# Modeling all alternative solutions for highly renewable energy systems

arXiv: https://arxiv.org/abs/2010.00836 · HTML: https://mlsanity.com/paper/2010.00836/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning](https://mlsanity.com/paper/2512.23284/) (2025)

## Abstract (arXiv)

As the world is transitioning towards highly renewable energy systems, advanced tools are needed to analyze such complex networks. Energy system design is, however, challenged by real-world objective functions consisting of a blurry mix of technical and socioeconomic agendas, with limitations that cannot always be clearly stated. As a result, it is highly likely that solutions which are techno-economically suboptimal will be preferable. Here, we present a method capable of determining the continuum containing all techno-economically near-optimal solutions, moving the field of energy system modeling from discrete solutions to a new era where continuous solution ranges are available. The presented method is applied to study a range of technical and socioeconomic metrics on a model of the European electricity system. The near-optimal region is found to be relatively flat allowing for solutions that are slightly more expensive than the optimum but better in terms of equality, land use, and implementation time.