Paper
13 May 2024 Research on regional air conditioning load aggregation model and control strategy
Xiaomin Zheng, Ligang Zhu, Ninglang Zheng
Author Affiliations +
Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 13159AG (2024) https://doi.org/10.1117/12.3024666
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China
Abstract
With the increase of regional air conditioning load, factors such as user comfort, willingness and controllability continue to affect the load side demand. In order to improve the accuracy of the air conditioning load aggregation model, user comfort, willingness and controllability have to be considered, and system operation, market transaction and user power consumption data have to be integrated to improve the load side big data analysis ability. This paper proposes a regional air conditioning load aggregation model considering the influence of multiple factors, and chooses Qiantang District of Hangzhou as the research area to accurately forecast the air conditioning load, and puts forward appropriate control strategies. The results show that when considering the probability of electricity consumption at each time, the load peaks twice at 15:00 and 24:00. Through data mining, it is found that the regional air conditioning load control strategy can be formulated by time-sharing and scenario-specific. According to the principle of adjustable load priority and adjustable air conditioner type priority, the design is based on the adjustable load total and the control simplicity, and the three types of air conditioning are matched with multi-line VRV air conditioner, air-cooled heat pump and water machine unit.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xiaomin Zheng, Ligang Zhu, and Ninglang Zheng "Research on regional air conditioning load aggregation model and control strategy", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 13159AG (13 May 2024); https://doi.org/10.1117/12.3024666
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KEYWORDS
Power consumption

Data modeling

Air temperature

Power grids

Buildings

Monte Carlo methods

Process modeling

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