Projects

The project AGISTIN (Advanced Grid Interface for innovative Storage Integration) proposes to develop grid integration architectures for energy storage with on-site renewables and emerging DC end uses. This includes the DC coupling approach considered in current PV + storage hybrids, extending it to include end use, grid users and system integrators as well as hybrid grid coupling approaches. With an optimized combined grid coupling, industrial grid users can benefit from the avoidance of additional hardware, reducing costs, improved operational efficiency, flexibility and self-consumption as compared to the mere parallel AC connection approach.

Further information on the project:

• Project website
• Press release on the project start
• The Influence of Grid-Forming Loads on Transient Stability
  This paper was presented at the 22nd Wind & Solar Integration Workshop (2023) and published in the workshop’s proceedings.

The scope of AI4REALNET covers the perspective of AI-based solutions addressing critical systems (electricity, railway, and air traffic management) modelled by networks that can be simulated, and are traditionally operated by humans, and where AI systems complement and augment human abilities. It has two main strategic goals: 1) to develop the next generation of decision-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, and 2) to boost the development and validation of novel AI algorithms, by the consortium and AI community, through existing open-source digital environments capable of emulating realistic scenarios of physical systems operation and human decision-making.
The core elements are: a) AI algorithms mainly composed by supervised and reinforcement learning, unifying the benefits of existing heuristics, physical modelling of these complex systems and learning methods, as well as, a set of complementary techniques to enhance transparency, safety, explainability and human acceptance; b) human-in-the-loop decision making for co-learning between AI and humans, considering integration of model uncertainty, human cognitive load and trust; c) autonomous AI systems relying on human supervision, embedded with human domain knowledge and safety rules.
The AI4REALNET framework will be validated in 6 uses cases driven by industry requirements, across 3 network infrastructures with common properties. The use cases are focused on critical challenges and tasks of network operators, considering strategic long-term goals, such as decarbonisation, digitalisation, and resilience to disturbances, and are formulated in a unified sequential decision problem where many AI and non-AI algorithms can be applied and benchmarked.

Further information on the project:

• Project website

The scientific objective of the DeV-KopSys-2 project is to investigate the role of individual technology options (e.g. electromobility, PtX fuels) for achieving the climate targets in transport using model-based scenarios against the background of the interactions of decarbonization in the transport sector with other developments. Of particular interest are bandwidths of global PtX export potentials up to the year 2045/2050, framework conditions of the electricity and gas market on the European level, and the expansion of the electricity grids in Germany with regard to electromobility and the expansion of renewable energies on the regional level.

Further information on the project:

• Project website

Technical progress in the production of electrical energy in offshore plants is leading to significantly lower construction and operating costs. In the planned dissertation, different aspects and options for the future use of the offshore energy potential of the North Sea will be investigated. The technical scope of the project will include the production of electrical energy, transport via direct current grids and conversion into hydrogen. In concrete terms, the technical potential and the economic framework parameters will be investigated and compared for the following scenarios. Subsequently, the individual scenarios will be evaluated comparatively and recommendations for the further expansion of the existing energy systems and the need for research and development will be derived.

The project InterACDC is to identity the possibly unwanted interactions among inverter-based generations (IBGs), synchronous machines (SMs) and HVDC transmission systems taking into account the bidirectional AC-DC dynamic couplings by:

1. establishing a more complete system model,
2. examining the applicability of existing stability analysis methods for the stability assessment of future hybrid AC/DC grids,
3. developing the required enhancements to the most promising analysis method,
4. investigating the stability impacts of various HVDC and IBG control schemes as can be classified into grid-following, grid-forming etc. and
5. developing bidirectional AC-DC dynamic coupling indicators and analyzing their stability impacts. Analytical results will be validated by time-domain stimulations and laboratory tests.

The ongoing transformation of the European energy sector is crucial to reduce CO2 emissions and increase security of supply and independence from energy imports. A pillar of this transformation is increased power generation from renewable resources, especially wind power and photovoltaics, but also electrification of the heating and mobility sector. These generators and consumers of electrical energy are increasingly connected to the power grid via power electronic converters. Furthermore, DC lines and subnetworks are built to reduce power conversion losses and achieve cost-efficient network strengthening. Due to these trends, power systems are increasingly hybrid AC/DC systems, and their dynamic is highly influenced by power electronic devices.

