Analysis of Hopf bifurcation with forecast uncertainties in load/generation

Krishan, Ram; Verma, Ashu; Mishra, Sukumar; Bijwe, P.R.

doi:10.1049/iet-gtd.2016.1316

Cited by 8 publications

(5 citation statements)

References 37 publications

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“…The base point is basically any stable operating point for the system under consideration. Here we use the stable point from [24] and values of the small-signal stable base point are given in Appendix Table II First, we discuss the GP learning process in one -dimensional voltage magnitude |V 3 | state subspace. The Fig.…”

Section: Simulations and Discussionmentioning

confidence: 99%

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Probabilistic robust small-signal stability framework using gaussian process learning

Pareek

Nguyen

2020

Electric Power Systems Research

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Section: Simulations and Discussionmentioning

confidence: 99%

“…The base point used in the simulations presented in this paper are taken from [24]. The real and reactive power generation values and voltage magintudes at generator buses is at base point are given in the table below.…”

Section: Appendixmentioning

confidence: 99%

Probabilistic robust small-signal stability framework using gaussian process learning

Pareek

Nguyen

2020

Electric Power Systems Research

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“…Moreover, time and weather are two major factors for the definitive component of demand changes. Daily and weekly changes in demand depend on the behaviour of patterns of different energy consumers [15]. Also, the transmission line parameters are always subject to variations, which could be due to changes in environmental and operation conditions [16].…”

Section: Introductionmentioning

confidence: 99%

Robust design of fuzzy‐based power system stabiliser considering uncertainties of loading conditions and transmission line parameters

Shokouhandeh

Jazaeri

2019

IET Generation, Transmission & Distribution

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“…Stability Certification via GP Learning related to complex formulation and inaccurate first and second-order approximations of sensitivities of eigenvalues. The detailed review of these techniques and approaches can be found in [240][241][242][243]. Regardless of the method employed, all these PSSS assessment methods try to solve specific uncertainty issues considering a typical PDF for uncertainties in the wind, solar, or load.…”

Section: Small-signal Stability: Concept and Methodsmentioning

confidence: 99%

“…The base point is basically any stable operating point for the system under consideration. Here we use the stable point from [242] and values of the small-signal stable base point are given in Table 4.1 First, we discuss the GP learning process in one -dimensional voltage magnitude V 3 state subspace. Figure 4.2 shows the learning mechanism for c as a function of V 3 with three sampling points shown as red triangles (m = 3).…”

Section: Numerical Case Studies For Prsmentioning

confidence: 99%

Analytical approximations and decision-making techniques for power systems under uncertainty

Pareek¹

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The integration of distributed energy resources and increasing adoption of electric vehicles continue to drive uncertainty in power systems to an unprecedented level.In view of reduced applicability of traditional analysis and decision-making methods, this dissertation aims to address the need for new approaches, by attempting to solve three di↵erent sets of problems.This dissertation first proposes an analytical power flow approximation and develops a closed-form power flow framework. The thesis proposes a novel framework using Gaussian process regression to learn node voltage as a closed-form function of e↵ective bus load or injection vector. The proposed approximation is valid over a subspace of load, where the 'subspace' is used to describe a hypercube within which uncertain loads/injections lie. The approximation framework explains the system's behavior under uncertainty via Gaussian process hyper-parameter based interpretability. The proposed method achieves low mean absolute error of order 10 5 (per unit) in voltage magnitude and 10 4 (rad.) in angle when tested on 33-Bus and 56-Bus systems. Also, the proposed framework is used to develop an optimal steady-state voltage control framework using a linear voltage-power relationship. Simulation results on the 69-Bus system have shown the independence of approximation error with system loading and uncertainty levels. Therefore, control mechanisms developed based on the proposed analytical approximation are suitable under uncertainty. The framework also addresses the challenges posed by independent entity integration (e.g., peer-to-peer energy trading), as independent operational decisions of prosumers a↵ect the entire network. The proposed probabilistic feasibility set construction method-for privacy-preserving feasibility assessment-is tractable and handles non-parametric injection uncertainties by applying the worst-case performance analysis theorem over the proposed closed-form power flow approximation. The developed method's ability to capture the variations in probabilistic feasibility set for storage connected at a node in a 33-Bus network is shown via numerical simulations under di↵erent levels of total renewable penetration and uncertainty.xviiContents Statement of Originality iii

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Analysis of Hopf bifurcation with forecast uncertainties in load/generation

Cited by 8 publications

References 37 publications

Probabilistic robust small-signal stability framework using gaussian process learning

Probabilistic robust small-signal stability framework using gaussian process learning

Robust design of fuzzy‐based power system stabiliser considering uncertainties of loading conditions and transmission line parameters

Analytical approximations and decision-making techniques for power systems under uncertainty

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