Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources

Abstract

Generating hydrogen by electrolysis in an alkaline system with a green power source consisting of wind turbines (WTs) and photovoltaic (PV) power is a promising and sustainable way to produce clean hydrogen to reduce greenhouse gas emissions. This study utilized TRNSYS 16 software to perform a dynamic simulation of a hydrogen system. TRNSYS, which stands for Transient System Simulation Program, is a software package designed for simulating the dynamic behaviour of thermal and electrical energy systems. It is widely used to analyze and optimize the performance of various energy systems. This system incorporated a PV power source and a WT for electricity generation, along with an electrolyzer for hydrogen production. The analysis was carried out to evaluate variable weather conditions, specifically wind speed, solar radiation, and temperature. These factors have a direct impact on the system’s performance, influencing the available power as a consequential outcome. The results reveal that, given the specific climate conditions in the Markham zone, Toronto, the integrated renewable system is capable of consistently providing electricity and meeting the load demand throughout the entire year. However, it is noteworthy that on cold days when solar radiation is limited, the WT emerges as the most effective and efficient power source. The analysis also indicates that the system reliably supplies enough energy to meet the laboratory’s load demand. Moreover, the system’s performance is particularly impressive with the WT as the power source, as it can generate a maximum of 9.03 kg of hydrogen per month. In contrast, the PV power source yields only 0.58 kg H₂. Additionally, the cost per kilogram of hydrogen (kg H₂) is considerably lower when the WT is used, at USD 0.55/kg H₂, while it rises to USD 1.5/kg H₂ when PV is the power source. These findings underscore the significance of using the most suitable power source, such as a WT, in specific climatic conditions and regions in terms of both performance and cost-effectiveness.

1. Introduction

1.1. Background

Global energy consumption has experienced substantial growth over the past ten years, with a 100% increase between 2010 and 2020 [1]. The International Energy Agency (IEA) has recently reported that the annual global energy consumption per capita stands at 22,000 kWh. This upward trend is expected to persist due to population growth and improvements in living standards [2]. Notably, approximately 85% of this energy is derived from fossil fuels such as coal, oil, and gas, which release greenhouse gases [3]. To address the global energy demand sustainably and substantially reduce greenhouse gas emissions, renewable energy sources emerge as the most effective and viable alternative. Information provided by the European Environment Agency (EEA) suggests that without the adoption of renewable energy since 2005, carbon dioxide emissions would have been over 10% higher [4]. According to research findings, nearly one-third of global energy consumption can be attributed to the building sector. Nevertheless, the proliferation of household appliances and the construction of new buildings for various purposes are contributing to the growth of energy consumption [5]. This increased energy demand leads to a rise in greenhouse gas emissions, which is a significant and adverse consequence [6]. Solar and wind energies, as clean and abundant renewable energy sources, hold great promise for integration into buildings to achieve net-zero-energy buildings. However, the effective storage of solar and wind energies is critical for harnessing these clean energy sources. In recent years, hydrogen has gained popularity as a viable means of storing renewable energy [7]. It is projected that the global demand for hydrogen will see a substantial increase by 2050 [8]. Hydrogen possesses several appealing characteristics aside from its evident trait of clean combustion. Notably, it stands out as the most abundant element in the universe (although often inconveniently bonded to other elements such as oxygen or carbon). Of particular importance are its useful properties in terms of relative transportability (e.g., compared to heat) and long-term storage capacity. This last point is beneficial not only for the intermittency problem described above but also for energy security and the resilience of systems in a broad sense. Hydrogen appears to have both technical and economic advantages for long-term storage [9]. Moreover, hydrogen is characterized by its high calorific value, lightweight properties, and high gravimetric energy density, which offers a renewable energy source that can aid in phasing out the use of fossil fuels [10]. As a result of the growing interest in hydrogen as a clean and carbon-free energy source, there have been numerous endeavours to integrate it into buildings for long-term energy storage [11]. A hydrogen energy system typically includes solar panels and wind turbines for generating the required electrical load for the building, an electrolyzer for hydrogen production, a hydrogen storage tank, and a fuel cell for converting electrochemical energy into electrical energy.

