基于系统动力学的氢需求量中长期预测

doi:10.11930/j.issn.1004-9649.202208038

中国电力 ›› 2023, Vol. 56 ›› Issue (10): 11-21.DOI: 10.11930/j.issn.1004-9649.202208038

• 面向新型电力系统的氢能及其系统集成控制关键技术 • 上一篇下一篇

基于系统动力学的氢需求量中长期预测

袁铁江¹(), 张一瑾¹(), 杨紫娟¹^,²(), 蒋东方³()

1. 大连理工大学电气工程学院，辽宁大连　116024
2. 西安科技大学电控学院，陕西西安　710054
3. 国网能源研究院有限公司，北京　102209

收稿日期:2022-08-10 出版日期:2023-10-28 发布日期:2023-10-31
作者简介:袁铁江（1975—），男，博士，教授，从事氢能与电力、化石能源系统集成技术等研究，E-mail： ytj1975@dlut.edu.cn
张一瑾（1998—），女，通信作者，硕士，从事氢负荷预测、电源低碳规划研究，E-mail： 754097877@qq.com
杨紫娟（1998—），女，硕士研究生，从事氢能与电力、化石能源系统集成研究，E-mail： 1315633224@qq.com
基金资助:
国网经济技术研究院有限公司自主立项项目（基于新能源云的电力系统碳排放达峰路径研究，ZZKJ-2021-03）

Medium and Long-Term Hydrogen Load Prediction Based on System Dynamics

Tiejiang YUAN¹(), Yijin ZHANG¹(), Zijuan YANG¹^,²(), Dongfang JIANG³()

1. College of Electrical Engineering, Dalian University of Technology, Dalian 116024, China
2. School of Electronic Control, Xi’an University of Science and Technology, Xi’an 710054, China
3. State Grid Energy Research Institute Co., Ltd., Beijing 102209, China

Received:2022-08-10 Online:2023-10-28 Published:2023-10-31
Supported by:
This work is supported by the Set Up Projects of State Grid Economic Research Institute Co., Ltd. (Study on Peak Path of Carbon Emission in Power System Based on New Energy Cloud, No.ZZKJ-2021-03)

摘要/Abstract

摘要：

“双碳”背景下氢能将在各种领域发挥巨大作用，开展氢需求中长期预测具有重要意义。基于系统动力学方法建立了省级氢需求中长期预测模型。首先将氢能需求分为工业、供热和交通3大领域，考虑各子系统内部因素的相互作用以及经济发展、政策支持等外部因素的影响，分析因果关系，构建系统预测方程；其次设定系统参数，采用最小二乘法方程回归得到方程常数，基于该省发展规划利用灰色模型设定表函数参数，并将模拟结果与历史数据进行对比，结果表明模型误差较小，适用于该省氢需求预测；最后利用所建立的系统动力学模型对该省的氢需求量进行了预测。

关键词: 系统动力学, 氢需求预测, 因果分析, 系统流程图, 灰色模型

Abstract:

Hydrogen energy will play a great role in various fields under the background of “double carbon”, it is important to carry out medium and long-term forecasting of hydrogen demand, and propose a medium and long-term forecasting model of provincial hydrogen demand based on the system dynamics approach. Firstly, the hydrogen demand was divided into three major areas, namely, industry, heating and transportation, and the interaction of internal factors of each subsystem and the influence of external factors such as economic development and policy support were considered to analyze the cause-effect relationship and construct the prediction equation, and on this basis, a flow chart of the total system hydrogen demand prediction was drawn. Secondly, the system parameters are set, and the equation constants are obtained by least squares equation regression, and the table function parameters are set based on the development plan of the province and the gray model. Finally, the system dynamics model built was used to forecast the hydrogen demand in the province.

Key words: system dynamics, hydrogen load prediction, causal analysis, system flow chart, grey model

袁铁江, 张一瑾, 杨紫娟, 蒋东方. 基于系统动力学的氢需求量中长期预测[J]. 中国电力, 2023, 56(10): 11-21.

