Posters
Come meet the poster presenters to ask them questions and discuss their work
We would like to invite you to come and see the posters at our upcoming conference. The posters will showcase a diverse range of research topics, and will give delegates an opportunity to engage with the authors and learn more about their work. Whether you are a seasoned researcher or simply curious about the latest developments in your field, we believe that the posters will offer something of interest to everyone. So please join us at the conference and take advantage of this opportunity to learn and engage with your peers in industry and the academic community.
PO386: Stochastic Modeling of Day-Ahead Wind Power Plant Forecast Errors
Đorđe Lazović, Teaching and Research Assistant, University of Belgrade
Abstract
Rising integration of renewable energy sources (RES) is reshaping power system planning and operation by introducing forecast uncertainty and a greater need for reserves and flexibility. To enhance calculation accuracy, capture a wider set of potential scenarios, and reduce decision-making risks, production planning and system operation models require a stochastic treatment of RES resources. This paper adopts a probabilistic framework for modeling day-ahead (DA) wind power plant (WPP) forecast errors, considering stochastic factors that trigger balancing energy activation in real time. The case study focuses on the Čibuk WPP (158.5 MW) in South Banat, Serbia, using two years of historical DA forecasts and metered active power at the grid connection point. To estimate the probability distribution of forecast errors with respect to planned generation levels, a non-parametric kernel density estimation (KDE) was employed. This approach captures both the stochasticity of wind speed forecasts and the probability of turbine outages caused by technical failures or wind speeds exceeding the cut-out threshold. Furthermore, temporal dependencies between hourly WPP generation forecast errors were analyzed using correlation matrices, while copula functions were applied to jointly model the empirical error distributions and their temporal correlations. As a results, the proposed methodology enables the generation of extensive sets of realistic WPP forecast error trajectories, as well as the formulation of day-ahead production schedules with defined confidence intervals. The proposed modeling framework provides several advantages, including the capability to formulate optimal power scheduling and bidding strategies for WPPs that explicitly capture wind speed uncertainty, as well as to improve both the reliability and accuracy of balancing capacity assessments necessary for accommodating WPP imbalances. The proposed methodology is universal and applicable to any WPP, providing valuable support to both system operators and market participants in making more robust and financially sustainable decisions.
