1 New ICT Solutions for Decentralized, Data-Intensive, and Distributed Processes

The decentralization of generation, consumer, and prosumers as new participants in flexibility markets, sector coupling, and the use of new decentralized storage technologies are leading to a massive increase in complexity in the planning and operation of energy systems. To reduce these complexities, the automation of operational processes in the energy system is vitally important.

The digitalization of the energy system thus becomes a necessity for the transformation of the energy system towards 100% renewables.

International Data Spaces are currently emerging as a European alternative to the data lake approach of hyperscalers. The basic idea of preserving companies’ sovereignty over their data and to promote the exchange of data for new data-driven services seems particularly promising in the energy industry, where energy system and energy economy processes are distributed across a whole range of devices and companies.

Based on the reference architectures of the International Data Space Association and the GAIA-X initiative, a European Energy Data Space is being created. The Energy Data Space supports the linking of data silos within the companies in order to enable innovative process automation for the energy industry while at the same time preserving the data sovereignty of the participating companies.

2 Use Cases in the Energy Data Space

The early use cases implemented in the Energy Data Space are laying the necessary foundations for a digital ecosystem in which more and more companies in the energy industry are establishing themselves, developing and providing digital tools for IDS-supported processes in the energy industry, and creating data structures for the standardized use of applications.

Using the complete value creation processes, demonstrators will be used to show that an efficient linking of information and software solutions is possible in a scalable manner in a European energy data space. In particular, the cross-company and sovereign data exchange should enable new processes that are able to increase the economic efficiency of the energy supply system without having to compromise on the security of supply. The following are some use cases that may seed future ecosystems.

2.1 Communications in Electrical Grids

The operation of transmission and distribution grids for electricity supply is a key part of the energy economy. The operation of grids requires information from a large number of generating units as well as an even larger number of electrical loads and consumption. Supply and demand have to match very closely with a very high time resolution to ensure the operation of the electricity system within the range of the grid codes and to provide reliable power supply to electricity consumers.

In addition to these characteristics, regulation adds complexity to transmission and distribution system operation as these systems are regulated monopolies governed by specific regulations to ensure fair access and competition in the electricity market under the European unbundling regulations.

Besides energy grids are critical infrastructures and as such subject to special regulations, e.g., on IT security and data confidentiality.

International Data Spaces can provide a framework and building blocks to meet the high requirements on ICT technology in grid operations. IDS use cases can build on existing standards for data that are already in place and increase efficiency in the existing system. Data spaces will make data easily available for further use like the training of AI models for forecasting or predictive maintenance.

Regulations may be translated into usage control policies and may enable data usage that is prohibited if the transfer of grid data was required. If the sensitive data is only used but not shared, new applications may become viable and benefit operational decisions.

2.2 Predictive Maintenance

As in other industries, predictive maintenance is a major trend to increase the equipment efficiency and availability by detecting anomalies in the operational behavior of assets and react with maintenance actions before a failure and unplanned downtime occurs.

Within the energy sector, wind farms are a good illustration of the advantages of data exchange and the formation of data ecosystems.

Wind turbines are similar worldwide in their design and function. This technical homogeneity makes it a promising scalable market for standardized applications if data can be accessed and interpreted easily. In the present state, manufacturers largely rely on proprietary data management approaches and offer digital solutions as added services to their customers.

The full potential of data, however, is only unlocked by combining different data sources and services based on them. To resolve the conflicts around data use and for establishing collaboration in data ecosystems in the wind industry, secure, i.e., trustworthy, infrastructures and independence are needed.

The vision of an Energy Data Space is one of a purpose-built, domain-specific, and prosperous data ecosystem (Fig. 20.1). Following the overall picture of a Silicon Economy, it combines the data basis of operational and plant data with digital platforms and cloud technologies. Everything is networked and interoperable. Different participants, services, and data assets are found and dynamically used via brokers. The case for predictive maintenance for wind farms has been described in a white paper [1] by Fraunhofer IEE and IML.

Fig. 20.1
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Participants and connections for predictive maintenance in an energy data space (©2020, Fraunhofer IML)

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This information from predictive maintenance applications is particularly valuable for the plant operator and the maintenance service provider. Furthermore, the evaluation of this data is also interesting for the manufacturer of wind turbines as they may promote product development and design the turbines more efficiently and more resistant to faults. The same applies to component suppliers. In addition, there are other interest groups that can benefit from access to data such as consulting services or insurance companies.

Another potential is that anomaly detection data and results are assessed by experts for specific components or types of equipment. Enriched by their expert knowledge, the identification of the fault or the prediction of possible damage is complemented. In the context of a data economy, new business relationships between stakeholders can thus emerge and new business models developed along the usual business processes around operation and maintenance. The various functionalities in the business process are performed by different participants in the data ecosystem.

