Ensemble-Based Uncertainty Quantification for Smart Grid Co-simulation

Steinbrink, Cornelius; Lehnhoff, Sebastian; Klingenberg, Thole

doi:10.1007/978-3-319-31559-1_16

Cited by 7 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next steps include the test evaluation criteria, test refinement, and optimally mapping the research infrastructure to the experiment specification. Future work will include the application of uncertainty quantification (UQ) methods to the test specification and evaluation process, including UQ annotations in the model library, which will enable UQ of the associated co-simulation experiments [32].…”

Section: Evaluation Discussion and Outlookmentioning

confidence: 99%

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

Meer

Pálenský

Heussen

et al. 2017

2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

View full text Add to dashboard Cite

Abstract-The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification.

show abstract

Section: Evaluation Discussion and Outlookmentioning

confidence: 99%

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

Meer

Pálenský

Heussen

et al. 2017

2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES)

View full text Add to dashboard Cite

show abstract

“…They are used to describe the behavior of a system that, for various reasons, is not suited to be built knowledge-based. They are used in a broad range of use cases in the energy domain: starting from the calculation and optimization of energy savings (Beisheim et al 2019;Nagpal et al 2019;Vazquez-Canteli et al 2019) and the replacement of specific simulation models Dimitrov 2019) over surrogate models for (micro)grids (Baumann et al 2019;Balduin 2018;Grundel et al 2019) to the use in uncertainty and reliability assessment (Blank and Lehnhoff 2014;Slot et al 2020;Steinbrink 2016). This list is far from complete and there are also other approaches such as in Gerster (2018) who use surrogate models to build a decoder function abstracting from technical system specifications.…”

Section: Related Workmentioning

confidence: 99%

“…The ongoing transformationof the power system requires the involvement of new technologies and methodologies to meet the requirements that arise during this process. Since the power grid is a safety-critical infrastructure, simulation and hardware-in-theloop are used for the development and testing of such new technologies (Steinbrink et al 2017). Smart grid simulations comprise also other domains, such as the Information and Communication Technology (ICT) domain, each of which are developed in their own simulation environment.…”

Section: Introductionmentioning

confidence: 99%

Evaluating different machine learning techniques as surrogate for low voltage grids

2020

View full text Add to dashboard Cite

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.

show abstract

“…3. Difference between specific and generic co-simulation [39] An important concept in co-simulation is model instantiation. That means that several virtual objects are derived from the same model to simulate the behaviour of a number of similar systems.…”

Section: Co-simulation Frameworkmentioning

confidence: 99%

Simulation-Based Validation of Smart Grids – Status Quo and Future Research Trends

Steinbrink

Lehnhoff

Rohjans

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical energy systems with a high(er) penetration of fluctuating renewable generation and controllable loads. As a result of these developments the validation on the system level becomes much more important during the whole engineering and deployment process, today. In earlier development stages and for larger system configurations laboratory-based testing is not always an option. Due to recent developments, simulation-based approaches are now an appropriate tool to support the development, implementation, and rollout of smart grid solutions. This paper discusses the current state of simulation-based approaches and outlines the necessary future research and development directions in the domain of power and energy systems.

show abstract

Ensemble-Based Uncertainty Quantification for Smart Grid Co-simulation

Cited by 7 publications

References 6 publications

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

Evaluating different machine learning techniques as surrogate for low voltage grids

Simulation-Based Validation of Smart Grids – Status Quo and Future Research Trends

Contact Info

Product

Resources

About