Maintaining system stability and situation awareness in such hybrid systems is a challenge for system operators on distribution and transmission level and creates a need for SCADA systems that are adaptable and help operators to tackle the challenges posed by the rapid transformation of the grids. InterSCADA is committed to developing and providing an open-source, vendor-independent SCADA system for operators. This will enable them to quickly adapt to sudden system perturbations, implement new monitoring and control functions, and maintain situational awareness.

The InterSCADA platform will implement a set of relevant functions as microservices, creating a modular SCADA system for system operators. The InterSCADA solutions will be deployed and tested in demonstration sites in four different countries, involving both distribution and transmission system operators. Furthermore, InterSCADA will provide recommendations to grid codes and standards for hybrid AC/DC systems, aiming to facilitate the transformation of the power grid while ensuring the maintenance of stability and reliability.

The "kurSyV" project addresses the curative system management of the 110 kV distribution grid in the InnoSys sense. The aim is to use the advantages of curative system management also in the distribution grid with the flexibilities available there, to enable the higher utilisation of the grid with the highest level of safety at the same time, thus integrating more renewable generation plants into existing grids at short notice and to increase the time window for required grid expansion by using operational measures.

The focus of the project is on the development of concrete sample solutions as well as necessary adaptations and developments in order to integrate them into the system management. In addition, the project should be designed in such a way that concrete results and implementations are already achieved during the project period, which can then be analysed in the further course of the project with regard to their applicability, effect and practical relevance.

For this purpose, research questions in the fields of "(temporary) higher utilisation through curative flexibilities", "Control system and system-related requirements for curative operation management" and "Evaluation of the effects of curative system management on network planning aspects" are to be addressed.

Within the sub-project, the University of Kassel will develop optimization-based methods to determine the flexibility requrements and potentials for a curative system operation. Using these methods, comprehensive studies are to be conducted to determine the requirements and potentials in specific 100 kV distribution grids. Finally, the potentials of a curative system operation to reduce the required grid expansion are to be estimated by the University of Kassel.

In various studies concerning system analysis and planning of energy systems, power grids, renewable energy plants, operational management strategies and energy markets, weather model data are required as input for many simulation models. Consequently, the used data and their accuracy have a direct impact on the results, subsequent measures and energy development paths. Up to now different weather model data and processing methods have been used in many system analysis studies. This means that the results can only be interpreted to a limited extent because of the missing knowledge about the performance of the processed weather data. This gap of model transparency leads also to the fact that results of different studies cannot be compared easily. In addition, various studies and expert interviews have shown that the currently used model data has significant weaknesses from the user’s point of view.
The overarching goal of the project includes the creation of a new, user-friendly, open, optimized and high resolution meteorological data set for Germany and its establishment as a meteorological standard data set within system analysis and energy economy. The data should cover 15 years of time series with a resolution of 250 x 250 m and containing all parameters that are relevant for system analysis (wind, global radiation, temperature etc.). In addition, the aim is to provide information on the temporal and spatial uncertainty of the meteorological time series.
Early user involvement ensures that the data set is developed in accordance to user requirements. For the technical implementation, new approaches from the field of weather model reanalysis ensembles, but also the application of fluid mechanical, statistical and machine learning methods are used. At the end of the project, the new data set will be evaluated by experienced system analysts to demonstrate its performance and to analyze the sensitivity of system analysis issues with regard to weather model data.