2.3 Energy Management Gateway: From the Perspective of an SME

Smartrplace is a spinoff company of Fraunhofer IEE and offers fully integrated Smart Building energy solutions for offices, schools, shopping malls, and community buildings. The initial core of the product is a building automation solution that enables demand-driven individual room control of heating, ventilation, and air conditioning. As a next step, Smartrplace offers the full digitalization of building energy supply. This also includes “smart districts,” which comprise entire residential complexes or neighborhoods. The number of players and stakeholders that need to be considered in such solutions leads to complex requirements. These players include tenants, janitors, building cleaners, facility managers, etc. There are significantly higher requirements in the areas of maintenance and reporting compared to individual buildings.

This also means a tight integration of operational planning of local electricity and heat energy supply as well as integration of electric vehicle charging requirements. Within the SINTEG beacon program, Fraunhofer IEE has worked with many partners in the C/sells project to look at both sides of the activation of flexibilities: their provision by plant operators, suppliers, and service providers and their use by grid operators. The researchers have paid particular attention to the backbone of activation—information and communication technology infrastructures and smart metering. With the EnergyPilot a new energy management software for activating flexibilities was developed that can be used in conjunction with the Virtual Power Plant offered by the institute or alternative control systems.

With Fraunhofer IEE’s EnergyPilot, the Smartrplace solution can be expanded into an energy management system that mobilizes the flexibility of consumers and generators in the building for the grids (Fig. 20.2).

For SMEs providing smart energy solutions for building operation, the IDS architecture and standard provides an excellent basis for scaling, as very different systems can be mapped via the cloud. The servers of the various operators and manufacturers can easily be made EDS-capable, which has high potential of facilitating the integration of the different interfaces at the field level in each case.

Relevant logic in the form of connectors for data processing can either be installed at the data source in a protected environment and perform data pre-processing, or at the data user side. In the first case, only the aggregated data or data computed specifically for the use case needs to be transferred, which saves transfer volume or is often the only viable solution for large amounts of data, while ensuring that no unauthorized use of the full base data occurs.

As significant parts of the API are publicly available [2] and decisive components are offered as Open Source under the Apache License [3], IDS implementations can be implemented more and more easily. Furthermore Fraunhofer provides a range of information, services, and additional implementations [4].

Fig. 20.2
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Energy Management with the Energy Pilot Software for operation planning and control (©2020, Fraunhofer IEE)

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3 Early Demonstration Projects

A number of projects to initially demonstrate the use of the technology of the IDS in the energy sector have already been started. Within the EU Horizon 2020 program, the R&D project platoon [5] is working in a 3-year project since the beginning of the year 2020 on the digitalization of the energy sector through data governance for multi-party data exchange via IDS-based connectors. The French energy utility company ENGIE is coordinating the project with 19 partners among which are TECNALIA, Fraunhofer IAIS, and several European universities as research partners.

The project’s objective is to demonstrate the use of the IDS in seven pilot use cases:

  1. 1.

    Predictive Maintenance of Wind Farms

  2. 2.

    Electricity Balance and Predictive Maintenance

  3. 3.

    Electricity Grid Stability, Connectivity, and Life Extension

  4. 4.

    Office Building: Operation Performance with Physical Models and IA Algorithms

  5. 5.

    Advanced Energy Management System and Spatial (multi-scale) Predictive Models in the Smart City

  6. 6.

    Energy Efficiency and Predictive Maintenance in the Smart Tertiary Building Hubgrade

  7. 7.

    Energy Management of Microgrids

The European strategy for data calls for a “Common European energy data space, to promote a stronger availability and cross-sector sharing of data, in a customer-centric, secure and trustworthy manner” (European Commission [6], S. 22), which will be further developed with additional European research funding which will be available from the Horizon Europe program.

3.1 Fraunhofer Demonstration Project “EnDaSpace”

As a kick-off for the demonstration of data spaces in the energy sector, Fraunhofer funded a project to learn and show how the IDS works with energy data. Data from a wind turbine operated by Fraunhofer was provided to the EMS-EDM PROPHET® as digital service to calculate schedules for the operation of an electrolyser at the Fraunhofer power-to-gas research facility (Fig. 20.3).

Fig. 20.3
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Elements in the IDS role model in the EnDaSpace demonstration project (©2020, Fraunhofer IEE)

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All data was communicated through IDS connectors. A subset of operational data of the wind turbine had been defined, pre-aggregated, and provided as a data resource. The EMS has been extended to be able to communicate through the IDS Connector with other IDS instances. To achieve this functionality, the internal time series management was wrapped as an interface to a message broker.