1.2. Literature Survey

Hydrogen, recognized as the most abundant element in the universe, presents itself as a potential alternative to fossil fuels due to its high energy content and substantial contributions to sustainable development [12]. Given the increasing environmental pollution and depletion of fossil fuel resources, there has been a growing focus on hydrogen production from renewable waste heat and electricity, which boasts the advantage of emitting no greenhouse gases [13]. In comparison to other emerging methods of hydrogen generation, thermochemical water-splitting cycles exhibit superior efficiency, cost-effectiveness, and environmental friendliness. Some explanations for this can be summarized as follows:

Efficiency: Thermochemical water-splitting cycles are designed to operate at high temperatures, which can enhance the overall system efficiency. The use of high-temperature heat sources, such as concentrated solar energy, can contribute to the efficiency of the water-splitting process.

Cost-Effectiveness: Thermochemical processes may benefit from the utilization of abundant and low-cost feedstocks, such as water and renewable heat sources. Some thermochemical water-splitting cycles aim to minimize the need for expensive materials or catalysts, contributing to cost-effectiveness.

Environmental Friendliness: Thermochemical water-splitting cycles, when powered by renewable energy sources like solar or geothermal, can be considered environmentally friendly, as they do not rely on fossil fuels. Some cycles are designed to produce hydrogen with minimal or zero greenhouse gas emissions, aligning with environmental sustainability goals [14]. Numerous researchers have explored the techno-economic and environmental aspects of solar–wind energy hydrogen production cycles.

The produced hydrogen is classified based on the method and source of generation. The technologies used for hydrogen generation in Canada are grey, blue, and green, as shown in Figure 1. Hydrogen is a versatile energy carrier that can be generated from several different pathways, and this diversity of raw materials creates resilience in Canada’s energy system. Hydrogen can help regions that depend on energy imports become more energy-independent. Hydrogen can also be an energy carrier to connect different energy systems into a more flexible and optimal integrated energy system. Moreover, Canada is currently a major producer of hydrogen, with about 3 million tons produced each year, mainly through steam methane reforming for industrial purposes, including fuel refining and fertilizer production; nitrogen fertilizer is ranked among the world’s top ten gas producers of hydrogen [15]. While steam methane reforming alone is not considered a path toward low-carbon hydrogen, Canada is well positioned to transition to a clean path in the future. In

Table 1. Common hydrogen feedstocks, production pathways, and CO₂ emissions [16,17].

Production Process	Feedstock and Energy Source	Pros and Cons	Examples	CO2 Emissions/H2 Generation
GREY Produced by steam methane reformation without carbon capture and sequestration (CCS)	Feedstock: natural gas, gasified coal	Pros: lowest cost, abundant Cons: highest carbon intensity	Canada produces approximately 3 million tons of grey hydrogen per year, primarily for industrial use	9 kg CO2e/kg H2
BLUE Produced from fossil fuels by steam methane reformation, pyrolysis, or other processes with carbon capture and sequestration (CCS).	Feedstock: natural gas, coal, crude bitumen	Pros: low-cost, abundant, low CI, pyrolysis offers scale and siting flexibility Cons: SMR pathway siting is constrained by CCUS, and the feedstock is not renewable	Alberta’s Quest project	2 to 3 kg CO2e/kg H2.
GREEN Produced from water by electrolysis using renewable electricity such as hydroelectricity, wind, or solar.	Feedstock: water Energy source: renewable electricity	Pros: lowest carbon intensity, scalable Cons: highest cost, opportunity cost-competitive with electrification demand	Air Liquide’s 20 MW electrolyzer plant in Becancour, projects developing in BC to support hydrogen fuelling network	0.8 (wind) to 3.4 (solar) kg CO2e/kg H2
NUCLEAR Produced from water by electrolysis or high temperatures from nuclear energy	Feedstock: water Energy source: uranium/nuclear electricity	Pros: low carbon intensity Cons: limited availability and siting constraints	Feasibility study planned in Bruce County

, the different production routes are described in terms of raw material inputs and estimated carbon intensity. Canada has one of the lowest IC power supplies in the world due to its hydroelectric generation capacity and tier 1 nuclear site status, abundant fossil fuel reserves, and state-of-the-art CO₂ storage geological technology.

The most widely used technologies contributing to advances in hydrogen production are methane reforming, electrolysis, waste gasification, and coal gasification.

Table 2. Comparison of hydrogen production technologies.

