Distributed Privacy-Preserving Stochastic Optimization for Available Transfer Capacity Assessment in Power Grids Considering Wind Power Uncertainty

Shaolian Xia, Huaqiang Xiong, Yi Dong (), Mingyu Yan, Mingtao He and Tianyu Sima
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Shaolian Xia: Developing Department, Central China Branch of State Grid Corporation of China, Wuhan 430077, China
Huaqiang Xiong: Developing Department, Central China Branch of State Grid Corporation of China, Wuhan 430077, China
Yi Dong: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Mingyu Yan: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Mingtao He: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tianyu Sima: State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Mathematics, 2025, vol. 13, issue 19, 1-0

Abstract: The uneven expansion of renewable energy generation in different regions highlights the necessity of accurately assessing the available transfer capability (ATC) in power systems. This paper proposes a distributed probabilistic inter-regional ATC assessment framework. First, a spatiotemporally correlated wind power output model is established using wind speed forecast data and correlation matrices, enhancing the accuracy of wind power forecasting. Second, a two-stage probabilistic ATC assessment optimization model is proposed. The first stage minimizes both generation cost and risk-related costs by incorporating conditional value-at-risk (CVaR), while the second stage maximizes the power transaction amount. Thirdly, a privacy-preserving two-level iterative alternating direction method of multipliers (I-ADMM) algorithm is designed to solve this mixed-integer optimization problem, requiring only the exchange of boundary voltage phase angles between regions. Case studies are performed on the 12-bus, the IEEE 39-bus and the IEEE 118-bus systems to validate the proposed framework. Hence, the proposed framework enables more reliable and risk-aware intraday ATC evaluation for inter-regional power transactions. Moreover, the impacts of risk parameters and wind farm output correlations on ATC and generation cost are further investigated.

Keywords: probabilistic optimization; privacy preserving; uncertainty of wind power; alternating direction method of multipliers; available transfer capability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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