考虑氢能需求的甘肃省电力行业低碳转型路径研究

doi:10.11930/j.issn.1004-9649.202211012

摘要/Abstract

摘要：

氢能是清洁能源发展的重要方向，在电力、工业、交通、建筑供热等领域有着广泛的应用前景，氢能的综合利用对于电力行业的低碳转型至关重要。基于支持向量回归（SVR）模型对各领域的氢能需求进行预测，利用新能源电制氢能量转换关系等效替代为电负荷；考虑不确定性对供电和氢能设备投资成本进行预测；以预测数据、初始装机情况与减排目标为输入，各类电源发展规模、装机占比、碳排放量等为输出，建立规划模型。以甘肃省为例，分析不同场景下电源结构和碳排放量变化情况，以及氢能在电力行业低碳转型过程中的影响，为电力行业的低碳转型路径规划提供参考。

关键词: 氢能, 低碳转型, 需求预测, 规划模型

Abstract:

Hydrogen energy is an important direction in the development of clean energy and has a wide range of application prospects in the fields of power, industry, transportation and building heating, etc. The comprehensive utilization of hydrogen energy is crucial to the low-carbon transformation of the power industry. This paper forecasts the hydrogen energy demand in various fields based on the SVR model and various social data, and uses the energy conversion relationship between power consumption and hydrogen production to equivalently replace the hydrogen energy, while forecasting the investment cost of various types of equipment; the planning model is established with the forecast data, the initial installation situation and carbon emission target as inputs, and the development scale of various power sources, the proportion of installed power and carbon emission as outputs; taking Gansu Province as an example, the planning model is analysed. The impact of hydrogen energy participation in the low-carbon transformation of the power industry is explored, providing reference for the planning of the low-carbon transformation path of the power industry in Gansu Province.

Key words: hydrogen energy, low-carbon transition, demand prediction, planning model

张学强, 董龙, 王栋. 考虑氢能需求的甘肃省电力行业低碳转型路径研究[J]. 中国电力, 2023, 56(12): 262-272.

Xueqiang ZHANG, Long DONG, Dong WANG. Study on Low-Carbon Transition Path of Power Industry in Gansu Province Considering Hydrogen Energy Demand[J]. Electric Power, 2023, 56(12): 262-272.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202211012

https://www.electricpower.com.cn/CN/Y2023/V56/I12/262

图/表 16

图 1 氢能产-输-储-用综合利用模式

Fig.1 Comprehensive utilization of hydrogen energy

图 2 SVR算法流程

Fig.2 Flow chart of SVR algorithm

图 3 电源单位投资成本

Fig.3 Unit investment cost of power supply

图 4 氢能设备投资成本

Fig.4 Hydrogen energy equipment investment cost

表 1 各领域氢负荷历史数据

Table 1 Historical data of hydrogen load in diffirent areas

年份	合成甲醇/t	合成氨/t	原油加工量/万t	公交车/辆	城市天然气供气总量/亿m³
2007	57687.00	777460.5	—	—	—
2008	62062.00	658704.5	—	—	—
2009	54008.30	761022.8	—	—	—
2010	50514.72	763833.1	1383.5	4382	7.29
2011	364619.90	733376.0	1613.5	4965	8.81
2012	564232.00	431337.9	1520.5	5214	11.21
2013	506165.30	700655.1	1554.2	5359	13.40
2014	727115.90	575393.0	1446.4	5488	15.92
2015	—	—	1424.3	5275	16.19
2016	—	—	1341.5	5233	16.86
2017	—	—	1440.8	5850	20.37
2018	—	—	1440.0	6519	23.51
2019	—	—	1465.6	7314	25.20
2020	—	—	1467.5	6408	25.44

图 5 用氢量预测曲线

Fig.5 Prediction curve of annual hydrogen quantity

图 6 规划区域氢等效电负荷

Fig.6 The equivalent electric load of hydrogen in the planning region

图 7 预测电负荷

Fig.7 The predicted electricity load

图 8 场景1规划结果

Fig.8 Planning results in scenario 1

图 9 场景1电力与工业煤制氢碳排放量

Fig.9 Carbon emissions from electricity and industrial coal to hydrogen in scenario 1

图 10 场景2规划结果

Fig.10 Planning results in scenario 2

图 11 场景2碳排放

Fig.11 Carbon emission in scenario 2

图 12 场景3规划结果

Fig.12 Planning results in scenario 3

图 13 场景3碳排放

Fig.13 Carbon emission in scenario 3

图 14 场景2、3下氢能设备容量变化情况

Fig.14 Change in capacity of hydrogen energy equipment under scenarios 2 and 3

表 2 各场景下的成本和碳减排量

Table 2 Cost and carbon emission

场景	投资成本/元	运维成本/元	综合成本/元	电力总碳排量/t	2020—2050年发电碳排减少量/t	新能源制氢减排总量/t
1	1.4296×10¹⁰	8.1523×10⁸	1.5111×10¹⁰	1.7909×10⁹	2.41488×10⁷	—
2	1.5083×10¹⁰	8.3701×10⁸	1.5921×10¹⁰	1.7909×10⁹	2.41488×10⁷	7.2927×10⁷
3	1.4680×10¹⁰	8.2015×10⁸	1.5499×10¹⁰	1.7349×10⁹	2.8421×10⁷	7.2927×10⁷

