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

ANaPlan Plus aims to identify the optimization potential in energy grids, considering new degrees of freedom from the cross-sectoral view. The core element is a methodology for integral infrastructure planning with digital support. With this, electricity and natural gas supply grids can be adapted to complex future scenarios.

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.

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.

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 project "OwnPV-Outlook – PV self-consumption as an efficient, sustainable and robust element of the future energy system" aims to investigate and evaluate the integration and development of PV self-supply systems into the future energy system. The project evaluates possible and probable designs of future frameworks for PV self-supply systems, considering economical, technical, behavioural and regulatory aspects. The aim is to narrow this range by determining an energetically and economically efficient and thus sustainable integration of PV self-supply systems into the energy system, taking into account the technological innovation in diverse PV application cases.

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.

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 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.