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A data-driven framework for unit commitment considering ramping and forecasting information
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DOI：10.3389/fenrg.2025.1693639.png)
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Abstract
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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.
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