参考文献 28

1	康重庆, 姚良忠. 高比例可再生能源电力系统的关键科学问题与理论研究框架[J]. 电力系统自动化, 2017, 41 (9): 2- 11.
	KANG Chongqing, YAO Liangzhong. Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2017, 41 (9): 2- 11.
2	张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46 (8): 189- 207.
	ZHANG Shenxi, WANG Danyang, CHENG Haozhong, et al. Key technologies and challenges of low-carbon integrated energy system planning for carbon emission peak and carbon neutrality[J]. Automation of Electric Power Systems, 2022, 46 (8): 189- 207.
3	周原冰, 杨方, 余潇潇, 等. 中国能源电力碳中和实现路径及实施关键问题[J]. 中国电力, 2022, 55 (5): 1- 11.
	ZHOU Yuanbing, YANG Fang, YU Xiaoxiao, et al. Realization pathways and key problems of carbon neutrality in China’s energy and power system[J]. Electric Power, 2022, 55 (5): 1- 11.
4	郭庆来, 兰健, 周艳真, 等. 基于混合智能的新型电力系统运行方式分析决策架构及其关键技术[J]. 中国电力, 2023, 56 (9): 1- 13.
	GUO Qinglai, LAN Jian, ZHOU Yanzhen, et al. Architecture and key technologies of hybrid-intelligence-based decision-making of operation modes for new type power systems[J]. Electric Power, 2023, 56 (9): 1- 13.
5	齐步洋, 卓振宇, 杜尔顺, 等. 考虑储能装置寿命的电网侧规模化电化学储能规划与评估方法[J]. 中国电力, 2023, 56 (8): 1- 9, 47.
	QI Buyang, ZHUO Zhenyu, DU Ershun, et al. Planning and assessment method of large-scale electrochemical energy storage in power grids considering battery aging[J]. Electric Power, 2023, 56 (8): 1- 9, 47.
6	姜海洋, 杜尔顺, 金晨, 等. 高比例清洁能源并网的跨国互联电力系统多时间尺度储能容量优化规划[J]. 中国电机工程学报, 2021, 41 (6): 2101- 2115.
	JIANG Haiyang, DU Ershun, JIN Chen, et al. Optimal planning of multi-time scale energy storage capacity of cross-national interconnected power system with high proportion of clean energy[J]. Proceedings of the CSEE, 2021, 41 (6): 2101- 2115.
7	彭生江, 孙传帅, 妥建军, 等. 面向统一能源系统的中长期氢负荷预测[J]. 中国电力, 2022, 55 (1): 84- 90.
	PENG Shengjiang, SUN Chuanshuai, TUO Jianjun, et al. Medium and long-term hydrogen load forecast for unified energy system[J]. Electric Power, 2022, 55 (1): 84- 90.
8	MI Y Z, LIU C Y, YANG J Y, et al. Low-carbon generation expansion planning considering uncertainty of renewable energy at multi-time scales[J]. Global Energy Interconnection, 2021, 4 (3): 261- 272. DOI
9	杜尔顺, 孙彦龙, 张宁, 等. 适应低碳电源发展的低碳电网规划模型[J]. 电网技术, 2015, 39 (10): 2725- 2730.
	DU Ershun, SUN Yanlong, ZHANG Ning, et al. Transmission expansion planning model facilitating low-carbon power sources' development[J]. Power System Technology, 2015, 39 (10): 2725- 2730.
10	李政, 陈思源, 董文娟, 等. 碳约束条件下电力行业低碳转型路径研究[J]. 中国电机工程学报, 2021, 41 (12): 3987- 4001.
	LI Zheng, CHEN Siyuan, DONG Wenjuan, et al. Low carbon transition pathway of power sector under carbon emission constraints[J]. Proceedings of the CSEE, 2021, 41 (12): 3987- 4001.
11	项目综合报告编写组. 《中国长期低碳发展战略与转型路径研究》综合报告[J]. 中国人口·资源与环境, 2020, 30 (11): 1- 25.
12	张运洲, 张宁, 代红才, 等. 中国电力系统低碳发展分析模型构建与转型路径比较[J]. 中国电力, 2021, 54 (3): 1- 11.
	ZHANG Yunzhou, ZHANG Ning, DAI Hongcai, et al. Model construction and pathways of low-carbon transition of China’s power system[J]. Electric Power, 2021, 54 (3): 1- 11.
13	侯金鸣, 孙蔚, 肖晋宇, 等. 电力系统关键技术进步与低碳转型的协同优化[J]. 电力系统自动化, 2022, 46 (13): 1- 9.
	HOU Jinming, SUN Wei, XIAO Jinyu, et al. Collaborative optimization of key technology progress and low-carbon transition of power systems[J]. Automation of Electric Power Systems, 2022, 46 (13): 1- 9.
14	FAN J L, SHEN S, XU M, et al. Cost-benefit comparison of carbon capture, utilization, and storage retrofitted to different thermal power plants in China based on real options approach[J]. Advances in Climate Change Research, 2020, 11 (4): 415- 428. DOI
