Posters

Abstract

Understanding Correlation Patterns in Wind Energy: A Data Driven Path to smarter Investments and Planning Renewable, solar and wind, energy generation exhibit significant temporal and spatial variability due to the fluctuating nature of natural resources. However, a deeper understanding of the correlation patterns between different geographic locations can provide critical insights for multiple business applications. Correlation among wind generation sites influences the aggregated variability of a portfolio, affecting financial risk, capacity factors, and revenue potential. Exploring these relationships is essential for strategic planning in the renewable energy sector. This study investigates the spatial correlation of wind energy across different European regions using wind speed data from the ECMWF ERA-5 reanalysis dataset. Cross-spatial correlation matrices were computed for all possible grid cell combinations, providing a comprehensive view of the resource synchronicity across the region. To simplify the complexity of these correlation structures, a hierarchical clustering algorithm was applied, using the correlation matrices as a distance metric. This approach allowed for the automatic segmentation of the geography into regions with similar behaviour, revealing patterns of synchronicity and divergence in renewable resource variability. The clustering analysis offers a powerful tool for several critical applications: * Portfolio/PPA Optimization: By identifying regions with low cross-correlation, asset owners and investors can design geographically diverse portfolios that minimize overall production variability and reduce financial risk exposure. * Energy Pricing and Revenue Forecasting: Correlation patterns between wind resource availability and market prices can be leveraged to optimize energy bidding strategies, considering how simultaneous generation affects market saturation and pricing. The clustering process also identifies regions where similar capture prices for renewable energy are expected. * Renewable Expansion Planning: Regulators can use this data-driven segmentation to guide renewable energy growth strategies, ensuring balanced capacity expansion and minimizing curtailment risks. * Hybrid Project Design: The clustering insights can be extended to optimize the combination of wind and solar resources, identifying areas where complementary generation patterns can enhance the overall capacity factor of hybrid plants. * Power-to-X Applications: Understanding correlation patterns can support the development of Power-to-X technologies by identifying regions with consistent renewable generation patterns, allows to design geographically diverse generation sources which maximizes the efficiency transforming renewable power to clean fuels or chemicals. By revealing the underlying correlation patterns through advanced clustering techniques, this study provides a structured framework for improving decision-making in renewable energy development, risk management, and market participation. The results emphasize the importance of regional correlation patterns as a foundation for strategic energy planning and portfolio management