Regionally-specific scenarios for smart grid simulations

Blank, Marita; Gandor, Malin; Niese, Astrid; Scherfke, Stefan; Lehnhoff, Sebastian; Sonnenschein, Michael

doi:10.1109/powereng.2015.7266328

Cited by 4 publications

(4 citation statements)

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“…The first step of this process starts with the generation of so-called samples through sampling strategies. We use 2 https://github.com/stbalduin/memobuilder Proceedings of the Federated Conference on Computer Science and Information Systems pp. 185-192 DOI: 10.15439/2019F242 ISSN 2300-5963 ACSIS, Vol.…”

Section: A Choosing the Sampling Strategymentioning

confidence: 99%

“…18 the term sampling strategy for the application of a sampling design, i. e. the theoretical construction to generate samples. These consist of a set of possible input combinations X = {x (1) , x (2) , ..., x (n) } for the original simulation model f (x) and their corresponding outputs y calculated by the simulation model. A sampling strategy defines which input combinations will be chosen to generate samples and it is important to pick the most relevant data to generate a good model.…”

Section: A Choosing the Sampling Strategymentioning

confidence: 99%

“…This can lead to a slowed down performance of a smart grid simulation. Performance plays a role especially in large-scale setups, such as in the research projects Smart Nord [1] or D-Flex [2], which are required for the evaluation of new Smart Grid algorithms or sustainability assessments. Furthermore, simulation models might be supplied by industrial stakeholders and thus must be considered as intellectual property that should not be disclosed to partners.…”

Section: Introductionmentioning

confidence: 99%

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Tool-assisted Surrogate Selection for Simulation Models in Energy Systems

Balduin

Oest

Blank-Babazadeh

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Surrogate models have proved to be a suitable replacement for complex simulation models in various applications. Runtime considerations, complexity reduction, and privacy concerns play a role in the decision to use a surrogate model. The choice of an appropriate surrogate model though is often tedious and largely dependent on the individual model properties. A tool can help to facilitate this process. To this end, we present a surrogate modeling process supporting tool that simplifies the process of generation and application of surrogate models in a co-simulation framework. We evaluate the tool in our application context, energy system co-simulation, and apply it to different simulation models from that domain with a focus on decentralized energy units.

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Section: A Choosing the Sampling Strategymentioning

confidence: 99%

Section: A Choosing the Sampling Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tool-assisted Surrogate Selection for Simulation Models in Energy Systems

Balduin

Oest

Blank-Babazadeh

et al. 2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Self Cite

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“…Even with co-simulation, building such a large, cross-domain simulation environment is still a complex task. In addition, depending on the size and complexity of the underlying scenario, the simulation of the overall system can become very slow [3]. To overcome this, surrogate models can be used to reduce the simulation time of some of the components in the environment.…”

Section: Introductionmentioning

confidence: 99%

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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