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.