摘要：New technologies and methodologies for smart grid applications cannot be tested in the real power grid, since it is a safety-critical infrastructure, therefore simulation and co-simulation is utilized. Simulation models itself can rely on quite complex calculations and therefore slow down the simulation. But even less complex models can lead to performance issues when used in large numbers in large-scale setups. The use of surrogate models is one way to improve the performance of simulation systems when the simulation models are slow, but the performance gain diminishes, when the simulation models are already quite fast. This abstract presents a new PhD project, which proposes a method to combine several simulation models into one surrogate model using correlations and other interdependencies of the simulation models. The goal is to further improve the performance gain not only for slower, but also for less complex simulation models, thus enable even larger simulation setups.
关键词：Surrogate models ; Machine learning ; Simulation ; Co-simulation ; Energy system ; Smart grid
