Spatial representation of renewable technologies in generation expansion planning models

The increasing share of intermittent Renewable Energy Sources (iRES) in the energy mix leads to a growing need for their accurate representation in energy system models. In this paper, we analyze how the results of generation planning models are impacted by the spatial representation used to model intermittent renewable technologies. It is shown that the spatial resolution, and to a lesser extent the spatial aggregation method used in a planning model, have a significant influence on the obtained system cost. The effect is quantified in a case study of European geographical scale, by varying the spatial resolution from highly resolved (hundreds of sites) to highly aggregated (1 site). A resolution-dependency of the system cost is observed under multiple renewable generation targets, and different renewable capacity restrictions. In doing so, we highlight the importance of implementing an appropriate capacity limit, especially when a high spatial resolution is used. Additionally, basic spatial aggregation methods-to process high resolution input data-are compared.

Automated summary

In this paper, we analyze the impact of the spatial representation of intermittent renewable technologies on generation planning models. The study finds that the spatial resolution and aggregation method significantly influence system costs, especially at high spatial resolutions. The paper also compares basic spatial aggregation methods for processing high resolution input data.