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Int2Grids - Integration of intelligent neighbourhood networks into integrated networks

Working Group:WG Optimization and Optimal Control
Leadership: Prof. Dr. Christof Büskens ((0421) 218-63861, E-Mail: bueskens@math.uni-bremen.de )
Processor: Dr. Chathura Wanigasekara (E-Mail: chathura@uni-bremen.de)
Funding: Bundesministerium für Wirtschaft und Energie
Project partner: IAV, Ingenieurgesellschaft Auto und Verkehr, Gifhorn
Arbeitsgruppe Optimierung, TU Ilmenau
OFFIS e.V. – Institut für Informatik
Time period: 01.05.2020 - 30.04.2023
Bild des Projekts Int2Grids - Integration von intelligenten Quartiersnetzen in Verbundnetze With the introduction of the Renewable Energy Sources Act in 2000, the framework conditions for the economically profitable production of renewable energies were created in Germany. In the meantime, over a third of the electrical power fed into the grid each year comes from renewable energies. Therefore, the associated change towards this decentralized energy supply should be considered in future electric grid management.
Neighbourhood networks are a possible way to achieve decentralized energy supply. Flexibilities in neighbourhood networks are sometimes only integrated in aggregated form into a super-ordinate interconnected network management. A stronger breakdown of the flexibilities would lead to a higher optimization potential. In doing so, first individual optimization goals such as minimizing cost, maximizing stability and efficiency will be pursued for every neighbourhood network. A multi-objective optimization which might include conflicting objectives will be implemented. As the final solution for such a multi-objective optimization problem, an optimal trade-off of among the target functions will be considered.
The project then aims to integrate locally optimized neighbourhood networks into the integrated super-ordinate network in such a way that both optimization goals of the neighbourhood networks (in a lower level) and higher-level goals such as the network stability of the super-ordinate interconnected network are achieved. Therefore, a bi-level optimization strategy will be used in this project. The whole system of neighbourhood and interconnected networks will then be implemented as a multi-agent system (in which the goals of super-ordinate interconnected network have higher priority) and will be solved as a distributed, combinatorial optimization problem.