Process	Advantages	Disadvantages	Efficiency
Steam reforming	Hydrogen yield is generally higher than 50% at T > 600 °C. Relative stability during transition operation.	Potential high level of carbonaceous material formation. COX Production Unsteady yield. An External Heat transfer device is required, therefore resulting in system complexity and potentially higher cost.	75–86
Coal gasification	Coal gasification is a cleaner, less polluting method of coal processing. It releases less carbon when burned, and other polluting gases, such as CO2, can be easily separated. The syngas derived from coal gasification can also be further treated to create fuels like gasoline and diesel.	H2 production with value-added product (coke and oil) production. Coal gasification is a process that consumes more energy and water than traditional methods.	61–74
Waste gasification	Waste reduction, low-cost substrate, low pollutant generation, commercialized stage.	Impurities, unstable rate of H2 production, geographical suitability	35–45
Electrolysis	Commercialized and greener technology. O2 is a by-product costly process.	Stationary technology, high cost	72–80

shows a comparison between hydrogen generation techniques and their efficiencies with the merits and disadvantages of every technology [18].

1.2.1. Steam Methane Reforming (SMR)

SMR is currently the most widely used method of hydrogen production, accounting for more than 75% of global hydrogen production. The cost of producing hydrogen through SMR varies according to the price of natural gas, the main raw material. This process involves the reaction of natural gas with water vapour to produce hydrogen and carbon dioxide. Carbon dioxide can be captured and stored, helping to reduce greenhouse gas emissions. The cost of hydrogen produced using SMR ranges from USD 1.5 to USD 3.50 per kilogram of hydrogen.

1.2.2. Coal Gasification

Coal gasification involves the reaction of coal with water vapour and oxygen to produce hydrogen and carbon monoxide. The carbon monoxide can then react with the water vapour to produce more hydrogen and carbon dioxide. The cost of hydrogen production by coal gasification is highly dependent on the price of coal and the type of technology used. The cost of hydrogen produced by coal gasification ranges from USD 2 to USD 6 per kilogram of hydrogen.

1.2.3. Waste Gasification

Gasification is a thermochemical process in which solid fuel can be converted into more valuable gaseous fuel. These raw materials are often locally available as surplus waste, so gasification allows them to be transformed into a resource with both energy and environmental value. From a chemical point of view, gasification can be considered incomplete combustion promoted by an oxidizing agent (e.g., air, pure oxygen, H₂O, CO₂, and mixtures). Unlike combustion, where heat is obtained, and exhaust gases include CO₂, and water vapour, in gasification, the produced gas also contains H₂, CO₂, and a small amount of CH₄. Often called producer gas or syngas, this gas has a calorific value that depends on the gasification agent used: low (4–6 MJ/Nm³dry) if produced with air and medium (10–15 MJ/Nm³dry) if produced with oxygen and/or steam [19]. Syngas with a low calorific value is often suitable for direct use for combined heat and power (CHP) purposes, even on small and medium scales. Syngas with an average calorific value is considered to have a higher value, suitable not only for direct use but also for more advanced process chains to produce gaseous or liquid fuels such as SNG and H₂, MeOH, diesel, gasoline, and DME, as well as green chemicals. (e.g., acetic acid, alcohol, aldehyde) [20,21,22].

1.2.4. Electrolysis

Electrolysis involves the use of electricity to split water into hydrogen and oxygen. The cost of producing hydrogen by electrolysis depends on the cost of electricity and the type of electrolysis technology used. Two main types of electrolysis technologies exist: alkaline water without membrane and alkaline water with proton-exchange membranes (PEMs). Alkaline electrolysis is a mature and relatively inexpensive technology, with production costs ranging from USD 2 to USD 4 per kilogram of hydrogen. PEM electrolysis is a newer and more expensive technology, with production costs ranging from USD 4 to USD 6 per kilogram of hydrogen. An enhancement was achieved in the water electrolysis system by using nickel electrodes, adding a pulsed voltage and magnetic force to improve the hydrogen generation rate when hydrogen was produced by the electrolysis of water, and studying related parameters, such as the effects of magnetohydrodynamics and the potential energy increase [23,24]. Experiments have shown that the Lorentz force of the magnetic field changes the convection direction of the electrolyte, affecting the flow of bubbles during electrolysis. When a magnetic field is applied, the current density increases by about 15% at room temperature, the distance between the electrodes is 2 mm, and the potential is 4 V. This changes the mass transfer rate at the electrode surface and inside the electrolyte. This reduces the electrochemical polarization of the diffuse layer, further increasing hydrogen production efficiency. With a 10% duty cycle and a 10 ms duty cycle, nearly 88% of the total power was converted, and the current density increased by 680 mA/cm², an increase of about 38%. In general, the energetic and magnetic potentials enhance each other when added under an appropriate ground energy and ground potential. Additionally, a static magnetic field (SMF) was applied to water electrolysis to increase the hydrogen yield. However, the influence of a dynamic magnetic field (DMF) on water electrolysis has also been investigated [25]. This study used a DMF to increase the hydrogen yield in water electrolysis. The DMF is generated by rotating a plate-shaped magnet. As a result, the DMF produces 23.1 mL of H₂. This is almost double the 12.1 mL H₂ in the SMF. The DMF increases the likelihood of hydrogen formation by weakening covalent bonds and hydrogen bonds and increasing ion mobility due to additional magnetic field strength.

