Data Clustering Method for Probabilistic Power Flow in Microgrids

Seyed Farhad Zandrazavi, Alejandra Tabares Pozos () and John Fredy Franco
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Seyed Farhad Zandrazavi: São Paulo State University
Alejandra Tabares Pozos: Los Andes University
John Fredy Franco: São Paulo State University

A chapter in Handbook of Smart Energy Systems, 2023, pp 1133-1154 from Springer

Abstract: Abstract Microgrids are paving the way for the integration of renewable energy-based distributed resources. Operators must deal with uncertainties linked to renewable generation and electric load fluctuations. One of the reliable tools for steady-state analysis of microgrids is probabilistic power flow (PPF). In this chapter, the concept of PPF is introduced via a literature review. Then, the detailed power flow formulation is presented for microgrids with or without reconfigurability characteristics. In the next part, the K-means algorithm is presented, and it is explained how this algorithm, combined with the LAPO algorithm, can help to model data clustering-based PPF for microgrid steady-state analysis. Moreover, it describes how to take advantage of different probability density functions, such as Beta, Gaussian, and Weibull distributions, to model uncertainties regarding solar photovoltaic generation, electric demand, and wind power generation. Last but not least, four different case studies are simulated, and the results are visualized and discussed to simplify the learning process.

Keywords: Data clustering; Microgrids; Probabilistic power flow; Renewable energy; Uncertainty (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_150

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DOI: 10.1007/978-3-030-97940-9_150

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