摘要
发电企业作为发电环节的主体具备高碳排放的特征,准确预估其在不同情景下的碳排放量有助于"双碳"目标的实现.目前碳排放量的预估聚焦于国家、部门、行业尺度,鲜少涉及企业尺度的研究.以湖北能源集团为例,基于皮尔逊相关性分析识别火力发电量的影响因素,通过神经网络、决策树、支持向量机(线性核函数、多项式核函数、径向基函数、Sigmoid核函数)、装袋算法、随机森林、线性回归、逐步回归算法分别构建火力发电量预测模型,并依据多种评价指标优选模型.随后以火力发电量和碳排放强度为边界条件,通过抽水蓄能发电、水电站低成本电解水制氢储能来削减企业的火力发电量,通过碳捕捉和碳封存技术来降低企业的碳排放强度.基于削减火力发电量和降低碳排放强度设定"结构减排方案"、"技术减排方案"、"综合减排方案",推演各情景下的碳排放量.研究结果表明:利用线性回归、逐步回归模型预估火力发电量的效果良好;2030 年"结构减排方案""技术减排方案""综合减排方案"下碳排放量分别为 1 648.89 万～1 934.07 万t、1 778.07 万～1 820.94 万t、1 345.75 万～1 571.77 万t,2050 年"结构减排方案""技术减排方案""综合减排方案"下碳排放量分别为0～1 117.79 万t、615.43 万～649.70 万t、0～299.20 万t.本文提出的研究思路和方法可为企业尺度的碳排放量预估提供参考.
Abstract
The power generation sector,as the primary entity in the power generation process,possesses characteristics of high carbon emissions.Accurately predicting its carbon emissions under different scenarios contributes to the achievement of the"Dual Carbon"goal.Currently,prediction of carbon emission focus on national,sectoral,and industry scales,with limited research at the enterprise level.Taking Hubei Energy Group as an example,this research employed Pearson correlation analysis to identify factors influencing thermal power generation.Various modeling techniques,including neural networks,decision trees,support vector ma-chines(linear kernel,polynomial kernel function,radial basis function,Sigmoid kernel function),bagging algorithms,random for-ests,linear regression,and stepwise regression,were then used to construct models for predicting the thermal power generation.Models were selected based on multiple evaluation criteria.Subsequently,with thermal power generation and carbon emission in-tensity as boundary conditions,we proposed measures to reduce thermal power generation for the enterprise.This involved utilizing pumped storage for power generation,low-cost electrolysis of water at hydroelectric stations for hydrogen storage,and employing carbon capture and storage technologies to decrease the carbon emission intensity of the enterprise.Based on reduced thermal pow-er generation and lowered carbon emission intensity,"structural emission reduction scenarios","technological emission reduction scenarios"and"comprehensive emission reduction scenarios"were devised to deduce carbon emissions under various scenarios.The research findings indicated that using linear regression and stepwise regression models to predict thermal power generation was effective.In 2030,under the"structural emission reduction scenario","technological emission reduction scenario"and"comprehensive emission reduction scenario",carbon emissions were projected to range from 16.488 9 million tons to 19.340 7 million tons,17.780 7 million tons to 18.209 4 million tons,and 13.457 5 million tons to 15.717 7 million tons,respectively.In 2050,under the same scenarios,carbon emissions were projected to range from 0 to 11.177 9 million tons,6.154 3 million tons to 6.497 0 million tons,and 0 to 2.992 0 million tons,respectively.The proposed research approach and methods can provide a ref-erence for estimating carbon emissions at the enterprise level.
基金项目
国家重点研发计划项目(2021YFC3200305)
国家自然科学基金项目(51879194)
湖北能源集团股份有限公司咨询服务项目(EN00-SJY-FW-2022095)
维普
万方数据