According to the roadmap to achieving a zero-carbon strategy and the previous survey, electrolysis is the most economical and efficient method to produce hydrogen and meet the zero-carbon target. Additionally, electrolysis is the main method for producing green hydrogen.

Various electrolysis systems developed for water electrolysis include alkaline water electrolysis (AWE), proton-exchange membranes (PEMs), alkaline anion-exchange membranes (AEMs), and solid oxide water electrolysis (SOE). Different materials and operating conditions are used in these systems; however, the operating principle is the same. Depending on different operating temperatures, low- and high-temperature water electrolysis is also possible.

Table 3. The characteristics of the main electrolysis technologies.

Low-Temperature Electrolysis				High-Temperature Electrolysis
	Alkaline (OH−) Electrolysis		Proton-Exchange (H+) Electrolysis	Oxygen Ion (O2−) Electrolysis
Charge carrier	OH−	OH−	H+	H+	O2−	O2−
Temperature	20–80 °C	20–200 °C	20–200 °C	500–1000 °C	500–1000 °C	750–900 °C
Electrolyte	Liquid	Solid (polymer)	Solid (polymer)	Solid (ceramic)	Solid (ceramic)	Solid (ceramic)
Anodic reaction	4OH− → 2H2 + O2 + 4e	4OH− → 4H2 + O2 + 4e	2H2O → 2H2 + O2 + 4e	2H2O → 2H2 + O2 + 4e	O2− → ½ O2 + 2e	O2− → ½ O2 + 2e
Anodes	Ni > Co > Fe	Ni-based	Ir O2, Ru O2	Perovskites with protonic–electronic conductivity	La Sr1-xMnO3+Y-stabilized ZrO2	La Sr1-xMnO3+Y-stabilized ZrO2
Cathodic Reaction	2H2O + 4e → 4OH + 2H2	2H2O + 4e → 4OH + 2H2	4H+ + 4e → 2H2	4H+ + 4e → 2H2	H2O + 2e → 2H2+ O2	H2O + 2e → H2+ O2 CO2 + 2e → CO+ O2
Cathodes	Ni alloys	Ni, Ni-Fe, NiFe2O4	Pt/C, MoS2	Ni-ceramets	Ni-YSZ	Ni-YSZ
Efficiency	59–70%		68–82%	Up to 100%	Up to 100%
Applicability	Commercial	Laboratory scale	Near-term commercialization	Laboratory scale	Demonstration	Laboratory scale
Advantages	Low capital cost, relatively stable, mature technology	Combination of alkaline and H+ PEM electrolysis	Compact design, fast response/startup, high-purity H2	Enhanced kinetics and thermodynamics: lower energy demand, low capital cost	Direct production of syngas
Disadvantages	Corrosive electrolyte, gas permission, slow dynamics	Low OH− conductivity in polymeric membranes	High-cost polymeric membrane, acids: noble metals	Mechanically unstable electrodes (cracking), safety issues: improper sealing
Challenges	Improved durability/reliability and oxygen evolution	Improved electrolyte	Reduction noble-metal utilization	Microstructural changes in the electrodes: delamination, blocking of TPBs, passivation	C deposition, microstructural changes in electrolytes

shows these methods with different working conditions and characteristics.

Alkaline water electrolysis is considered the most appropriate method for an electrolysis system to produce hydrogen for many reasons, such as operating at low temperatures (60 to 80 °C), with aqueous KOH and/or NaOH as an electrolyte; the concentration of the electrolyte is about 20–30%. In the alkaline electrolyzer, nickel material is used as the electrode; the purity of the hydrogen produced is about 99%. However, the alkaline mist in the generated gas must be removed for desorption to be commonly used; the maximum operating current density of an alkaline electrolyzer is less than 400 mA/cm², and the power consumption for H₂ production is about 4.5 to 5.5 kWh/Nm³ with an efficiency of about 60%. This method can work directly with renewable energy sources due to its low energy consumption to produce hydrogen.

