Modeling and control strategy for hydrogen production systems coupled with PV and battery storage

. Environmental conditions can significantly affect the performance of photovoltaic (PV) hydrogen production systems, resulting in fluctuations in PV output and suboptimal hydrogen production. In order to solve these problems, a voltage stabilization control based approach has been implemented for a photovoltaic integrated hydrogen production system, which is based on an existing hydrogen production model coupled to PV storage. The aim of this approach is to enhance system stability, improve the quality of photovoltaic power generation, and optimize hydrogen production. The strategy includes maximum power point tracking (MPPT) control for the PV system, as well as coordinated control of the electrochemical energy storage system to ensure stable bus voltage and efficient use of solar resources. The effectiveness of this control strategy was demonstrated by simulation.


1.Preface
PV hydrogen production through electrolysis of water is one of the "Green Hydrogen" production technologies. However, during the PV hydrogen production process, the quality of the output electrical energy may fluctuate due to environmental factors [1][2], thus affecting hydrogen production efficiency. To address this problem, energy storage systems can be deployed to mitigate PV power fluctuations and achieve smooth power output. According to the characteristics of the energy storage system and the control strategy of the PV energy system inverter, the PVstorage coupling hydrogen production system can achieve stable hydrogen production capabilities. Therefore, research on control strategies for PV-storage coupled hydrogen production systems is crucial for addressing PV power integration issues and improving hydrogen production system stability.
Currently, research progress has been made on control strategies for PV-storage coupled hydrogen production systems. To achieve balance in the PV-storage microgrid system, some studies [3] have incorporated energy storage units into the PV system grid connection process to effectively detect PV and grid information for peak shaving and filling. However, control accuracy may be affected by environmental changes and may lead to incorrect identification of the maximum power point, thus affecting the stability of the PV storage microgrid system. Other studies [4][5][6][7] have developed Proton Exchange Membrane (PEM) hydrogen production models, distributed control strategies, and improved Maximum Power Point Tracking (MPPT) algorithms for PV systems.
However, these studies did not fully consider the energy balance in response to light fluctuations, the state of charge (SOC) of energy storage batteries, and the dynamic characteristics of hydrogen production rate.
To address instability in the DC bus and inefficient hydrogen production under environmental variation for PV-storage coupled hydrogen production systems [8], this study develops a comprehensive model for PV systems, electrochemical energy storage systems [9], and PEM electrolysis cells [10][11]. In order to maximize the use of PV energy, MPPT control is used to track the output power of the PV system. In addition, we introduce adaptive coordination control of the electrochemical energy storage system to stabilize the DC bus, smooth hydrogen production, and implement the PEM cell dynamics model for hydrogen production.

2..Modeling of Light-Induced Hydrogen Production System with Energy Storage
Coupling.

2.1Structure of photovoltaic storage coupling hydrogen production system
The photo-storage coupled hydrogen production system model investigated in this study is presented in Figure 1. It consists of a photovoltaic system model, an electrochemical energy storage system model, and a PEM electrolyzer model. The photovoltaic system and energy storage system are connected directly to the DC bus via a converter. MPPT control and adaptive control strategies are employed, and the load terminal is connected to the PEM electrolyzer to monitor real-time hydrogen production rates and hydrogen storage pressure.

Photovoltaic System
The photovoltaic system consists of various photovoltaic cells connected either in series or in parallel. Each individual photovoltaic cell has a photoelectric current generator, a diode, and a resistor that causes energy loss. A photovoltaic cell can be modeled as follows [12]: Where: and are parameters for photovoltaics, is the short circuit current, is the output voltage of the photovoltaic cell, is the open circuit voltage, is the operating current at the maximum power point of the light, and is the operating voltage at the maximum power point.

