Fei Teng

TL;DR: In this paper, a mixed integer linear programming (MILP) formulation for stochastic unit commitment is proposed to optimize system operation by simultaneously scheduling energy production, standing/spinning reserves and inertia-dependent fast frequency response in light of uncertainties associated with wind production and generation outages.

TL;DR: In this paper, the benefits of frequency response support from wind power plants are quantified for the future Great Britain power system with different wind energy penetration levels and frequency response requirements, and the impact of the uncertainty associated with the quantity of wind plants being online and the energy recovery effect are also analyzed.

TL;DR: A novel frequency-constrained stochastic unit commitment model is proposed which co-optimizes energy production along with the provision of synchronized and synthetic inertia, enhanced frequency response, primary frequency response and a dynamically-reduced largest power infeed.

TL;DR: The concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range.

TL;DR: In this article, a data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNNs) in predicting the remaining useful life (RUL) for rolling element bearings (REBs).