IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p364-d307778.html
   My bibliography  Save this article

Wind-Thermal-Nuclear-Storage Combined Time Division Power Dispatch Based on Numerical Characteristics of Net Load

Author

Listed:
  • Xin Sui

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
    State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

  • Shengyang Lu

    (Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

  • Hai He

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
    State Grid Anshan Electric Power Supply Company, Anshan 114000, China)

  • Yuting Zhao

    (State Grid Anshan Electric Power Supply Company, Anshan 114000, China)

  • Shubo Hu

    (School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China)

  • Ziqian Liu

    (Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

  • Hong Gu

    (School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China)

  • Hui Sun

    (School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

In order to satisfy the strategic needs of energy sustainable development, renewable energy has developed rapidly and the power systems have been transformed to a new generation of power systems. In the renewable energy power generation technologies, the fastest developing wind power generation are highly intermittent and fluctuating. When high penetration of renewable power connects to the power grid and participates in the system dispatch, there will be more difficulties and challenges in the energy balance control. In this paper, a wind-thermal-nuclear-storage combined time division power dispatch strategy based on numerical characteristics of net load is proposed, where a specific thermal generating mode and an unconventional nuclear generating mode are discussed. In the strategy, the dispatch time division method is introduced in detail and the sample entropy theory is used to calculate the net load complexity. An adaptive thermal generating mode is determined according to the numerical characteristics of the net load. The nuclear generating modes of constant power operation, time division operation, and net load tracking time division operation are compared and analyzed, respectively. Finally, the wind-thermal-nuclear-storage combined time division power dispatch strategy aiming at decreasing the ramping power of thermal generators is achieved, and the increasing of the participation of pumped storage and improving of the continuous and steady operation time of thermal generators are realized. The experiment simulation is developed on an actual provincial power system in the northeast of China. The results verify that the thermal generator ramping power in the case based on SampEn are reduced, and the participation of pumped storage is improved. When both of the thermal generating mode and nuclear generating mode are according to the changing of net loads, the ramping powers of thermal generators are further decreased.

