Effects of inertia, load damping and dead-bands on frequency histograms and frequency control of power systems

Giudice, Davide del; Brambilla, A.; Grillo, Samuele; Bizzarri, Federico

doi:10.1016/j.ijepes.2021.106842

Cited by 20 publications

(13 citation statements)

References 32 publications

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“…Theorem 1. Given scalars h 1 , h 2 , µ 1 and µ 2 , the system (7) satisfying condition (8) is asymptotically stable, if there exists positive definite matrices S i2 , 4), such that the following matrix inequalities hold forḣ 1t…”

Section: Lemma 3 ([39]mentioning

confidence: 99%

“…In order to maintain the power grid frequency (an important index of power quality) fixed or within a small allowable range, a load frequency control (LFC) strategy is a common technique equipped in the power systems [1][2][3][4][5][6][7]. With the development and expansion of the power grid, a dedicated independent communication network has been unable to meet the operation of the power grid, although the small transmission delay in the dedicated independent communication network can be ignored [8].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays

et al. 2021

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The stability problem for load frequency control (LFC) of power systems with two time-varying communication delays is studied in this paper. The one-area and two-area LFC systems are considered, respectively, which are modeled as corresponding linear systems with additive time-varying delays. An improved stability criterion is proposed via a modified Lyapunov-Krasovskii functional (LKF) approach. Firstly, an augmented LKF consisting of delay-dependent matrices and some single-integral items containing time-varying delay information in two different delay subintervals is constructed, which makes full use of the coupling information between the system states and time-varying delays. Secondly, the novel negative definite inequality equivalent transformation lemma is used to transform the nonlinear inequality to the linear matrix inequality (LMI) equivalently, which can be easily solved by the MATLAB LMI-Toolbox. Finally, some numerical examples are presented to show the improvement of the proposed approach.

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Section: Lemma 3 ([39]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays

et al. 2021

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“…In power systems, D is quite complicated because it depends on the load type and time characteristics [13,[15][16][17]. In this study, the D value was estimated from 82 contingency events and separated into three operation-shift intervals (first shift: 00:00-09:00, second shift: 09:00-17:00, and third shift: 17:00-24:00).…”

Section: B Conventional Assessment Of the Load Damping (D Value) Using The Swing Equationmentioning

confidence: 99%

“…In our case, the feature sets presented in Table II are the input x's of (17), where y's are the output representing the frequency deviation ∆f. Finally, the predicted frequency nadir can be obtained using (11).…”

Section: B Model Selectionmentioning

confidence: 99%

“…Conventionally, FRRs are evaluated using frequency nadir prediction in frequency response models (FRMs) [12][13][14]. However, the load damping of a FRM changes with load conditions in a power system [15][16][17]. Moreover, the time-varying response of the speed droop governor in a generator unit strongly affects the unit's ability to support a frequency disturbance event.…”

Section: Introductionmentioning

confidence: 99%

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A Supervised Learning Scheme for Evaluating Frequency Nadir and Fast Response Reserve in Ancillary Service Market

Chiu

Chang-Chien

2021

IEEE Access

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Power system operators evaluate the frequency security of the system by predicting the frequency nadir, which is assumed to indicate the impact of a sudden loss of a generating resource. Recently, frequency nadir prediction has become more challenging because renewables have penetrated and significantly changed the generation portfolio within the system. Conventionally, the frequency nadir is determined using a frequency response model where the features-load damping, system inertia, and effective governor response-are assumed to be known. However, these key features are not easily obtained in a power system that continuously changes during daily operation. This study proposes a supervised learning scheme that traces these key features. It also proposes a new feature-the power gap rate-that better reflects the influence of the load on the system frequency than that of the load damping. Feature importance recognition and the construction of a frequency nadir model (FNM) are realized using the proposed supervised learning scheme. The proposed FNM achieved 54% higher accuracy than the conventional method. Finally, the FNM is implemented in a planning process that quantifies the capacity of the fast responsive reserve (FRR). In two renewable penetration cases, the proposed FRR procurement successfully secured the frequency nadir above the security criterion.

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Introduction

Giudice

Bizzarri

Linaro

et al. 2022

Modular Multilevel Converter Modelling and Simulation for HVDC Systems

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Effects of inertia, load damping and dead-bands on frequency histograms and frequency control of power systems

Cited by 20 publications

References 32 publications

Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays

Enhanced Stability Criteria of Network-Based Load Frequency Control of Power Systems with Time-Varying Delays

A Supervised Learning Scheme for Evaluating Frequency Nadir and Fast Response Reserve in Ancillary Service Market

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