Flexible time aggregation for energy systems modelling

Abstract

With high shares of renewable generation and a reliance on storage, modelling large scale energy systems is computationally challenging. One factor driving the complexity of these models is the need for a high temporal resolution over a long period; a typical baseline is modelling all 8760 hours in a year. While simple methods such as down-sampling and segmentation are effective at reducing the number of time-steps in a model, there is potential for more sophisticated simplifications. In this work, we propose a flexible time aggregation framework where individual components in the systems (e.g. generators, storage units) may be modelled at a lower time resolution. We base the method on the theory of aggregation in linear programming, giving the possibility for provable bounds on the resulting objective value. These ideas have only been explored in a limited fashion in the context of energy systems modelling, and we highlight their potential for large scale energy system models and the next steps for research.