IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4406-d1470210.html
   My bibliography  Save this article

Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification

Author

Listed:
  • Hua Li

    (Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710048, China)

  • Xiangfei Qiu

    (State Grid Shaanxi Electric Power Company, Xi’an 710046, China)

  • Qiuyi Xi

    (Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710048, China)

  • Ruogu Wang

    (Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710048, China)

  • Gang Zhang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Yanxin Wang

    (Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710048, China)

  • Bao Zhang

    (School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model.

Suggested Citation

  • Hua Li & Xiangfei Qiu & Qiuyi Xi & Ruogu Wang & Gang Zhang & Yanxin Wang & Bao Zhang, 2024. "Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification," Energies, MDPI, vol. 17(17), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4406-:d:1470210
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4406/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4406/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Lücken & Benjamín Barán & Carlos Brizuela, 2014. "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 707-756, July.
    2. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    3. Xiao, Jun & Lin, Xiqiao & Jiao, Heng & Song, Chenhui & Zhou, Huan & Zu, Guoqiang & Zhou, Chunli & Wang, Dan, 2023. "Model, calculation, and application of available supply capability for distribution systems," Applied Energy, Elsevier, vol. 348(C).
    4. Xianfeng Xu & Ke Wang & Yong Lu & Yunbo Tian & Liqun Hu & Ming Zhong, 2023. "Research on Performance Evaluation Index System and Assessment Methods for Microgrid Operation in the Port Area," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    5. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
    Full references (including those not matched with items on IDEAS)

    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. Byungsung Lee & Haesung Lee & Hyun Ahn, 2020. "Improving Load Forecasting of Electric Vehicle Charging Stations Through Missing Data Imputation," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    3. Wang, Yue & Shi, Jianmai & Wang, Rui & Liu, Zhong & Wang, Ling, 2018. "Siting and sizing of fast charging stations in highway network with budget constraint," Applied Energy, Elsevier, vol. 228(C), pages 1255-1271.
    4. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    5. Jefimowski, Włodzimierz & Szeląg, Adam & Steczek, Marcin & Nikitenko, Anatolii, 2020. "Vanadium redox flow battery parameters optimization in a transportation microgrid: A case study," Energy, Elsevier, vol. 195(C).
    6. Wang, Haibing & Zhao, Anjie & Khan, Muhammad Qasim & Sun, Weiqing, 2024. "Optimal operation of energy hub considering reward-punishment ladder carbon trading and electrothermal demand coupling," Energy, Elsevier, vol. 286(C).
    7. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    8. Yue Wang & Zhong Liu & Jianmai Shi & Guohua Wu & Rui Wang, 2018. "Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network," Energies, MDPI, vol. 11(9), pages 1-21, September.
    9. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    10. Chen-Yu Chang & Pei-Fang Tsai, 2022. "Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    11. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    12. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    13. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    14. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    15. Shin-Ki Hong & Sung Gu Lee & Myungchin Kim, 2020. "Assessment and Mitigation of Electric Vehicle Charging Demand Impact to Transformer Aging for an Apartment Complex," Energies, MDPI, vol. 13(10), pages 1-23, May.
    16. Chen, Changming & Wu, Xueyan & Li, Yan & Zhu, Xiaojun & Li, Zesen & Ma, Jien & Qiu, Weiqiang & Liu, Chang & Lin, Zhenzhi & Yang, Li & Wang, Qin & Ding, Yi, 2021. "Distributionally robust day-ahead scheduling of park-level integrated energy system considering generalized energy storages," Applied Energy, Elsevier, vol. 302(C).
    17. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
    18. Mu Li & Yingqi Liu & Weizhong Yue, 2022. "Evolutionary Game of Actors in China’s Electric Vehicle Charging Infrastructure Industry," Energies, MDPI, vol. 15(23), pages 1-20, November.
    19. Christian Lücken & Carlos A. Brizuela & Benjamín Barán, 2022. "Clustering-based multipopulation approaches in MOEA/D for many-objective problems," Computational Optimization and Applications, Springer, vol. 81(3), pages 789-828, April.
    20. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(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:17:y:2024:i:17:p:4406-:d:1470210. 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.