A Collaborative Neurodynamic Optimization Approach to Distributed Chiller Loading
Summary
This study introduces a novel neurodynamic optimization method for efficient distributed chiller loading, effectively handling complex power functions and constraints. The approach improves energy management in multi-chiller systems.
Area of Science:
- Engineering
- Computer Science
- Operations Research
Background:
- Distributed chiller systems face challenges with nonconvex power consumption and cardinality constraints.
- Optimizing chiller loading is crucial for energy efficiency and operational cost reduction.
Purpose of the Study:
- To develop a collaborative neurodynamic optimization approach for distributed chiller loading.
- To address nonconvex objective functions and binary variables in optimization problems.
Main Methods:
- Formulated a cardinality-constrained distributed optimization problem using an augmented Lagrangian function.
- Developed a collaborative neurodynamic optimization method with coupled recurrent neural networks.
- Employed a meta-heuristic rule for repeated reinitialization of neural networks.
Main Results:
- Demonstrated the efficacy of the proposed approach on two multi-chiller systems.
- Showcased superior performance compared to several baseline methods.
- Validated the approach using manufacturer-provided chiller system parameters.
Conclusions:
- The collaborative neurodynamic optimization method effectively solves distributed chiller loading problems with nonconvexities.
- The approach offers a promising solution for enhancing energy efficiency in complex chiller systems.
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