Stochastic simulations of wind and solar processes for reliable renewable energy decision-making

This study explores the stochastic analysis of wind and solar meteorological processes, focusing on simultaneous simulations at small scales while preserving marginal distribution, periodicities and dependence structure. Using historical data from Amsterdam Schiphol Airport as a case study, the analysis employs the Hurst-Kolmogorov process to model variability and long-term dependence.

By integrating the Hurst-Kolmogorov framework with recent stochastic modelling algorithms, synthetic time series are generated to emulate realistic patterns of wind and solar variability. Special attention is given to assessing the correlation between wind and solar processes, as their interplay significantly influences the balance and reliability of renewable energy systems.  These simulations aim to enhance the reliability of renewable energy resource assessments, supporting decision-making for infrastructure design and offering practical applications beyond the case study to broader renewable energy systems planning.