BRIEF RESEARCH REPORT article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1693639
This article is part of the Research TopicGrid Stability and Optimized Operation in Renewable Energy Grid SystemsView all 7 articles
A Data-driven Framework for Unit Commitment Considering Ramping and Forecasting Information
Provisionally accepted
- 1Guizhou Power Grid Co., Ltd, Guiyang, China
- 2Dongfang Electronics Co., Ltd, Shandong, China
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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.
Keywords: Load clustering, load forecasting, LSTM network, Security Constrained Unit Commitment, ramping constraint
Received: 27 Aug 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Chen, Fu, Lan, Hao, Yang and Weng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sheng Chen, shchen_gzcsg@126.com
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