A data-driven framework for unit commitment considering ramping and forecasting information

A data-driven framework was proposed in this paper to enhance the accuracy of load power forecasting and improve the economy and reliability of security-constrained unit commitment (SCUC) scheduling. The loads in each time period are clustered into several distinct scenarios firstly and each scenario exhibits a unique fluctuation boundary, which is quantitatively characterized using the proposed fluctuation indicator. Based on historical data, we evaluated the boundaries of fluctuations at different confidence levels. Then a data-driven framework is proposed to improve the accuracy of evaluating these indices. The effectiveness of this framework is validated using a Long Short-Term Memory (LSTM) network, and the results show that the proposed framework reduced the average error by 45.5% compared to traditional frameworks. Finally, a SCUC optimization model is formulated with these indices results, and case studies were conducted on an IEEE 30-bus system to demonstrate the effectiveness of the proposed method.