Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?

TL;DR

This study evaluates the feasibility of disaggregating low-resolution capacity expansion models into high-resolution models, revealing that simplified models often lead to infeasible solutions with significant load-shedding.

Contribution

It introduces three methods for disaggregating low-res models and assesses their feasibility through operational dispatch simulations, highlighting the limitations of country-level modeling.

Findings

Re-optimisation yields the best disaggregation results.

Simplified models still cause 3-7% load-shedding in high-res simulations.

Disaggregation methods vary in computational effort and accuracy.

Abstract

Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally intensive. The simplification mixes sites with different renewable features while ignoring transmission lines that can cause congestion. As a consequence, the results may represent an infeasible system when the capacities are fed back at higher spatial detail. Thus far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is feasible. This is challenging since there is no unique way to invert the clustering. This article is split into two parts to tackle these challenges. First, methods to disaggregate spatially low-resolved results are presented: (a) an uniform distribution of regional results across its original highly-resolved regions, (b) a re-optimisation for each region separately, (c) an approach that minimises the "excess electricity". Second, the resulting highly-resolved models' feasibility is investigated by running an operational dispatch. While re-optimising yields the best results, the third inverse method provides comparable results for less computational effort. Feasibility-wise, the study design strengthens that modelling countries by single regions is insufficient. State-of-the-art reduced models with 100-200 regions for Europe still yield 3%-7% of load-shedding, depending on model resolution and inverse method.