Probabilistic upscaling and aggregation of wind power forecasts

Abstract

Background: Wind power forecasts of the expected wind feed-in for the next hours or days are necessary to integrate the generated volatile wind energy into power systems. Most forecasting models predict in some sense the best value, but they ignore the other possible outcomes which may arise because of forecasting uncertainties. Probabilistic forecasts, on the other hand, also predict a distribution of possible outcomes with their respective probabilities that specific power values will occur and therefore have higher information content. In this work, we address two problems that hinder the introduction of probabilistic forecasts in practice: (1) no measurement data are available for some wind farms and (2) the flexible aggregation of probabilistic forecasts for changing wind farm portfolios. Methods: We present an approach based on copulas that can solve both problems by modeling the spatial correlation structure between reference wind farms. By sampling from the resulting joint probability distribution, probability forecasts can be upscaled from reference wind farms to wind farms without power measurements. Furthermore, the results can be aggregated to probability forecasts of portfolios of arbitrary and changing size. Results: We perform experiments by applying our procedure to three use cases. The results are quantitatively evaluated with different probabilistic scores. For single target wind farms, our approach is as good as a state-of-the-art reference even if no data are available for the wind farm under consideration. For portfolios, our approach also allows forecasts to be made if no data is available for some wind farms and also to aggregate flexible portfolios of changing sizes, which was not possible before. Conclusion: Our work solves two problems that hindered the introduction of quantile probabilistic forecasts in the application. This work opens a pathway for many different applications, e.g., predictive grid securities with stochastic optimization, better marketing of renewable energies, or allow to compensate for forecast errors in various applications.

China's flexibility challenge in achieving carbon neutrality by 2060

“…Portfolio theories have shown how geographical dispersion can reduce the system variability of wind and solar power (Hu et al, 2019). Moreover, it also helps to reduce the uncertainty in forecasting renewable energy output, because forecasting the aggregate output over multiple locations is more accurate than that in a single location (Henze et al, 2020).…”

“…Provincial grids are largely self-balancing with only limited interconnections. By 2019, the total interconnecting capacity among regions in China was 146 GW, accounting for only 14% of the maximum load at monthly frequency 14 . This means a big gap compared to some countries such as Germany and Denmark 15 .…”

Energy landscapes of today and tomorrow

“…The topics included in this issue range from wind power to photovoltaic systems [1][2][3] and from biomass to bioliquids and biofuels for heat, electricity, and transport [4][5][6], (1A, 3A). They cover the entire innovation process, ranging from scientific research to innovative approaches for technology implementation and the politics of energy landscapes [5,7] edition comments on the societal debate on the benefits and risks of Germany's "Energiewende" and the conservation aspects of future energy landscapes [8,9].…”