On the Impact of Auto-Correlation of Stochastic Processes on the Transient Behavior of Power Systems

Adeen, Muhammad; Milano, Federico

doi:10.1109/tpwrs.2021.3068038

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…For the former, the fact that discrete variables may have dynamic effects that disappear when they are relaxed (see for example the region of attraction of discrete on-load tap changers [21]; the limit cycle originated by the series of two discrete under-load tap changers described in [22]; and the real-world example described in Section 1.1). For the latter, the average model loses the information on higher order statistical momenta, such as the standard deviation of the variables [23] and the potentially destabilizing effect of correlation [24] and autocorrelation [25] of stochastic processes. using various resistances, capacitances, voltage sources and diodes.…”

Section: Sdaesmentioning

confidence: 99%

Micro-flexibility: Challenges for power system modeling and control

Chatzivasileiadis

Aristidou

Dassios

et al. 2023

Electric Power Systems Research

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Section: Sdaesmentioning

confidence: 99%

Micro-flexibility: Challenges for power system modeling and control

Chatzivasileiadis

Aristidou

Dassios

et al. 2023

Electric Power Systems Research

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“…In our research, CIG infrastructure has not been implicated as a source for VLFO; however it can strongly influence system parameters that appear to sensitize the power systems to VLF modes, these parameters include generator dispatch, active The purpose of installing renewable generation is to displace conventional generators, which reduces synchronous inertia on a power system, changing VLF modes and making the system more sensitive to perturbations such as transient generation/load imbalances and short circuits. Stochastic generation can also be a trigger that exposes sensitized oscillatory modes (Adeen and Milano, 2021). Displayed in Figure 11 is a fluctuation that moved the system frequency.…”

Section: Variability Of Wind Generation Possibly Exciting Vlfosmentioning

confidence: 99%

Active power control from wind farms for damping very low-frequency oscillations

et al. 2022

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Timely remote activation of frequency response, provided by converter-based generation, can improve the damping of very low-frequency (VLF) oscillations. The research presented is based on both power system models and actual data from phasor measurement units (PMU) on the Irish power system. The performance of active power control (APC) is investigated, and variations in wind speed, droop, time lag and resource capacity demonstrate their effectiveness at damping wide-spectrum VLF modes. PMU data that captures the activation and deactivation of APC at wind farms is presented and analyzed; it demonstrates how APC control effectively dampens VLF modes on the Irish system. These observations are supported by sensitivity analysis carried out a power system model in DIgSILENT PowerFactory. These results demonstrate the improvement in VLF mode stability that APC can provide at wind generation. It is demonstrated that minimal amounts of generation curtailment and modest droop settings are sufficient to see substantial VLF mode damping. Index Terms—Active Power Control, Oscillation damping, Very Low-Frequency Oscillations, PMU.

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“…The autocorrelation, in turn, defines how fast or slow a process can vary from one time lag to another, which in turn, defines the harmonic content of the process itself. For this reason, two processes with identical probability distribution but with different autocorrelations may have significantly different effects on the dynamic response of a system [90].…”

Section: Stochastic Differential-algebraic Equationsmentioning

confidence: 99%

Power system modelling as stochastic functional hybrid differential‐algebraic equations

Milano

Liu

Murad³

et al. 2022

IET Smart Grid

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This paper presents the software tools developed for the research project Advanced Modelling for Power System Analysis and Simulation (AMPSAS) funded by Science Foundation Ireland from 2016 to 2021. The main objective of AMPSAS was the development of novel analytical and computational tools to understand, efficiently design, and optimise ever-changing modern power systems and smart grids, through modelbased approaches. In particular, the paper discusses (i) stochastic differential equations for modelling power systems, which are subject to large stochastic perturbations (e.g. wind and solar generation); (ii) the effect of controller and modelling imperfections, for example, delays, discontinuities, and digital signals, on both local and area-wide regulators in power systems; and (iii) the stability analysis and dynamic performance of power systems modelled through stochastic, delay and hybrid implicit differential-algebraic equations. The software tool developed during the execution of AMPSAS integrates areas of applied mathematics, automatic control, and computer science. Several implementation features and open challenges of this software tool are also discussed in the paper. A variety of examples that illustrates the features of this software tool are based on a dynamic model of the all-island Irish transmission system.

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On the Impact of Auto-Correlation of Stochastic Processes on the Transient Behavior of Power Systems

Cited by 13 publications

References 15 publications

Micro-flexibility: Challenges for power system modeling and control

Micro-flexibility: Challenges for power system modeling and control

Active power control from wind farms for damping very low-frequency oscillations

Power system modelling as stochastic functional hybrid differential‐algebraic equations

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