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为解决省级天然气规划中发展目标的合理确立与发展潜力的比较问题,开展天然气负荷预测模型的创新探索。通过数据归类、相关性分析以及与发达国家的对标分析,利用曲面拟合方法建立了中国经济驱动型省级天然气负荷中长期预测模型,绘制了天然气消费潜力、GDP以及一次能源消费的三维坐标系,定量分析了十省/市2030年天然气消费需求。结果表明:各省未来天然气需求旺盛,在GDP复合增长率为5%情景下,十省/市在2030年天然气需求总量达3 155×108 m3,是2021年的2.5倍。其中,天然气消费绝对增量和增速最大的分别为广东省和湖南省。研究结果可为天然气市场调节与发展提供建议与思路。
In order to solve the problems of reasonable establishment of construction objectives and comparison of development potential in provincial natural gas programming, the innovative exploration of natural gas load prediction model was carried out. Through data classification and correlation analysis, as well as benchmarking analysis with developed countries, the medium- and long-term prediction model of economic driven provincial natural gas load in China was established by surface fitting method. Meanwhile, the three-dimensional coordinate system of natural gas consumption potential, GDP and primary energy consumption was drawn. The natural gas consumption demand of ten provinces/cities in 2030 was quantitatively analyzed. The results indicated that the future demand for natural gas in each province is strong. Under the scenario of a compound GDP growth rate of 5%, the total demand for natural gas in the ten provinces/cities will reach 315.5 billion cubic meters in 2030, which is 2.5 times that of 2021. Among them, Guangdong Province and Hunan Province have the largest absolute increase and the highest growth rate of natural gas consumption, respectively. The research not only predicted the natural gas demand of economic driven provinces in China, but proposed a three-dimensional visualization and universal regional natural gas load prediction model.
[1]严宇,谭羽非,张碧波.盐穴型地下储气库调峰优化控制[J].油气储运,2009,28(3):7.[doi:10.6047/j.issn.1000-8241.2009.03.003]
YAN Yu,TAN Yufei.Optimization Control on Peak Shaving of Salty-cave Underground Gas Storage[J].Oil & Gas Storage and Transportation,2009,28(10):7.[doi:10.6047/j.issn.1000-8241.2009.03.003]
[2]周登极,邢同胜,张麟,等.大数据背景下天然气管网数据挖掘与应用[J].油气储运,2021,40(03):271.[doi:10.6047/j.issn.1000-8241.2021.03.005]
ZHOU Dengji,XING Tongsheng,ZHANG Lin,et al.Data mining and its application in natural gas pipeline network under the context of big data[J].Oil & Gas Storage and Transportation,2021,40(10):271.[doi:10.6047/j.issn.1000-8241.2021.03.005]
[3]吴岩,刘喆,李灿,等.西气东输系统城市燃气用户负荷预测[J].油气储运,2021,40(04):386.[doi:10.6047/j.issn.1000-8241.2021.04.004]
WU Yan,LIU Zhe,LI Can,et al.Load forecasting of urban gas users in West-to-East Gas Pipeline System[J].Oil & Gas Storage and Transportation,2021,40(10):386.[doi:10.6047/j.issn.1000-8241.2021.04.004]
[4]徐鹏,杜景勃,刘伟.城镇天然气负荷预测方法研究进展[J].油气储运,2023,42(05):481.[doi:10.6047/j.issn.1000-8241.2023.05.001]
XU Peng,DU Jingbo,LIU Wei.Progress of research on load forecasting method for city gas[J].Oil & Gas Storage and Transportation,2023,42(10):481.[doi:10.6047/j.issn.1000-8241.2023.05.001]