The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system’s flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations.

In the context of a high proportion of renewable energy integrated to the power grid, the net load may has significant fluctuations, and it is necessary to quantify peak regulation demand of power system. This paper stablishes a peak regulation demand prediction model based on long short-term memory (LSTM) method by training historical data. The typical data such as load and renewable energy output are selected as the input vector, and correlation coefficients are used to process and simplify the input vector. The historical prediction errors are used to set margins for peak regulation demand prediction. The case study shows that the proposed model can effectively predict the peak regulation demand of the power system.