Broad Ranges of Investment Configurations for Renewable Power Systems, Robust to Cost Uncertainty and Near-Optimality

This repository contains the entire scientific project, including code and manuscript.

Abstract

To achieve ambitious CO$_2$ emission reduction targets quickly, the planning of energy systems must accommodate societal preferences, e.g.~regarding transmission reinforcements or onshore wind parks, and must also acknowledge uncertainties of technology cost projections. To date, however, many models lean towards only minimising system cost and only using a single set of cost projections. Here, we address both criticisms in unison. While taking account of cost uncertainties, we apply multi-objective optimisation techniques to explore trade-offs in a fully renewable European electricity system between rising system cost and the deployment of individual technologies for generating, storing and transporting electricity. We identify boundary conditions that must be met for cost-efficiency that are robust to how cost developments will unfold; for instance, we find that some grid reinforcement and long-term storage alongside large wind capacities appear essential. We reveal that near the cost-optimum a broad spectrum of technologically diverse options exist, which allows policymakers to make trade-offs regarding unpopular infrastructure.

Repository Structure

• data contains additional input data sources
• notebooks contains results analysis and plotting scripts, as well as created graphics
• scripts contains Python scripts of the core workflow
• results contains workflow outputs
• paper contains the adjunct manuscript
• subworkflows contains links to dependent workflows including PyPSA-Eur and PyPSA-Eur-MGA

Installation

mamba env create -f environment.yaml

Usage

Set configuration in config.yaml and config.pypsaeur.yaml.

Run

snakemake -call build_all_surrogates

License

Different licenses apply to different parts of the repository. See specifications here.