The growth in volatile renewable energy (RE) generation is accompanied by an increasing network load and an increasing demand for storage units. Household storage systems and micro power plants, in ...particular, represent an uncertainty factor for distribution networks, as well as transmission networks. Due to missing data exchanges, transmission system operators cannot take into account the impact of household storage systems in their network load and generation forecasts. Thus, neglecting the increasing number of household storage systems leads to increasing forecast inaccuracies. To consider the impact of the storage systems on forecasting, this paper presents a new approach to calculate a substation-specific storage forecast, which includes both substation-specific RE generation and load forecasts. For the storage forecast, storage systems and micro power plants are assigned to substations. Based on their aggregated behavior, the impact on the forecasted RE generation and load is determined. The load and generation are forecasted by combining several optimization approaches to minimize the forecasting errors. The concept is validated using data from the German transmission system operator, 50 Hertz Transmission GmbH. This investigation demonstrates the significance of using a battery storage forecast with an integrated load and generation forecast.
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.
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