<figure><img src="image_1.png" /></figure>

HAL
open science

Impact of Operating Load Limits on the Control
Strategy and Performance of Alkaline Electrolyzer
Arrays in Renewable Energy Systems

Xiongzheng Wang, Xin Meng, Gongzhe Nie, Haoran Yang

► To cite this version:

Xiongzheng Wang, Xin Meng, Gongzhe Nie, Haoran Yang. Impact of Operating Load Limits on the Control Strategy and Performance of Alkaline Electrolyzer Arrays in Renewable Energy Systems. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0078 . hal-05081985

HAL Id: hal-05081985

https://utc.hal.science/hal-05081985v1

Submitted on 23 May 2025

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Impact of Operating Load Limits on the Control Strategy and Performance of Alkaline Electrolyzer Arrays in Renewable Energy Systems

Xiongzheng Wang, Xin Meng, Gongzhe Nie, Haoran Yang
Electrical Engineering School of Sichuan University / ChengDu 610065, China
Tel.: +86 18292875730
E-Mail: mengxin_pe@163.com

## Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 52207217.

## Keywords

«Electrolysis», «Hydrogen», «Renewable energy systems», «Control strategy», «Operating load limits».

## Abstract

This study examines the impact of varying operating load limits on control strategies and performance of alkaline electrolyzer arrays in renewable energy-based hydrogen production. Lower load limits improve hydrogen production by 10.63%, especially during low wind availability, but cause slight imbalances in load distribution.

## Introduction

Hydrogen energy is increasingly recognized as a cornerstone in the transition to a sustainable energy future due to its versatility, high energy density, and potential to significantly reduce greenhouse gas emissions. As a clean energy carrier, hydrogen plays a crucial role in decarbonizing various sectors, including transportation, industry, and power generation. Its importance is further amplified when considering the global shift towards renewable energy sources, which provide an environmentally friendly and sustainable method for hydrogen production [1]-[3]. Hydrogen production from renewable energy sources, such as wind, solar, and hydropower, not only leverages abundant natural resources but also addresses the pressing need to reduce dependence on fossil fuels. This synergy between hydrogen energy and renewable energy sources is vital for achieving global energy sustainability goals, as it enables the generation of green hydrogen, a zero-emission fuel that holds the

promise of a cleaner and more resilient energy system [4]-[6].

Electrolyzers, as the core components of renewable energy-based hydrogen production systems, include Alkaline Electrolyzers (ALE), Proton Exchange Membrane Electrolyzers (PEM), and Solid Oxide Electrolyzers (SOEC). Among them, Alkaline Electrolyzers are technologically mature and have relatively low costs, making them widely adopted in renewable energy hydrogen production systems [7].

Currently, the manufacturing capability of individual alkaline electrolyzers has reached the megawatt scale, necessitating the parallel connection of multiple units to the bus via rectifiers for large-scale hydrogen production. The volatility of renewable energy leads to issues such as power point drift during electrolyzer operation and frequent startups, which significantly affect equipment lifespan and hydrogen production efficiency. Current research on improving the performance of renewable energy-based hydrogen production systems primarily focuses on refining control strategies and optimizing operations. For instance, Reference [8] controls parallel electrolyzers to operate in a power-sharing mode, but the average hydrogen production power remains significantly below 50% of the rated power, resulting in very low efficiency during periods of low hydrogen production. Reference [9] proposes sequentially connecting and disconnecting parallel electrolyzers based on different operating points, setting power intervals for start-stop operations, thereby reducing the frequency of startups and shutdowns. References [10] and [11] employ rotational scheduling to balance the operating time of different electrolyzers, thereby extending their lifespan. However, these studies primarily focus on predetermined operational strategies for the electrolyzers, without examining the impact of different minimum load limits under the same control strategy on system performance.
According to the working principle of alkaline electrolyzers, oxygen is generated at the anode and hydrogen at the cathode, with the primary function of the membrane being to separate the anode and cathode to prevent the explosive mixture of the produced hydrogen and oxygen. However, due to the limitations of current membrane technology, gases may pass through the membrane to some extent, particularly the smaller hydrogen molecules. When the input power is below the rated level, the risk of hydrogen and oxygen mixing increases. To ensure safety, it is essential to determine the minimum operating load of the electrolyzer. Additionally, operating at low power accelerates the aging of the electrolyzer [12].

