Optimising for the long game: methodological challenges in energy system optimisation pathways

TL;DR

This paper reviews methodological challenges in energy system optimisation pathways, focusing on model foresight, biases, resolution trade-offs, and investment dynamics over decades.

Contribution

It systematically analyzes how modelling choices affect long-term energy pathway results and offers recommendations for improving model formulation and communication.

Findings

Identifies biases from end effects and foresight choices.

Highlights trade-offs between model resolution and long-term planning.

Provides guidelines to improve model transparency and accuracy.

Abstract

Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.