计及多关联因素的电力行业碳排放权分配方案

潘险险; 余梦泽; 隋宇; 郇嘉嘉; 洪海峰; 林俐

doi:10.7500/AEPS20190606001

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计及多关联因素的电力行业碳排放权分配方案

能源转型与电力支撑 | 更新时间：2020-07-14

- 计及多关联因素的电力行业碳排放权分配方案
- Allocation Scheme of Carbon Emission Rights for Power Industry Considering Multiple Correlated Factors
- 电力系统自动化 2020年44卷第1期页码：35-42
- 作者机构：
  
  1. 广东电网有限责任公司电网规划研究中心，广东省广州市 510080
  2. 新能源电力系统国家重点实验室（华北电力大学），北京市 102206
- 作者简介：
  
  潘险险(1989—)，女，通信作者，硕士，工程师，主要研究方向：电力系统分析与控制、碳排放权交易。E-mail：panxianxianpxx@163.com
  余梦泽(1980—)，男，博士，高级工程师，主要研究方向：电力系统分析与控制、碳排放权交易。E-mail：yumengze@gd.csg.cn
  隋宇(1983—)，男，硕士，高级工程师，主要研究方向：电力系统分析与控制、碳排放权交易。E-mail：suiyu@gd.csg.cn
- 基金信息：
  
  国家自然科学基金重大项目(51190103);已申请国家发明专利（申请号：201711168457.3）
- DOI：10.7500/AEPS20190606001
  中图分类号：
- 收稿日期：2019-06-06，
  
  修回日期：2019-09-12，
  
  网络出版日期：2019-11-19，
  
  纸质出版日期：2020-01-10
- 稿件说明：
移动端阅览
潘险险,余梦泽,隋宇等.计及多关联因素的电力行业碳排放权分配方案[J].电力系统自动化,2020,44(01):35-42.

PAN Xianxian,YU Mengze,SUI Yu,et al.Allocation Scheme of Carbon Emission Rights for Power Industry Considering Multiple Correlated Factors[J].Automation of Electric Power Systems,2020,44(01):35-42.
潘险险,余梦泽,隋宇等.计及多关联因素的电力行业碳排放权分配方案[J].电力系统自动化,2020,44(01):35-42. DOI： 10.7500/AEPS20190606001.

PAN Xianxian,YU Mengze,SUI Yu,et al.Allocation Scheme of Carbon Emission Rights for Power Industry Considering Multiple Correlated Factors[J].Automation of Electric Power Systems,2020,44(01):35-42. DOI： 10.7500/AEPS20190606001.

摘要

电力行业碳排放权的初始分配是保证碳交易市场有效运作的基础和关键。通过对电力行业碳排放过程的特征挖掘，进行指标分解，提取影响碳排放分配结构体系的多关联影响因素；其次，结合专家的知识和经验，基于改进的层次分析法确定各指标的主观权重；接着，采用改进的熵权系数法进行指标映射，构建新的识别参数体系，自适应确定各指标的客观权重；然后，通过主客观权重的优化组合，确定各指标的综合权重，建立电力行业碳排放权的分配模型。最后，以某省区为算例进行对比仿真分析，仿真结果表明：与传统考虑单个因素的分配方案相比，文中方案计及了多关联因素的影响，得到的理论测算结果能更客观地反映地区碳减排权责的实际状况，避免了过于主观或过于客观的片面评估结果。

Abstract

The initial allocation of carbon emission rights for the electric power industry is the basis and key to ensure the effective operation of the carbon trading market. Through the mining of the characteristics of the carbon emissions in the electric power industry

the index decomposition is carried out to extract the multiple correlated factors that affect the carbon emission allocation system. Secondly

based on the knowledge and experience of experts

the subjective weight of each indicator is determined based on the improved analytic hierarchy process. Then

the improved entropy weight method is used to map the indicators

and a new identification parameter system is constructed. The objective weights of each indicator are determined adaptively. Finally

through the optimized combination of subjective and objective weights

the comprehensive weights of each indicator are determined. A case study of a province using comparative simulation analysis is performed to examine the effectiveness of the proposed method. Compared with traditional schemes that consider single factor

the proposed model that considers multiple correlated factors can reflect the actual responsibilities of regional carbon emission reduction more objectively. It can avoid excessively subjective or excessively objective one-sided evaluation results.

关键词

Keywords

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