LAPSE:2019.0986
Published Article
LAPSE:2019.0986
Control Strategy for Inverter Air Conditioners under Demand Response
Yanbo Che, Jianxiong Yang, Yuancheng Zhao, Siyuan Xue
September 5, 2019
Air conditioning loads are important resources for demand response. With the help of thermal energy storage capacity, they can reduce peak load, improve the reliability of power grid operations, and enhance the emergency capacity of a power grid, without affecting the comfort of the users. In this paper, a virtual energy storage model for inverter air conditioning loads, which reflects their operating characteristics and is more conducive to practical application, is established. Two parts are involved in the virtual energy storage model: An electrical parameter part, based on the operating characteristics, and a thermal parameter part, based on the equivalent thermal parameter model. The control function and restrictive conditions of the virtual energy storage are analyzed and a control strategy, based on virtual state-of-charge ranking, is proposed. The strategy controls the inverter air conditioners through re-assigning indoor temperature set-points within the pre-agreed protocol interval and gives priority those with a higher virtual state of charge. As a result, electric power consumption is reduced while the temperature remains unchanged, so that a shortage in the power system can be compensated for as much as possible, while the comfort of users is guaranteed. Simulation and example analyses show that the strategy is effective in controlling air conditioning loads. Additionally, the influences of load reduction target magnitude and communication time-step on the performance of the control strategy are analyzed.
Keywords
demand response, inverter air conditioner, virtual energy storage, virtual state of charge
Suggested Citation
Che Y, Yang J, Zhao Y, Xue S. Control Strategy for Inverter Air Conditioners under Demand Response. (2019). LAPSE:2019.0986
Author Affiliations
Che Y: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China [ORCID]
Yang J: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Zhao Y: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Xue S: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
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Journal Name
Processes
Volume
7
Issue
7
Article Number
E407
Year
2019
Publication Date
2019-07-01
Published Version
ISSN
2227-9717
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PII: pr7070407, Publication Type: Journal Article
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LAPSE:2019.0986
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doi:10.3390/pr7070407
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Sep 5, 2019
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Sep 5, 2019
 
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Calvin Tsay
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