The necessary transformation of energy systems towards net zero greenhouse gas emissions provides a plethora of new research challenges. New interconnections between different energy sectors, such as power, heat and mobility increase the system’s complexity, thus requiring stronger interconnections and data exchange between different research communities. In this context, the digitalisation towards cyber-physical energy systems (CPES) alleviates change in many respects: First, it drives and enables new levels of automation and sector-coupling and thus allows to address new optimisation potentials towards energy efficiency. Second, the long-envisioned idea of a direct integration of new stakeholders, right down to private households, becomes feasible based on a broad range of new participation opportunities. Third, digitalisation creates the conditions for improving energy research itself and for fostering a change towards transdisciplinary energy system development and research. Thus, digitalisation in energy systems equally affects technical, social, and societal topics, as well as the mode of research of the energy system research community.
A cyber-physical system is a composite of electro-mechanical components with IT interfaces and functionalities that are connected via a communication system. It is characterised by its high degree of complexity. The formation of cyber-physical systems arises from the interconnection of embedded systems through communication networks. The concept formation follows the need for a new theoretical basis for the study and development of large, distributed, complex systems. Energy informatics specialises this need for a theoretical foundation for the research and development of energy systems that are CPS but with very special properties and unique characteristics. It is counted among society’s critical infrastructures, as such it is an indispensable lifeline of modern societies, it is of cross-continental size (in case of the European synchronous system from North Africa to Scandinavia, from Ireland to Asia), energy systems exhibit almost instantaneous propagation speed of phenomena and instabilities as well as ubiquitous conflicting goals by its interconnected actors (e.g. monetary, technical, national/international political interests). Research on CPES requires the integration of relevant subdomains/-disciplines that are now the focus of the international energy informatics community.
Individual research efforts towards CPES heavily rely on modelling or (co-)simulation-based approaches: Either system components spanning different sectors must be improved and combined, or interactions between a large number of actors, possibly with differing modelling approaches, need to be integrated. Either way, tracking of models, their respective versions and states, together with the input data creates a complex software and data management challenge, which needs to be addressed and solved in each research project.
Thus, NFDI4Energy focuses on supporting activities surrounding reproducible research and best practices in the domain of energy system modelling and simulation. This includes models, workflows, (standardised) datasets, benchmark or reference scenarios, in short: various digital objects (DO) needed to conduct repeatable experiments across scales, domains, and modelling approaches within the energy sector. These range from detailed component simulations of energy equipment up to analytical socio-economic models of whole continents.
To support this wide range of approaches, NFDI4Energy develops and bundles a suite of community services through a common platform for the research community. These services support the different phases of the typical research and transfer cycle of projects in energy system research.
Research and Transfer Cycle in Energy System Research
Today’s interdisciplinary energy system research is mostly project-oriented. This gives rise to a cyclical time-frame for most research endeavours, which are aligned with the time frames of typical research and development projects (2-5 years). We conceptualise this process as an idealised research and transfer cycle as composed of 5 phases, as shown in Figure 1. NFDI4Energy aims at supporting this cycle based on community services. While this Section outlines the research activities and common challenges per phase, Services describes key services aimed to address these challenges.
Figure 1 CPES research and transfer cycle; 5 phases with indicative activities
During phase I, a research group identifies its core competencies and expertise, e.g. modelling methods, simulation techniques, or particular knowledge with respect to technological, engineering, economic or societal aspects. The aim is a complementary match in relation to its immediate partners, while searching for suitable industry partners with matching research and business goals to form consortia. Typical challenges involve finding and establishing contacts with potential research partners. This is particularly true for newly formed groups and young researchers, and especially challenging in fast-moving fields, like energy system research, which has gained traction, size, and speed during the last two decades, making it hard to follow the pace of new developments. Additionally, the interdisciplinarity of this field has increased dramatically, making it more difficult to find and connect with new relevant partners.
Phase II, occurring during proposal development and during the first project stage, includes defining relevant scenarios and experimental setup, which should be reviewed by the relevant research community through conference participation or white paper publication. This phase can benefit from findable curated, opinionated best-practice guidelines and demonstrations of existing best practices for experimental setups, research data management as well as privacy concerns. These concerns are more common knowledge in the social sciences, but not yet established practice in the more engineering and modelling oriented parts of energy system research, which on the other hand relies on community-driven processes and established practices, e.g. in electrical engineering.
During phase III, partner collaboration takes the centre stage as individual models and data are integrated, interfaces are configured, and tools and laboratories are coupled. CEPS research is often based on simulating behaviour and, therefore, it requires multiple different data and models as input. Finding suitable upstream models, data sources or a clear lack thereof is still a major challenge and has not yet benefited from standardisation. Furthermore, there are a multitude of segmented community-curated repositories and libraries for energy system research but no documented standard to follow for data providers of energy system model data and metadata models for simulation authors, so that plug & play interoperability could be accomplished for downstream users. This leads to friction and inhibits the reuse of DO in research. Therefore, a lot of effort is needed by each individual research group for strikingly similar tasks, e.g. repetitive time series preparation for demand curves or meteorological capacity factors for fluctuating renewable sources.
In phase IV, results are extracted and their persistence is ensured. Also, the results are part of public consultation and discourse. A prominent challenge lies in the large inferential distance between the complex inner workings of a (distributed) simulation model in energy system research and its outputs, which often require specialised knowledge to be interpreted. Also, it is almost never possible for laypersons to interact with those research methods directly to gain an intuitive understanding of their mode of operation. This understanding is crucial to build trust for energy system research in the public and society in general. Providing easy-to-interpret model results with process guidelines for public discussion and validation on top of a complex simulation model is a hard problem, on which even modest progress will be valuable.
Phase V, identification of research gaps and challenges for follow-up activities, closes the loop of the whole research and transfer cycle and is the precursor step to phase I. In this phase, the direction and next steps for future research are identified. Challenges here lie in selecting which of the many follow-up questions to pursue, which method and approach to tackle next, and which past paths to abandon. By including politics and society, the amount of possible follow-up questions can be reduced in a meaningful way, since energy system research claims to be useful for the energy transition. Also, this phase could profit from a more structured overview about other current projects, project ideas, and both technical and methodical suggestions.