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Electrical Engineering

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20.06.2024 | Original Paper

Data-driven virtual power plant aggregation method

verfasst von: Xueyan Bai, Yanfang Fan, Ruixin Hao, Jiaquan Yu

Erschienen in: Electrical Engineering | Ausgabe 1/2025

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Abstract

Virtual power plant needs to use advanced coordinated control technology to aggregate a large amount of new energy to reliably meet the regulatory needs of the superior power grid. Currently, virtual power plant aggregation technology considering reliability effectively alleviates the problems of low reliability of traditional virtual power plants and poor absorption capacity of new energy. However, in the process of solving the optimization scheme, the traditional optimization solution based on physical models is faced with great challenges due to the complex characteristics such as diversity and heterogeneity of virtual power plant aggregation models. Therefore, a data-driven virtual power plant aggregation method is proposed. The dispatching characteristics of different virtual power plant clusters can be effectively expressed by using the load data, the historical dispatching data of virtual power plant clusters and the data-driven technology. The packaging model reflecting the reliability difference of virtual power plant assemblies is established. The results show that the calculation results indicate that the root mean square error of the model is only 0.2134. Compared to LSTM training model and BP neural networks, the RMSE has decreased by 44.22% and 54.41%, respectively, while the MAE has decreased by 48.32% and 57.84%, respectively. This method has good accuracy. At the same time, this method provides a new method for complex and heterogeneous power system dispatching operation of China's new power system.

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Titel: Data-driven virtual power plant aggregation method
verfasst von: Xueyan Bai
Yanfang Fan
Ruixin Hao
Jiaquan Yu
Publikationsdatum: 20.06.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering / Ausgabe 1/2025
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-024-02544-z

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