This work proposes a new Modelling-to-Generate Alternatives (MGA) method for Energy System Optimisation Models (ESOMs) using a Genetic Algorithm (GA).
Instead of generating each alternative one by one, the GA aims to optimise for a diverse set of alternatives, meaning they cover the space of possible alternatives as evenly as possible.
Such a diverse set of alternatives has the potential to improve the decision-making process by accelerating the extraction of stakeholder requirements and finding more agreeable compromises.
Before designing the algorithm, we investigate what diversity metric is most suitable to optimise.
The components of the GA are designed to exploit useful properties of ESOMs to increase efficiency.
The performance of the GA is tested in terms of output quality and scalability for increasingly large ESOMs, showing promising performance in terms of output quality for a similar computational burden as state-of-the-art MGA methods.
A potential issue caused by the curse of dimensionality is formulated, requiring further investigation on its impact on the quality of the method's output.
We show the generated output of applying the proposed method to the European power system, which encourages further testing of the method on increasingly large ESOMs.

This paper introduces and studies the Multi-Level Bin Packing Problem with Time Windows. This is a NP-hard problem with real-world applications in logistics. This problem states that items with certain sizes need to be packed inside bins without exceeding their capacity. These bins then again need to be assigned to other bins while minimizing the cost of the used bins. At the same time, each item has a time interval and there must be a common time point between all the items that are packed in the same bin that should be as early as possible. Not much research has been done regarding this problem. In this paper, we suggest multiple models that can be solved using Integer Programming and compare their performance. The results show slight differences in performance between the models.