Decentralized Management of Commercial HVAC Systems
With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in ove...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/11/3024 |
| _version_ | 1797532957090512896 |
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| author | Samy Faddel Guanyu Tian Qun Zhou |
| author_facet | Samy Faddel Guanyu Tian Qun Zhou |
| author_sort | Samy Faddel |
| collection | DOAJ |
| description | With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling. |
| first_indexed | 2024-03-10T11:07:44Z |
| format | Article |
| id | doaj.art-d2483fafd7c745a1bb4c12f041a65507 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T11:07:44Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d2483fafd7c745a1bb4c12f041a655072023-11-21T21:03:38ZengMDPI AGEnergies1996-10732021-05-011411302410.3390/en14113024Decentralized Management of Commercial HVAC SystemsSamy Faddel0Guanyu Tian1Qun Zhou2Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USADepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USADepartment of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USAWith the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.https://www.mdpi.com/1996-1073/14/11/3024commercial HVACmicrogridsdistributed optimizationmulti-objectivecosts |
| spellingShingle | Samy Faddel Guanyu Tian Qun Zhou Decentralized Management of Commercial HVAC Systems Energies commercial HVAC microgrids distributed optimization multi-objective costs |
| title | Decentralized Management of Commercial HVAC Systems |
| title_full | Decentralized Management of Commercial HVAC Systems |
| title_fullStr | Decentralized Management of Commercial HVAC Systems |
| title_full_unstemmed | Decentralized Management of Commercial HVAC Systems |
| title_short | Decentralized Management of Commercial HVAC Systems |
| title_sort | decentralized management of commercial hvac systems |
| topic | commercial HVAC microgrids distributed optimization multi-objective costs |
| url | https://www.mdpi.com/1996-1073/14/11/3024 |
| work_keys_str_mv | AT samyfaddel decentralizedmanagementofcommercialhvacsystems AT guanyutian decentralizedmanagementofcommercialhvacsystems AT qunzhou decentralizedmanagementofcommercialhvacsystems |