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.

The energy transition is an urgent and challenging subject in research and for society. For this, transitioning to renewable energies is one key element of the energy transition, but renewable energies have drawbacks with regard to their reliability of power supply. The weather as well as day and night periods induce a very high volatility, that requires multiscale coordination of power supply and demand. Therefore, the power grid will undergo a drastic change from a puely demand-driven towards a supply and demand-driven network. The operation of these highly coordinated future smart grids will create huge data volume which needs to be managed appropriately. In the scientific domain as well as in operations FAIR Digital Objects (FDOs) are envisaged to be a key technology for this task. FDOs provide access to metadata allowing applications to automatically retrieve the referenced data, interpret it semantically and support energy researchers and operational staff to handle it in a more sustainable way. In Schweikert et al. (2022) a concept is proposed which allows to describe different aspects of an object. Arbitrary schemas and ontologies can be utilized and a coherent object graph is provided by using FDOs. As an conceptual example the authors employed a photovoltaic system (PV system). The data of this system is divided into two kinds: static data and dynamic data. Static data is further divided into structural composition metadata, which describes the entities of the PV system and their relations to each other, and master data which are used to describe the properties of the different entities (comparable to a technical information data sheet). Dynamic data is the measurement data which is acquired at several locations within the PV system. The structural composition metadata is described by using an in-house developed ontology called PV Ontology based on the Web Ontology Language*1. The master data is described using the standards IEC 61850*2, GeoJSON*3 and SensorML*4 (for the master data of the sensors). The dynamic data - the structure of the measurement data and its geo-position - is also described with SensorML. By describing every entity in the PV system using the mentioned schemas, a lot of description objects (instances of the schemas) are created. For instance, a PV module is comprised of three description objects, one written in IEC 61850 (technical data sheet), another in GeoJSON (geo-position and dimensions), and the third is the ontology instance where the PV module is represented as a node in the ontology instance graph. How can these three objects be linked with each other to make clear that the containing information is about this PV module? Schweikert et al. (2022) uses FDOs to create these associations. The profile used in the FDOs is the Helmholtz Kernel Information Profile (KIP) (Pfeil et al. 2022) which is an extension of the Research Data Alliance (RDA)*5 KIP (Weigel et al. 2018). It introduces several new properties, inter alia, the property hasMetadata. This property allows to reference further FDOs providing metadata to the current one. Using the Helmholtz KIP Schweikert et al. (2022) constructs the description of the PV system as follows: An FDO is created for the ontology instance (hereinafter referred to as ONT-FDO). For every entity of the system with description objects an FDO (Bridge-FDO) is created. The digitalObjectLocation of these FDOs is referencing the ONT-FDO plus adding the ID of the corresponding entity in the ontology instance as fragment identifier. This bridges the border between the ontology and the FDOs and allows unambiguous referencing of ontology graph nodes. An application using the PV ontology and a given Bridge-FDO can infer the position of the entity in the PV system. For every description object an FDO is created and linked to its entity by using the hasMetadata property on the Bridge-FDO. Alternatively, if one wants to reduce the number of FDOs, a collection (e.g, using RDA's Collection Recommendations (Weigel et al. 2017)) containing all description objects can be created and referenced through the Bridge-FDO by creating an FDO for the collection. Lastly, a final Bridge-FDO can be created pointing with its digitalObjectLoaction to the PV system root node and referencing all other Bridge-FDOs with its hasMetadata property. Schweikert et al. (2022) did not implement the discussed concept. In the present work we introduce for the first time an implementation of the concept, in which we develop an application that allows users to browse and visualize all the data (structural design, technical information of the components and measurement data) of a PV system. A user provides a persistent identifier (PID) to the application and the application starts to resolve all the data associated with the PID. Three possible cases of a given PID can occur: First, the PID of the entire PV system is entered, the application retrieves the data for all components of the system and presents them to the user. Second, the PID of one component is entered, the application retrieves all the data of this component and presents them to the user, and an option to browse the complete system is offered. In these two cases it is assumed that the entered PID belongs to a Bridge-FDO. In the last case a PID of a description object is entered, then the application behaves as in the second case. The implementation verifies the concept in which any information about the PV system can be obtained by any starting node in the object graph spanned by the FDOs. It also provides insight into the applicability in a real-world use case uncovering possible problems and pitfalls. It also gives energy researchers and operational staff a useful tool to browse and visualize information about a PV system. This work is an essential contribution to use FDOs for accessing, visualizing and studying similarly modeled systems.