The electrolyser was adapted to read and make use of the schedules in its control system.

Based on wind power forecasts and electricity market prices, an optimal schedule for producing green hydrogen was calculated daily. The optimization model was created in such a way that it prefers to sell the generated energy at the energy market until a certain limit of the energy price is exceeded which then triggers the start of hydrogen production.

A digital platform based on FIWARE technology was used to visualize operational wind data. Applications for anomaly detection in predictive maintenance and for post-construction yield assessment and a virtual met mast have been developed for operation directly with the IDS connectors or within the FIWARE environment.

In addition to the technical demonstrator, further use cases for future applications have been developed for oncoming projects.

3.2 Bauhaus.MobilityLab

With the vision “Innovation by experiment,” the Bauhaus.MobilityLab (BML-EcoSys) started in July 2020 as a real-world laboratory in the district Brühl of the city Erfurt, Thuringia, Germany. The consortium for this research project, funded by the Federal Ministry for Economic Affairs and Energy (BMWi), consists of the Fraunhofer IOSB, other research institutions, companies, universities, and the Thuringian state capital Erfurt. A complete list of all partners can be found at bauhausmobilitylab.de. Erfurt presents itself as a representative of a typical German city and is therefore ideally suited for a real-world laboratory that includes the sectors of mobility, logistics, and energy.

The real-world laboratory in the district Brühl is in its way unique in Germany. It is the first one that does a fusion between the different sectors of mobility, logistics, and energy. Next to experiments executed as part of the project, BML opens the laboratory for customers. They should be supported in developing and evaluating new data-driven business models by linking data sources available on the platform with own data sources. This is supported by the possibility to integrate existing artificial intelligence (AI) models, either provided by the platform or developed by the customer. The laboratory will not be set up with defined and hard-coded methods of experiments but will allow the user to be able to supply new methods or link existing methods and data in new ways.

The goal of the BML-EcoSys is to bring together several roles and stakeholders:

  • Laboratory customers develop or test new services within the real-world laboratory.

  • Laboratory users live, work, or visit the district Brühl and use the services that are developed or tested within the real-world laboratory.

  • Infrastructure partners supply the necessary basic infrastructure (mobility, electricity, logistics) in the real-world laboratory. In addition to physical infrastructure, partners can bring in new data sources, which can be shared (based on defined policies) with laboratory customers.

  • The laboratory operator takes care of the laboratory and maintains the platform. He also is responsible to support the laboratory customers.

A typical use case of the BML-EcoSys could be a laboratory customer that is developing a new e-mobility service by providing e-bikes for rental. Since the success of such a service depends on various factors, it should be evaluated in a practical experiment.

The BML platform provides access to data, infrastructure, as well as analytic methods to easily realize the service. With the involvement of the people as laboratory customers, the idea can be evaluated in a realistic environment. Data collected during the experiment can be evaluated by analysis methods available on the platform to further improve the service offering. Incentives can be given to users to encourage the usage of the e-bikes, for example, when the forecasted energy price rises above a certain limit and the recharging would be inexpensive in the next hours. Another threshold could be the air quality: if it drops below a certain limit, the customer could motivate the users to use his e-bikes instead of a car to help to make the air quality better. There are an unlimited number of use cases in the fields of energy, mobility, and logistics that are imaginable.

In the project, great importance is attached to the sovereignty of data. Like mentioned before, different organizational actors are part of the BML-EcoSys platforms. Therefore, data from many different providers, sensors, and systems are integrated in a common data sharing platform. This allows to link different data sources and to combine multiple of them during the analysis. Freely sharing data is not possible due to legal, regulatory, or economic reasons. The available data (e.g., current energy usage, traffic, air quality) leads to an advantage in the market. Therefore, actors are usually willing to share data in a controllable and restricted way. In addition, shared data like the mentioned energy usage is subject to privacy restrictions like the GDPR. It is important that the sovereignty of the individual datasets is always guaranteed so that usage can be strictly controlled and misuse can be prohibited. To ensure this, the BML platform relies on the reference architecture of the IDS. Inside the platform data is only available through the IDS. For every data source, this allows to define and enforce usage policies in a fine-grained way. One of the main goals of the BML platform is to provide an open and extensible structure. This allows data providers to offer their data to laboratory customers either free of charge or for a fee and to expand the platform with data from participants for evaluation purposes.

