Demand response scheduling using derivative-based dynamic surrogate models

Abstract

When assessing demand response to solve optimal scheduling problems, the optimization algorithm needs to be coupled with the process model in order to quantify the behavior of the monitored variable. For negligible transients, a first approximation consists of applying the steady state correlation between input and output variables. On the contrary, when dynamics show a relevant bias with respect to the estimated steady state response, a more accurate model is required. When coupling dynamic models of entire chemical processes with the optimization algorithm, the computational effort drastically increases. In those cases, the model should be simplified without losing its accuracy.In this research work, we propose a derivative-based approach for dynamic surrogate modeling applied to an ethylene oxide production scheduling problem. Thanks to its general validity, once derived, it can be applied to any setpoint trajectory. This approach allows to reduce the computational time of the optimization algorithm by orders of magnitude, providing results with the same accuracy as the detailed process simulation.