Hydrogen does not compete with direct electrification but can help increase renewable energy penetration by providing time-shifting and energy storage capabilities. Although the main source of renewable energy in Canada is hydropower, which has inherent energy storage capabilities, its wind energy capacity has grown steadily over the past decade. Wind power is one of the fastest-growing sources of electricity in the world and Canada, currently accounting for 4% of national electricity generation, with Ontario and Quebec leading the way in terms of capacity. For example, Prince Edward Island, with 98% of its local electricity generation coming from wind power, is currently dependent on imported power from New Brunswick’s dispatched grid. Hydrogen can be a dispatchable energy solution to increase energy independence. It can also be used directly for heating in winter as a hybrid system to offset the demand for seasonal peak electric heating. The versatility of hydrogen as an energy carrier provides customizable options for every region of Canada [26]. While solar technologies have achieved a significant level of technological maturity, several challenges persist, including high initial investment costs and the intermittent nature of energy production. In contrast, wind power is recognized as one of the most abundant and sustainable renewable resources, frequently used for domestic applications due to its cost-effectiveness in energy production. Combining solar and wind sources to generate cost-effective, clean, and reliable energy output is a prudent approach. The subsequent section includes various works exploring the integration of solar technologies and wind power systems for multi-generation purposes, encompassing hydrogen production, electricity generation, and heating and cooling, with a focus on techno-economic and environmental considerations. Ishaq et al. introduced and analyzed an innovative multi-generation system that combines a wind turbine and a high-temperature solar collector with a CuCl₂ thermochemical hydrogen cycle, achieving a satisfactory performance efficiency of 50% [27]. In 2019, O. Nematollahi et al. [28] evaluated wind and solar data from selected meteorological stations in Sistan and Baluchistan province, Iran, for hydrogen production as a clean fuel. A detailed techno-economic analysis was conducted for utilizing wind and solar energies of 500 W/m² each. Evaluating hydrogen production from several small wind turbines showed that up to 39.7 tons of hydrogen could be produced annually. It was found that under optimal conditions, the cost of generating 1 kWh of electrical energy with wind turbines was USD 0.08 cheaper than the market energy unit price when both wind and solar energies were used.

Hasan and Genç [29] examined the economic aspects of a hybrid solar–wind system combined with electrolyzers. Their results indicated that selling surplus power to the local electricity grid could reduce hydrogen production costs by approximately 0.03 USD/m³.

In 2019 O. Nematollahi et al. [28] evaluated wind and solar data from selected meteorological stations in Sistan and Baluchistan province, Iran, for hydrogen production as a clean fuel. A detailed techno-economic analysis was conducted for utilizing wind and solar energies of both 500 W/m². Evaluating hydrogen production from several small wind turbines showed that up to 39.7 tons of hydrogen could be produced annually. It was found that under optimal conditions, the cost of generating 1 kWh of electrical energy with wind turbines was 8 cents cheaper than the market energy unit price when both wind and solar energies were used.

Al-Buraiki and Al-Sharafi [30] designed a wind–solar system that focuses on hydrogen production and consumption for Dhahran City, Saudi Arabia. According to their findings, the suggested system could independently supply the building’s electricity demand and reduce carbon dioxide emissions by 9.6 tons annually. As part of a low-carbon community, Parra et al. [31] developed a hydrogen energy storage system that incorporated a polymer electrolyte membrane electrolyzer, a metal hydride tank, and a proton-exchange membrane fuel cell unit. Notably, this system demonstrated both flexibility and efficiency for long-term and mid-term energy storage, achieving a round-trip efficiency of 52%. In a separate study, Izadi et al. [32] investigated hybrid renewable energy systems integrated with buildings in four distinct locations, ultimately leading to the creation of zero-energy buildings. The simulated system, implemented using TRNSYS software, encompassed solar parabolic troughs, wind turbines, and a hydrogen storage unit. According to simulation results, solar systems and wind turbines could, respectively, produce 35% and 49% of the electricity required by buildings in each city, noting that solar energy is significantly less effective than wind energy for storing hydrogen during the winter. However, renewable resources and hydrogen storage systems had the potential to generate 70% to 88% of the electricity needed by buildings. Another study conducted by Wei et al. [33] utilized TRNSYS software to assess the potential, storage, and consumption of electricity in Saskatoon, Canada, a city with high solar and wind power potential. The findings suggested that solar panels had a considerably higher energy generation capacity, indicating their superior potential compared to wind turbines. To examine the transient behaviour of a residential energy system, Mansir et al. [34] developed a model using TRNSYS software to fulfil the heating, cooling, domestic hot water, and electricity demands of a residential building. This study also presented comparative analyses of hydrogen fuel cells and conventional batteries. Based on the results, HVAC systems utilizing hydrogen storage had a capital cost twice that of systems using batteries. Nevertheless, employing hydrogen storage systems with larger capacities resulted in improved performance.

