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This paper reports on implementation of gradient computation for real-time dynamic optimization, where the dynamic models can be Modelica models. Analytical methods for gradient computation based on sensitivity integration is compared to finite difference-based methods. A case study reveals that analytical methods outperforms finite difference-methods as the number of inputs and/or input blocks increases.

Using Modelica models in real time dynamic optimization – gradient computation