基于“能源大脑”的城市区域碳排放实时计算方法

doi:10.16183/j.cnki.jsjtu.2021.364

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (9): 1111-1117.doi: 10.16183/j.cnki.jsjtu.2021.364

• 新型电力系统与综合能源 • 下一篇

基于“能源大脑”的城市区域碳排放实时计算方法

陈赟¹, 沈浩¹, 王佳裕¹, 赵文恺¹, 潘智俊¹, 王晓慧¹, 肖银璟²()

1.国网上海市电力公司浦东供电公司,上海 200122
2.上海交通大学电子信息与电气工程学院,上海 200240

收稿日期:2021-09-18 出版日期:2022-09-28 发布日期:2022-10-09
通讯作者: 肖银璟 E-mail:bard-ee@sjtu.edu.cn
作者简介:陈赟(1982-),女,浙江省台州市人,硕士,高级工程师,从事电网数字化转型及双碳技术研究.
基金资助:
国家电网有限公司科技项目(52094021000A)

Real-Time Calculation of Carbon Emissions in County-Level Administrative Regions Based on ‘Energy Brain’

CHEN Yun¹, SHEN Hao¹, WANG Jiayu¹, ZHAO Wenkai¹, PAN Zhijun¹, WANG Xiaohui¹, XIAO Yinjing²()

1. Pudong Power Supply Company, State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China
2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2021-09-18 Online:2022-09-28 Published:2022-10-09
Contact: XIAO Yinjing E-mail:bard-ee@sjtu.edu.cn

摘要/Abstract

摘要：

现有碳排放计算方法不能很好地满足碳排放区域逐渐细化和实时需求.为保证碳排放责任分摊的实时和准确,提出一种城市区域碳排放实时计算方法.利用改进的K-means聚类算法,对城市区域能源负荷的运行时段和运行场景进行聚类组合,得到典型碳排放特征.将区域单位电力碳排放量作为碳排放指标;归类运行时段和场景,计算各簇单位电力碳排放量和城市区域碳排放总量.基于中国东部某地区 “能源大脑”中部分能源消费历史数据进行验证,结果表明:该聚类方法和碳排放指标可以有效地实时计算城市区域碳排放总量.

关键词: 碳排放计算, 能源大脑, 城市区域, K-means聚类算法, 单位电力碳排放量

Abstract:

Existing calculation methods of carbon emission cannot well meet the needs of gradual refinement and real-time of carbon emission regions. In order to ensure the real-time and accuracy of carbon emissions responsibility allocation, a real-time calculation method of carbon emissions in urban regions is proposed. The improved K-means clustering algorithm is used to cluster and combine the operating periods and operating scenarios of the urban area energy load,so as to obtain the typical carbon emission characteristics. The regional unit electricity carbon emission is proposed as a carbon emission indicator, the operating period and scenario are classified, and the unit electricity carbon emission and the total carbon emission of urban regions for each cluster are calculated. The proposed algorithm is verified based on part of the historical data of energy consumption in the energy brain of a certain region in eastern China. The results show that the clustering method and carbon emission indicators can effectively calculate the total carbon emission of urban regions in real-time.

Key words: carbon emission calculation, energy brain, urban region, K-means clustering algorithm, unit electricity carbon emission

中图分类号:

F407.6
X 321

陈赟, 沈浩, 王佳裕, 赵文恺, 潘智俊, 王晓慧, 肖银璟. 基于“能源大脑”的城市区域碳排放实时计算方法[J]. 上海交通大学学报, 2022, 56(9): 1111-1117.

CHEN Yun, SHEN Hao, WANG Jiayu, ZHAO Wenkai, PAN Zhijun, WANG Xiaohui, XIAO Yinjing. Real-Time Calculation of Carbon Emissions in County-Level Administrative Regions Based on ‘Energy Brain’[J]. Journal of Shanghai Jiao Tong University, 2022, 56(9): 1111-1117.

图/表 4

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基于“能源大脑”的城市区域碳排放实时计算方法

Real-Time Calculation of Carbon Emissions in County-Level Administrative Regions Based on ‘Energy Brain’

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