考虑电力行业碳排放的全国碳价预测

doi:10.11930/j.issn.1004-9649.202308084

中国电力 ›› 2024, Vol. 57 ›› Issue (5): 79-87.DOI: 10.11930/j.issn.1004-9649.202308084

• 新型能源体系下电碳协同市场机制及优化运行 • 上一篇下一篇

考虑电力行业碳排放的全国碳价预测

王一蓉¹(), 陈浩林¹(), 林立身²(), 唐进²()

1. 国家电网有限公司大数据中心，北京　100052
2. 北京中创碳投科技有限公司，北京　100007

收稿日期:2023-08-21 接受日期:2023-12-12 出版日期:2024-05-28 发布日期:2024-05-16
作者简介:王一蓉（1979—），女，博士，正高级工程师，从事金融科技研究，E-mail：yirong-wang@sgcc.com
陈浩林（1996—），男，硕士，工程师，从事机器学习研究，E-mail：sgccchenhaolin@163.com
林立身（1991—），男，通信作者，硕士，从事碳定价与碳金融研究，E-mail：linlishen@sino-carbon.cn
唐进（1979—），男，博士，高级工程师，从事企业碳管理研究，E-mail：tangjin@sino-carbon.cn
基金资助:
国家电网有限公司大数据中心科技项目（SGSJ0000KFJS2200034）。

National Carbon Price Prediction Considering Carbon Emissions from the Power Industry

Yirong WANG¹(), Haolin CHEN¹(), Lishen LIN²(), Jin TANG²()

1. Big Data Center of State Grid Corporation of China, Beijing 100052, China
2. SinoCarbon Innovation & Investment Co., Ltd., Beijing 100007, China

Received:2023-08-21 Accepted:2023-12-12 Online:2024-05-28 Published:2024-05-16
Supported by:
This work is supported by Science and Technology Project of the Big Data Center of State Grid Corporation of China (No.SGSJ0000KFJS2200034).

摘要/Abstract

摘要：

为更好预测全国碳价走势，基于带有外生变量的自回归差分移动平均模型（autoregressive integrated moving average with exogenous variable model，ARIMAX），分履约期和非履约期使用不同的外生变量分别构建了全国碳价预测模型。首先，基于对全国碳市场制度规则研究和交易特征分析，识别出全国碳价在非履约期主要受参与者预期的影响，在履约期碳价主要受企业履约需求驱动；其次，在模型训练方面，采用一种自回归差分移动平均模型，在不同阶段引入不同的外生变量来提升碳价预测效果；最后，基于全国碳市场第一履约期真实价格数据验证结果表明，所提的全国碳价预测模型在准确性方面优于基准模型。

关键词: 电力行业, 碳排放, 碳配额, 价格预测, 全国碳市场, ARIMAX模型

Abstract:

In order to better predict the trend of national carbon prices, a national carbon price prediction model is constructed based on the autoregressive integrated moving average with exogenous variable model (ARIMAX), using different exogenous variables during the fulfillment and non-fulfillment period. Firstly, based on research on the institutional rules of the national carbon market and analysis of trading characteristics, it is found that the national carbon price is mainly influenced by the expectations of participants during the non-fulfillment period, and is mainly driven by the fulfillment demand of enterprises during the fulfillment period. Secondly, in terms of model training, an autoregressive differential moving average model is adopted to introduce different exogenous variables at different stages to improve the effectiveness of carbon price prediction. Finally, the real price data of the first compliance period in the national carbon market are used for verification, and the results show that the proposed national carbon price prediction model in this article is superior to the benchmark model in terms of accuracy.

Key words: power industry, carbon emission, carbon allowances, price forecast, national carbon market, ARIMAX model

王一蓉, 陈浩林, 林立身, 唐进. 考虑电力行业碳排放的全国碳价预测[J]. 中国电力, 2024, 57(5): 79-87.

Yirong WANG, Haolin CHEN, Lishen LIN, Jin TANG. National Carbon Price Prediction Considering Carbon Emissions from the Power Industry[J]. Electric Power, 2024, 57(5): 79-87.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202308084

https://www.electricpower.com.cn/CN/Y2024/V57/I5/79

图/表 6

图 1 全国碳市场整体运行框架流程

Fig.1 The overall operation process of the national carbon market in China

图 2 2021年7月6日—12月31日全国碳市场配额收盘价和成交量走势

Fig.2 Closing price and trading volume trend of the national carbon market quotas in China during July 7th and December 31st, 2021

图 3 2021年7月16日—10月31日电力行业碳排放量和全国碳市场配额收盘价走势

Fig.3 Carbon emissions from the power industry and the closing price trend of the national carbon market quotas in China during July 16th and October 31st, 2021

图 4 非履约期模型预测价格与实际价格对比

Fig.4 Comparison between fulfillment period model predicted prices and actual prices

表 1 模型预测价格与实际价格误差

Table 1 Errors between model predicted prices and actual prices

	E_MA	E_RMS	E_MS	E_MAP/%	E_SMAP/%
非履约期	0.96	0.98	1.77	1.96	1.95
履约期	0.45	0.67	0.74	0.98	0.98

图 5 履约期模型预测价格与实际价格对比

Fig.5 Comparison between fulfillment period model predicted prices and actual prices

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考虑电力行业碳排放的全国碳价预测

National Carbon Price Prediction Considering Carbon Emissions from the Power Industry

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考虑电力行业碳排放的全国碳价预测

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