In this paper, first, considering the dynamic characteristics of the alkaline electrolyzer, a multi-state transformation model of the electrolyzer is established, and then the daisy chain control strategy is introduced. Finally, four types of electrolyzers with different minimum operating power are selected, and the real wind power output data are used for simulation analysis in Python. The main contribution of this article is as follows: The hydrogen production of the electrolyzer array can be improved by selecting the electrolyzer with a lower operating load limit. The total hydrogen production of the four electrolyzers with a 10% operating load lower limit was 74.64 kg higher than that of the electrolyzers with a 50% limit, representing an approximate 10.63% increase over the original total production.

This article is organized as follows. In Section 2, the system structure, the multi-state transition model of the electrolyzer, and the daisy chain control strategy are introduced. In Section 3, a simulation is conducted using Python, and the results are analyzed. Finally, Section 4 concludes the article.

# System modeling

## A. System structure

The system structure described in this paper is illustrated in Fig. 1, where the electrolyzers are connected in parallel to the AC bus through their respective rectifiers and are powered by wind turbines. The AC bus is also connected to the public grid, which supplies power only when wind generation is insufficient to keep the electrolyzers in standby mode, thereby reducing the frequency of electrolyzer shutdowns.

<figure><img src="image_3.png" /><figcaption>Fig. 1: Schematic diagram of system topology</figcaption></figure>

## B. Multi-state transition model

To investigate the impact of the lower limit of operating load on the control strategy of the electrolyzer array, it is necessary to base this study on an accurate and suitable multi-state transition model of alkaline electrolyzers [13]. Currently, the classification of electrolyzer states into production, standby, and off has gained widespread recognition [14], as shown in Fig. 2.

<figure><img src="image_4.png" /><figcaption>Fig. 2: Electrolyzer state transition</figcaption></figure>

In the off state, the electrolyzer neither produces hydrogen nor consumes energy. In the standby state, the electrolyzer does not produce hydrogen, but it consumes 5% of its rated power to maintain the temperature of the alkaline solution and to operate auxiliary equipment; at this time, the electrolyzer can be considered a fixed load. In the production state, the electrolyzer produces hydrogen according to the input power within its load range; thus, it can be considered a variable load.

## C. Daisy chain control strategy

The daisy chain control strategy sequentially starts electrolyzers based on the wind power input. When the wind power exceeds the minimum operating power of the electrolyzers, the first unit is activated and its operating power is increased
as wind power continues to rise. Each subsequent electrolyzer is only started when the previous one is operating at its rated power and the remaining wind power is sufficient to exceed the minimum operating power of the next unit, continuing in this manner until the last electrolyzer is also running at rated power. As shown in Fig. 3, the state of four electrolyzers under daisy chain control strategy at $P_{wind}=3P_{ele}$ is presented, the shaded area indicates that the electrolyzer is under minimum operating power threshold, green area indicates the electrolyzer is operating and red area indicates the electrolyzer is shut down. The diagram shows that EL1, EL2, and EL3 are operating at rated power, while EL4 is in off state. However, when the difference between available wind power and Electrolytic hydrogen production power is less than the minimum operating power threshold of next electrolyzer, as indicated by the red arrow in the diagram, the unutilized portion of wind power and energy waste exists.

<figure><img src="image_5.png" /><figcaption>Fig. 3: Schematic diagram of daisy chain control strategy</figcaption></figure>

## Simulation results and discussion

To explore the impact of various lower limits of operating loads on the adoption of a basic start-stop control strategy in an alkaline electrolyzer array, a model was constructed in Python for simulation studies. The 24-hour output of a 4MW wind farm was utilized, as illustrated in Fig. 4. Four alkaline electrolyzers, each with a rated power of 1MW, were chosen to form an electrolyzer array, comprising four groups in total. The operating load lower limits for each group were set at 50%, 30%, 20%, and 10%, respectively.

<figure><img src="image_6.png" /><figcaption>Fig. 4: Wind power output curve</figcaption></figure>

Each group of four electrolyzers initially operated at their rated power, with operating load lower limits set at 50%, 30%, 20%, and 10% of the rated power, respectively. The power curves for each electrolyzer are presented in Fig. 5. As observed in Fig. 5, the power curves of the electrolyzers under the four different operating load lower limits almost entirely overlap during high and rated power operations. This indicates that lowering the operating load lower limit exerts minimal impact on their performance at high and rated power levels.

The impact of different operating load lower limits on the control strategy of electrolyzers is most evident during periods of low wind power. For example, during the 6-12 hour period shown in Fig. 4, wind power is low and highly variable. From the power variation curves of electrolyzer EL1 in Fig. 5(a) during the 10-12 hour period and electrolyzer EL2 in Fig. 5(b) during the 6-9 hour period, it can be observed that the electrolyzer with a 10% rated power load lower limit operated for the longest duration and absorbed the most wind power, while the electrolyzer with a 50% rated power load lower limit operated for the shortest duration and absorbed the least wind power. Therefore, lowering the operating load lower limit of the electrolyzer can enhance its ability to absorb wind power during periods of low wind power.

