4.6 Review

Use of Forecasting in Energy Storage Applications: A Review

Journal

IEEE ACCESS
Volume 9, Issue -, Pages 114690-114704

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3103844

Keywords

Forecasting; Predictive models; Wind forecasting; Wind power generation; Training; Renewable energy sources; Industries; Energy storage; energy forecasting; control; battery

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In the past decade, there has been a significant shift towards renewable energy sources due to the increasing demand for energy in a sustainable manner. However, the dependency of renewable energy generation on weather conditions poses a major challenge. Energy storage is crucial for harnessing renewable energy and managing stochasticity in generation. Current research has focused on the importance of forecasting for effective energy storage management, but further studies are needed to understand the actual value of forecasts in different energy storage applications.
During the last decade there has been a major shift towards renewable energy sources to fulfill the increasing demand for energy in a sustainable manner. However, a major challenge with renewable energy generation is its dependency on weather conditions. Energy storage is deemed instrumental to harness renewable energy by providing a means to overcome stochasticity in renewable generation. Nonetheless, the operation of energy storage is not trivial due to its energy limitation and degradation behavior. Many works in literature consider forecasts as a cornerstone for effective management of energy storage for various grid applications. However, little work has been devoted to studying the actual value of forecast for energy storage management, which is highly dependent on the use case. This paper presents a review of the state of the art in the use of forecasts for energy storage management, identifying the estimated value of forecast with respect to baseline management approaches that do not rely on forecasts. The paper also discusses research pathways that would focus on improving forecast only on the energy storage applications that can benefit from it.

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