Application Research On The Gas Load Feature In Chong Qing

Gas load forecasting relate to safety, reliability of gas supply system and economic benefits of the gas company’s and so on. So it becomes a hot subjdet. The feature of and historical data is the basic for gas load forecasting, but, in recent years, most research focused on the establishment and application of mathematical models and lack investigation of gas load. In addition, the more mature the mathematical model is, the more stringent requirement will be for historical load data and influence factor. It’s difficult for gas companies to use the gas load forecasting complex model for the lack of technical personnel.Firstly, this study focused on the gas load research and anlysis in Chongqing. Secondly, the author established a simplified model of household gas load forecasting according to the demand from gas company. Then, a strategy to allot the amount of gas supply to household, commercial, industrial load was built in this paper. Finally, the strategy was used to finish the pipe network hydraulic simulation in different condition. The main contents are as follows:The condition of gas supply system’s load in the area from 2013 to 2015 was surveyed which was used to calculate heat consumption index. Even calculate monthly peak coefficient, daily peak coefficient and hourly peak coefficient. This paper stated consumption law of commercial and industrial users’ natural gas, and the causes to be formed is analyzed.Carrying out gas load research in Chongqing: classified the residential gas loads into the villa building, multi-layer building, high-rise building lever, excluding outlier by Grubbs method, amending the outlier through the characteristic curve method. Finally, get the 24-hours asymmetrical coefficient curve and 95% confidence interval about the three residential buildings by normal fitting. Selecting all types of mathematical model, choose quadratic equation to regressing weekly gas load and weekly average temperature.Using the historical gas load data in the same area to verify the model, establish the pipe network hydraulic simulation model and load distribution scripts based on pipeline parameters. The results of simulation prove the reasonableness of load estimation and allocation. If the reasonability of the distribution model can be proved, it can be used to calculate peak pressure and maximum network capacity. Furthermore, optimization of pipe network can be achieved. This model innovatively combined load forecasting and actual pipe network allocation.In summary, the author comes up with a systematic method for gas companies to get the flow and pressure of pipe network by hydraulic simulation. With limited survey data from the residents and commercial users and Gas Company daily manage. The simulation results can give information on the spots without SCADA system monitoring. Besides, the characteristics of residential gas load from the survey can be use in designing and optimizing gas pipe network in this area.