On mitigating wind energy variability with storage

Reducing fluctuations and mitigating the variability of wind energy is essential for its integration in grids, electricity markets, microgrids, and distributed generation settings. In this work, we present a storage-based wind power smoothing system that uses novel optimization algorithms to reduce the variability of wind energy. The system considers forecasted and actual energy generated, battery size, and energy prices and determines export rates that have low variability and maximize either the energy exported or revenue earned. Our optimization algorithms are novel as they model an equivalent relaxed buffer system that uses only linear constraints and allows the computation of optimal smoothing solutions in an efficient manner. This enables the system to be used in an online manner in real time as well as in planning and operations. The smoothing system and the mathematical models and programs used in optimization are presented along with preliminary simulation results that demonstrate the need and effectiveness of the system with the help of real wind energy data. Finally, we compare wind smoothing to video smoothing and point out the important similarities and differences.