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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
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    References listed on IDEAS

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    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).
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    4. 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).
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    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).

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