A driving force for the realization of a sustainable energy supply in Europe is the integration of distributed, renewable energy resources. Due to their dynamic and stochastic generation behaviour, utilities and network operators are confronted with a more complex operation of the underlying distribution grids. Additionally, due to the higher flexibility on the consumer side through partly controllable loads, ongoing changes of regulatory rules, technology developments, and the liberalization of energy markets, the system's operation needs adaptation. Sophisticated design approaches together with proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a so-called smart grid. While reaping the benefits that come along with those intelligent behaviours, it is expected that the system-level testing will play a significantly larger role in the development of future solutions and technologies. Proper validation approaches, concepts, and corresponding tools are partly missing until now. This paper addresses these issues by discussing the progress in the integrated Pan-European research infrastructure project ERIGrid where proper validation methods and tools are currently being developed for validating smart grid systems and solutions.
Objective Computing patients’ similarity is of great interest in precision oncology since it supports clustering and subgroup identification, eventually leading to tailored therapies. The availability of large amounts of biomedical data, characterized by large feature sets and sparse content, motivates the development of new methods to compute patient similarities able to fuse heterogeneous data sources with the available knowledge. Materials and Methods In this work, we developed a data integration approach based on matrix trifactorization to compute patient similarities by integrating several sources of data and knowledge. We assess the accuracy of the proposed method: (1) on several synthetic data sets which similarity structures are affected by increasing levels of noise and data sparsity, and (2) on a real data set coming from an acute myeloid leukemia (AML) study. The results obtained are finally compared with the ones of traditional similarity calculation methods. Results In the analysis of the synthetic data set, where the ground truth is known, we measured the capability of reconstructing the correct clusters, while in the AML study we evaluated the Kaplan-Meier curves obtained with the different clusters and measured their statistical difference by means of the log-rank test. In presence of noise and sparse data, our data integration method outperform other techniques, both in the synthetic and in the AML data. Discussion In case of multiple heterogeneous data sources, a matrix trifactorization technique can successfully fuse all the information in a joint model. We demonstrated how this approach can be efficiently applied to discover meaningful patient similarities and therefore may be considered a reliable data driven strategy for the definition of new research hypothesis for precision oncology. Conclusion The better performance of the proposed approach presents an advantage over previous methods to provide accurate patient similarities supporting precision medicine.
The complexity of a smart grid with a high share of renewable energy resources introduces several issues in testing power equipment and controls. In this context, real-time simulation and Hardware in the Loop (HIL) techniques can tackle these problems that are typical for power system testing. However, implementing a convoluted HIL setup in a single infrastructure can be physically impossible or can increase the time required to test a smart grid application in detail. This paper introduces the Joint Test Facility for Smart Energy Networks with Distributed Energy Resources (JaNDER) that allows users to exchange data in real-time between two or more infrastructures. This tool enables the integration of infrastructures, exploiting the synergies between them, and creating a virtual infrastructure that can perform more experiments using a combination of the resources installed in each infrastructure. In particular, JaNDER can extend a HIL setup. In order to validate this new testing concept, a coordinated voltage controller has been tested in a Controller HIL setup where JaNDER was used to interact with an actual On Load Tap Changer (OLTC) controller located in a remote infrastructure. The results show that the latency introduced by JaNDER is not critical; hence, under certain circumstances, it can be used to expand the real-time testing without affecting the stability of the experiment.
This chapter deals with the coupling of smart grid laboratories for joint experiments. Therefore, various possibilities are outlined and a reference implementation is introduced. Finally, the vision of a distributed, virtual research infrastructure is presented.
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