The impact of different operating load lower limits on the control strategy of electrolyzers is also reflected in the startup time of the electrolyzer. As seen in Fig. 4, wind power gradually increases from 0 during the 15-18 hour period, and the electrolyzers start up sequentially according to the simple start-stop control strategy. From the power variation curve of electrolyzer EL1 in Fig. 5(a) during the 16-18 hour period, it can be observed that the electrolyzer with a 10% rated power load lower limit started producing hydrogen the earliest, while the electrolyzer with
a 50% rated power load lower limit did not start until 17:00, making it the latest to start. Therefore, lowering the operating load lower limit of the electrolyzer can advance its startup time when wind power increases.

<figure><img src="image_7.png" /><figcaption>Fig. 5: Power curve of electrolyzer with different lower limit of operating load (a) EL1; (b) EL2; (c) EL3; (d) EL4</figcaption></figure>

As seen in Fig. 6, with the decrease in the operating load lower limit, the hydrogen production of each electrolyzer shows an increasing trend. The hydrogen production of EL1 increased from 344.7129 kg to 370.2023 kg, representing a 7.39% increase over the original hydrogen production. EL2's hydrogen production increased from 238.2664 kg to 258.7395 kg,

representing an 8.59% increase over the original hydrogen production. The hydrogen production of EL3 increased from 70.1784 kg to 97.1557 kg, representing a 38.44% increase over the original hydrogen production. Finally, the hydrogen production of EL4 increased from 49.4281 kg to 51.1367 kg, representing a 3.46% increase over the original hydrogen production.

<figure><img src="image_8.png" /><figcaption>Fig. 6: Hydrogen production of each electrolyzer</figcaption></figure>

EL2 experienced a slight reduction in production when the operating load lower limit was set to 30% of its rated power, compared to when the lower limit was set to 50%. The primary reason is that when the operating load lower limit was set to 50%, the four electrolyzers began their cold start when wind power exceeded 0.5 MW. By the time the electrolyzers completed their cold start, the wind power had already exceeded 1.5 MW, allowing EL1 and EL2 to almost simultaneously complete their startup and begin hydrogen production. In contrast, for the electrolyzers with lower operating load limits, the wind power after the cold start could only support the operation of EL1, as the remaining power was less than the lower limit required for EL2. Although there was a slight reduction in hydrogen production for EL2 when the operating load lower limit was set to 30% of its rated power, the total hydrogen production still increased, as shown in Fig. 6. The total hydrogen production of the four electrolyzers with a lower limit set to 50% of rated power was 74.64 kg less than that of the electrolyzers with a lower limit of 10% of rated power, accounting for approximately 10.63% of the total production. Therefore, lowering the operating load lower limit of the electrolyzers can significantly increase the hydrogen production of the electrolyzer array.
<figure><img src="image_9.png" /><figcaption>Fig. 7: Total hydrogen production</figcaption></figure>

The load factor is used to measure the balance between operating power and time among electrolyzers. The calculation formula is as follows:

$$load\ factor = \frac{\sum_{t=1}^{T} p_{i,t}^{ele} \Delta t}{\sum_{t=1}^{T} Capacity_{i} \Delta t} \quad (1)$$

Where $P_{i,t}^{ele}$ represents the power of the electrolyzer at time t, $\Delta t$ represents the operating time of the electrolyzer, and $P_{rated}$ represents the rated power of the electrolyzer.

The smaller the standard deviation of the load factors among the four electrolyzers, the more balanced the distribution of operating power and time among them. As shown in Fig. 8, the standard deviation of the four electrolyzers under the four different operating load lower limits increases as the load lower limit decreases, but the increase is quite limited. From the power variation curve of EL4 in Fig. 5(d), it can be observed that the power curves for EL4 under the four different operating load lower limits almost overlap. As the operating load lower limit decreases, the ability of the electrolyzers to absorb wind power during periods of low wind power increases. This allows more of the smaller wind power to be utilized, leading to increased operating time and hydrogen production for EL1 and EL2, which does not contribute to reducing the standard deviation. However, the limited increase in the standard deviation is mainly due to the inherent control effect of the simple start-stop control strategy. As shown in Fig. 9, the load factors of the four electrolyzers with a 50% operating load lower limit are highly unbalanced.

