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Modeling And Optimization Of Natural Gas CCHP System In Alpine Region

Posted on:2022-07-26Degree:MasterType:Thesis
Country:ChinaCandidate:H B ZhangFull Text:PDF
GTID:2492306572959509Subject:Power Engineering
Abstract/Summary:PDF Full Text Request
CCHP system with natural gas as the core is a typical comprehensive energy supply mode to realize terminal energy consumption and heterogeneous energy supply optimization.The East China Russia natural gas pipeline enters from Heihe City,Heilongjiang Province,and passes through Heilongjiang,Jilin,Inner Mongolia,Liaoning and other provinces,providing an effective supplement of natural gas resources for Northeast China.Natural gas combined cooling,heating and power system has been demonstrated and applied in the south of Beijing,but how to further improve its economy is still a problem to be solved.Under the requirements of the national goal of coal to gas and double carbon,how to realize the natural gas combined cooling,heating and power supply which can meet the economic requirements in the alpine region with 50% lowgrade heat supply demand is a hot research topic and an urgent problem to be solved.In this paper,the model of natural gas combined cooling,heating and power system in Typical Alpine region is established,and the system optimization,operation economy and forecast analysis are carried out.The main achievements are as follows:Firstly,three typical natural gas CCHP systems are analyzed,and the mathematical models of gas turbine,internal combustion engine,high parameter small steam boiler and system including waste heat recovery device and supplementary combustion device are established.Through the analysis of thermodynamic characteristics,the energy efficiency change rules of thermoelectric conversion,waste heat utilization and steam parameters on the external cooling,heat and electricity demand of the system are obtained,and the configuration schemes of different systems are proposed.Secondly,taking the climate conditions in Harbin as the background,taking the residential buildings with 10000 square meters of usable area as the research object,this paper analyzes the cold,heat and electricity demand of different seasons,typical days and typical users in the whole year by using the load index method.The maximum ratio of heating load to electric load in winter is 12,while the maximum ratio of cooling load to electric load in summer is 4.5,showing significant difference among heating,cooling and electric demand.According to the load demand of different energy,the operation economy of independent gas turbine system,internal combustion engine system and steam boiler system is studied,and the selection suggestions of main equipment are given when the cooling and heating load accounts for different proportions.Taking the change of online electricity price and natural gas price as the influencing factors,the typical daily operation schemes in winter and summer are compared and analyzed.The cost of the combined mode is 1.766 million yuan,which is 10.7%,7.8% and 18.3% less than that of the three single equipment subsystems.Under the condition of multi energy input,the simulation and optimal operation method of the cooling,heating and power system are studied,and the critical curves of typical daily cost and natural gas price in winter and summer are obtained.The BP neural network algorithm is used for simulation prediction verification.The predicted values of three typical systems are compared with the real values,and the error is between 1% and 2%,which confirms the rationality of the method.The main equipment selection,modeling,system modeling,solution and optimization analysis method,BP neural network prediction and simulation method of CCHP system presented in this paper can be applied to the planning and operation scheduling optimization of natural gas CCHP system in alpine region,which has a good theoretical support for the construction of multi energy and heterogeneous integrated energy system.
Keywords/Search Tags:alpine region, CCHP, system configuration and operation optimization, Simulation prediction
PDF Full Text Request
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