15	SEPULVEDA N A, JENKINS J D, DE SISTERNES F J, et al. The role of firm low-carbon electricity resources in deep decarbonization of power generation[J]. Joule, 2018, 2 (11): 2403- 2420. DOI
16	毛健雄, 郭慧娜, 吴玉新. 中国煤电低碳转型之路: 国外生物质发电政策/技术综述及启示[J]. 洁净煤技术, 2022, 28 (3): 1- 11.
	MAO Jianxiong, GUO Huina, WU Yuxin. Road to low-carbon transformation of coal power in China: a review of biomass co-firing policies and technologies for coal power abroad and its inspiration on biomass utilization[J]. Clean Coal Technology, 2022, 28 (3): 1- 11.
17	WITKOWSKI A, RUSIN A, MAJKUT M, et al. Analysis of compression and transport of the methane/hydrogen mixture in existing natural gas pipelines[J]. International Journal of Pressure Vessels and Piping, 2018, 166, 24- 34. DOI
18	丁剑, 方晓松, 宋云亭, 等. 碳中和背景下西部新能源传输的电氢综合能源网构想[J]. 电力系统自动化, 2021, 45 (24): 1- 9.
	DING Jian, FANG Xiaosong, SONG Yunting, et al. Conception of electricity and hydrogen integrated energy network for renewable energy transmission in western China under background of carbon neutralization[J]. Automation of Electric Power Systems, 2021, 45 (24): 1- 9.
19	邱玥, 周苏洋, 顾伟, 等. “碳达峰、碳中和”目标下混氢天然气技术应用前景分析[J]. 中国电机工程学报, 2022, 42 (4): 1301- 1321.
	QIU Yue, ZHOU Suyang, GU Wei, et al. Application prospect analysis of hydrogen enriched compressed natural gas technologies under the target of carbon emission peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42 (4): 1301- 1321.
20	任洲洋, 罗潇, 覃惠玲, 等. 考虑储氢物理特性的含氢区域综合能源系统中长期优化运行[J]. 电网技术, 2022, 46 (9): 3324- 3333.
	REN Zhouyang, LUO Xiao, QIN Huiling, et al. Mid/long-term optimal operation of regional integrated energy systems considering hydrogen physical characteristics[J]. Power System Technology, 2022, 46 (9): 3324- 3333.
21	张红, 袁铁江, 谭捷, 等. 面向统一能源系统的氢能规划框架[J]. 中国电机工程学报, 2022, 42 (1): 83- 94.
	ZHANG Hong, YUAN Tiejiang, TAN Jie, et al. Hydrogen energy system planning framework for unified energy system[J]. Proceedings of the CSEE, 2022, 42 (1): 83- 94.
22	白雪松. 国内外氢气的生产和消费[J]. 化工技术经济, 2003, 21 (12): 18- 25.
	BAI Xuesong. An analysis on the production and consumption of hydrogen in the world and China[J]. Chemical Techno-Economics, 2003, 21 (12): 18- 25.
23	KURTZ J, BRADLEY T, WINKLER E, et al. Predicting demand for hydrogen station fueling[J]. International Journal of Hydrogen Energy, 2020, 45 (56): 32298- 32310. DOI
24	HUANG J S, LI W, WU X Y. Forecasting the hydrogen demand in China: a system dynamics approach[J]. Mathematics, 2022, 10 (2): 205. DOI
25	罗凤章, 张旭, 杨欣, 等. 基于深度学习的综合能源配电系统负荷分析预测[J]. 高电压技术, 2021, 47 (1): 23- 32.
	LUO Fengzhang, ZHANG Xu, YANG Xin, et al. Load analysis and prediction of integrated energy distribution system based on deep learning[J]. High Voltage Engineering, 2021, 47 (1): 23- 32.
26	LIU W J, SUN L, LI Z L, et al. Trends and future challenges in hydrogen production and storage research[J]. Environmental Science and Pollution Research, 2020, 27 (25): 31092- 31104. DOI
27	FAN G F, PENG L L, HONG W C, et al. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression[J]. Neurocomputing, 2016, 173, 958- 970. DOI
28	TAHMASBI R, HASHEMI S M. Modeling and forecasting the urban volume using stochastic differential equations[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15 (1): 250- 259. DOI

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