Renewable energy forecasting optimization for system operator and trader

This paper focuses on the significance of renewable energy infeed forecast and presents two optimization methods. In contrast to usual procedures, this paper does not show an optimization of weather forecast but rather highlights optimization methods and possibilities to improve the forecast of renewable energy infeed i. e. in a control area based on multiple forecast provider time-series. Thereby, a sufficiently accurate and reliable infeed of highly volatile energy sources is essential for transmission system operators (and others) to optimize the Day-Ahead Congestion Forecast (DACF). Moreover, it is crucial for energy traders/producers like Virtual Power Plants (VPP) to increase the profits with an optimized energy placement at different markets. For this purpose, the paper presents two approaches (Least Squares Method and Energy Minimization Method) to improve the overall forecast by using provider data. Furthermore, the paper demonstrates the influence of a security slope and other factors in different case studies.