提升风电消纳的绿氢钢铁冶炼系统动力学建模

doi:10.11930/j.issn.1004-9649.202310099

摘要/Abstract

摘要：

富裕风电电解水制氢，耦合高污染的钢铁冶炼过程，既促进炼钢产业低碳转型，又可有效提升风电消纳能力。为了模拟绿氢钢铁冶炼对风电消纳能力的影响，综合考虑钢铁冶炼的可靠性、氢能发展的经济性以及风力发电的环境性，确定绿电-氢能-钢铁冶炼耦合系统内部交互关系和反馈机制；结合钢铁行业发展趋势和绿氢替代潜力，建立了提升风电消纳的绿氢钢铁冶炼系统动力学模型；同时考虑到政府支持力度与制氢技术的发展，构建差异化情景，分析不同条件下的氢能需求规模和风电消纳水平。基于新疆案例表明，加大氢冶金支持力度和提高制氢电解效率，能够有效提升钢铁行业的绿氢替代率并加速绿氢成本降低，进而实现风电的全额消纳，有效解决“弃风”难题。

关键词: 风电消纳, 绿氢需求, 钢铁行业, 清洁冶炼, 系统动力学

Abstract:

Rich wind power electrolysis of water to produce hydrogen, coupled with high-pollution steel smelting process, not only promotes the low-carbon transformation of the steel-making industry, but also effectively improves the wind power consumption capacity. In order to simulate the influence of green hydrogen steel smelting on wind power accommodation capacity, considering the reliability of steel smelting, the economy of hydrogen energy development and the environment of wind power generation, the internal interaction and feedback mechanism of green electricity-hydrogen energy-steel smelting coupling system are determined. Combined with the development trend of iron and steel industry and the potential of green hydrogen substitution, a dynamic model of green hydrogen iron and steel smelting system for improving wind power consumption is established. At the same time, considering the government support and the development of hydrogen production technology, a differentiated scenario is constructed to analyze the scale of hydrogen demand and the level of wind power consumption under different conditions. Based on the case of Xinjiang, it is shown that increasing the support of hydrogen metallurgy and improving the efficiency of hydrogen electrolysis can effectively improve the replacement rate of green hydrogen in the iron and steel industry and accelerate the reduction of green hydrogen cost, so as to realize the full consumption of wind power and effectively solve the problem of abandoned wind.

Key words: wind power consumption, green hydrogen demand, iron and steel industry, clean smelting, system dynamics

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.

Zhuan ZHOU, Shuai MIAO, Tiejiang YUAN. System Dynamics Modeling of Green Hydrogen Steel Smelting to Improve Wind Power Consumption[J]. Electric Power, 2024, 57(8): 36-45.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310099

https://www.electricpower.com.cn/CN/Y2024/V57/I8/36

图/表 11

图 1 绿电-氢能-钢铁冶炼耦合系统的构建流程

Fig.1 Construction process of green electricity-hydrogen energy-steel smelting coupling system

图 2 绿电-氢能-钢铁冶炼耦合系统的因果关系

Fig.2 The causality diagram of the coupling system of green electricity-hydrogen energy-steel smelting

表 1 关键参数设置

Table 1 Key parameter settings

参数	初始值	参数	初始值
地区生产总值/亿元	9630.8	加工废钢率/%	4.5
总人口/万人	2428	绿氢成本/(元·kg^–1)	29.9
生铁产量/万t	849.92	绿氢价格/(元·kg^–1)	53
粗钢产量/万t	868.37	电解效率/%	65
钢材产量/万t	1087.64	电解槽年利用小时数/h	3000
人均饱和水平/(t·人^–1)	12	上网电价/(元·(kW·h)^–1)	0.25
税率/%	15	风力发电量/(亿kW·h)	220
自产废钢率/%	4.25	风电年利用小时数/h	3025

表 2 模型检验结果

Table 2 Model test results

年份	地区生产总值			钢材产量			风力发电量
年份	真实值/亿元	模拟值/亿元	相对误差/%	真实值/万t	模拟值/万t	相对误差/%	真实值/(亿kW·h)	模拟值/(亿kW·h)	相对误差/%
2016	9630.80	9630.80	0	1087.64	1087.64	0	220.00	221.50	0.68
2017	11159.90	11145.52	0.13	1299.64	1308.93	0.71	312.70	318.35	1.81
2018	12809.40	12771.65	0.29	1322.65	1339.92	1.31	360.26	370.60	2.87
2019	13597.10	13765.31	1.24	1367.93	1382.23	1.05	407.16	425.35	4.47
2020	13800.70	14272.4	3.42	1420.52	1459.04	2.71	435.60	453.51	4.11
2021	16311.60	15993.52	1.95	1467.68	1460.06	0.52	503.60	515.75	2.41

图 3 生铁、粗钢、钢材、人均钢铁量的预测结果

Fig.3 Prediction results of pig iron, crude steel, steel and per capita steel quantity

图 4 钢铁总存量、废钢产量的预测结果

Fig.4 Prediction results of total steel stock and scrap steel production

图 5 灵敏度分析

Fig.5 Sensitivity analysis diagram

图 6 氢能总需求量

Fig.6 Total demand for hydrogen energy

图 7 电解槽装机容量、绿氢成本的预测结果

Fig.7 Prediction results of electrolytic cell installed capacity and green hydrogen cost

图 8 绿氢回报

Fig.8 Green hydrogen returns

图 9 弃电量、消纳水平的预测结果

Fig.9 Prediction results of abandoned electricity and consumption level

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