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Energies
Volume 18
Issue 24
10.3390/en18246633
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids

by
Tingting Cheng
1,*,
Xirui Jiang
1,
Zheng Fan
1,
Yanan Wu
1,
Ying Mu
2,
Dashun Guan
2,
Dongliang Zhang
2 and
Ying Bai
2
1
National Key Laboratory of Advanced Power Transmission Technology, China Electric Power Research Institute, Beijing 102209, China
2
Economic & Technological Research Institute, State Grid Shandong Electric Power Company, Jinan 255000, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(24), 6633; https://doi.org/10.3390/en18246633
Submission received: 9 November 2025 / Revised: 16 December 2025 / Accepted: 16 December 2025 / Published: 18 December 2025
(This article belongs to the Special Issue Advances in Power System and Renewable Energy)
Versions Notes

Abstract

As renewable energy penetration increases, the volatility and uncertainty of photovoltaic generation and load demand pose significant challenges to power-system stability. This paper proposes a data-driven probabilistic load-flow method that employs a Gaussian mixture model (GMM) to model uncertainties in photovoltaic generation and load demand. Cumulative quantity analysis is then applied to conduct probabilistic load-flow studies, quantifying the impact of these uncertainties on the power system. Building upon this foundation, a two-layer optimization model is constructed to optimize the siting, capacity, and operational strategies of energy storage systems. Experimental results demonstrate that this method effectively reduces the probability of voltage-limit violations, ensures the reliability of supply–demand balance, and enhances system stability and reliability even under fluctuating PV generation and load-demand conditions.
Keywords: data-driven; probabilistic load flow; energy storage planning; interconnected power grid; bi-level programming model data-driven; probabilistic load flow; energy storage planning; interconnected power grid; bi-level programming model

Share and Cite

MDPI and ACS Style

Cheng, T.; Jiang, X.; Fan, Z.; Wu, Y.; Mu, Y.; Guan, D.; Zhang, D.; Bai, Y. Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids. Energies 2025, 18, 6633. https://doi.org/10.3390/en18246633

AMA Style

Cheng T, Jiang X, Fan Z, Wu Y, Mu Y, Guan D, Zhang D, Bai Y. Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids. Energies. 2025; 18(24):6633. https://doi.org/10.3390/en18246633

Chicago/Turabian Style

Cheng, Tingting, Xirui Jiang, Zheng Fan, Yanan Wu, Ying Mu, Dashun Guan, Dongliang Zhang, and Ying Bai. 2025. "Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids" Energies 18, no. 24: 6633. https://doi.org/10.3390/en18246633

APA Style

Cheng, T., Jiang, X., Fan, Z., Wu, Y., Mu, Y., Guan, D., Zhang, D., & Bai, Y. (2025). Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids. Energies, 18(24), 6633. https://doi.org/10.3390/en18246633

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