Wind Power Forecast Error-Driven Day-Ahead Scheduling Method for Power Systems with Flexibility Chance Constraints

In recent years, renewable energy sources (RESs) have been increasingly integrated into power systems to address energy crises and environmental concerns. However, the inherent uncertainty and volatility of RESs pose significant challenges to the secure and cost-effective operation of power systems, such as increased risk of curtailment and supply shortages. Enhancing power system flexibility is an effective way to address these challenges. However, existing studies on flexibility primarily focus on its evaluation, while limited research has considered incorporating flexibility directly into scheduling models. To address this issue, this paper proposes a day-ahead scheduling model with flexibility chance constraints (DASFCC), driven by wind power forecast errors. First, historical wind power data are used to model the forecast error and derive a probability density function characterizing the flexibility demand. Next, the flexible supply capabilities of coal-fired units are analyzed. Then, the DASFCC model is formulated and reformulated as a mixedinteger linear programming problem. Finally, numerical results demonstrate the validity of the DASFCC model in improving the accommodation of wind power and reducing operational costs.