Wind power hydrogen production systems considering uncertainty: multi-time scale operation strategy

doi:10.11949/0438-1157.20241287

Abstract

Abstract:

With the increasing penetration of renewable energy into the grid, the volatility of wind energy and load uncertainty have an increasingly significant impact on the stable operation of the system. Hydrogen storage emerges as a key technology for achieving renewable energy consumption and mitigating fluctuations. This paper proposes a multi-time scale optimal scheduling strategy considering source-load uncertainty and electrolyzer start-stop characteristics, based on wind power hydrogen production to meet downstream hydrogen load demands. The method integrates a multi-state model of the electrolyzer and state-switching constraints. In the day-ahead scheduling stage, a two-stage robust optimization method is adopted, combined with the hot and cold start-stop characteristics of the electrolyzer, to minimize the day-ahead operating cost and cope with the worst-case scenario. In the intra-day adjustment stage, model predictive control (MPC) is used to dynamically adjust each unit in the system based on real-time wind power data and day-ahead optimization results. To verify the effectiveness of the proposed method, a simulation analysis of the electrolytic hydrogen production system is conducted under typical day scenarios obtained through clustering. The results show that, compared with the traditional scheduling scheme, the proposed method can reduce operating costs by about 5%—6% and significantly reduce the number of start-stop cycles of the electrolyzer, thereby improving system stability.

Key words: wind power hydrogen production, multi-timescale, uncertainty, cold-hot start and stop

摘要：

随着可再生能源在电网中的渗透率不断提高，风能的波动性以及负荷不确定性对系统稳定运行的影响越来越显著。氢储能作为实现可再生能源消纳和平抑波动的关键技术显得尤为重要。基于风电制氢以满足下游氢气负荷，并针对实际运行中电解槽频繁启停的问题，提出了一种考虑源荷不确定性和电解槽启停特性的多时间尺度系统最优调度策略。该方法集成了电解槽多状态模型和状态切换约束。在日前调度阶段，采用两阶段鲁棒优化方法，同时结合电解槽冷热启停特性，以最小化日前运行成本为目标，尝试应对最恶劣情景。在日内调整阶段，基于实时风电数据和日前优化结果，通过模型预测控制（MPC）对系统中的各单元进行动态调整。为了验证所提方法的有效性，在聚类获得的典型日场景下进行了电解制氢系统仿真分析。结果表明，与传统调度方案相比，所提方法可将运行成本降低5%~6%，同时大幅减少电解槽的启停次数，提高系统稳定性。

关键词: 风电制氢, 多时间尺度, 不确定性, 冷热启停

CLC Number:

TQ 151.1

Pengwei LIAO, Qinghui LIU, An PAN, Jiayue WANG, Xiaogui FU, Siyu YANG, Hao YU. Wind power hydrogen production systems considering uncertainty: multi-time scale operation strategy[J]. CIESC Journal, 2025, 76(6): 2743-2754.

廖鹏伟, 刘庆辉, 潘安, 王嘉岳, 符小贵, 杨思宇, 余皓. 考虑不确定性的风电制氢系统：多时间尺度运行策略[J]. 化工学报, 2025, 76(6): 2743-2754.

Figures/Tables 13

Table 1 China renewable energy hydrogen projects

序号	项目名称	地点	签约时间	详情
1	辽宁省朝阳县风光储氢一体化项目	辽宁	2022/1	规划建设300 MW级压缩空气储能电站，配套建设900 MW风电、300 MW光伏、2×500 m³/h（标准工况）的电解水制氢项目
2	兴安盟废弃矿区风光储氢一体化项目	内蒙古	2022/2	规划建设900 MW风电、300 MW光伏、200 MW/800 MWh压缩空气储能与2×500 m³/h（标准工况）电解水制氢项目
3	宣化风储氢综合智慧能源示范项目	河北	2022/4	装机容量200 W的风电、30 MW/60 MWh储能站及500 m³/h（标准工况）制氢站
4	鄂尔多斯市乌审旗风光融合绿氢化工示范项目二期、乌兰察布10×10⁴ t/a风光制氢一体化示范项目	内蒙古	2022/12	鄂尔多斯市并网型项目：400 MW风电，制氢2×10⁴ t/a 乌兰察布并网型项目：2546 MW（风电1742 MW、光伏804 MW），制氢10×10⁴ t/a
5	乌兰察布兴和县风光发电制氢合成氨一体化项目	内蒙古	2023/1	规划建设350 MW风电、150 MW光电，年制氢25700 t
6	国际氢能冶金示范区新能源制氢联产无碳燃料项目	内蒙古	2023/1	规划建设30×10⁴ t/a新能源制氢、120×10⁴ t绿氨及配套5000 MW风电绿色冶金化工产业示范区
7	张家口风氢一体化源网荷储综合示范工程项目	河北	2023/7	规划建设200 MW风电、2.4×10⁴ m³/h（标准工况）制氢站、配套储氢装置及40 MW氢燃料电池发电系统。80%风电用于绿氢生产，年产绿氢约1×10⁴ t

Fig.1 Structural diagram of the wind-powered hydrogen production system

Fig.2 Electrolyzer performance characteristics

Fig.3 The schematic overview of the proposed strategy in this paper

Table 2 C&CG algorithm process

C&CG 算法

1 输入初始不确定度集 μ，设 LB =-∞, UB =+∞

2 求解主要问题，得到最优解( x, α, y )，令 UB = α

3 将主问题得到的 x 代入子问题

4 解决子问题 $m a x μ ∈ U m i n y ∈ Ω x, μ c T y$

5 令 $L B = m a x L B, f (x)$

6 如果 $U B - L B ≥ ε$ 则

7 跳到步骤2，将步骤4中得到的最新不确定变量 μ 代入主要问题

8 否则

9 输出最优解 x, y

10 结束

Table 2 C&CG algorithm process

C&CG 算法

1 输入初始不确定度集 μ，设 LB =-∞, UB =+∞

2 求解主要问题，得到最优解( x, α, y )，令 UB = α

3 将主问题得到的 x 代入子问题

4 解决子问题 $m a x μ ∈ U m i n y ∈ Ω x, μ c T y$

5 令 $L B = m a x L B, f (x)$

6 如果 $U B - L B ≥ ε$ 则

7 跳到步骤2，将步骤4中得到的最新不确定变量 μ 代入主要问题

8 否则

9 输出最优解 x, y

10 结束

Fig.4 Typical daily wind turbine power

Fig.5 Hydrogen load data

Fig.6 Intraday forecasted wind power output and error setting

Fig.7 Intraday forecasted hydrogen load and deviation

Fig.8 Total cost graph with different forecast errors

Fig.9 Robust optimization results for typical day 1

Fig.10 Intraday adjustment results for typical day 1

Table 3 Comparison of the number of starts and stops of different strategies

场景	顺序分配策略	本文模型
1	17	0
2	15	0

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