Data-Driven Modeling for Photovoltaic Power Output of Small-Scale Distributed Plants at the 1-s Time Scale

Under the condition of a small time scale (e.g. second), distributed photovoltaic (PV) power generation output has the problems of strongly fluctuating and difficult to accurately simulate. It affects the control strategy and operation mode of hybrid energy systems. To address this problem, a data-d...

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Bibliographic Details
Published inIEEE access Vol. 12; pp. 117560 - 117571
Main Authors Wei, Jia, Yang, Weijia, Li, Xudong, Wang, Junsong
Format Journal Article
LanguageEnglish
Published IEEE 2024
Subjects
Accuracy
Analytical models
Data models
data-driven
fluctuation characteristics
Fluctuations
hybrid energy system
Hybrid power systems
Photovoltaic
Photovoltaic systems
Power generation
Predictive models
second-time scale
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Summary:Under the condition of a small time scale (e.g. second), distributed photovoltaic (PV) power generation output has the problems of strongly fluctuating and difficult to accurately simulate. It affects the control strategy and operation mode of hybrid energy systems. To address this problem, a data-driven small-scale distributed PV plant power output model on a 1-second time scale is proposed for the generation of second-by-second PV power output scenarios in hybrid energy systems. Firstly, this work analyzes the characteristics of PV power output at the 1-second time scale based on the probability distribution of power output fluctuations. Secondly, an index system that characterizes the PV power output fluctuation characteristics at the 1-second time scale is constructed. Then, using the data-driven method, a BP neural network model is constructed to simulate the PV power output at the 1-second time scale. Finally, a simulation is performed using the measured data from the PV plant. The findings demonstrate that compared to PV power output models in seconds based on Pearson systematic random numbers: (1) The correlation coefficient (r) of the proposed model is more than 0.8, in a higher degree of fit; (2) The root mean square error (RMSE) of the proposed model achieves 0.005, generally representing a 37.12% reduction. Overall, both the time scale and model accuracy of this model have deep potential value in PV power output modeling and system regulation.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3446790