Data-Based Distributionally Robust Stochastic Optimal Power Flow—Part II: Case Studies

This is the second part of a two-part paper on data-based distributionally robust stochastic optimal power flow. The general problem formulation and methodology have been presented in Part I (Y. Guo, K. Baker, E. Dall'Anese, Z. Hu, and T.H. Summers, “Data-based distributionally robust stochastic optimal power flow-Part I: Methodologies,” IEEE Trans. Power Syst., 2018.). Here, we present extensive numerical experiments in both distribution and transmission networks to illustrate the effectiveness and flexibility of the proposed methodology for balancing efficiency, constraint violation risk, and out-of-sample performance. On the distribution side, the method mitigates overvoltages due to high photovoltaic penetration using local energy storage devices. On the transmission side, the method reduces N-1 security line flow constraint risks due to high wind penetration using reserve policies for controllable generators. In both cases, the data-based distributionally robust model-predictive control algorithm explicitly utilizes forecast error training datasets, which can be updated online. The numerical results illustrate inherent tradeoffs between the operational costs, risks of constraints violations, and out-of-sample performance, offering systematic techniques for system operators to balance these objectives.