Measuring effective area inertia to determine fast-acting frequency response requirements

Wilson, Douglas; Yu, James; Al-Ashwal, Natheer; Heimisson, Birkir; Terzija, Vladimir

doi:10.1016/j.ijepes.2019.05.034

Cited by 71 publications

(49 citation statements)

References 7 publications

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“…In this context, one of the core project activities, directly related to frequency control, was monitoring of power system attributes directly affected by the increased penetration of nonsynchronous generation. In this context, novel approaches for monitoring of power system inertia were proposed [169][170][171]. One of particular research questions of the project was definition of regional inertia and approaches for its monitoring [170].…”

Section: Discussion On Future Challengesmentioning

confidence: 99%

Smart frequency control in low inertia energy systems based on frequency response techniques: A review

Cheng

Azizipanah‐Abarghooee

Azizi

et al. 2020

Applied Energy

133

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Section: Discussion On Future Challengesmentioning

confidence: 99%

Smart frequency control in low inertia energy systems based on frequency response techniques: A review

Cheng

Azizipanah‐Abarghooee

Azizi

et al. 2020

Applied Energy

133

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“…Increasing both Kp and Ki widens the control bandwidth whilst gains at low frequencies and rejection of steady-state errors are mainly increased by increasing the value of Ki. Using (16) to (19) for analysis and controller design, different step-responses can be obtained as shown in Fig. 4 with fixed Kp = 0.001 and different values of Ki .…”

Section: Conventional Direct Power Controlmentioning

confidence: 99%

“…However, microgrids with increasing levels of power electronicbased sources, decrease the available inertia of the power system due to the decreasing ratio of the total system kinetic energy with respect to the total power of the system [15]. Systems with reduced inertia may be susceptible to large frequency deviations beyond the permitted limits and a high rate of change of frequency (ROCOF), leading to system failure and power blackout due to the activation of protection devices [16]- [17]. Accordingly, such microgrids may be categorized as grids with low inertia, especially when they are islanded or isolated, operating with DEGs that have relatively low inertia constants compared with those of typical synchronous generators.…”

Section: Introductionmentioning

confidence: 99%

Model‐based control of a VSC‐based power generator with synthetic inertia provision in an isolated micro‐grid

Hardan¹,

Norman²,

Leithead

2020

IET gener. transm. distrib.

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Distributed energy resources such as solar PV, wind and energy storage systems are increasingly being utilized within isolated and grid-connected microgrids. Their integration has prompted the move towards more advanced coordinated control for flexible, efficient and secure operation of microgrid-based power systems. This paper presents a new power control method based on a canonical inverse-model and implied synthetic inertia (SI), which is implemented on a VSC-based power generator (VSC-PG) for frequency response support. The control method augments a traditional power controller whilst the SI technique is blended with a new switching mechanism which enhances the performance of the VSC-PG when it is utilized with SI provision capability. The switching mechanism acts only when necessary, without contradicting the main frequency regulating sources. For assessment, the above method and SI technique have been designed and modelled for a 630kVA VSC-PG with DC-link batterybank. The VSC-PG was connected within an isolated/remote microgrid formed by an 11kV feeder, 1MVA diesel generator and variable load. The performances of the proposed power controller and SI provider have been tested and analysed using detailed Matlab/Simulink simulations and found to compare favourably with methods published in the literature.

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“…Online identification can be further divided into two kinds of methods. The first kind is to calculate the inertia contributed by all online traditional generators (TGs) [17], [18], which only yields a lower bound of the system inertia. The second method is to use the power disturbance data recorded by the PMU to identify system inertia during the online stage [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

Liu¹,

Hu²,

Mujeeb³

2021

Preprint

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The highly fluctuated renewable generations and electric vehicles have undergone tremendous growth in recent years. The majority of them are connected to the grid via power electronic devices, resulting in wide variation ranges for several key parameters in the frequency response model (FRM) such as system inertia and load damping factor. In this paper, an automatic generation control (AGC) method considering the uncertainties of these key parameters is proposed. First, the historical power system operation data following large power disturbances are used to identify the FRM key parameters offline. Second, the offline identification results and the normal operation data prior to the occurrence of the disturbance are used to train the online probability estimation model of the FRM key parameters. Third, the online estimation results of the FRM key parameters are used as the input, and the model predictive-based AGC signal optimization method is developed based on distributionally robust optimization (DRO) technology. Case studies conducted on the IEEE 118-Bus System show that the proposed AGC method outperforms the widely utilized PI-based control method in terms of performance and efficiency.<br>

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Measuring effective area inertia to determine fast-acting frequency response requirements

Cited by 71 publications

References 7 publications

Smart frequency control in low inertia energy systems based on frequency response techniques: A review

Smart frequency control in low inertia energy systems based on frequency response techniques: A review

Model‐based control of a VSC‐based power generator with synthetic inertia provision in an isolated micro‐grid

Automatic Generation Control Considering Uncertainties of the Key Parameters in the Frequency Response Model

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