Suggested Citation

  • Xin Sui & Shengyang Lu & Hai He & Yuting Zhao & Shubo Hu & Ziqian Liu & Hong Gu & Hui Sun, 2020. "Wind-Thermal-Nuclear-Storage Combined Time Division Power Dispatch Based on Numerical Characteristics of Net Load," Energies, MDPI, vol. 13(2), pages 1-24, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:364-:d:307778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/364/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/2/364/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shaker, Hamid & Zareipour, Hamidreza & Wood, David, 2016. "Impacts of large-scale wind and solar power integration on California׳s net electrical load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 761-774.
    2. Shubo Hu & Hui Sun & Feixiang Peng & Wei Zhou & Wenping Cao & Anlong Su & Xiaodong Chen & Mingze Sun, 2018. "Optimization Strategy for Economic Power Dispatch Utilizing Retired EV Batteries as Flexible Loads," Energies, MDPI, vol. 11(7), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Yuwei & Yang, Yuanjuan & Fei, Haoran & Song, Minghao & Jia, Mengyao, 2022. "Wasserstein and multivariate linear affine based distributionally robust optimization for CCHP-P2G scheduling considering multiple uncertainties," Applied Energy, Elsevier, vol. 306(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Shubo Hu & Feixiang Peng & Zhengnan Gao & Changqiang Ding & Hui Sun & Wei Zhou, 2019. "Sample Entropy Based Net Load Tracing Dispatch of New Energy Power System," Energies, MDPI, vol. 12(1), pages 1-23, January.
    3. Dunguo Mou, 2018. "Wind Power Development and Energy Storage under China’s Electricity Market Reform—A Case Study of Fujian Province," Sustainability, MDPI, vol. 10(2), pages 1-20, January.
    4. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    5. Rauner, Sebastian & Eichhorn, Marcus & Thrän, Daniela, 2016. "The spatial dimension of the power system: Investigating hot spots of Smart Renewable Power Provision," Applied Energy, Elsevier, vol. 184(C), pages 1038-1050.
    6. Hui Sun & Peng Yuan & Zhuoning Sun & Shubo Hu & Feixiang Peng & Wei Zhou, 2018. "Distribution Network Congestion Dispatch Considering Time-Spatial Diversion of Electric Vehicles Charging," Energies, MDPI, vol. 11(10), pages 1-17, October.
    7. Antonelli, Marco & Desideri, Umberto & Franco, Alessandro, 2018. "Effects of large scale penetration of renewables: The Italian case in the years 2008–2015," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3090-3100.
    8. Ye, Liang-Cheng & Lin, Hai Xiang & Tukker, Arnold, 2019. "Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China," Energy, Elsevier, vol. 167(C), pages 708-714.
    9. Wang, Zhenni & Wen, Xin & Tan, Qiaofeng & Fang, Guohua & Lei, Xiaohui & Wang, Hao & Yan, Jinyue, 2021. "Potential assessment of large-scale hydro-photovoltaic-wind hybrid systems on a global scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    10. Dunguo Mou, 2019. "Pumped storage hydro power’s function in the electricity market under the electricity deregulation background in China – A case study of Fujian province," Energy & Environment, , vol. 30(6), pages 951-968, September.
    11. Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.
    12. Mou, Dunguo & He, Xiaoping, 2019. "Developing large-scale energy storage to alleviate a low-carbon energy bubble," Energy Policy, Elsevier, vol. 132(C), pages 62-74.
    13. Seong-Hyeon Ahn & Jin-Hee Hyun & Jin-Ho Choi & Seong-Geun Lee & Gyu-Gwang Kim & Byeong-Gwan Bhang & Hae-Lim Cha & Byeong-Yong Lim & Hoon-Joo Choi & Hyung-Keun Ahn, 2023. "Load-Following Operation of Small Modular Reactors under Unit Commitment Planning with Various Photovoltaic System Conditions," Energies, MDPI, vol. 16(7), pages 1-16, March.
    14. Pierro, Marco & Perez, Richard & Perez, Marc & Moser, David & Cornaro, Cristina, 2020. "Italian protocol for massive solar integration: Imbalance mitigation strategies," Renewable Energy, Elsevier, vol. 153(C), pages 725-739.
    15. Cany, C. & Mansilla, C. & Mathonnière, G. & da Costa, P., 2018. "Nuclear contribution to the penetration of variable renewable energy sources in a French decarbonised power mix," Energy, Elsevier, vol. 150(C), pages 544-555.
    16. Pierro, Marco & Perez, Richard & Perez, Marc & Prina, Matteo Giacomo & Moser, David & Cornaro, Cristina, 2021. "Italian protocol for massive solar integration: From solar imbalance regulation to firm 24/365 solar generation," Renewable Energy, Elsevier, vol. 169(C), pages 425-436.
    17. Suyang Zhou & Yuxuan Zhuang & Wei Gu & Zhi Wu, 2018. "Operation and Economic Assessment of Hybrid Refueling Station Considering Traffic Flow Information," Energies, MDPI, vol. 11(8), pages 1-20, July.
    18. Yujing Sun & Fei Wang & Bo Wang & Qifang Chen & N.A. Engerer & Zengqiang Mi, 2016. "Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems," Energies, MDPI, vol. 10(1), pages 1-20, December.
    19. Tang, Yuchen & Cheng, John W.M. & Duan, Qinwei & Lee, Cheuk Wing & Zhong, Jin, 2019. "Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies," Renewable Energy, Elsevier, vol. 133(C), pages 1099-1107.
    20. Zha, Donglan & Jiang, Pansong & Zhang, Chaoqun & Xia, Dan & Cao, Yang, 2023. "Positive synergy or negative synergy: An assessment of the carbon emission reduction effect of renewable energy policy mixes on China's power sector," Energy Policy, Elsevier, vol. 183(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:364-:d:307778. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.