IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i22p5725-d1522024.html
   My bibliography  Save this article

An Optimization Strategy for Unit Commitment in High Wind Power Penetration Power Systems Considering Demand Response and Frequency Stability Constraints

Author

Listed:
  • Minhui Qian

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
    National Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing 100192, China)

  • Jiachen Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Dejian Yang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Hongqiao Yin

    (School of Electrical Engineering, Southeast University, Nanjing 210003, China)

  • Jiansheng Zhang

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration.

Suggested Citation

  • Minhui Qian & Jiachen Wang & Dejian Yang & Hongqiao Yin & Jiansheng Zhang, 2024. "An Optimization Strategy for Unit Commitment in High Wind Power Penetration Power Systems Considering Demand Response and Frequency Stability Constraints," Energies, MDPI, vol. 17(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5725-:d:1522024
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/22/5725/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/22/5725/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Zhang, Xian & Chan, K.W. & Wang, Huaizhi & Hu, Jiefeng & Zhou, Bin & Zhang, Yan & Qiu, Jing, 2019. "Game-theoretic planning for integrated energy system with independent participants considering ancillary services of power-to-gas stations," Energy, Elsevier, vol. 176(C), pages 249-264.
    3. Sun, Qirun & Wu, Zhi & Ma, Zhoujun & Gu, Wei & Zhang, Xiao-Ping & Lu, Yuping & Liu, Pengxiang, 2022. "Resilience enhancement strategy for multi-energy systems considering multi-stage recovery process and multi-energy coordination," Energy, Elsevier, vol. 241(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. Dong, Lei & Sun, Shiting & Zhang, Shiming & Zhang, Tao & Pu, Tianjiao, 2024. "Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme," Energy, Elsevier, vol. 304(C).
    2. Yang, Dechang & Wang, Ming & Yang, Ruiqi & Zheng, Yingying & Pandzic, Hrvoje, 2021. "Optimal dispatching of an energy system with integrated compressed air energy storage and demand response," Energy, Elsevier, vol. 234(C).
    3. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    4. Lan, Puzhe & Han, Dong & Xu, Xiaoyuan & Yan, Zheng & Ren, Xijun & Xia, Shiwei, 2022. "Data-driven state estimation of integrated electric-gas energy system," Energy, Elsevier, vol. 252(C).
    5. Liu, Hong & Zhao, Yue & Gu, Chenghong & Ge, Shaoyun & Yang, Zan, 2021. "Adjustable capability of the distributed energy system: Definition, framework, and evaluation model," Energy, Elsevier, vol. 222(C).
    6. Yu, Haiquan & Zhou, Jianxin & Si, Fengqi & Nord, Lars O., 2022. "Combined heat and power dynamic economic dispatch considering field operational characteristics of natural gas combined cycle plants," Energy, Elsevier, vol. 244(PA).
    7. Peng Liu & Tieyan Zhang & Furui Tian & Yun Teng & Miaodong Yang, 2024. "Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory," Energies, MDPI, vol. 17(24), pages 1-20, December.
    8. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    9. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    10. Tan, Hong & Yan, Wei & Ren, Zhouyang & Wang, Qiujie & Mohamed, Mohamed A., 2022. "Distributionally robust operation for integrated rural energy systems with broiler houses," Energy, Elsevier, vol. 254(PC).
    11. Li, Bo & Li, Xu & Su, Qingyu, 2022. "A system and game strategy for the isolated island electric-gas deeply coupled energy network," Applied Energy, Elsevier, vol. 306(PA).
    12. Lv, Hang & Wu, Qiong & Ren, Hongbo & Li, Qifen & Zhou, Weisheng, 2024. "A two-stage decision-making approach for optimal design and operation of integrated energy systems considering multiple uncertainties and diverse resilience needs," Energy, Elsevier, vol. 305(C).
    13. Chen, Xi & Wang, Chengfu & Wu, Qiuwei & Dong, Xiaoming & Yang, Ming & He, Suoying & Liang, Jun, 2020. "Optimal operation of integrated energy system considering dynamic heat-gas characteristics and uncertain wind power," Energy, Elsevier, vol. 198(C).
    14. Wang, Yunqi & Qiu, Jing & Tao, Yuechuan & Zhang, Xian & Wang, Guibin, 2020. "Low-carbon oriented optimal energy dispatch in coupled natural gas and electricity systems," Applied Energy, Elsevier, vol. 280(C).
    15. Wang, Shouxiang & Wang, Shaomin & Zhao, Qianyu & Dong, Shuai & Li, Hao, 2023. "Optimal dispatch of integrated energy station considering carbon capture and hydrogen demand," Energy, Elsevier, vol. 269(C).
    16. Chen, Xiaoyuan & Jiang, Shan & Chen, Yu & Lei, Yi & Zhang, Donghui & Zhang, Mingshun & Gou, Huayu & Shen, Boyang, 2022. "A 10 MW class data center with ultra-dense high-efficiency energy distribution: Design and economic evaluation of superconducting DC busbar networks," Energy, Elsevier, vol. 250(C).
    17. Qian, Lanping & Bai, Yang & Wang, Wenya & Meng, Fanyi & Chen, Zhisong, 2023. "Natural gas crisis, system resilience and emergency responses: A China case," Energy, Elsevier, vol. 276(C).
    18. Wu, Yanjuan & Wang, Caiwei & Wang, Yunliang, 2024. "Cooperative game optimization scheduling of multi-region integrated energy system based on ADMM algorithm," Energy, Elsevier, vol. 302(C).
    19. Mu, Chenlu & Ding, Tao & Qu, Ming & Zhou, Quan & Li, Fangxing & Shahidehpour, Mohammad, 2020. "Decentralized optimization operation for the multiple integrated energy systems with energy cascade utilization," Applied Energy, Elsevier, vol. 280(C).
    20. Salehi, Javad & Namvar, Amin & Gazijahani, Farhad Samadi & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction," Energy, Elsevier, vol. 240(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:22:p:5725-:d:1522024. 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.