<figure><img src="image_10.png" /><figcaption>Fig. 8: Standard deviation of load factors of four electrolyzers with different lower limits of operating loads</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9: Load factor for four electrolyzers at 50%</figcaption></figure>

## Conclusion

This study, grounded in a dynamic transition model of electrolyzers, compared and analyzed the effects of four different operating load lower limits (10%, 20%, 30%, and 50%) on the control outcomes of an alkaline electrolyzer array utilizing a basic start-stop control strategy. The Python-based simulation revealed that the effects of different operating load lower limits are most pronounced during periods of low wind power. Lowering the operating load lower limit enhances the electrolyzers' ability to absorb wind power during low wind periods and advances their startup time as wind power increases. Consequently, reducing the operating load lower limit can significantly increase the hydrogen production of the electrolyzer array. The total hydrogen production of the four electrolyzers with a 10% operating load lower limit was 74.64 kg higher than that of the electrolyzers with a 50% limit, representing an approximate 10.63% increase over the original total production.
However, while lowering the operating load lower limit slightly disrupts the balance of the load factors among the electrolyzers, the effect is minimal.

References

[1]. A. Ursua, L. M. Gandia, and P. Sanchis, ‘Hydrogen Production From Water Electrolysis: Current Status and Future Trends’, Proceedings of the IEEE, vol. 100, no. 2, pp. 410-426, Feb. 2012.

[2]. J. Li, G. Li, S. Ma, Z. Liang, Y. Li, and W. Zeng, ‘Modeling and Simulation of Hydrogen Energy Storage System for Power-to-gas and Gas-to-power Systems’, Journal of Modern Power Systems and Clean Energy, vol. 11, no. 3, pp. 885-895, May 2023.

[3]. G. Pan, W. Gu, Y. Lu, H. Qiu, S. Lu, and S. Yao, ‘Optimal Planning for Electricity-Hydrogen Integrated Energy System Considering Power to Hydrogen and Heat and Seasonal Storage’, IEEE Transactions on Sustainable Energy, vol. 11, no. 4, pp. 2662–2676, Oct. 2020.

[4]. J. Wu, J. Xiao, J. Hou, J. Zhang, C. Jin, and R. Han, ‘Generation Potential and Economy Analysis of Green Hydrogen in China’, in 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), May 2022, pp. 4477-4482.

[5]. R. V. Vardhan, R. Mahalakshmi, R. Anand, and A. Mohanty, ‘A Review on Green Hydrogen: Future of Green Hydrogen in India’, in 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Apr. 2022, pp. 303–309.

[6]. Nawel. Outili and A. Hassen. Meniai, ‘Patent Mapping and Analysis for Green and Clean Hydrogen Production Technology’, in 2023 14th International Renewable Energy Congress (IREC), Dec. 2023, pp. 1–5.

[7]. J. Wang, J. Wen, J. Wang, B. Yang, and L. Jiang, ‘Water electrolyzer operation scheduling for green hydrogen production: A review’, Renewable and Sustainable Energy Reviews, vol. 203, p. 114779, Oct. 2024.

[8]. M. K. Cho et al., ‘Alkaline anion exchange membrane water electrolysis: Effects of electrolyte feed method and electrode binder content’, Journal of Power Sources, vol. 382, pp. 22–29, Apr. 2018.

[9]. R. Fang and Y. Liang, ‘Control strategy of electrolyzer in a wind-hydrogen system considering the constraints of switching

times', International Journal of Hydrogen Energy, vol. 44, no. 46, pp. 25104-25111, Sep. 2019.

[10]. X J Shen, C Y Nie and H Lyu, "Coordination control strategy of wind power-hydrogen alkaline electrolyzer bank considering electrothermal characteristics", Transactions of China Electrotchnical Society, vol. 36, pp. 463-472, 2021.

[11]. W. Won, H. Kwon, J.-H. Han, and J. Kim, ‘Design and operation of renewable energy sources based hydrogen supply system: Technology integration and optimization’, Renewable Energy, vol. 103, pp. 226–238, Apr. 2017.

[12]. Y. Xia, H. Cheng, H. He, and W. Wei, ‘Efficiency and consistency enhancement for alkaline electrolyzers driven by renewable energy sources’, Commun Eng, vol. 2, no. 1, Art. no. 1, May 2023.

[13]. C. Varela, M. Mostafa, and E. Zondervan, ‘Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach’, International Journal of Hydrogen Energy, vol. 46, no. 14, pp. 9303–9313, Feb. 2021.

[14]. Y. Zheng, S. You, H. W. Bindner, and M. Münster, ‘Optimal day-ahead dispatch of an alkaline electrolyser system concerning thermal–electric properties and state-transitional dynamics’, Applied Energy, vol. 307, p. 118091, Feb. 2022.