Towards ontology-based network model management and data integration for smart grids

Schumilin, Artem; Stucky, Karl-Uwe; Sinn, Frederic; Hagenmeyer, Veit

doi:10.1109/mscpes.2017.8064530

Cited by 11 publications

(4 citation statements)

References 23 publications

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“…In future work, collection support will be implemented using [54] which is developed within HMC. We plan to implement the presented concept also with other tools, like an in-house development using RDF technology [55] or a microservice-based approach as described in [52]. A third candidate is an electronic lab notebook allowing to use arbitrary schemas models [56].…”

Section: Discussionmentioning

confidence: 99%

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

et al. 2023

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Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.

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Section: Discussionmentioning

confidence: 99%

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

et al. 2023

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“…IoT sensor data with a semantic reasoning layer that supports explicitly embedding domain knowledge into several machine learning tasks such as data exploration, feature engineering and model creation [7]- [11]. A promising research direction to create data-driven models for power systems that can be explicitly informed from knowledge-based systems is the use of probabilistic graphs [12]- [14].…”

Section: Knowledge-based Systems Have Recently Emerged To Enrichmentioning

confidence: 99%

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

Fusco¹,

Eck²,

Gormally³

et al. 2021

Preprint

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The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data-and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.

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“…Schumilin et al [82] introduced harmonization using the CIM extension, which contains the measurement data and time-series data defined in IEC 61850. In order to represent a pair of corresponding elements between the two models, a new class is introduced at the point of integration.…”

Section: Mapping and Extensionmentioning

confidence: 99%

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

et al. 2020

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Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

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Towards ontology-based network model management and data integration for smart grids

Cited by 11 publications

References 23 publications

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks

A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

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