Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control

Zexu Chen, Jing Shi, Zhaofang Song, Wangwang Yang and Zitong Zhang
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Zexu Chen: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Jing Shi: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Zhaofang Song: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Wangwang Yang: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Zitong Zhang: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 2, 1-16

Abstract: In recent years, demand response (DR) has played an increasingly important role in maintaining the safety, stability and economic operation of power grid. Due to the continuous running state and extremely fast speed of response, the aggregated inverter air conditioning (IAC) load is considered as the latest and most ideal object for DR. However, it is easy to cause load rebound when the aggregated IAC load participates in DR. Existing methods for controlling air conditioners to participate in DR cannot meet the following three requirements at the same time: basic DR target, load rebound suppression, and users’ comfort. Therefore, this paper has proposed a genetic algorithm based temperature-queuing control method for aggregated IAC load control, which could suppress load rebound under the premise of ensuring the DR target and take users’ comfort into account. Firstly, the model of the aggregated IAC load is established by the Monte Carlo method. Then the start and end time of DR are selected as the main solution variables. A genetic algorithm is used as the solving tool. The simulation results show that the proposed strategy shows better performance in suppressing load rebound. In the specific application scenario of adjusting the frequency fluctuation of the microgrid, the results of the case show that this strategy can effectively control the frequency fluctuation of the microgrid. The effectiveness of the strategy is verified.

Keywords: demand response; power system; smart grid; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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