Tiejiang YUAN, Yijin ZHANG, Zijuan YANG, Dongfang JIANG. Medium and Long-Term Hydrogen Load Prediction Based on System Dynamics[J]. Electric Power, 2023, 56(10): 11-21.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202208038

https://www.electricpower.com.cn/CN/Y2023/V56/I10/11

图/表 17

参考文献 29

1	KHATOON S, IBRAHEEM, SINGH A K, et al. Analysis and comparison of various methods available for load forecasting: an overview[C]//2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH). Ghaziabad, India. IEEE, 2015: 243–247.
2	李开卷. 地区电网中长期电力负荷预测研究[D]. 南昌: 南昌大学, 2020.
	LI Kaijuan. Study on medium and long-term electricity load forecasting in regional power grids[D]. Nanchang: Nanchang University, 2020.
3	廖旎焕, 胡智宏, 马莹莹, 等. 电力系统短期负荷预测方法综述[J]. 电力系统保护与控制, 2011, 39 (1): 147- 152. DOI
	LIAO Nihuan, HU Zhihong, MA Yingying, et al. Review of the short-term load forecasting methods of electric power system[J]. Power System Protection and Control, 2011, 39 (1): 147- 152. DOI
4	MA T, JI J, CHEN M. Study on the hydrogen demand in China based on system dynamics model[J]. International Journal of Hydrogen Energy, 2010, 35 (7): 3114- 3119. DOI
5	PARK S Y, KIM J W, LEE D H. Development of a market penetration forecasting model for hydrogen fuel cell vehicles considering infrastructure and cost reduction effects[J]. Energy Policy, 2011, 39 (6): 3307- 3315. DOI
6	RAHMOUNI S, SETTOU N, NEGROU B, et al. GIS-based method for future prospect of hydrogen demand in the Algerian road transport sector[J]. International Journal of Hydrogen Energy, 2016, 41 (4): 2128- 2143. DOI
7	张红, 袁铁江, 谭捷. 统一能源系统氢负荷中长期预测[J]. 中国电机工程学报, 2021, 41 (10): 3364- 3372. DOI
	ZHANG Hong, YUAN Tiejiang, TAN Jie. Medium and long-term forecast of hydrogen load in unified energy system[J]. Proceedings of the CSEE, 2021, 41 (10): 3364- 3372. DOI
8	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.
9	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
10	邓斌, 张楠, 王江, 等. 基于LTC-RNN模型的中长期电力负荷预测方法[J]. 天津大学学报(自然科学与工程技术版), 2022, 55 (10): 1026- 1033.
	DENG Bin, ZHANG Nan, WANG Jiang, et al. Medium- and long-term power load forecasting method based on LTC-RNN model[J]. Journal of Tianjin University(Science and Technology), 2022, 55 (10): 1026- 1033.
11	王其藩. 系统动力学-修订版[M]. 北京: 清华大学出版社, 2009.
12	李肖冰. 基于系统动力学的中国能源供求预测模型研究[D]. 包头: 内蒙古科技大学, 2015.
	LI Xiaobing. Research on China energy supply and demand forecasting model based on system dynamics[D]. Baotou: Inner Mongolia University of Science and Technology, 2015.
13	陈智锴. 基于系统动力学的工业园区电力需求分析与预测[D]. 北京: 北京交通大学, 2019.
	CHEN Zhikai. Analysis and forecast of power demand in industrial parks based on system dynamics[D]. Beijing: Beijing Jiaotong University, 2019.
14	刘亚琼. 基于系统动力学的广东省电力需求预测模型的研究[D]. 广州: 华南理工大学, 2016.
	LIU Yaqiong. Research on Guangdong power demand forecasting model based on system dynamics[D]. Guangzhou: South China University of Technology, 2016.
15	陈蓉珺, 何永秀, 陈奋开, 等. 基于系统动力学和蒙特卡洛模拟的电动汽车日负荷远期预测[J]. 中国电力, 2018, 51 (9): 126- 134.
	CHEN Rongjun, HE Yongxiu, CHEN Fenkai, et al. Long-term daily load forecast of electric vehicle based on system dynamics and Monte Carlo simulation[J]. Electric Power, 2018, 51 (9): 126- 134.
