Yumin Zhang

TL;DR: A data-driven adaptive robust optimization method for the unit commitment of bulk power systems with high-level wind power integration is proposed, different from the conventional robust unit commitment methods, and the distributional uncertainty of wind power is well respected in the proposed approach.

TL;DR: A power system state forecasting model based on long-short term memory neural network is established, which can solve the problem of missing data combining power flow calculation and has high accuracy and robustness.

TL;DR: A risk-based two-stage robust unit commitment (RUC) model is proposed to analyze the admissibility of wind power and can flexibly adjust the uncertainty set by optimizing the operational risks including the wind spillage risk and load shedding risk.

TL;DR: A novel real-time autonomous dynamic reconfiguration (ADR) method to reduce the cost of power loss and switch action of distribution network based on the deep learning (DL) algorithm that can be decision-making from the historical control dataset and the real- time system state.

TL;DR: A decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems and the startup/shutdown variables of the thermal units and the variables in TG + ADN +-MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system.