Research On Multi-Objective Optimization Of Power Structure Based On Carbon Trading

In recent years,the global warming caused by the greenhouse effect has received more and more attention.A series of external economic problems caused by climate warming have a negative impact on the global’s economic and social development.So,the world jointly take the low-carbon reduction measures to reduce CO2 emissions.From the Kyoto Protocol to the Doha Amendment to the Kyoto Protocol to the Paris Agreement,as the first carbon emitters and the largest developing country,China has been actively involved in global climate governance.As the largest carbon dioxide emissions industry,the power industry undertakes the task of lowcarbon emission reduction.Therefore,the research of power structure optimizes,reduce carbon emissions from the root,has a certain practical significance to achieve low-carbon power industry development.In this paper,the background and significance of the optimization of power supply structure under low carbon condition are introduced.On this basis,the carbon trading mechanism and power supply model and algorithm of power industry are reviewed.And the related theories of power structure optimization are expounded,including the theory and method of power supply planning,the connotation and principle of carbon trading mechanism and so on.Then,the power structure of Hebei Province is analyzed in detail in Hebei Province,and the energy situation and power generation situation of Hebei Province are listed.The power structure of the province is compared with the power structure of our country,and the potential of low-carbon emission reduction in Hebei province is analyzed in detail.Next,the multiobjective decision-making method and Pareto correlation definition are introduced.On this basis,we establish the multi-objective optimization model,the model includes two objective functions include economic cost and carbon emission and multiple constraints.To verify the practicability of the model,this paper takes Hebei Province as an example to forecast the electricity demand in Hebei Province from 2016 to 2020 by the BP neural network based on genetic algorithm.And collect the other data needed for the analysis model.Finally,through the empirical analysis of power structure optimization in Hebei Province,it is concluded that the development of power structure in Hebei Province from 2016 to 2020 is based on the analysis of the power structure of Hebei Province during the "13th Five-Year Plan" The feasibility of multi-objective optimization model of power structure based on carbon transaction is verified,which can provide reference for the optimization of power supply structure in other provinces.Based on this,we choose the non-dominated sorting genetic algorithm based on fast classification to optimize the power structure during the "13th FiveYear Plan" in Hebei Province.Finally,through the empirical analysis of power structure optimization in Hebei Province,this paper draws out the development trend of power structure in Hebei Province from 2016 to 2020,and validates the feasibility of multi-objective optimization model of power structure based on carbon transaction,which can provide reference for other provinces’ power structure optimization research.