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Adaptive time division power dispatch based on numerical characteristics of net loads

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  • Hu, Shu-bo
  • Gao, Zheng-nan
  • He, Hai
  • Cao, Wen-ping
  • Zhao, Yu-ting
  • Zhou, Wei
  • Gu, Hong
  • Sun, Hui

Abstract

Energy sustainable development is of strategic importance across the world. This is especially the case in China as it is the world’s largest energy consumer. However, renewable energies such as wind and solar energy are characterized with high intermittence and fluctuation. As the penetration of renewable power in the energy system increases, it is getting more challenging in power dispatch. In this paper, an adaptive time division power dispatch strategy based on numerical characteristics of net loads is proposed. In this strategy, a dispatch time division method is introduced in detail to divide the dispatching time according to the numerical characteristics of net loads. The sample entropy theory is utilized to calculate the complexity of the net loads, depending on which a specific thermal generating mode is provided. The experiment simulation is developed on an actual provincial power system in the northeast of China. The test results have confirmed that in the adaptive time division power dispatch strategy, the ramping power of thermal generators are decreased, the continuous and steady operation time of thermal generators are improved. As a result, this new strategy, can lead to improved energy efficiency of power systems as well as significant environmental benefits.

Suggested Citation

  • Hu, Shu-bo & Gao, Zheng-nan & He, Hai & Cao, Wen-ping & Zhao, Yu-ting & Zhou, Wei & Gu, Hong & Sun, Hui, 2020. "Adaptive time division power dispatch based on numerical characteristics of net loads," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220311336
    DOI: 10.1016/j.energy.2020.118026
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    Cited by:

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    2. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Yan, Zhiyu, 2022. "A Wasserstein metric-based distributionally robust optimization approach for reliable-economic equilibrium operation of hydro-wind-solar energy systems," Renewable Energy, Elsevier, vol. 196(C), pages 204-219.
    3. Shubo Hu & Zhengnan Gao & Jing Wu & Yangyang Ge & Jiajue Li & Lianyong Zhang & Jinsong Liu & Hui Sun, 2022. "Time-Interval-Varying Optimal Power Dispatch Strategy Based on Net Load Time-Series Characteristics," Energies, MDPI, vol. 15(4), pages 1-23, February.
    4. Wang, Yajun & Wang, Jidong & Cao, Man & Kong, Xiangyu & Abderrahim, Bouchedjira & Yuan, Long & Vartosh, Aris, 2023. "Dynamic emission dispatch considering the probabilistic model with multiple smart energy system players based on a developed fuzzy theory-based harmony search algorithm," Energy, Elsevier, vol. 269(C).
    5. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    6. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong & Ma, Zeyang, 2023. "A novel two-layer nested optimization method for a zero-carbon island integrated energy system, incorporating tidal current power generation," Renewable Energy, Elsevier, vol. 218(C).

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