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Computer Science XI - Modeling and Simulation

DigiSWM – AI and advanced data analytics for an interplay between power, heat and mobility – subarea of simulation

Project Description

The integration of renewable energy sources in the private sector is made possible in particular by coupling the consumption sectors of electricity, heat and mobility. In order to leverage the enormous potential, energy solutions for the private sector must be consistently thought through. However, sector coupling and the digitization required for it also increase the demands on the players in the development, parameterization, optimization and marketing of the technologies. Comprehensive energy data (from systems, consumption, and behavior) and AI processes can help enable new energy services, optimize grid operations, and promote greater adoption of sustainable energy technologies. The project aims to harness the potential from existing energy data for such applications. The project's field-tested Big Data Analytics (BDA) toolbox will support households and utilities with machine learning technology to drive sector coupling.

Our contribution to the project: The simulation

The conceptual design, implementation and evaluation of the simulation is our contribution in this project. First of all, we integrate the real electricity demand time series (in the form of real measured smart meter data) as well as the network topology of the power grid from Hassfurt (i.e. households, transformer stations, etc.) into the simulation. Based on this, we can simulatively provide a freely definable number of households with PV systems, battery storage, heat pumps, and wallboxes for charging an electrically powered automobile. The new resulting load flows can be analyzed at all levels of the grid, i.e. household, transformer and overall grid level. Furthermore, we use simulation to explore the integration and effects of intelligent control strategies for the individual components such as battery storage or heat pumps.