Contribution type: Lightning talk and Poster
Addressing Spatial Resolution Challenges in Wind Resource Assessment for Energy System Modeling
Presenter: Florian Scheiber
Description:
Future energy systems increasingly rely on weather-driven variable renewable energy sources. As a result, the accuracy, resolution, and statistical consistency of meteorological inputs have become key considerations in energy system modelling (ESM). In particular, wind power estimates strongly depend on local wind speed characteristics, including both distributional properties and temporal variability. However, widely used meteorological datasets such as ERA5 often provide insufficient spatial detail for assessing wind resources at scales relevant to future ESMs. At the same time, higher-resolution datasets are frequently limited in temporal coverage, geographical extent, or accessibility.
This work discusses current challenges associated with deriving validated, transparent high-resolution wind resource inputs for energy system applications. Building on ERA5 reanalysis data as a commonly used starting point, we present an approach for estimating statistical properties and time series of wind speed at 250 m resolution. Preliminary results suggest that methodological choices can influence derived wind speed characteristics.
Beyond the methodological aspects, the contribution highlights the broader challenge of translating meteorological datasets into model-ready renewable energy inputs. Throughout this process, assumptions and uncertainties are introduced that often remain implicit, despite their relevance for subsequent power generation estimates and downstream energy system analyses. The contribution aims to foster discussion on transparent and reproducible workflows, uncertainty awareness, and the role of meteorological preprocessing within the open energy modelling community.
Background:
The contribution addresses the interface between meteorological datasets and energy system modelling. In addition to model structure and optimisation methods, the derivation of renewable generation inputs plays an important role in shaping model assumptions and results. The presented work aims to contribute to a transparent discussion of assumptions, uncertainties, and preprocessing workflows involved in transforming meteorological data into model-ready inputs.