Die Verfügbarkeit von Flächen ist eine elementare Voraussetzung zur Erreichung der Ausbauziele für die Windenergie. Daher gibt die Bundesregierung vor, dass die Bundesländer Flächen für die Windenergienutzung ausweisen müssen. Um einen schnellen Ausbau der Windenergie zu gewährleisten, ist es von zentraler Bedeutung, dass die ausgewiesenen Flächen zeitnah und effizient genutzt werden. Es ist deshalb wichtig, auftretende Restriktionen frühzeitig zu identifizieren, deren Auswirkungen zu quantifizieren und realistische Szenarien für die Bebauung der Windenergieflächen abzuschätzen. Im ihrem Teilprojekt entwickelt die Universität Kassel Verfahren und Modelle zur Analyse und Abschätzung der Auswirkungen von Restriktionen auf die installierbare Windenergieanlagenleistung auf den Windenergieflächen. Hierfür werden umfangreiche Daten beschafft und aufbereitet. Auf Basis existierender Windparks werden GIS-Analysen zu den Auswirkungen von Restriktionen auf die Bebauungsdichte und die Windparklayouts in verschiedenen Regionen untersucht. Aufbauend auf den Ergebnissen der Analysen entwickelt die Universität Kassel innovative Verfahren zur verbesserten Abschätzung zu den installierbaren Leistungen auf den Windenergieflächen unter Berücksichtigung der Restriktionen. Hierfür werden einfache Verfahren zur Leistungsabschätzung und komplexere Verfahren zur Windenergieanlagenplatzierung unter Berücksichtigung der Standortcharakteristik sowie der lokalen Restriktionen entwickelt und als Bestandteil einer Gesamtmethodik zur Ertragsabschätzung durch die Partner implementiert. Die Universität Kassel nutz diese Gesamtmethodik dann insbesondere zur Analyse des Potenzials für Nachverdichtungen von bereits bebauten Windenergieflächen. Die wissenschaftlichen Ergebnisse werden publiziert und mit der Windindustrie, Politik und Regionalplanung diskutiert.

Further information on the project:

• Project website at the Fachagentur Wind und Solar
• Project website at the Fraunhofer IEE

The overall objective of the "OASES" proposal is the development and demonstration of a sustainable AU-EU ecosystem for energy system modelling based on open-source software and open access data. The project will build easy-to-use modelling workflows for different spatial scales. The workflows will utilize the RES data developed in the project as well as data and tools from other similar high quality efforts (e.g. "PyPSA meets Africa" initiative). The workflows will be used by six example case studies, each with different scope, that can be replicated using code, data, tutorials, and documentation from the proposed project. By so doing, the project enables local actors to learn and perform energy system scenario analysis relevant for their needs.
e2n from University of Kassel will develop open tools for (1) detecting already installed wind energy and photovoltaic (PV) systems using satellite imagery, digital orthophotos and machine learning approaches and (2) for improving resource assessment, spatial distribution of new wind and PV systems and time series generation.

The overall aim of the project is to tap the potential of probabilistic information for grid operation planning and to develop robust decision-making procedures based on probabilistic risk analyses and suitable optimization procedures for taking stochastic input variables into account. These are to be tested for their practical suitability for integrating uncertainties into operational planning processes as part of a field test.

The following sub-goals, which build on and complement each other, are derived from this overarching goal
1. Uncertainties are determined and made visible for selected processes
2. Risks are derived and evaluated for various options for action on the basis of the uncertainties made explicit
3. Optimal decisions can be made taking the risks into account
4. The added value of risk-based decisions is investigated
5. Technical barriers are overcome
6. A concept for implementing the results is drawn up

The project "Redispatch 3.0" shall on the one hand improve the integration of facilities in the low voltage grid and on the other hand improve the collaboration and information exchange between the DSO and the TSO to advance the Redispatch 2.0. The goals are increasing the share of renewable energy due to a higher degree of capacity utilization, lowering the operation and investment costs of the DSOs, and the exploitation of grid-supportive capacities from decentralized facilities, especially for the provisioning of ancillary services. Additionally, the project researches (near) real-time and resilient concepts for digitalization, which is a precondition for reactive power system management.
In the context of the subproject, the department of energy management and power system operation of the University of Kassel develops optimization tools suitable for the reactive real-time grid operation. The optimization tools are used in the case that a contingency could not be preventively removed by the grid operation planning due to forecasting uncertainties. To this end, appropriate optimization algorithms will be developed for every voltage level with focus on the low and medium voltage level. The optimizations on the different voltage levels have to be efficiently coordinated across all voltage levels with as few data exchange between the voltage levels as possible.

We want to assess and improve the ability of the distributed, multimodal and smart energy system to withstand challenges - its resilience. We consider the future electrical power system that strongly depends on information and communication technology and is interconnected to gas and heating systems. The modelling of this complex system needs to be as efficient as possible, taking into account only the most relevant aspects in terms of resilience. Otherwise, performing computer simulations for resilience assessment is not feasible in terms of computational complexity. Towards this end, we propose the following methodological approaches: The description of the subsystems and interconnections as services, the abstraction into stochastic activity nets, the modelling as interoperable agents, and the use of multi-level optimisation for the analytical identification of resilience-relevant modelling parameters on the one hand and of the scope of challenges beyond known, high probability events on the other hand.

