Study On Comprehensive Post Evaluation Of Offshore Wind Power Project

The State Council of China has released “The Action Plan for Carbon Dioxide Peaking by2030”,where it clearly stipulated: develop new energy,and by 2030,the installed gross capacity of wind power and solar power should be up to more than 1.2 billion kilowatt.Offshore wind power is an important area of renewable energy development.In 2021,the new scale of offshore wind power in China reached 16.9 million kilowatt.Compared with onshore wind power,there are multiplied complexity and challenge of offshore wind power.It is of great significance to investigate how to judge whether the offshore wind power project meets the investment expectation,how to improve the operation and management level of offshore wind power project on the basis of finding risks and problems,which can be referred to for subsequent investment decisions.Therefore,the post evaluation of offshore wind power projects that have been running is increasingly important.On the basis of the characteristics of offshore wind power projects,this thesis chose proper qualitative and quantitative indicators from four aspects: post evaluation for process,post evaluation for wind condition,post evaluation for business condition and post evaluation for finance,and constructs the post evaluation indicator system of offshore wind power projects.Firstly,when determining the constant weight,the thesis introduced the network analytic hierarchy process optimized by three interval numbers and entropy weight method to comprehensively weight each indicator subjectively and objectively,and then this thesis created a "penalty" state variable weight vector according to the variable weight theory to modify the constant weights of unqualified indexes.Finally,the thesis made a comprehensive post evaluation of offshore wind power projects on the basis of the grey cloud whitening weight clustering model,and then got the evaluation grade.In terms of determining the weight: firstly,the thesis introduced three interval numbers to improve the network analytic hierarchy process,of which the purpose is not only to keep the range of interval values,but also to highlight the value with the biggest possibility,and this can reflect the preferences of decision makers more accurately.Secondly,the "penalty" state variable weight vector is used to modify the constant weight,and the punishment of unqualified indicators can reflect its short board effect.In terms of evaluation model: this thesis used cloud comment to quantify qualitative indicators into numerical values,and this is closer to human thinking mode.The introduction of cloud model into the traditional grey clustering whitening weight function can describe the randomness,fuzziness and incompleteness of the evaluation level boundary information effectively.