Machine intelligent forecasting based penalty cost minimization in hybrid wind‐battery farms.

Summary: Modern‐day hybrid wind farm operation is fundamentally dependent on the accuracy of short‐term wind power forecasts. However, the inevitable error in wind power forecasting limits the power transfer capability to the utility grid, which calls for battery energy storage systems to furnish the deficit power. This manuscript addresses a wind forecasting based penalty cost minimization solution for hybrid wind‐battery farms. We choose six wind farm sites (three offshore and the other three onshore) to study machine intelligent forecasting based solutions and compare the performance of a wavelet‐Twin support vector regression (TSVR) based wind power forecasting model with ε‐Twin support vector regression, Random forest, and Gradient boosted machines, for penalty cost minimization. We access the penalties that arise as power imbalances along with the battery system's cost. We find that TSVR based wind power forecasting method results in a minimum global operational cost for all the wind farm sites under study. [ABSTRACT FROM AUTHOR]