16	LOPEZ-ARBOLEDA E, SARMIENTO A T, CARDENAS L M. Policy assessment for electromobility promotion in Colombia: a system dynamics approach[J]. Transportation Research Part D:Transport and Environment, 2023, 121, 103799. DOI
17	熊亚林, 刘玮, 高鹏博, 等. “双碳”目标下氢能在我国合成氨行业的需求与减碳路径[J]. 储能科学与技术, 2022, 11 (12): 4048- 4058.
	XIONG Yalin, LIU Wei, GAO Pengbo, et al. Demand of hydrogen energy in China’s synthetic ammonia industry and carbon reduction path under the goal of “double carbon”[J]. Energy Storage Science and Technology, 2022, 11 (12): 4048- 4058.
18	SHARIFZADEH M, SIKINIOTI-LOCK A, SHAH N. Machine-learning methods for integrated renewable power generation: a comparative study of artificial neural networks, support vector regression and Gaussian process regression[J]. Renewable and Sustainable Energy Reviews, 2019, 108, 513- 538. DOI
19	贾亮. 我国合成氨及下游产品工业消费现状与预测[J]. 化学工业, 2012, 30 (S1): 38- 41. DOI
	JIA Liang. Consumption situation and prediction of ammonia and its downstream industries in China[J]. Chemical Industry, 2012, 30 (S1): 38- 41. DOI
20	张卫峰, 季玥秀, 马骥, 等. 中国化肥消费需求影响因素及走势分析: Ⅰ化肥供应[J]. 资源科学, 2007, 29 (6): 162- 169. DOI
	ZHANG Weifeng, JI Yuexiu, MA Ji, et al. Factors affecting fertilizer demand and supply in China[J]. Resources Science, 2007, 29 (6): 162- 169. DOI
21	王建建, 胡辰树. 我国氢燃料电池专用车发展现状与趋势分析[J]. 专用汽车, 2021, (4): 51- 55. DOI
	WANG Jianjian, HU Chenshu. Development status and trend analysis of special purpose vehicle for hydrogen fuel cell in China[J]. Special Purpose Vehicle, 2021, (4): 51- 55. DOI
22	周静. 天然气管线掺混氢气的特性分析[D]. 抚顺: 辽宁石油化工大学, 2020.
	ZHOU Jing. Characteristic analysis of natural gas pipeline mixed with hydrogen[D]. Fushun: Liaoning Shihua University, 2020.
23	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.
24	张烘玮, 赵杰, 李敬法, 等. 天然气掺氢输送环境下的腐蚀与氢脆研究进展[J]. 天然气工业, 2023, 43 (6): 126- 138.
	ZHANG Hongwei, ZHAO Jie, LI Jingfa, et al. Research progress on corrosion and hydrogen embrittlement in hydrogen-natural gas pipeline transportation[J]. Natural Gas Industry, 2023, 43 (6): 126- 138.
25	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
26	彭生江, 杨德州, 孙传帅, 等. 基于氢负荷需求的氢能系统容量规划[J]. 中国电力, 2023, 56 (7): 13- 20, 32.
	PENG Shengjiang, YANG Dezhou, SUN Chuanshuai, et al. Capacity planning of hydrogen production and storage system based on hydrogen load demand[J]. Electric Power, 2023, 56 (7): 13- 20, 32.
27	尚娟, 鲁仰辉, 郑津洋, 等. 掺氢天然气管道输送研究进展和挑战[J]. 化工进展, 2021, 40 (10): 5499- 5505.
	SHANG Juan, LU Yanghui, ZHENG Jinyang, et al. Research status-in-situ and key challenges in pipeline transportation of hydrogen-natural gas mixtures[J]. Chemical Industry and Engineering Progress, 2021, 40 (10): 5499- 5505.
28	卢子敬, 李子寿, 郭相国, 等. 基于多目标人工蜂鸟算法的电-氢混合储能系统最优配置[J]. 中国电力, 2023, 56 (7): 33- 42.
	LU Zijing, LI Zishou, GUO Xiangguo, et al. Optimal configuration of electricity-hydrogen hybrid energy storage system based on multi-objective artificial hummingbird algorithm[J]. Electric Power, 2023, 56 (7): 33- 42.
29	魏震波, 魏平桉, 郭毅, 等. 考虑需求侧管理和碳交易的电-气互联网络分散式低碳经济调度[J]. 高电压技术, 2021, 47 (1): 33- 44.
	WEI Zhenbo, WEI Pingan, GUO Yi, et al. Decentralized low-carbon economic dispatch of electricity-gas network in consideration of demand-side management and carbon trading[J]. High Voltage Engineering, 2021, 47 (1): 33- 44.

