Paper
16 January 2025 Load adjustable potential assessment method for large industrial users based on decadal time scale
Bing Yu, Ao Li, Xin Yin, Yichi Zhang, Dunnan Liu, Zhenyu Ma, Jinbo Cui, Xiujun Li, Changsheng Su, Zihao Wang
Author Affiliations +
Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 1344747 (2025) https://doi.org/10.1117/12.3044971
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China
Abstract
With the rapid development of the power market and the large-scale integration of renewable energy, the load adjustment capacity of the power system has become increasingly important. Large industrial users, as the main load component of the power system, have significant load adjustment potential. This paper proposes a load adjustable potential assessment method for large industrial users based on the decadal time scale. By analyzing various adjustable potential indicators within the decade, the TOPSIS method is used to comprehensively evaluate the load adjustment potential of large industrial users. First, collect historical load data and related environmental data of large industrial users; then, quantify the indicators on the decadal time scale; finally, propose corresponding load adjustment strategy suggestions based on the indicator analysis results. The actual example shows that the evaluation method and indicator system proposed in this paper are reasonable and effective.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Bing Yu, Ao Li, Xin Yin, Yichi Zhang, Dunnan Liu, Zhenyu Ma, Jinbo Cui, Xiujun Li, Changsheng Su, and Zihao Wang "Load adjustable potential assessment method for large industrial users based on decadal time scale", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 1344747 (16 January 2025); https://doi.org/10.1117/12.3044971
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KEYWORDS
Reflection

Renewable energy

Data processing

Radar

Lithium

Matrices

Power consumption

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