IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v273y2023ics036054422300662x.html
   My bibliography  Save this article

An efficient full-response analytical model for probabilistic production simulation in fast frequency response reserve planning

Author

Listed:
  • Li, Zifeng
  • Guo, Litao
  • Yu, Samson S.
  • Zhang, Mingli
  • Ren, Yupeng
  • Zhang, Na
  • Li, Weidong

Abstract

Due to the variation in loads and generations, maintaining system frequency stability is a critical task in power system planning and control. Considering the frequency dynamics in probabilistic production simulation during the fast frequency response reserve planning process, it is particularly important to analyze the massive system operation scenario quickly and accurately. In this paper, an analytical model based on the demand of probabilistic production simulation is proposed to describe the frequency dynamics in fast frequency response reserve planning. Specifically, the droop efficient of units seen as the standardized gain is adopted for the same kind of resource single-machine aggregation in a multi-resource power system. Based on this, a first-order inertia fitting model of speed governors is used to describe the frequency dynamic changes of frequency. In addition, to avoid the inaccuracy in the dynamic analysis, the system state after disturbance is considered as the initial state in the zero-input response instead of the rated state. Simulations and benchmark comparisons are performed on the IEEE RTS-79 system with various power disturbances to verify the superior performance of the proposed model. The analytical method takes only 1464.03 s, 7.67 times faster than the simulation method for 100 years’ probabilistic production simulation. Moreover, the effectiveness of the model is also verified in a large-scale province-level power system. The results show that the proposed model can make a reasonable trade-off between the calculation accuracy and computation time so as to meet the need of probabilistic production simulation in fast frequency response reserve planning.

Suggested Citation

  • Li, Zifeng & Guo, Litao & Yu, Samson S. & Zhang, Mingli & Ren, Yupeng & Zhang, Na & Li, Weidong, 2023. "An efficient full-response analytical model for probabilistic production simulation in fast frequency response reserve planning," Energy, Elsevier, vol. 273(C).
    • Handle: RePEc:eee:energy:v:273:y:2023:i:c:s036054422300662x
    DOI: 10.1016/j.energy.2023.127268
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422300662X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127268?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    2. Yin, Yue & Liu, Tianqi & Wu, Lei & He, Chuan & Liu, Yikui, 2021. "Frequency-constrained multi-source power system scheduling against N-1 contingency and renewable uncertainty," Energy, Elsevier, vol. 216(C).
    3. Liu, Jizhen & Yao, Qi & Hu, Yang, 2019. "Model predictive control for load frequency of hybrid power system with wind power and thermal power," Energy, Elsevier, vol. 172(C), pages 555-565.
    4. Wei, Zhinong & Yang, Li & Chen, Sheng & Ma, Zhoujun & Zang, Haixiang & Fei, Youdie, 2022. "A multi-stage planning model for transitioning to low-carbon integrated electric power and natural gas systems," Energy, Elsevier, vol. 254(PC).
    5. Yu, Dongmin & Zhu, Haoming & Han, Wenqi & Holburn, Daniel, 2019. "Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system," Energy, Elsevier, vol. 173(C), pages 554-568.
    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. Li, Xuehan & Wang, Wei & Ye, Lingling & Ren, Guorui & Fang, Fang & Liu, Jizhen & Chen, Zhe & Zhou, Qiang, 2024. "Improving frequency regulation ability for a wind-thermal power system by multi-objective optimized sliding mode control design," Energy, Elsevier, vol. 300(C).
    2. Sebastian, Oliva H. & Carlos, Bahamonde D., 2024. "Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints," Renewable Energy, Elsevier, vol. 232(C).

    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. Chaudhary, Aniket Karan & Roy, Satyabrata & Guha, Dipayan & Negi, Richa & Banerjee, Subrata, 2024. "Adaptive cyber-tolerant finite-time frequency control framework for renewable-integrated power system under deception and periodic denial-of-service attacks," Energy, Elsevier, vol. 302(C).
    2. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).
    3. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    4. Li, Kun & Wei, Lishen & Fang, Jiakun & Ai, Xiaomeng & Cui, Shichang & Zhu, Mengshu & Wen, Jinyu, 2024. "Incentive-compatible primary frequency response ancillary service market mechanism for incorporating diverse frequency support resources," Energy, Elsevier, vol. 306(C).
    5. Ou, Kai & Yuan, Wei-Wei & Kim, Young-Bae, 2021. "Development of optimal energy management for a residential fuel cell hybrid power system with heat recovery," Energy, Elsevier, vol. 219(C).
    6. Qinjin Zhang & Xuzhou Zhuang & Yancheng Liu & Chuan Wang & Haohao Guo, 2019. "A Novel Autonomous Current-Sharing Control Strategy for Multiple Paralleled DC–DC Converters in Islanded DC Microgrid," Energies, MDPI, vol. 12(20), pages 1-22, October.
    7. Wang, Haibing & Zheng, Tianhang & Sun, Weiqing & Khan, Muhammad Qasim, 2023. "Research on the pricing strategy of park electric vehicle agent considering carbon trading," Applied Energy, Elsevier, vol. 340(C).
    8. Jiang, Boyou & Guo, Chuangxin & Chen, Zhe, 2024. "Frequency constrained unit commitment considering reserve provision of wind power," Applied Energy, Elsevier, vol. 361(C).
    9. Kong, Xiaobing & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2023. "Stable feedback linearization-based economic MPC scheme for thermal power plant," Energy, Elsevier, vol. 268(C).
    10. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    11. Zhou, Xianyang & Zhou, Dequn & Ding, Hao & Zhao, Siqi & Wang, Qunwei, 2023. "Low-carbon transition of China's provincial power sector under renewable portfolio standards and carbon cap," Energy, Elsevier, vol. 283(C).
    12. Duan, Zhu & Liu, Hui & Li, Ye & Nikitas, Nikolaos, 2022. "Time-variant post-processing method for long-term numerical wind speed forecasts based on multi-region recurrent graph network," Energy, Elsevier, vol. 259(C).
    13. Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    14. Ye, Lin & Jin, Yifei & Wang, Kaifeng & Chen, Wei & Wang, Fei & Dai, Binhua, 2023. "A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment," Applied Energy, Elsevier, vol. 351(C).
    15. Tan, Jin & Wu, Qiuwei & Zhang, Menglin & Wei, Wei & Liu, Feng & Pan, Bo, 2021. "Chance-constrained energy and multi-type reserves scheduling exploiting flexibility from combined power and heat units and heat pumps," Energy, Elsevier, vol. 233(C).
    16. Pravat Kumar Ray & Akash Bartwal & Pratap Sekhar Puhan, 2024. "Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 4124-4142, August.
    17. Rubén López-Rodríguez & Adriana Aguilera-González & Ionel Vechiu & Seddik Bacha, 2021. "Day-Ahead MPC Energy Management System for an Island Wind/Storage Hybrid Power Plant," Energies, MDPI, vol. 14(4), pages 1-33, February.
    18. Aktaş, Ahmet & Kırçiçek, Yağmur, 2020. "A novel optimal energy management strategy for offshore wind/marine current/battery/ultracapacitor hybrid renewable energy system," Energy, Elsevier, vol. 199(C).
    19. Wu, Xuewei & Zhang, Bin & Nielsen, Mads Pagh & Chen, Zhe, 2024. "Multi-stage planning of integrated electricity-gas-heating system in the context of carbon emission reduction," Applied Energy, Elsevier, vol. 358(C).
    20. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wang, Haixia & Ba, Yu, 2024. "Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

    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:eee:energy:v:273:y:2023:i:c:s036054422300662x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.