Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

Genzhu Wu, Weilin Zhong (), Muyang Liu, Xiqiang Chang, Xianlong Shao and Ruo Mo
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Genzhu Wu: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Weilin Zhong: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Muyang Liu: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Xiqiang Chang: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Xianlong Shao: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China
Ruo Mo: School of Electrical Engineering, Xinjiang University, Urumqi 830046, China

Energies, 2024, vol. 17, issue 20, 1-17

Abstract: As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface ( POI ) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5 % .

Keywords: inertia estimation; virtual power plant; equivalent inertia constant; regularization algorithm; point of interface (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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