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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.15649 |
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| _version_ | 1866916911417131008 |
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| author | Guo, Zhong Barooah, Prabir |
| author_facet | Guo, Zhong Barooah, Prabir |
| contents | We describe a framework of modeling a central chilled
water plant (CCWP) that consists of an aggregate
cooling coil, a number of heterogeneous chillers and
cooling towers, and a chilled water-based thermal
energy storage system. We improve upon existing component
models from the open literature using a constrained
optimization-based framework to ensure that the models
respect capacities of all the heat exchangers (cooling
coils, chillers, and cooling towers) irrespective of
the inputs provided. As a result, the proposed model has a wider
range of validity compared to existing models; the
latter can produce highly erroneous outputs when inputs are not
within normal operating range. This
feature is essential for training learning-based
controllers that can choose inputs beyond normal operating conditions and is lacking in currently available
models. The overall plant model is
implemented in Matlab and is made publicly
available. Simulation of a CCWP with closed loop
control is provided as an illustration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Central Chilled Water Plant Model for Designing Learning-Based Controllers Guo, Zhong Barooah, Prabir Systems and Control We describe a framework of modeling a central chilled water plant (CCWP) that consists of an aggregate cooling coil, a number of heterogeneous chillers and cooling towers, and a chilled water-based thermal energy storage system. We improve upon existing component models from the open literature using a constrained optimization-based framework to ensure that the models respect capacities of all the heat exchangers (cooling coils, chillers, and cooling towers) irrespective of the inputs provided. As a result, the proposed model has a wider range of validity compared to existing models; the latter can produce highly erroneous outputs when inputs are not within normal operating range. This feature is essential for training learning-based controllers that can choose inputs beyond normal operating conditions and is lacking in currently available models. The overall plant model is implemented in Matlab and is made publicly available. Simulation of a CCWP with closed loop control is provided as an illustration. |
| title | A Central Chilled Water Plant Model for Designing Learning-Based Controllers |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2508.15649 |