Impact of static and dynamic load models on security margin estimation methods

Hagmar, Hannes; Tuấn, Lê Anh; Eriksson, Robert

doi:10.1016/j.epsr.2021.107581

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Based on the discussion in section 2.1.4, the effective droop (R e f f.droop ) is given in eqn. (17), displayed by the system after disturbance, is estimated to be 91.4076%.…”

Section: System Self-regulation (D Sel F )mentioning

confidence: 99%

“…). The SGs and synchronous motors have an inherent capability to support the grid during frequency fluctuations by adapting its output, as they operate at the same frequency as the grid (50 Hz) [17]. However, Variable Frequency/Speed Drives (VFD/VSD) based motors operate at a wide frequency range (0 to 2x50 Hz) [18], so they are more prone to stalling, which can worsen frequency response during disturbance.…”

Section: Inertial Response (Ir)mentioning

confidence: 99%

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Quantifying System Inertia and Primary Frequency Response Residual Sources of Low-inertia Power System: A Comprehensive SBCM-PFA Approach

Basumatary,

Shukla

2023

Preprint

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<p>This paper presents a comprehensive framework, which includes a quantification procedure for various Frequency Response Residual-Sources (FRR-Ss), including System Inertia, $M_{sys}$ ( = 2H), System Self-regulation ($D_{self}$), Governor Response ($FR_{Gov.}$), and Effective Governor-Droop ($R_{eff.droop}$) performance. This framework leverages Phasor Measurement Unit (PMU) data, capturing sequence-of-events frequency dynamics from a critical location following a cascaded tripping event. Within this framework, a time duration of 20 sec, starting from the onset of the disturbance, employs suitable approaches to analyze measured frequency in two network cases: `Case-I' real-time simulation in IEEE-24 bus system (RSCAD/RTDS) and `Case-II' real-frequency event measurements from the Indian power system for the aforementioned FRR-Ss quantification. The quantified results for `Case-I' are as follows: $M_{sys}$ = 20.9855 sec, $D_{self}$ = 2.477 p.u, $FR_{Gov.}$ = 10.680 p.u, and $R_{eff.droop}$ = 9.363\%, and results for `Case-II' are 5.0289 sec, 1.2116 p.u, 1.094 p.u, and 91.4076\% respectively. This effort enhances an improved understanding of system frequency dynamics and estimating the frequency response requirements during cascaded tripping events of low-inertia power systems. Finally, the paper provides insights into operational measures and highlights various challenges faced.</p>

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“…Based on the discussion in section 2.1.4, the effective droop (R e f f.droop ) is given in eqn. (17), displayed by the system after disturbance, is estimated to be 91.4076%.…”

Section: System Self-regulation (D Sel F )mentioning

confidence: 99%

Section: Inertial Response (Ir)mentioning

confidence: 99%

Quantifying System Inertia and Primary Frequency Response Residual Sources of Low-inertia Power System: A Comprehensive SBCM-PFA Approach

Basumatary,

Shukla

2023

Preprint

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“…Long-term voltage stability involves slow-acting equipment devices such as generator current limiters, LTC, and controlled loads. The attempt to restore power consumption of the dynamic load is usually the reason for long-term voltage instability [15], [16]. So, that is important to consider the load model in long-term voltage study.…”

Section: B Dynamic Load Modelmentioning

confidence: 99%

Coordination of Load Tap Changer and Distributed Energy Resources to Improve Long-term Voltage Stability

Tran

Nguyen

et al. 2022

2022 IEEE 7th International Energy Conference (ENERGYCON)

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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“…There are many current modeling methods for power load forecasting, and power load forecasting models with good performance have emerged [3]. Traditional models include models such as linear regression and moving averages, which collect and analyze the historical data of power loads, from which they find the changing law between power loads and factors and fit the future value of power loads according to the changing law [4][5][6]. The traditional model assumes that the power load is a fixed and unchanging trend, such as an upward trend or a downward trend [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Traceless Kalman filter threshold estimation for distributed power loads

Bao,

Zhang,

2023

Applied Mathematics and Nonlinear Sciences

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Load forecasting is an important part of smart grid construction, energy management, and sustainable design of power systems, and has a great impact on the reliable operation of power grids, facility planning and other decisions. In this paper, we utilize the traceless transform of the UKF algorithm to obtain the sigma feature points generated by the mean value of the high-frequency wavelet components of the power signals and combine them with the EKF algorithm. We put forward a novel neural network hybrid Kalman TUKF algorithm, which will be used to carry out simulation experiments on the distributed electric loads and to estimate the threshold value of the loads in the numerical experiments. The results show that in comparison with the actual measurements, the TUKF algorithm improves by 34.7% in the RMSE metrics, 38.7% in the MAE metrics, and 40.6% in the MAPE metrics compared to the PFWNN. The TUKF algorithm is closer to the real curves and has the best prediction performance for all the time intervals of the prediction. The change of the threshold value has a more obvious effect on the prediction accuracy, and the best effect is in the scale δ = 0.5, i.e., the threshold frequency is selected as the middle value of the intermediate frequency.

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Impact of static and dynamic load models on security margin estimation methods

Cited by 6 publications

References 17 publications

Quantifying System Inertia and Primary Frequency Response Residual Sources of Low-inertia Power System: A Comprehensive SBCM-PFA Approach

Quantifying System Inertia and Primary Frequency Response Residual Sources of Low-inertia Power System: A Comprehensive SBCM-PFA Approach

Coordination of Load Tap Changer and Distributed Energy Resources to Improve Long-term Voltage Stability

Traceless Kalman filter threshold estimation for distributed power loads

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