PV system model reduction for reliability assessment studies

Gafurov, Tokhir; Prodanović, Milan; Usaola, Julio

doi:10.1109/isgteurope.2013.6695420

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Given the need to reduce large power systems, [20] used a linear system reduction method and the work was successful for small-signal stability while [21] performed a parametric MOR aimed at preserving parameters related to decentralised power system devices such as stabilisers. Additionally, MOR has been used to obtain reduced models of PV systems [22,23], battery energy storage systems [24] and of microgrids [25], using the singular perturbation technique. Albeit not explored in this paper, another important class of MOR methods is based on Krylov subspaces (moment matching).…”

Section: Network Reduction In Power System Analysis -Related Workmentioning

confidence: 99%

Model order reduction for reliability assessment of flexible power networks

Ndawula

Hernando‐Gil

et al. 2021

International Journal of Electrical Power & Energy Systems

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Model order reduction (MOR) has demonstrated its robustness and wide applicability in simulating large-scale mathematical models in the engineering research domain. In this paper, MOR techniques are applied to quantify relevant reliability metrics of power distribution systems and the impact associated with the integration of different smart grid technologies. To the best of the authors' knowledge, this is the first application of MOR techniques of balanced truncation to derive reliability models of electricity networks, which exhibit a reduced number of equivalent components and thus simplify the complexity for network analysis. The extensive case studies presented, based on both radial and meshed systems, demonstrate that the proposed technique allows for a faster reliability assessment through Monte Carlo simulation while preserving high accuracy. The proposed methodology can also be applied to systems endowed with photovoltaic and energy storage technologies, emphasising that this approach represents a promising starting point for reliability analysis of more complex systems, which are normally characterised by a large penetration of these distributed energy resources.

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Section: Network Reduction In Power System Analysis -Related Workmentioning

confidence: 99%

Model order reduction for reliability assessment of flexible power networks

Ndawula

Hernando‐Gil

et al. 2021

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

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“…This paper explores an intermediate solution and proposes reasonably reduced models of PTR and tower-based solar plants. The work represents the continuation of [9], where the authors focused on modelling of photovoltaic systems.…”

Section: Nomenclaturementioning

confidence: 99%

“…The performance of the new CSP models is analysed as in [9] primarily based on the annual and monthly prediction deviations and cumulative distributions of the net electrical output. The individual errors for each of the three calculation steps ( I b , n ⇒ Q sf ⇒ P csp ⇒ P csp,net ) are also estimated.…”

Section: Model Validationmentioning

confidence: 99%

Modelling of concentrating solar power plant for power system reliability studies

Gafurov

Usaola

Prodanović

2015

IET Renewable Power Generation

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Growing share of concentrating solar power (CSP) plants in power systems creates the need for including these renewable sources in power system reliability studies. As such studies analyse the grid on a global scale and start to be performed increasingly by Monte Carlo simulations, modelling of CSP production has to be reasonably simplified in order to reduce the calculation time. The model simplification also concerns the minimisation of the required specific knowledge and input data which enables power system engineers to evaluate the impact of CSP without substantial expertise in its underlying physics and by focusing on the key design parameters. There is a large variety of accurate CSP simulation programs and models, and yet neither of them offers the required simplicity. This study addresses this gap by proposing reduced models for predicting energy output of parabolic trough and central receiver systems. The calculation procedure and its underlying assumptions are presented. The adequacy of the models is demonstrated by comparing their predictions with that of the System Advisor Model for different case scenarios.

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