# Robust capacity expansion modeling for renewable energy systems

## TL;DR

This paper introduces a method to plan renewable energy systems that account for weather variability, ensuring reliable supply while keeping costs low.

## Contribution

A novel algorithm for robust capacity expansion planning that iteratively adjusts for weather uncertainty in renewable energy systems.

## Key findings

- Robust systems increase annual costs by 1.6-2.9%, but prevent supply gaps during extreme weather.
- Non-robust systems can face up to 50% loss of load during atypical weather periods.
- Using average weather years and iteratively adjusting solutions is a best practice for reliable modeling.

## Abstract

Future greenhouse gas neutral energy systems will be dominated by renewable energy technologies providing variable supply subject to uncertain weather conditions. For this setting, we propose an algorithm for capacity expansion planning: We evaluate solutions optimized on a single years’ data under different input weather years, and iteratively modify solutions whenever supply gaps are detected. These modifications lead to solutions with sufficient capacities to overcome periods of cold dark lulls and seasonal demand/supply fluctuations. A computational study on a German energy system model for 40 operating years shows that preventing supply gaps, i.e., finding a robust system, increases the total annual cost by 1.6-2.9%. In comparison, non-robust systems display loss of load close to 50% of total demand during some periods. Results underline the importance of assessing the feasibility of energy system models using atypical time-series, combining dark lull and cold period effects.

•In energy system modeling, disregarding weather variability leads to misinvestment•Average weather years fail to capture intra-/inter-year variability•Best practice: Use average years, but validate and iteratively adjust solutions•Protection against weather variability requires only 2%–3% additional annual costs

In energy system modeling, disregarding weather variability leads to misinvestment

Average weather years fail to capture intra-/inter-year variability

Best practice: Use average years, but validate and iteratively adjust solutions

Protection against weather variability requires only 2%–3% additional annual costs

Applied sciences