Quantifying the uncertainty imposed by inaccurate modeling of active distribution grids

Vorwerk, Johanna; Markovic, Uros; Aristidou, Petros; Hug, Gabriela

doi:10.1016/j.epsr.2022.108426

Cited by 5 publications

(12 citation statements)

References 7 publications

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“…The employed grid model is based on [8], which has been extended for this work. Standard models are used for the lines, transformers, induction motors, and synchronous machines.…”

Section: A Grid Modelmentioning

confidence: 99%

“…Unlike [8], where the ATL power and initial load per node P 0 are assumed to be known, in this study, only the latter is known while the load distribution among thermal, static, and dynamic load is uncertain. Thus, the initial static background load power P l,0 is formulated as: where f im and f atl are the initial load shares of the IM and ATL, respectively.…”

Section: A Grid Modelmentioning

confidence: 99%

“…The latter time scales range in hours, while those of interest for this study range in seconds. Refer to [8] for a detailed model description.…”

Section: A Grid Modelmentioning

confidence: 99%

“…Unlike the implementation in [8] where most ATLs share the same nominal power S b , in this implementation, it is adjusted by the load factor LF to consider different initial loading conditions of the ATLs during MC simulations. The base power S b of the ATL is adjusted according to S b = f atl P 0 /LF, where the load factor is randomly chosen.…”

Section: A Grid Modelmentioning

confidence: 99%

“…The method is suitable for low inertia systems. However, all of these equivalents are only valid for the operating point they were designed for, and only [6] assesses the uncertainty imposed by DNs on the TN level in the derivation of dynamic equivalents [8].…”

Section: Introductionmentioning

confidence: 99%

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Using Quantile Forecasts for Dynamic Equivalents of Active Distribution Grids under Uncertainty

Vorwerk¹,

Zufferey²,

Aristidou³

et al. 2022

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While distribution networks (DNs) turn from consumers to active and responsive intelligent DNs, the question of how to represent them in large-scale transmission network (TN) studies is still under investigation. The standard approach that uses aggregated models for the inverter-interfaced generation and conventional load models introduces significant errors to the dynamic modeling that can lead to instabilities. This paper presents a new approach based on quantile forecasting to represent the uncertainty originating in DNs at the TN level. First, we aquire a required rich dataset employing Monte Carlo simulations of a DN. Then, we use machine learning (ML) algorithms to not only predict the most probable response but also intervals of potential responses with predefined confidence. These quantile methods represent the variance in DN responses at the TN level. The results indicate excellent performance for most ML techniques. The tuned quantile equivalents predict accurate bands for the current at the TN/DN-interface, and tests with unseen TN conditions indicate robustness. A final assessment that compares the MC trajectories against the predicted intervals highlights the potential of the proposed method.

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“…The employed grid model is based on [8], which has been extended for this work. Standard models are used for the lines, transformers, induction motors, and synchronous machines.…”

Section: A Grid Modelmentioning

confidence: 99%

Section: A Grid Modelmentioning

confidence: 99%

“…The latter time scales range in hours, while those of interest for this study range in seconds. Refer to [8] for a detailed model description.…”

Section: A Grid Modelmentioning

confidence: 99%

Section: A Grid Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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