The increasingly visible effects of climate change necessitate a fundamental transformation of energy systems toward renewable sources. While the Fukushima event led to a particularly strong change in energy policies in Germany, resulting in the so-called Energiewende, or energy transition, the trend toward renewables is visible worldwide. Here, we outline how major challenges of the energy transition have led to a strong need for essential contributions from the computer science community to maintain stability and security of supply, particularly for the electric power grid. As a result, the new discipline of Energy Informatics has emerged which is addressing this highly interdisciplinary and dynamic field of research and development.
While there are numerous demanding aspects of the energy transition, there are several major problem areas which show why contributions from Energy Informatics are urgently needed:
Volatility. In tomorrow's energy system, electric power will be provided mainly by photo-voltaic modules on rooftops and in larger field installations, and by wind power plants, onshore as well as offshore. Being weather-dependent, this energy supply is inherently volatile and only partially controllable. Therefore, the classic principle "supply follows demand" is no longer sufficient, rather we need to follow the supply with demand, which requires the demand side to become sufficiently flexible and adaptable. In this context, the optimal use of storage, for example batteries, will become very relevant.
Uncertainty. In addition to the increasingly intermittent character of renewable energy supply, predictions of energy supply and demand have become significantly more difficult. Beyond the weather-dependent uncertainty of supply, the behavior of consumers is changing—by using new applications such as heat pumps and electric vehicles, for example, and by using demand-side management in response to time-dependent pricing—and hence no longer necessarily corresponds to standard load profiles. Therefore, the transition and distribution system operators as well as the balancing responsible parties need more information on the actual behavior of end customers and their actual energy schedules, and they must respond more dynamically and more often, almost in real time, to observed deviations from expected energy schedules.
Decentralization. While in traditional power grids electricity was generated by a few large power plants, fed into the highest voltage level grid, transmitted over long distances, and distributed locally down to the low voltage connection points of the end customer, in tomorrow's power grid electricity will be generated at millions of low voltage locations where it will either be consumed directly or fed into the grid, which will have to collect and (re) distribute the energy bidirectionally. Local power in-feed can lead to congestions due to insufficient cable capacity resulting in voltage increase. The opposite effect can occur because of the emergence of large new loads from recharging the batteries of electric vehicles. Both effects are hardly visible to the distribution system operators (DSOs), even if they have remote information about the status of their substations. Another implication of the trend from centralized to decentralized power generation is the gradual disappearance of large power plants as the traditional providers of ancillary energy system services, again leading to an increasing demand for flexibility in decentralized energy schedules.
Modified dynamic characteristics of the power system. Traditionally power systems have been controlled as electromechanical systems. A grid dominated by power electronics is rather a software-defined system that is capable of dynamic response way faster than a traditional grid based on large power plants. Hence, we need faster control responses and smarter IT solutions.8
Obviously, the essential prerequisite for dealing with these major problems is the availability of information on the status of all active components of the power grid, which shows the need for digitalization. But while in the traditional power grid the transmission system operators (TSOs) were responsible for dealing with stability problems of frequency and voltage, in tomorrow's grid the DSOs and even the energy managers of facilities, buildings, and homes will have to respond locally to such quality problems, mainly with respect to voltage but to some extent even in response to frequency deviations as long as they are part of aggregated service providers. Hence, there is an inherent need for an energy information and control network with distributed system intelligence, and for a multitude of locations where adequate control decisions can be derived, mostly from locally available data.
Consequences. To cope with the inherent volatility, uncertainty, and decentralization of energy supply from renewable sources it is necessary to discover and exploit the flexibility of load schedules related to demand and supply. This can only be achieved by analysis of available data and by interaction with entities which are responsible for the operation of relevant devices, including (smart) homes and buildings, industrial processes, and those related to the energy requirements of electric vehicles. For this, the computer science community is challenged to provide the necessary adequately designed methods and tools. In this way the tasks of informatics are extended from information and communication technologies to operational technologies and even to real-time control, which underlines requirements for a joint multidisciplinary effort of computer scientists, power engineers, and control engineers. Beyond technical problems they also must consider essential economic and legal issues in this highly regulated critical infrastructure. All of this is summarized in the term Energy Informatics. A simplified structural view on decentralized activities in the power grid is provided in Figure 1.
Due to strict space constraints, we can only highlight a few of the essential topics which have to be addressed by Energy Informatics and cannot include an adequate overview of the numerous European locations performing related research. Nevertheless, in addition to brief descriptions and examples of our own research we include links and references to related publications, projects, and institutions which provide further links to relevant research activities.
Provide adequate information on current and historic status of the energy system. The prerequisite for providing information is the availability of sensors like smart meters which can measure the current status of energy-relevant devices, and of an infrastructure for delivering measured data values to authorized recipients. A common requirement is the capability to support bidirectional exchange of information to transmit information on dynamic, time-variant tariffs from energy suppliers, or to send control signals from active external market participants (like Demand-Side Managers or DSOs) to controllable local systems. There is a range of topics related to the design and utilization of such an infrastructure, like concerns with respect to security and privacy, or the appropriate choice of temporal and spatial granularity of data (see, for example, Kroener et al.6).