The project REWARDS deals with the conception of different system scenarios with endangered energy security and the development of suitable model constraints and scenario tree structures for their implementation in the stochastic modelling and optimization framework EMPRISE. After developing an understanding of the concepts of energy security, energy sovereignty, and resilience in integrated energy systems, the identification and structuring of shocks and slow-burn processes that significantly threaten energy security follows. After a structural derivation of storylines, which cover a broad range of system developments that can be regularly expected in the future, these storylines are combined with the shocks and slow-burn processes to form so-called storyline-shock combinations. For their representation in EMPRISE, parameterization concepts are developed that enable the implementation of new model restrictions and scenario tree structures. By applying and further developing EMPRISE, an evaluation of possible trade-offs between energy security and additional costs for energy system transformation can be performed. By open-sourcing the EMPRISE framework including the new methodological approaches and interfaces as well as the use and generation of open-source datasets, the project follows open-science principles.
At the University of Kassel, the definitions of energy security, energy sovereignty, and resilience in integrated energy systems are developed. A list of possible shocks and slow-burn processes that pose a threat to energy security is compiled. Subsequently, new model constraints are implemented in the EMPRISE framework. Furthermore, the University of Kassel performs all simulations based on the storyline-shock combinations with the EMPRISE framework and analyses the repercussions on the German transmission grid with pandapower.

Further information on the project:

• Project website at Fraunhofer IEE

RobustPlan aims to iteratively evaluate feedback between regional generation, energy markets and the grid. This also includes feedback between the use of flexibilities on the electricity market and their opportunity costs. The modeling of operational measures to take these degrees of freedom into account in grid expansion planning is another key objective of the project.

SENERGY NETS aims at demonstrating the technical and economic capability of multi-energy systems to decarbonize the heating and cooling, power and gas sectors through renewable energy sources produced locally as well as sector integration, by primarily focusing on promising infrastructure and business models.
To do so, SENERGY-NETS will develop a set of tools and platforms (up to TRL7/8) aimed to optimise the planning of District Heating and Cooling as well as distribution grids with sector coupling consideration and allow the provision of flexibility services to Distribution and Transmission System Operators.
These solutions will be implemented on three pilot sites located in Milan (IT), Ljubljana (SI) and Paris (FR) and their replicability will be tested in two additional real case studies presenting alternative climatic, economic and geographic conditions in Västerås (SW) and Cordoba (ES).
The SENERGY NETS solutions will be adapted to the main stakeholders at the different phases of the projects development involving sector coupling: long term planning, design and simulation, operational planning, valorisation, evaluation and replication.
The project will evaluate the benefits through a consolidated methodology developed to estimate the overall value created by sector integration, relying on the current economic, regulation and market rules and assess the impacts on the European power system.
SENERGY NETS relies on a strong trans-disciplinary consortium involving 17 organisations located in 7 European countries, involving renowned experts from public authorities, infrastructure providers, research institutions, entrepreneurs and consumers associations.
Altogether, they will provide the necessary knowledge, expertise and capacities to develop, demonstrate and evaluate developed tools and services enabling the integration of multi-energy systems to provide flexibility to the power system, and ultimately enable the decarbonisation of the energy system.

Further information on the project:

• Project website

The goal of the University of Kassel e2n is to develop an AI-OPF, which can realize the flexibility determination under consideration of uncertainties from forecasts and schedules, plants with local Q-control, regulatory and network conditions such as N-1 network security, protection configurations and possibly costs of Q-procurement processes. The OPF should be able to determine the Q-flexibility range on the part of the TSO as well as the Q-demands at the grid interconnection points and the characteristic parameters for individual plants or grid clusters.  The uncertainty from day-ahead/short-term forecasts and measurements will be evaluated by an AI-based method to be developed and will be coupled with the AI-OPF. The functionality of the developed method will also be verified in simulation and field test context and brought into online operation of the flexibility platform.

