Method for determining peak shaving capacity of pipeline
A peak-shaving and pipeline technology, which is applied in the field of determining pipeline peak-shaving capabilities, and can solve problems such as pipeline emergency deployment capabilities and peak-shaving capabilities
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Embodiment 1
[0045] Take 6 users A, B, C, D, E, and F in the pipe network as an example. Among them, the fluid consumption of A, B, C, and D is more than 500,000 cubic meters per day, which is a typical user.
[0046] It is now necessary to obtain the pipeline peak-shaving capacity on December 2, 2016, December 2016, the fourth quarter of 2016, and one year in 2016, based on the data actually recorded by the pipeline network.
[0047] For the peak shaving capacity of the pipeline on December 2, 2016, first obtain the first unevenness coefficient of each typical user, according to the formula first day unevenness coefficient = (the maximum value of the fluid volume used by the user per hour - the user per hour Average amount of fluid used) / Average amount of fluid used by the user per hour. For user A, the maximum amount of fluid used per hour by the user within 24 hours on December 2, 2016 was 2 million cubic meters per day, and the average fluid volume per hour used by the user within 24 h...
Embodiment 2
[0052] Take 6 users A, B, C, D, E, and F in the pipe network as an example. Among them, the fluid consumption of A, B, C, and D is more than 500,000 cubic meters per day, which is a typical user.
[0053] It is now necessary to obtain the pipeline peak-shaving capacity on December 2, 2017, December 2017, the fourth quarter of 2017, and within one year of 2017. Since the current time is July 27, 2017, December 2, 2017 The peak-shaving capacity of pipelines in Japan, December 2017, the fourth quarter of 2017, and one year in 2017 is the peak-shaving capacity of pipelines in the future. Therefore, according to the existing data, the corresponding value for the preset time period range in the future is obtained. The average value of the first uneven coefficient of the preset time period that has occurred, that is, the corresponding data of December 2, 2016, December 2016, and the fourth quarter of 2016 are obtained based on the data actually recorded by the pipeline network. , the...
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