Data analytics for energy status data. As energy-related data will be available at high spatial and temporal resolution, intelligent methods have to be designed for utilizing this valuable source of information. In this context, funded by the German Research Foundation (DFG), an interdisciplinary Research Training Groupa spread over 11 research groups at Karlsruhe Institute of Technology is dedicated to informatics methods for various challenges in the life cycle of energy status data, consisting of collection, analysis, deployment, and exploitation.
Innovative and scalable architectures for data platforms. The increased level of decentralization calls for scalable data platforms that are active mostly at the edge. Classical centralized SCADA architectures are not able to cope with the huge amount of data that must be processed. Solutions in these directions are emerging, mostly from the world of the open-source communities, as with the SOGNO projectb at Linux Foundation Energy.
Modeling, (co-)simulation, and prediction. The contributions of Energy Informatics crucially depend on adequate modeling and simulation of energy-relevant devices, systems, grids, and related processes, providing the ability to analyze and predict behavior under various conditions. This is particularly necessary for predicting potential congestions in the power grid, based on anticipated power flows in the grid and load profiles of relevant entities. New grid dynamics call for completely new modeling approaches for power systems, and corresponding software,7 which is also essential for the design of efficient wind energy systems, a major topic of the DTU Wind Energy Center.c Beyond the separate simulation of individual devices or aggregated entities, their interaction must be analyzed using co-simulation. As an example, the potential contribution of intelligent buildings for reducing congestion in the distribution grid can be investigated by a multihome simulation in combination with a simulation of the distribution grid.5 An interesting comparison of co-simulation frameworks is provided in Steinbrink et al.10 A particularly relevant modeling task is the discovery of the potential flexibility in the load profiles of entities (see Barth et al.1). Machine learning is essential for detecting degrees of freedom in the use of certain appliances by analyzing recorded load profile data streams (see Šikšnys9 and Förderer3).
The new discipline of Energy Informatics has emerged to address the strong need for essential contributions from the computer science community to maintain stability and security of supply, particularly for the electric power grid.
Energy Management Systems (EMS). The anticipated energy information network with distributed system intelligence crucially depends on an efficient and effective management of energy at various levels of the grid. At the end-customer level a home, building, or facility EMS must provide effective visualization of the energy status and user-friendly interaction with humans to detect local preferences enabling adequate optimization of schedules for local supply and demand. The EMS has to serve as the digital connection point for the next layer (the DSO or the regional EMS), translating external requests for load changes into appropriate schedule changes for local devices. Such an EMS depends on the availability of data and methods for data analysis, simulation, prediction, and optimization. An example of such an EMS is the Organic Smart Home,d which has been developed in a sequence of smart grid projects. A sample architecture for the interaction between an external market participant and a local charging station for an electric vehicle (EV) via a smart meter gateway and a local EMS is shown in Figure 2.
Figure 2. EV-charging controlled by an EMS with respect to directives from an active External Market Participant via a smart meter gateway complying also with consumer preferences (taken from Kroener et al.6)
Cyber security issues. The inherently growing intrusion of information and communication technologies into energy systems inevitably leads to new vulnerabilities of this highly critical infrastructure. Therefore, security, safety, and data protection have emerged as essential topics for the design and operation of smart energy grids. A particular challenge is to reconcile the seemingly contradictory requirements for functionality, real-time capability, privacy protection, and robustness against attacks and disruptions. Distributed energy systems should not only have a secure IT infrastructure, but also be resilient since attacks cannot be completely avoided. Relevant research on security issues is available from the German Competence Center for Applied Security Technologies,e the Queen's University Belfast center for Secure Digital Systems,f and the Norwegian Department of Information Security and Communication Technology.g
There is a growing research community on Energy Informatics in Europe which emerged mainly in the last decade. For example, the German Informatics Society established a special interest group on Energy Informatics which has strong ties with the newly formed ACM SIG Energy and its flagship conference, ACM e-Energy. Based on a joint German-Austrian-Swiss initiative, the DACH+ conference series on Energy Informaticsh evolved as an annual conference, moving cyclically through Germany (where it started in 2012), Austria, and Switzerland. Its objectives are to promote the research, development, and implementation of information and communication technologies in the energy domain and to foster the exchange between academia, industry, and service providers in the German-Austrian-Swiss region and its neighboring countries.
Energy Lab 2.0. The Energy Lab 2.0i at KIT is one of Europe's largest research infrastructures for renewable energy and the energy transition. The intelligent networking of environmentally friendly energy generators and storage methods are investigated. In addition, energy systems of the future are simulated and tested based on real consumer data.4 A plant network links electrical, thermal, and chemical energy flows as well as new information and communication technologies. The research aims at improving the transport, distribution, storage, and use of electricity and thus supports the energy transition.
