Posters

Abstract

In hybrid energy yield assessment, consisting of PV and wind energy, the industry traditionally relies on standard values and long-term mean values for critical wind energy loss calculations and commonly applies the typical meteorological year (TMY) approach for PV production assessment. While this approach simplifies the hybrid energy yield assessment process, it fails to account for the time-varying nature of key factors, leading to less accurate energy production estimates which are critical for a stable power supply above certain thresholds to keep electrolyzers for H2 production cost efficiently running. This study presents a state-of-the-art method that integrates concurrent time-variant approaches for PV and wind energy to improve the accuracy of time-variant hybrid energy yield assessments over hourly or sub-hourly timesteps. The study focuses on analyzing how wind energy losses such as wake effects, availability, electrical efficiency, turbine performance, ambient conditions, and curtailments fluctuate over time and the complexity of applying these concurrent PV and wind time-sensitive methods. By adopting these advanced time-variant approaches, the study demonstrates a significant enhancement in hybrid energy yield assessment accuracy. This approach provides a more detailed and reliable understanding of how dynamic factors influence hybrid energy yields. Additionally, using concurrent time-variant methods allows uncertainties to be calculated variably across the time series, providing a nuanced perspective of the uncertainty distribution over time. The findings show that transitioning from TMY and standard values and long-term mean values assumptions to time-variant methods reduces uncertainties, leading to more precise hybrid energy yield assessment and optimized resource management. This is particularly important for improving hybrid energy yield project scaling and assessments for the determination of stable power-to-X production. This work underscores the critical need for adopting advanced time-variant hybrid energy yield assessment techniques to maximize the efficiency and reliability of future hybrid renewable energy projects, particularly those supporting power-to-X applications.