Abstract
We present a method to turn the results of model-based optimisations into resilient and comprehensible control strategies. Our approach is to define priority lists for all available technologies in a district energy system. Using linear discriminant analysis and the results of the optimisations, these are then assigned to discrete time steps using a set of possible steering parameters. In contrast to the model-based optimisations, the deduced control strategies do not need predictions or even perfect foresight but solely rely on data about the present. The case study using priority lists presents results in terms of emissions and prices that are only about 5% off the linear optimum. Considering that the priority lists only need information about the present, the results of the control strategies obtained using the proposed method can be considered competitive.
Highlights
We present a method to turn the results of model-based optimisations into resilient and comprehensible control strategies
We compare the results of the model-optimisation and the results that we obtain with the deduced optimal control strategies
The class-definition- and classification-based control strategies are deduced from the linear optimisation results but do not depend on linear optimisation, models or perfect foresight anymore
Summary
We present a method to turn the results of model-based optimisations into resilient and comprehensible control strategies. In contrast to the model-based optimisations, the deduced control strategies do not need predictions or even perfect foresight but solely rely on data about the present. The case study using priority lists presents results in terms of emissions and prices that are only about 5% off the linear optimum. Considering that the priority lists only need information about the present, the results of the control strategies obtained using the proposed method can be considered competitive. We see a need for less data-intensive and more comprehensible control strategies that can provide competitive solutions even if forecasts are (temporarily) unavailable. Our approach is to deduce simple control strategies from the results obtained in model-based optimisations. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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