The Helmholtz Association (Anonymous 2022d), the largest association of large-scale research centres in Germany, covers a wide range of research fields employing more than 43.000 researchers. In 2019, the Helmholtz Metadata Collaboration (HMC) (Anonymous 2022f) Platform as a joint endeavor across all research areas of the Helmholtz Association was started to make the depth and breadth of research data produced by Helmholtz Centres findable, accessible, interoperable, and reusable (FAIR) for the whole science community. To reach this goal, the concept of FAIR Digital Objects (FAIR DOs) has been chosen as top-level commonality for existing and future infrastructures of all research fields. In doing so, HMC follows the original approach of realizing FAIR DOs based on globally unique, Persistent Identifiers (PID), e.g., provided by https://handle.net/, machine actionable PID Records and strong typing using Data Types like https://dtr-test.pidconsortium.eu/#objects/21.T11148/1c699a5d1b4ad3ba4956 registered in a Data Type Registry, e.g., http://dtr-test.pidconsortium.eu/. In all these areas, HMC can build on the great groundwork of the Research Data Alliance and the FAIR DO Forum. However, when it comes to realization, there are still some gaps that will have to be addressed during our work and will be raised in this presentation. For single FAIR DO components like PIDs and Data Types, existing infrastructures are already available. Here, the Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen (GWDG) (Anonymous 2022e) provides strong support with their many years of experience in this field. Within the framework of the ePIC consortium (Anonymous 2022c), the GWDG is offering on the one hand PID prefixes based on a sustainable business model, on the other hand GWDG is very active in terms of providing base services required for realizing FAIR DOs, e.g., different instances of Data Type Registries for accessing, creating, and managing Data Types required by FAIR DOs. Besides that, in the context of HMC we developed a couple of technical components to support the creation and management of FAIR DOs: The Typed PID Maker (Pfeil 2022b) providing machine actionable interfaces for creating, validating, and managing PIDs with machine-actionable metadata stored in their PID record, or the FAIR DO testbed, currently evolving into the FAIR DO Lab (Pfeil 2022a), serving as reference implementation for setting up a FAIR DO ecosystem. However, introducing FAIR DOs is not only about providing technical services, but also requires the definition and agreement on interfaces, policies, and processes. A first step in this direction was made in the context of HMC by agreeing on a Helmholtz Kernel Information Profile (http://dtr-test.pidconsortium.eu/#objects/21.T11148/b9b76f887845e32d29f7). In the concept of FAIR DOs, PID Kernel Information as defined by Weigel et al. (Weigel et al. 2018) is key to machine actionability of digital content. Strongly relying on Data Types and stored in the PID record directly at the PID resolution service, PID Kernel Information can be used by machines for fast decision making. The Helmholtz Kernel Information Profile is an attempt to introduce a top-level commonality across all digital assets produced within the Helmholtz Association and beyond to establish a basis for FAIR research data based on FAIR DOs. Hereby, the Helmholtz Kernel Information Profile integrates the recommendations of the RDA PID Kernel Information Working Group (Anonymous 2022b) as far as possible. By extending the Draft Kernel Information Profile (Weigel et al. 2018) with additional, mostly optional attributes, the Helmholtz Kernel Information Profile allows the adding of contextual information to FAIR DOs, e.g., research topic, or contact information, which is then available for machine decisions. Furthermore, additional properties for representing relationships between FAIR DOs, e.g, hasMetadata and isMetadataFor, were introduced to allow mutual relations between FAIR DOs. Currently, a demonstrator is implemented integrating the above components and services, i.e., PID Service, Data Type Registry, and Typed PID Maker. Fig. 1 outlines the architecture overview of the first version of the demonstrator. In this first version, in a semi-automatic workflow, a user enters a Zenodo (Anonymous 2022a) PID in a graphical Web frontend. A mapping component tries to fill automatically at least the properties required by the Helmholtz Kernel Information Profile using the obtained Zenodo metadata record. In a manual validation loop, the user may add or update certain properties before they are sent to an instance of the Typed PID Maker, validated against the Helmholtz Kernel Information Profile, and stored in the record of a newly registered PID using the services of the ePIC consortium. In addition, registered PID records are made searchable via the graphical frontend on top of a search index, e.g., realized using https://www.elastic.co/. After implementing this generic workflow, additional mappers supporting other repository platforms will be implemented based on the lessons learned, which will lead to a growing number of FAIR DOs and holds potential for providing significant benefits to scientists, e.g., a central point of contact for research data sets stored in different repositories, machine-actionable identification of relevant datasets, and creation of knowledge graphs representing relationships between data sets, repository platforms, researchers and research organizations. Furthermore, the gathered experience and its documentation will help others to apply the FAIR DO concept more easily, which will lead to an ever-growing collection of available FAIR DOs with an increasing quality and level of automation at creation time.

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