C/sells, SINTEG. In large-scale tests for the energy supply of the future and the digitalization of the energy sector in the German-funded Smart Energy Showcase—Digital Agenda for the Energy Transition (SINTEG)j program, more than 300 companies, research institutions, and municipalities worked together from 2016 to 2020. They formed five model regions, in which they developed and tested solutions for the energy supply of the future. To take an example, the C/sellsk project focused on developing a smart, cellular energy system, supported by an infrastructure system that enabled data to be exchanged securely between different cells, and a coordination sequence through which grid operators could communicate and act quickly, and for the most part autonomously. It also involved platforms for trading regional energy and flexibilities, leading to new services and products.
European projects. There are a considerable number of research projects on Energy Informatics funded through the EU's eighth Framework Programme Horizon 2020. One of the largest is the currently active One Network for Europe (OneNet) project.l Its scope is to create a fully replicable and scalable architecture that enables the whole European electrical system to operate as a single system in which a variety of markets allows the universal participation of stakeholders at every level, from small consumers to large producers, regardless of their physical location (see Figure 3). Led by the Fraunhofer Institute for Applied Information Technology, the project brings together a consortium of over 70 partners, including grid operators, key IT players, leading research institutions, and two European associations for grid operators. OneNet aims at creating the conditions for a new generation of grid services able to fully exploit demand response, storage, and distributed generation while creating fair, transparent, and open conditions for the consumer.
The energy transition toward renewables is driving the energy system into an epochal transformation of its basic principles. The outlined combination of challenges can only be addressed through a real multidisciplinary approach and strong cooperation among the different energy sectors necessarily based on thorough digitalization. Energy Informatics can be seen as the main enabler that orchestrates all other elements and promises increased efficiency, new sources of load flexibility, and the necessary resilience to cope with inevitable local disturbances in a highly decentralized system. Tomorrow's energy system must be at least as reliable as the current one: new solutions are needed to go beyond the current practice, and in addition, lack of implementation competence in the field considering more complex system structures further increases the challenges of the transition. Beyond the gradual transformation of existing structure and management of energy systems there are also approaches to design a fundamentally new type of power grid, inspired by the principles of the Internet of Data, dealing with energy packets and storage at every node of the grid (see De Din2), but it remains completely open to what extent this will lead to a viable concept. At least, intelligent utilization of ubiquitously available storage, mobile within EVs and stationary within buildings, will play a major role in tomorrow's energy system.
1. Barth, L., Hagenmeyer, V., Ludwig, N., and Wagner, D. How much demand side flexibility do we need? Analyzing where to exploit flexibility in industrial processes. In Proceedings of the 9th ACM Intern. Conf. Future Energy Systems. June 2018, 43–62; https://doi.org/10.1145/3208903.3208909
2. De Din, E., Monti, A., Hagenmeyer, V., and Wehrle, K. A new solution for the energy packet-based dispatching using power/signal dual modulation. In Proceedings of the 9th ACM Intern. Conf. Future Energy Systems. June 2018, 361–365; https://doi.org/10.1145/3208903.3208931
3. Förderer, K., Ahrens, M., Bao, K., Mauser, I., and Schmeck, H. Towards the modeling of flexibility using artificial neural networks in energy management and smart grids. In Proceedings of the 9th ACM Intern. Conf. Future Energy Systems. June 2018, 85–90; https://doi.org/10.1145/3208903.3208915
4. Hagenmeyer, V., et al. Information and communication technology in energy lab 2.0: Smart energies system simulation and control center with an open-street-map-based power flow simulation example. Energy Technology 4, 1 (Jan. 016) 145–162; https://doi.org/10.1002/ente.201500304
5. Kochanneck, S., Mauser, I., Phipps K., and Schmeck H. Hardware-in-the-loop co-simulation of a smart building in a low-voltage distribution grid. In Proceedings IEEE PES Innovative Smart Grid Technologies Conf. Europe, 2018, 1–6; https://doi.org/10.1109/ISGTEurope.2018.857174
6. Kroener, N., Förderer, K., Lösch, M., and Schmeck, H.: State-of-the-art integration of decentralized energy management systems into the German smart meter gateway infrastructure. Applied Science 10, 11 (2020), 3665; https://doi.org/10.3390/app10113665
7. Mirz, M., Vogel, S., Reinke, G., and Monti, A. DPsim—A dynamic phasor real-time simulator for power systems. SoftwareX 10, (2019), 100253; https://doi.org/10.1016/j.softx.2019.100253
9. Šikšnys, L., Pedersen, T. B., Aftab, M., and Neupane, B. Flexibility Modeling, Management, and Trading in Bottom-up Cellular Energy Systems. In Proceedings of the 10th ACM Intern. Conf. Future Energy Systems, June 2019, 170–180; https://doi.org/10.1145/3307772.3328296
10. Steinbrink, C., van der Meer, A. A., Cvetkovic, M., Babazadeh, D., Rohjans, S., Palensky, P., and Lehnhoff S. Smart grid co-simulation with MOSAIK and HLA: A comparison study. Computer Science - Research and Development 33, 1–2, (Feb. 2018), 135–143. Springer International Publishing; https://doi.org/10.1007/s00450-017-0379-y
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