Towards domain-specific surrogate models for smart grid co-simulation

Balduin, Stephan; Tröschel, Martin; Lehnhoff, Sebastian

doi:10.1186/s42162-019-0082-2

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…From both experiments, we concluded that the use of an ANN as the surrogate model in the reference work [5] was not a bad choice although the evidence was not provided in that very work. Additionally, we improved the model Speed-Up-Factor architecture to provide an even lower error and a higher speed-up.…”

Section: Discussionmentioning

confidence: 93%

“…One question when applying ML to power grid models is related to the gathering of training data. In [5], the authors pointed out the challenges of the sampling process for power grids. They tried to replace their set of load profiles using an empirical sampling strategy with kernel density estimation, but this method did not provide satisfactory results.…”

Section: Related Workmentioning

confidence: 99%

“…The simulation setup in [5] used the pandapower [35] implementation of the CIGRE LV benchmark grid [6]. This grid consisted of three subgrids: a residential subgrid, an industrial subgrid, and a commercial subgrid.…”

Section: Simulation Setupmentioning

confidence: 99%

“…We aimed to provide a well-founded basis for these crucial points, selected a variety of models, and improved the hyperparameter tuning (see Model selection). Hereafter, any mention of a part of the study (e. g., hypothesis, or surrogate model) is referring to the corresponding part in the reference [5].…”

Section: Hypothesismentioning

confidence: 99%

“…In a recent work, a surrogate model for a subsystem of such a multi-domain co-simulation environment was built [5]. Starting with the Conseil International des Grands Réseaux Électriques (CIGRE low voltage (LV) benchmark power grid model [6], the authors added several load models that were connected to certain buses in the grid.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluating different machine learning techniques as surrogate for low voltage grids

2020

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The transition of the power grid requires new technologies and methodologies, which can only be developed and tested in simulations. Especially larger simulation setups with many levels of detail can become quite slow. Therefore, the number of possible simulation evaluations decreases. One solution to overcome this issue is to use surrogate models, i. e., data-driven approximations of (sub)systems. In a recent work, we built a surrogate model for a low voltage grid using artificial neural networks, which achieved satisfying results. However, there were still open questions regarding the assumptions and simplifications made. In this paper, we present the results of our ongoing research, which answer some of these questions. We compare different machine learning algorithms as surrogate models and exchange the grid topology and size. In a set of experiments, we show that algorithms based on linear regression and artificial neural networks yield the best results independent of the grid topology. Furthermore, adding volatile energy generation and a variable phase angle does not decrease the quality of the surrogate models.

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Section: Discussionmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

Section: Hypothesismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating different machine learning techniques as surrogate for low voltage grids

2020

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Comprehensively Analyzing the Impact of Cyberattacks on Power Grids

Bader,

Serror,

Lamberts

et al. 2023

2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)

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Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

Veith¹,

Balduin²,

Wenninghoff³

et al. 2020

Preprint

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Modern cyber-physical systems (CPS), such as our energy infrastructure, are becoming increasingly complex: An ever-higher share of Artificial Intelligence (AI)-based technologies use the Information and Communication Technology (ICT) facet of energy systems for operation optimization, cost efficiency, and to reach CO 2 goals worldwide. At the same time, markets with increased flexibility and ever shorter trade horizons enable the multi-stakeholder situation that is emerging in this setting. These systems still form critical infrastructures that need to perform with highest reliability. However, today's CPS are becoming too complex to be analyzed in the traditional monolithic approach, where each domain, e.g., power grid and ICT as well as the energy market, are considered as separate entities while ignoring dependencies and side-effects. To achieve an overall analysis, we introduce the concept for an application of distributed artificial intelligence as a self-adaptive analysis tool that is able to analyze the dependencies between domains in CPS by attacking them. It eschews pre-configured domain knowledge, instead exploring the CPS domains for emergent risk situations and exploitable loopholes in codices, with a focus on rational market actors that exploit the system while still following the market rules.

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Towards domain-specific surrogate models for smart grid co-simulation

Cited by 3 publications

References 20 publications

Evaluating different machine learning techniques as surrogate for low voltage grids

Evaluating different machine learning techniques as surrogate for low voltage grids

Comprehensively Analyzing the Impact of Cyberattacks on Power Grids

Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence

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