Abstract
Compensating for photovoltaic (PV) power forecast errors is an important function of energy storage systems. As PV power outputs have strong random fluctuations and uncertainty, it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods. In this paper, a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation. A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance. The power forecast errors in different weather types are analyzed, and an energy storage system is used to compensate for the errors. The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system; the power and capacity of the energy storage unit are calculated at different confidence levels. The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction. The proposed method was validated using actual operating data from a PV power station. The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
