利用数据驱动方法平滑和优化工业园区的跨级蒸汽负荷

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaojie Lin , Xueru Lin , Wei Zhong , Feiyun Cong , Yi Zhou
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引用次数: 0

摘要

本研究以工业园区综合能源生产系统为重点,解决需求波动下的设备稳定负荷调度问题。提出了一种跨层次的蒸汽负荷平滑和优化方法,旨在通过负荷预测、负荷调度和负荷调节三个层次的整合,实现稳定的生产和最优的经济效益。与直接使用负荷预测值的传统方法不同,该方法将热网弹性作为供需之间的缓冲。为实现平稳调节,对设备负荷和运行参数的最小变化进行了限制。工业案例表明,负荷预测模型对中压蒸汽和低压蒸汽的平均绝对百分比误差分别为 2.44% 和 1.68%,符合精度要求。通过考虑热网弹性,修正后的供方负荷平稳性得到了有效改善。该方法使锅炉效率提高了 1.92%,平均煤耗降低了 0.92 吨/小时。与手动操作相比,所提出的模型可使发电量平均增加 5.69 兆瓦,煤电比平均降低 10.81%。这项研究验证了不同层面平滑整合的重要性,并分析了所提方法对负荷预测不确定性的有效响应。该方法展示了数据驱动方法在实现工业园区安全、经济和可持续生产方面的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-level steam load smoothing and optimization in industrial parks using data-driven approaches

This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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