汽油产量影响因素	关联度系数	柴油产量影响因素	关联度系数
公路里程	0.9364	原油加工量	0.9020
能源消费总量	0.9363	工业产值	0.8419
工业产值	0.9044	能源消费总量	0.8162
原油加工量	0.8656	公路里程	0.8142
地区生产总值	0.8344	地区生产总值	0.7258
交通业产值	0.8260	货运量	0.7242
人均可支配收入	0.7571	交通业产值	0.7169
私人汽车保有量	0.6261	民用客货车数量	0.6337
邮政业务总量	0.5852	邮政业务总量	0.5528

汽油产量影响因素	关联度系数	柴油产量影响因素	关联度系数
公路里程	0.9364	原油加工量	0.9020
能源消费总量	0.9363	工业产值	0.8419
工业产值	0.9044	能源消费总量	0.8162
原油加工量	0.8656	公路里程	0.8142
地区生产总值	0.8344	地区生产总值	0.7258
交通业产值	0.8260	货运量	0.7242
人均可支配收入	0.7571	交通业产值	0.7169
私人汽车保有量	0.6261	民用客货车数量	0.6337
邮政业务总量	0.5852	邮政业务总量	0.5528

掺氢比/%	CH₄占比/%	H₂占比/%	高热值/(MJ·m^–3)	密度 /(kg·m^–3）
0	96.114	0.0350	35.785	0.706
5	91.308	5.0333	34.082	0.674
10	86.507	10.032	32.378	0.643
15	81.699	15.028	30.676	0.612
20	76.891	20.028	28.975	0.581

掺氢比/%	CH₄占比/%	H₂占比/%	高热值/(MJ·m^–3)	密度 /(kg·m^–3）
0	96.114	0.0350	35.785	0.706
5	91.308	5.0333	34.082	0.674
10	86.507	10.032	32.378	0.643
15	81.699	15.028	30.676	0.612
20	76.891	20.028	28.975	0.581

第t–1年γ₃		第t年掺氢比/%
0≤γ₃＜0.0035		5
0.0035≤γ₃＜0.007		10
0.007≤γ₃＜0.0105		15
γ₃≥0.0105		20

基于系统动力学的氢需求量中长期预测

Medium and Long-Term Hydrogen Load Prediction Based on System Dynamics

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 29

相关文章 10

编辑推荐

Metrics

方程序号		参数估计值
（10）		A₁ = 0.045；A₂ = −1061.35；A₃ = 1229
（12）		A₄ = 1.319；A₅ = −5.176；A₆ = 3152.22
（14）		C₁ = 0.1587；C₂ = 0.0.3547；C₃ = −14.7019； C₄ = −0.0999；C₅ = 680.6551
（15）		C₆ = −0.0873；C₇ = 0.2671；C₈ = 25.3917； C₉ = 0.02941；C₁₀ = −373.0887
（22）		V₁ = −1.0347；V₂ = 8.2429；V₃ = 2857.612
（23）		V₄ = 3.2224；V₅ = 0.1233
（26）		H₁ = 0.6013；H₂ = 0.0197；H₃ = −5.6195
（28）		H₄ = 1.0513；H₅ = −0.01623；H₆ = −706.22

年份	真实值/t	模拟值/t	相对误差/%
2008	116261.34	120653.26	–3.64
2009	134320.52	132948.70	1.03
2010	134816.54	135149.83	–0.25
2011	129440.86	126231.25	2.54
2012	76131.14	69089.26	10.19
2013	123665.63	122645.21	0.83
2014	101556.86	106445.71	–4.59

年份	真实值/10⁸m³	模拟值/10⁸m³	相对误差/%
2011	7.29	6.91	–5.23
2012	8.81	8.15	–7.44
2013	11.21	11.42	1.88
2014	13.40	13.78	2.86
2015	15.92	15.70	–1.40
2016	16.19	17.14	5.88
2017	16.86	18.18	7.82
2018	20.37	20.65	1.37
2019	23.51	22.51	–4.26
2020	25.20	23.83	–5.46