Electrochemical Energy Storage System
To cope with fluctuations in output from photovoltaic systems, energy storage systems are often added as an intermediate link to support and release energy. Among these systems, electrochemical energy storage using lithium-ion batteries has gained popularity due to its simple structure and extended cycle life. Energy storage systems can be modeled as follows:

PEM electrolyzer system
PEM electrolyzers are a suitable solution for dealing with output fluctuations and intermittency in renewable energy, thanks to their wide range of operating current densities, adaptable power adjustment, and fast start-stop capabilities [13]. It has been observed that [14] PEM electrolyzer arrays exhibit exceptional MPPT tracking capabilities and a working curve that aligns with the maximum power curve of large-scale photovoltaic systems, as depicted in the figure 2. This paper presents the construction of a dynamic model for the PEM electrolyzer. Using MATLAB's Simulink software, a simulation model of the PEM electrolyzer is established, which consists of the cathode module, anode module, proton exchange membrane module, hydrogen storage module, and voltage module [15][16]. The mathematical representation of the dynamic structure of the PEM electrolyzer is shown in the figure 3.

Control structure of a photovoltaic storage coupled hydrogen production system
The actual operating conditions of the photovoltaic storage system are influenced by environmental fluctuations, which affect the dynamic balance of the system power and the stability of the bus voltage during the load start-stop process. To ensure stability and smooth operation, this paper proposes an adaptive coordinated control strategy that considers fluctuations in photovoltaic output and energy storage. The control strategy illustrated in Figure 4 independently adjusts the charging and discharging operating states of the energy storage system in response to changes in bus voltage. This maintains voltage stability at both ends of the PEM electrolyzer, ensures smooth bus voltage fluctuation of the photovoltaic storage system, and smoothes the hydrogen production process.
The MPPT converter control module in Figure 4 is based on the variable step perturbation observation MPPT algorithm. D0, Dt, and Dt-1 represent duty cycles for the initial setting, the current moment, and the previous moment, respectively. Pt and Pt-1 represent input power at current and previous moments, respectively, while Ut and Ut-1 represent input voltages at current and previous moments, respectively. ΔD is the interference step size, and for this paper, the interference step is set to 1.25x10^-4 through debugging.

PV MPPT control method
The MPPT algorithm is commonly classified into four methods: constant voltage tracking, short-circuit current proportional coefficient, conductance increment, and disturbance observation. While the constant voltage tracking and short-circuit current proportional coefficient methods are open-loop systems that directly control voltage, they have drawbacks such as inaccurate voltage tracking under external environmental changes and unstable tracking of maximum power output. The conductance increment method requires derivative calculations, making the algorithm more complex and slower. In contrast, the disturbance observation method, which approaches the high-power point, is a simple and effective way to track the maximum power of the photovoltaic system.
This study uses the MPPT algorithm based on a variable step disturbance observation to control the power of the photovoltaic system. Closed-loop control compares the measured voltage and power values, and cycle judgment is used to adjust the switch's duty cycle until maximum power is achieved under given conditions.

Adaptive Coordinated Control Method for Energy Storage
The adaptive coordinated control method for energy storage is based on the DC bus voltage as the power control signal. When the power output of the photovoltaic system is higher or lower than the rated load power, its charging and discharging functions are realized through the adaptive coordinated control method of the energy storage system to maintain the balance of the bus voltage and ensure stable system operation. To control the charging and discharging of the energy storage system, a droop control superimposed bidirectional DC-DC converter is used as the output interface of the lithium battery. This achieves output or absorption of the fluctuating energy of the photovoltaic power supply. The lithium battery is connected to the output through a bidirectional DC-DC converter, and the switch is controlled to turn on and off through the adaptive control method. This realizes the charging and discharging characteristics of the lithium battery, which cooperates with the photovoltaic system to supply power to the hydrogen production unit.

Conclusion
This paper proposes a coordinated control strategy that takes into account the adaptive energy storage balance and considers the fluctuation of photovoltaic output. The aim is to control the quality of photovoltaic power that is susceptible to environmental factors and stabilize the DC bus voltage at the reference value, thus achieving smooth hydrogen production. In addition, the proposed control model aims to integrate the photovoltaic storage system to optimize hydrogen production.