In order to help achieve the goal of carbon peak and carbon neutrality, the large-scale development and application of clean renewable energy, like wind generation and solar power, will become an important power source in the future. Large-scale clean renewable energy generation has the uncertain characteristics of intermittency, randomness, and volatility, which brings great challenges to the balance regulation and flexible operation of the power system. In addition, the rapid development of renewable energy has led to strong fluctuations in electricity prices in the power market. To ensure the safe, reliable, and economic operation of the power system, how to improve the power system flexibility in an uncertain environment has become a research hotspot. Considering the uncertainties, this article analyzes and summarizes the research progress related to power system flexibility from the perspective of power system planning, operation, and the electricity market. Aiming at the modeling technology of uncertainty, the related modeling methods including stochastic programming, robust optimization, and distributionally robust optimization are summarized from the perspective of mathematics, and the application of these methods in power system flexibility is discussed.

The penetration of photovoltaic (PV) power into modern power systems brings enormous economic and environmental benefits due to its cleanness and inexhaustibility. Therefore, accurate PV power forecasting is a pressing and rigid demand to reduce the negative impact of its randomness and intermittency on modern power systems. In this paper, we explore the application of deep learning based hybrid technologies for ultra-short-term PV power forecasting consisting of a feature engineering module, a deep learning-based point prediction module, and an error correction module. The isolated forest based feature preprocessing module is used to detect the outliers in the original data. The non-pooling convolutional neural network (NPCNN), as the deep learning based point prediction module, is developed and trained using the processed data to identify non-linear features. The historical forecasting errors between the forecasting and actual PV data are further constructed and trained to correct the forecasting errors, by using an error correction module based on a hybrid of wavelet transform (WT) and k-nearest neighbor (KNN). In the simulations, the proposed method is extensively evaluated on actual PV data in Limburg, Belgium. Experimental results show that the proposed hybrid model is beneficial for improving the performance of PV power forecasting compared with the benchmark methods.