The platform for the real-world laboratory is developed and implemented by the Fraunhofer IOSB. Like already mentioned, key elements of the platform are the sovereignty exchange of the data and the integration of AI components; in addition it provides an execution environment where customers can deploy and evaluate their services. The platform architecture is based on the reference architecture “Open Urban Platform (OUP)” that’s defined in the DIN SPEC 9135. Next to unprocessed—raw data—it’s foreseen to provide pre-processed data—smart data. This allows project partners and customers to pick the data sources needed to implement their service. Since not all of the available data sources are freely available and since customers might belong to different organizations, IDS connectors are the central point to declare and enforce data usage policies. By this every organization interacting with the BML platform has its own identity in the IDS ecosystem. To offer data on the BML platform, the data can be either copied to the platform or an external system can provide data through the IDS. Analogous data sources on BML can be accessed by external systems through the IDS. Most of the available data source considered in the BML project are time series. To store data in a common and standardized data model as well as to provide a powerful query interface, time series data is stored in the OGC SensorThings API on the platform. This is realized by using FROST®, an open source implementation of the standard, developed by the Fraunhofer IOSB. To enforce usage policies, the FROST® instances aren’t directly accessible inside the platform. An IDS connector is placed in front to implement this control. Therefore customers, as well as AI services (integrated using PERMA, a component developed by Fraunhofer IOSB or Kubeflow), can access all data sources only through IDS.

The software solution EMS-EDM-PROPHET® developed by Fraunhofer IOSB-AST for the energy sector offers a broad portfolio of algorithms for time series management, forecasting, and optimization as well as other essential functions in energy data management. Contract structures and network topologies can also be mapped using the software system. EMS-EDM-PROPHET® is the leading software solution in this domain. Therefore, to support the above-described use cases of the energy domain, a tight integration of PROPHET and the BML platform is needed.

Our goal is to develop services for the BML project that are optimized for operation in a cloud environment. For this, we will develop relevant components into a service-oriented or microservice architecture. We will use open-source technologies for the implementation. As a result, the developed services should be characterized by very good availability and security for the BML-EcoSys platform. One example will be an AI-supported method for an automated selection of forecast methods and models in the energy sector. This will be implemented as microservice with an IDS/EDS interface for making the automated selection of the forecast method and the method itself accessible though the BML-EcoSys platform. With this universal IDS/EDS Connector implemented, it’s possible to extend the forecast models in near future by more complex AI that will produce even better results. Such AI forecast models then could also fuse data between the different sectors that are available in the laboratory.

Next to the energy data, another example for shared data inside the BML platform is calendar information. To optimize the (AI-)based forecasting models, additional information like public holidays, long weekends, school holidays, or events in the city are necessary. In contrast to other data sources, most of this information is publicly available and no strict usage policies exist. To unify data access within the BML platform calendar information is also available through the IDS.

The Fraunhofer IOSB-AST has already contributed to the further development of the reference implementation of an IDS connector provided by Fraunhofer ISST and created a connection to the EMS-EDM-PROPHET® software system. This allows IDS-compliant data exchange with other data providers or data consumers.

4 Summary and Outlook

The dynamic development as part of the energy transition towards renewables with its large need for data and communication of producers and consumers of electricity as well as grid operators make the energy sector a very promising area for an Energy Data Space.

The prototypes being built in the early research project will spark a first round of early implementation across the energy sector. Specifically pilots and demonstrators will be available for wind energy data energy consumption in buildings and for grid operation. Soon afterwards sector coupling application connecting mobility and heat with the power sector will emerge. With the strong commitment and funding from the European Commission, this will come with the opportunity to scale solutions and applications on the international level within the forming of the European Energy Data Space.

In a second wave of adoption, first movers and early adopters will build up experience with data spaces, and hesitant market participants may build trust into IDS technology. This is the requirement to expand the ecosystem to additional participants and to start the use of central services. The strategy of grid operators may be of critical importance as ICT requirements for grid infrastructure affect all participants in the sector.

After first operational processes in the energy sector have moved into the Energy Data Space, there may follow another wave of exploring new options and business models within the IDS architecture and the evolved IDS components. This will include integration with other data spaces like materials or logistics and the exploitation of the growth of data and applications available.

This scenario may be facilitated by some factors that would be relevant for the acceptance and adoption of an Energy Data Space:

First, participation and coordination in the industry through the relevant industry associations will prove very helpful to agree on initial processes and governance models as well as on suitable information models and vocabulary.

Second, barriers for entry of new participants have to remain low as the energy sector includes a large number of SMEs that may shy away from initial investments. Therefore basic components to onboard to the data space should be available under open-source licenses. This also supports the building of transparency, trust, and a developer community.

Moderating these communities will be a major task for governing institutions such as the IDSA to enable an efficient collaboration within the sector.

The Energy Data Space is an evolving concept to manage the growing complexity and data intensity of the energy sector which is needed to achieve the common goal of a sustainable and renewable energy future.