Development of Methods for an Optimized Infeed Forecast of Renewable Energies

Substation Related Forecasts of Electrical Energy Storage Systems: Transmission System Operator Requirements

“…Here, the developed regional forecast method in [23] is extended by the weighted least square method, developed in [38], and applied and adjusted to a substation-specific forecast (see Figure 4). Furthermore, the focus of system operators has shifted from forecasting actual generation measurements to forecasting extrapolated generation time-series.…”

“…Furthermore, the focus of system operators has shifted from forecasting actual generation measurements to forecasting extrapolated generation time-series. Since the actual generation measurements are provided with a delay of one month, whereas the extrapolated generation time series are provided 15 min after the fulfillment time, in contrast to [23,38], a data gap of one month is no longer required. As indicated in Figure 4, first, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and the Weighted Least Square method (WLS) are used simultaneously to determine the weights w for combining the provider generation forecasts.…”

“…This loss function is, per its definition, part of the WLS method [38]. To assign a high weight to high deviations, the normalized Root Mean Square Error (RMSE) in (8), which is related to the installed wind and PV power, is used as the fitness function in the GA and PSO, whereas the fitness function is equated with the loss function [23].…”

“…To minimize the deviation of the RE generation forecast from the actual generation and thus optimally use these provider forecasts, a method for combining these forecasts is introduced below. In this way, the weighting of individual providers has a decisive influence on the quality of the combined forecast (CoF) and thus on the safe network operations as well as the profits of energy traders and producers [23]. According to equation (6), the CoF p c is calculated by multiplying the provider time-series P pro (the matrix with a column per provider and a row per power value) and the dynamic and optimal weightings w (the vector with a row per provider).…”

“…To make optimal use of these models, the idea is to combine them. Combining forecasts could yield positive and negative errors that compensate for each other, thus improving the forecasting quality [23]. Although combining forecasts is already a well-known method for improving forecast accuracy, with a wide range of approaches such as simple average and Bayesian methods, this approach is still underdeveloped [24].…”