A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Yan Li (), Zhiwen Liu, Chong Shao, Bingjun Lin, Jiayu Rong, Nan Dong, Buyun Su and Yuejia Hong
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Yan Li: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Zhiwen Liu: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Chong Shao: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Bingjun Lin: Beihai Power Supply Bureau Guangxi Power Grid Co., Ltd., Beihai 536000, China
Jiayu Rong: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Nan Dong: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Buyun Su: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China
Yuejia Hong: Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China

Energies, 2024, vol. 17, issue 20, 1-14

Abstract: Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.

Keywords: demand response; potential evaluation; fuzzy control; K-means algorithm (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: 2024
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