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
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()
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:
CHEN Yun1, SHEN Hao1, WANG Jiayu1, ZHAO Wenkai1, PAN Zhijun1, WANG Xiaohui1, XIAO Yinjing2()
Received:
2021-09-18
Online:
2022-09-28
Published:
2022-10-09
Contact:
XIAO Yinjing
E-mail:bard-ee@sjtu.edu.cn
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.
中图分类号:
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.
表1
用于能源用户聚类的典型日样本数据集(部分)
能源用 户编号 | 典型日1负荷/kWh | … | 2020年6月能源总消耗量 | ||||
---|---|---|---|---|---|---|---|
时间点1 | 时间点2 | 时间点3 | 电力/kWh | 天然气/m3 | 水/t | ||
42 | 1289.4 | 1158.15 | 1135.05 | … | 1833161 | 192491 | 11013 |
49 | 1535.2 | 1836 | 1757.6 | … | 1203562 | 60455 | 4400 |
61 | 395.4 | 406.2 | 421.2 | … | 601560 | 22971 | 3879 |
65 | 4521.3 | 6239.1 | 6516.3 | … | 4260270 | 0 | 17460 |
66 | 2171.3 | 1929.6 | 2106.1 | … | 1052040 | 6855 | 3224 |
71 | 5201 | 5279.4 | 5359.2 | … | 2673789 | 47696 | 9629 |
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