Behzadi et al. [35] put forward and thoroughly investigated an innovative strategy to effectively combine solar and wind energy sources through hydrogen storage. The goal was to enhance grid stability and reduce peak loads. The study utilized the Transient System Simulation (TRNSYS) tool and the Engineering Equation Solver program to perform a comprehensive assessment of the techno-economic and environmental aspects of a residential building in Sweden. A four-objective optimization, employing MATLAB with the grey wolf algorithm and an artificial neural network, was employed to find the optimal balance among various indicators. The results indicate that, under optimal conditions, there is an 80.6% improvement in primary energy savings, a 219% reduction in carbon dioxide emissions, a cost of 14.8 USD/h, and a purchased energy amount of 24.9 MWh. The analysis of the scatter distribution suggests that maintaining the fuel cell voltage and collector length at their minimum values is crucial, while the electrode area is not a significant parameter. The proposed renewable-driven smart system is capable of meeting 70% of the building’s energy needs throughout the year. Additionally, excess energy can be sold back to the local energy network, making this system a practical and viable alternative.

Li et al. [36] introduced and assessed a novel solar-based system that featured an Alkaline Fuel Cell (AFC) and an electrolyzer. Their research revealed that, in addition to significant electricity production and low exergy destruction, the waste heat generated by the AFC could be effectively utilized for co-generating electricity through a Stirling engine and providing cooling through an absorption chiller.

In a more recent study, Wang et al. [37] presented an environmentally friendly and efficient system designed for electricity and cooling production. Their research highlighted a total efficiency of 77.5%, primarily attributed to the high hydrogen-to-electricity conversion rate and the recovery of waste heat from AFCs.

Shen et al. [38] proposed an energy system based on hydrogen consumption/generation, which incorporated AFCs and an electrolyzer driven by a wind turbine. They demonstrated that carefully balancing the electricity demand and the capacity of the AFC and wind turbine, achieved through the proper sizing of the hydrogen storage tank, could lead to substantial primary energy savings and cost reductions.

According to the previous survey, there are some research gaps for hydrogen generation with the alkaline method. These gaps encompass several key parameters, including the following:

• Electrolyte Optimization: Improving ionic conductivity and addressing carbonate formation in electrodes to enhance the efficiency of alkaline systems.
• Electrode Design: Focusing on optimizing the surface area and structure of electrodes, as well as ensuring durability under a high current density during hydrogen generation.
• System Integration and Scale-Up: Transitioning from laboratory-scale to industrial-scale production and integrating with renewable energy sources.
• Environmental and Economic Assessment: Conducting lifecycle analyses and exploring cost reduction strategies for hydrogen production.
• Advanced Diagnostics and Real-Time Monitoring: Developing advanced tools for real-time monitoring and diagnostics.
• Hydrogen Purity: Finding efficient methods to purify hydrogen produced through alkaline processes to meet application-specific purity standards.

Due to that, it is seen that alkaline electrolysis is the most economical and efficient system. Nevertheless, the choice of the electrolysis system to be used is contingent upon certain factors, outlined as follows:

Economic Factors: Alkaline electrolysis systems typically use inexpensive materials, and they have been commercially available for a longer time, contributing to their perceived economic advantages.

Efficiency: Alkaline electrolysis is known for its relatively high efficiency, especially when operating at larger scales. Emerging technologies, such as high-temperature electrolysis, are also being explored for improved efficiency.

Technology Advancements: Research and development in electrolysis technologies are ongoing, and new advancements may influence the comparative advantages of different systems.

