
Data Source
AI summary
A system and method for optimizing management of a system of multiple chiller devices for circulating a chilled medium within an indoor environment determines a current state of the environment (e.g., ambient temperature, temperature of the medium entering and leaving the environment, inlet flow rate of chilled medium, target cooling load) and a current configuration of the chiller system, e.g., flow and outlet temperature setpoints of each active device and total energy consumption of the chiller system. Based on this information the chiller system controller solves (either offline or online) for an optimal chiller system configuration (and steps for achieving this configuration by adjusting chiller device flow and outlet temperature setpoints) for providing the required cooling load to the indoor environment while minimizing total energy consumption across the chiller system. If an optimal solution is found, the controller monitors its implementation.