| Title |
Developing Optimal Wind Power Offer Strategy for Maximizing Forecasting Payment by Modeling the Forecasting Error Distribution through the Levy Process for Different Wind Power Levels |
| Authors |
임정협(Jung-Hyeop Im) ; 고영준(Young-Jun Go) ; 정민규(Min-Kyu Jung) ; 김민성(Min-Sung Kim) ; 이두희(Duehee Lee) |
| DOI |
https://doi.org/10.5370/KIEE.2026.75.4.748 |
| Keywords |
Levy process; Logit transformation; Regime-switching error model; Renewable energy settlement; Sequence-to-Sequence model; Settlement optimization; Wind power forecasting |
| Abstract |
This study proposes a strategic framework for maximizing expected profits in renewable energy markets under step-wise payment structures. We propose a probabilistic submission method that integrates a Sequence-to-Sequence model with a logit transformation and a Levy process. First, a deep learning model such as Long Short?Term Memory generates a day-ahead normalized wind power trajectory using historical data and weather forecasts. The normalized trajectory is transformed into logit space so that forecasting errors can be modeled more effectively for bounded power values. Second, to capture heavy tails, asymmetry, and power-level?dependent uncertainty, forecasting errors are classified into regimes and modeled using a Levy process. The framework then combines the stochastic error model with the deterministic trajectory to generate realistic scenario distributions. By evaluating expected revenues over these distributions, the method derives the optimal submitted quantity that maximizes expected profit. |