Energies, Vol. 18, Pages 6633: Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids

Energies, Vol. 18, Pages 6633: Data-Driven Probabilistic Power Flow for Energy-Storage Planning Considering Interconnected Grids

Energies doi: 10.3390/en18246633

Authors:
Tingting Cheng
Xirui Jiang
Zheng Fan
Yanan Wu
Ying Mu
Dashun Guan
Dongliang Zhang
Ying Bai

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.