Renewables at >50% in European grids

In order to realize the overall goal of boosting the share of renewables in the European grids to at least 50%, the grid simulator is an important tool in development and operation of innovative optimization, The example of the German demonstration shows some innovative conceptions in using flexible data-driven grid simulator.

As described in EU-SysFlex’s previous blog about the new demands in Germany’s law on using the flexibility from renewable energy in solving DSO and TSO grid congestion, University of Kassel and Fraunhofer IEE are participating actively in research and development of the German Demonstrator. The demonstrator will operate in the control center of the German DSO MITNETZ STROM for aggregating and offering flexibility from the integrated distributed energy resources (DERs) to the TSO for congestion management. The demonstrator consists of advanced forecasting, state estimation and grid optimization with standardized data interface for grid control center (CIM/CGMES). The grid optimization takes into account the existing regulative and operational limits and thus finds optimized operating points for flexibility exploitation. This blog covers the topic of the simulation tool developed for the project and how it could be utilized during the development, field test and real operational phase of the German demonstrator. The conception of developing a flexible grid simulation tool for DSO grid with universal data-interface enables the utilization in further studies and demonstrations of grid operational optimization e.g. redispatch demonstration and integrations of market-based congestion management processes.

Figure 1 Structure and Data flow of real-time grid simulator in German demonstration of project EU SysFlex

Flexible quasi-dynamic grid simulator to perform time-domain simulation is developed in a data-driven fashion. The structure and data flow of the grid simulator is shown in Figure 1. Currently the grid simulator implements the model of DERs and automatic transformer tap changer, which are the most important factors in the operation of DSO grid. The grid simulator is developed in the programming language Python with the open-source tool pandapower as the power flow solver. The object-oriented programming language Python makes it simple to develop the grid simulator modularized. The individual model can be easily expanded and further open source tools for data integration e.g. database-interfacing/CIM-Parsing can be easily integrated. The grid simulator is designed in a data-driven fashion, which means with the data in expected format available, the creation and performing of simulation is fully automated. In German demonstration, the grid modelling is based on the standard CIM-format (automatically converted from CIM) and the assets can be easily configured through parameter definition.

Scenario simulation and demonstrator verification are the main functionalities of the grid simulator in the development phase. Testing scenarios for simulation can either be artificially generated with a time series generator or delivered from the grid control center in CIM-format, and then converted for the simulation. In offline modes, the grid simulator simulates the testing scenarios standalone, with which the asset-modes e.g. DER modes of offering ancillary service (reactive power for voltage stability) is user-defined in order to study the impacts on grid status from different behaviors. The process data of the simulation is archived in high-performance NoSQL database. Comparing to the traditional SQL database, the NoSQL database could handle large volumes of structured and unstructured data (advantages of NoSQL), which makes it suitable for the data integration of the real time grid simulation and results evaluation afterwards. In online mode, the demonstrator receives grid status in real time from the grid simulator and transmits optimized operating points (active and reactive set-points) for all or parts of controllable DERs to the grid simulator through the co-simulation tool called OpSim (Fraunhofer IEE, “OpSim: test- and simulation-environment for grid control and aggregation strategies”). The influences of applying these set-points for flexibility exploitation can be further analyzed. Besides the already accomplished module-level functionality verification, the functionality of demonstrator on the system level is now fully verified under the co-simulation context.

The concepts and the proposed process of “digital twin” in field-test and real operation phase is shown in Figure 2. For the innovations in grid operation optimization with the German demonstrator for DSO MITNETZ STROM, a parallel laboratory demonstrator will be running in Fraunhofer IEE. Both demonstrators have identical system structure and data interface. The only difference is that, the laboratory demonstrator is directly connected to the grid simulator instead of the grid control center and the physical assets behind it. In field test and real operation, the labor demonstrator will serve as a black box of the DSO demonstrator. When unexpected events occur, with real-time data synchronization with the DSO demonstrator through CIM/CGMES data interface, the labor demonstrator can simulate the scenarios with the grid simulator recursively. So that the reason for the occurrences of events can be extensively analyzed and optimization tool can be further improved.

Figure 2 Concept and process of digital twin in German demonstration of project EU SysFlex

Further research on the grid operation is very important on the road of archiving the goal of boosting the share of renewables in the European grids to at least 50%. The conceptions and practice in implementing flexible data-driven grid simulator during the development phase and as “digital-twin” during the field-test and real operation phase shows its potential and advantage in developing innovative optimization for power system operation, just as shown by the example of the German demonstration.

Written by: Zhenqi Wang (University of Kassel) and Dr. Sebastian Wende-von Berg (University of Kassel/Fraunhofer IEE) working on Grid simulation and Grid optimization of the German Demonstrator of flexibility services from resources connected to the distribution network

University of Kassel is the newest university in the state of Hessen with a current enrolment of approximately 24,000 students. The University of Kassel offers a wide range of undergraduate and postgraduate study programs in many fields and an interdisciplinary approach in a wide range of renewable energy integration projects. In the faculty of Electrical Engineering and Computer Science a Competence Centre for Decentral Electrical Energy Supply (KDEE) has been created. The department e²n (Energy Management and Power System Operation) has been established in close cooperation with the Fraunhofer Institute for Energy Economics and Energy System Technology (IEE) in Kassel, Germany.

The Fraunhofer Institute for “Institute for Energy Economics and Energy System Technology (IEE) has its core competence in energy management and system design. It is concentrated on system modelling and the associated analyses of political and economic options for action. The considered systems cover technical (producers, consumers, storage, grid, etc.) as well as economic components (energy market, business models, etc.). Research topics include among others the integrated simulation of future energy supply structures, system analyses and technology assessment, integration of markets and systems, interaction of the sectors electricity-heat- transport, international, national and regional energy concepts and their evaluation as well as energy solutions for the industry. Fraunhofer IEE consists of about 350 employees and has an annual budget of about 22Mio €. The departments, which are most active in EU SysFlex, are “Department of Grid Planning and Grid Operation” and “Energy Informatics” focus on research and development of new approaches for system operation strategies and solutions as well as of statistical and physical based models for the prediction of energy generation and consumption.

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Disclaimer: blog entries reflect individual views of the author(s) that may not reflect official positions or communication of the project / project consortium.





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