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

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.