In order to achieve the expansion targets for wind energy set by new government, it is necessary to develop a large number of wind farms in a short time. The basis for wind farm planning at a new site is the estimation of the expected energy yields and the selection of suitable wind turbines.
At present, the yield estimation is subject to high uncertainties. In addition, it is time-consuming and costintensive, especially due to the currently required one-year wind measurements. The aim of the project is therefore to enable qualitatively better yield estimates in a shorter time and at significantly lower costs through improvements along the entire process chain. To achieve this goal, procedures are being developed that provide a better data basis (e.g. reanalyses, roughness data) for the wind sector. In addition, innovative procedures from the field of data science such as machine learning or model ensembles are used at various points to enable an accurate estimation of energy yields in a shorter time.
The merging of the various methods and data into an overall process enables a high degree of automation of yield assessments in addition to the increase in quality. Ultimately, the project thus creates the basis for a reduction in project risks for planners and project developers. In addition, the developed methods can also be used for more precise regional potential assessments and thus contribute to better planning of wind energy expansion.
The University of Kassel is focusing its research on the development of procedures for the long-term correction of short-term wind measurements using artificial intelligence methods and the improved estimation of design wind conditions.

The aim of the project is to accelerate the introduction of the necessary ancillary services by generation plants and consumers in order to maintain system stability. The consortium will work together to identify and confirm the needs and challenges of the power grid in 2030 and the necessary ancillary services derived from this.

Methods for determining system requirements are to be (further) developed and implemented with the involvement of all key stakeholders. At the same time, the technical potentials and obstacles to the provision of necessary ancillary services by future generation and consumption systems will be analyzed and solution options developed to cover the identified requirements in the medium and long term. The solutions will be evaluated from a technical perspective and in terms of their pragmatic feasibility.

The sub-project of the University of Kassel contributes to this in particular through the evaluation and further development of methods and models for the evaluation of subsynchronous stability and harmonic stability as well as the quantification of requirements and remedial measures. One focus of this work is on the evaluation of subsynchronous stability and harmonic stability in hybrid AC-DC grids. In addition, the potential and challenges of instantaneous reserve provision by generators and loads that are subject to asymmetrical and otherwise non-continuous restrictions of current and power depending on the operating point are considered. In this context, the interaction between the type of grid-forming control, the associated current and power limitation and transient stability is also examined.

The project is closely interlinked with the System Stability Roadmap.

The BMBF flagship project TransHyDE pursues the common, overarching goal of mapping consistent model-based descriptions of possible hydrogen transport development perspectives with the help of scenarios. To this end, two complementary approaches are pursued: a stakeholder-driven approach and an independent systemic approach that considers infrastructure development, with a focus on green hydrogen, with a perspective on economic cost minimization.
The work of the e²n department focuses on the analysis of systemic interactions between thy hdrogen and electricity grid infrastructure. In particular, the infrastructure feedback effects of different locations are examined in detail by applying optimisation models.

The expected growth of both on and offshore wind energy is enormous and many new wind parks are planned for the coming years. Experience from the existing wind farms shows the importance of a proper micrositing of the wind turbines as well their efficient interconnection within the farm. In addition, bringing wind farms together into clusters toward a wind power plant concept might induce long distance negative interaction between the farms, reducing their expected efficiency. This might happen both on and offshore. The high amount of connected wind power and the expected increase during the coming years requires that this technology has to be prepared to take a more important role as of its contribution to the reliability and security of the electricity system.

The present proposal, WinDTwin, targets to develop and validate an offshore wind farm digital twin (DT) for highly accurate prediction of power production and energy demand of the end user. The DT will give users tailored access to high-quality information, services, models, scenarios, forecasts, and visualisations, as a central hub for offshore wind decision-makers, and will also serve as platform, offering users access to a comprehensive array of high-quality resources, services, models, scenarios, forecasts, and visualisations.

WinDTwin seeks to revolutionise the way industry professionals make informed choices. To reach WinDTwin expected impact, the ambitious innovation-led research proposed necessitates bringing together a range of skills and expertise which cannot be found within a single member country or institution. We have put together a unique team that has a broad range of expertise through the whole wind energy development process; ranging from the management of wind energy production and development of industrial codes, numerical methods, algorithms, ensuring the uptake of improved methodologies.

Further information on the project:

• Project website

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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for.