年份	公交车			重卡
年份	真实值/辆	模拟值/辆	相对误差/%	真实值/ 万辆	模拟值/ 万辆	相对误差/%
2011	4965	4766	–4.00	7.40	7.38	–0.28
2012	5214	5379	3.17	8.12	8.02	–1.20
2013	5359	5523	3.06	8.78	8.73	–0.56
2014	5488	5649	2.93	9.56	9.14	–4.41
2015	5275	5386	2.10	9.40	9.31	–0.95
2016	5233	5634	7.67	9.45	9.63	1.91
2017	5850	6161	5.32	9.78	9.97	1.96
2018	6519	6503	–0.25	10.34	10.54	1.96
2019	7314	7102	–2.90	10.78	11.09	2.82
2020	7307	6815	–6.73	11.66	11.28	–3.23

年份	合成氨系统/t	原油加工系统/t	供热系统/t	交通系统/t	总需氢量/t
2021	135936.43	53639.29	12554.15	32649.42	234779.29
2025	147731.64	54908.92	68923.98	97411.11	368975.65
2030	165003.69	57085.43	121284.44	141629.52	485003.08

[1]	朱瑞, 娄奇鹤, 靳晓凌, 刘畅. 适应新型电力系统的电网基建功能化投资结构演化研究[J]. 中国电力, 2024, 57(9): 194-204.
[2]	周专, 苗帅, 袁铁江. 提升风电消纳的绿氢钢铁冶炼系统动力学建模[J]. 中国电力, 2024, 57(8): 36-45.
[3]	彭生江, 孙传帅, 妥建军, 袁铁江. 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.
[4]	张金良, 周秀秀. 基于系统动力学的发电行业市场型碳减排政策影响分析[J]. 中国电力, 2020, 53(6): 114-123.
[5]	杜振东, 徐尔丰, 张笑弟, 刘敦楠, 沈舒仪. 绿色电力证书市场下中国各类电源规模及发电成本演化发展[J]. 中国电力, 2019, 52(7): 168-176.
[6]	陈蓉珺, 何永秀, 陈奋开, 董明宇, 李德智, 光峰涛. 基于系统动力学和蒙特卡洛模拟的电动汽车日负荷远期预测[J]. 中国电力, 2018, 51(9): 126-134.
[7]	马小敏, 高剑, 吴驰, 何锐, 龚奕宇, 李熠, 吴天宝. 基于灰色支持向量机的输电线路覆冰厚度预测模型[J]. 中国电力, 2016, 49(11): 46-50.
[8]	郑雅楠，单葆国，顾宇桂，刘伟，李庚银. 改进的电量灰色预测方法实证对比研究[J]. 中国电力, 2013, 46(6): 108-112.
[9]	章伟，邓院昌. 基于灰色-马尔可夫链的短期风速及风电功率预测[J]. 中国电力, 2013, 46(2): 98-102.
[10]	郑雅楠, 单葆国, 顾宇桂, 李庚银. 中长期电量灰色预测中数据预处理方法研究[J]. 中国电力, 2013, 46(10): 111-114.

年份	真实值/t	模拟值/t	相对误差/%
2011	64960.63	65089.34	0.20
2012	60567.31	59888.43	–1.12
2013	59165.66	59780.53	1.04
2014	56554.02	55507.57	–1.85
2015	53686.56	51691.33	–3.72
2016	49206.54	49719.33	1.04
2017	50648.84	51946.83	2.56
2018	50402.01	51966.85	3.10
2019	52077.10	53138.54	2.04
2020	52074.54	50614.46	–2.80

年份	真实值/t	模拟值/t	相对误差/%
2011	64960.63	65089.34	0.20
2012	60567.31	59888.43	–1.12
2013	59165.66	59780.53	1.04
2014	56554.02	55507.57	–1.85
2015	53686.56	51691.33	–3.72
2016	49206.54	49719.33	1.04
2017	50648.84	51946.83	2.56
2018	50402.01	51966.85	3.10
2019	52077.10	53138.54	2.04
2020	52074.54	50614.46	–2.80

模态框（Modal）标题

基于系统动力学的氢需求量中长期预测

Medium and Long-Term Hydrogen Load Prediction Based on System Dynamics

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 29

相关文章 10

编辑推荐

Metrics