Abstract
By mining the typical operating curve of an energy storage system, an understanding of the overall characteristics of the charge–discharge power of the system can be attained and a decisive support for the capacity allocation of the system can be provided. This paper proposes a typical operation curve mining algorithm based on a cloud model for the application scenario of using an energy storage system to suppress the power fluctuation of a photovoltaic (PV) power station. The frequency distribution of the charge–discharge power of the energy storage system in a longitudinal time series is decomposed into several cloud models with different granularities. By weighting and summing the expected values of the normal cloud model group, the typical operating curve of the energy storage system is obtained. To analyze the power distribution of the energy storage system and the typical operating curve under six typical weather types, a nonparametric estimation method was adopted to fit the power value distribution and the fitting deviation was calculated. The rationality of the extracted typical operating curve is verified and the capacity of the energy storage system is determined according to the obtained curve. The typical operating curve is used to configure the energy storage capacity of a 40 MWp PV plant and the result is 4.4984 MW·h, i.e., approximately 4.5 MW·h, which represents 11.25% of the installed capacity of the PV plant. This energy storage capacity affords a 95% probability of meeting the daily capacity requirements of the system. This shows that mining the typical operating curve is a valid method for configuring the energy storage capacity of a PV power station. Using the simulation examples, the effectiveness and the correctness of the method proposed in this paper are verified to provide theoretical support for the capacity allocation of energy storage systems. The authors of the article agree to the retraction of the article effective AUGUST 25, 2021.
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