Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part I: Methodologies

@article{Guo2019DataBasedDR,
  title={Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part I: Methodologies},
  author={Yi Guo and Kyri Baker and Emiliano Dall’Anese and Zechun Hu and Tyler Holt Summers},
  journal={IEEE Transactions on Power Systems},
  year={2019},
  volume={34},
  pages={1483-1492}
}
We propose a data-based method to solve a multi-stage stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The framework explicitly combines multi-stage feedback policies with any forecasting method and historical forecast error data. The objective is to determine power scheduling policies for controllable devices in a power network to balance operational cost and conditional value-at-risk of device and network constraint violations. These… CONTINUE READING
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