Change in Optimal Shares of PV and Wind in Hybrid Power Plants in Europe Due to Climate Variability Using the Monte Carlo Method

The article provides a comprehensive analysis of optimizing the component shares in hybrid solar and wind power plants across European countries. The study is based on long-term meteorological data from 1980–2022 with a spatial resolution of 0.25x0.25 degrees. Calculations of gross and net available energy were conducted using daily values of solar radiation, wind speed, and temperature, incorporating technical constraints. Optimal proportions for utilizing various energy sources were determined using Markowitz Portfolio Theory (MPT). Historical daily data on solar radiation, wind speed, and temperature, coupled with Mann-Kendall tests at a 5% significance level, were utilized to assess trends and identify climate variability over time. Statistical models employing Monte Carlo Methods were applied to forecast daily values of solar radiation, wind speed, and temperature over a 10-year horizon. Based on these forecasts, the shares of components in hybrid power plants were re-optimized, enabling an evaluation of the impact of predicted climate changes on the energy mix. To illustrate potential financial implications of these climatic changes, the study calculated variations in revenue from energy sales for a 2 MW hybrid system (1 MW solar, 1 MW wind) across individual countries. The analysis revealed disparities in revenue changes, with losses of up to -150 EUR/day in southern Europe but gains of up to +135 EUR/day in northern and central regions, such as the British Isles and eastern Germany. These findings emphasize the need for tailored energy strategies to mitigate economic risks in vulnerable areas while maximizing opportunities in regions forecasted to benefit from climatic shifts. The results are presented graphically and conclude with a discussion on the implications of climate change for the economic viability of hybrid energy systems. This study highlights the importance of integrating climatic forecasts, advanced modeling techniques, and financial analysis in the strategic planning of renewable energy infrastructure.