Therefore, this paper aims to address most of these key points, which will be discussed in the following sections.

The primary objective of this work is to conduct a comparative analysis of hydrogen generation rates and costs, specifically as influenced by the power sources of wind turbines and photovoltaic (PV) panels. This comparison aims to assess and contrast the efficiency, performance, and economic aspects of using wind energy versus solar energy for hydrogen production. By evaluating these two renewable energy sources, this study seeks to provide insights into the most effective and cost-efficient method for generating hydrogen. To achieve this objective, it is necessary to calculate and determine the capacity factor for both the wind turbine and PV panel used in the simulation. The capacity factor is a crucial metric that reflects how efficiently these renewable energy sources are utilized in generating power. By quantifying the capacity factor for each source, this study comparatively assesses their performance in terms of hydrogen generation rates and costs. For this, there is a need to determine the optimal configuration for a hydrogen energy system designed to meet the hourly energy demand of a small, off-grid laboratory situated in Toronto, Canada, using renewable resources. The study involved a comprehensive energy system simulation conducted with TRNSYS software to model the transient behaviour of the system over the course of a year.

The methodology employed in this paper is illustrated in Figure 2 as a flow chart, providing a visual representation of the sequence of the work and the process for obtaining and calculating the results.

2. TRNSYS Modelling

TRNSYS is a powerful software tool used to simulate the performance of dynamic systems, including those related to solar power. In this study, TRNSYS version 16 was employed to simulate the system’s performance over the course of a year. The simulations were conducted at hourly intervals, covering a total of 8760 h. The system under investigation primarily comprises solar panels, wind turbines, an electric controller, and an electrolyzer, and each of these components will be discussed in this section. As depicted in Figure 3, a schematic representation of two renewable energy simulations using TRNSYS software is presented. This schematic provides a clear and detailed visualization of the hydrogen system in TRNSYS software, emphasizing the interactions between renewable energy sources and hydrogen production. The following components and their interactions are clearly defined:

• Components, Configuration, and Input Data
  • Weather Data Import

    -

    Purpose: To import and process weather data, including solar radiation, wind speed, temperature, and other relevant meteorological parameters.

    -

    Configuration: Import weather data files.

  • Solar Photovoltaic (PV) System

    -

    Function: To simulate the electrical output of a solar PV array based on incident solar radiation and cell temperature.

    -

    Configuration: Define PV module characteristics (e.g., efficiency, area, tilt angle). Connect to the weather data component to receive solar radiation input.

  • Wind Turbine System

    -

    Function: To simulate the power output of wind turbines based on wind speed and turbine performance curves.

    -

    Configuration: Input turbine specifications (e.g., rated power, cut-in and cut-out wind speeds). Link to the weather data component for wind speed input.

  • Electrolyzer

    -

    Function: To model the hydrogen production process via electrolysis, converting electricity from renewable sources into hydrogen.

    -

    Configuration: Define electrolyzer characteristics (e.g., efficiency, operating range). Connect to the PV or wind turbine systems for electricity input.

  • Control Systems

    -

    Function: To manage the flow of electricity and hydrogen, ensuring optimal operation.

    -

    Configuration: Develop control algorithms to balance supply and demand.

• Simulation Setup and Execution
  • System Integration

    -

    Integrate all components by defining their interactions and data flow.

    -

    Ensure proper connections between renewable energy sources, the control system, and the electrolyzer.

  • Simulation Parameters

    -

    Set the simulation time step (hourly) and duration (9 months).

    -

    Configure output parameters to monitor (hydrogen production rate and system efficiency).

  • Running Simulations

    -

    Execute the simulation and monitor real-time performance.

• Data Analysis and Interpretation
  • Hydrogen Production Analysis

    -

    Analyze the hydrogen production rate from the electrolyzer over time.

    -

    Assess the impact of varying solar and wind inputs on hydrogen output.

  • Cost Analysis

    -

    Perform a cost analysis of hydrogen production.

    -

    Compare the cost of hydrogen generated from solar and wind energy.

Data Reader: This component serves the function of reading data from a data file at regular time intervals, converting it to the desired system of units, and then making it available to other components within the TRNSYS simulation as time-varying forcing functions. This component is versatile and can handle various types of data files. The data in the file must be organized in such a way that they are at constant time intervals. It provides a streamlined approach to handling weather data and solar radiation processing within the TRNSYS simulation environment.

Wind Turbine: This is a mathematical model used to model a Wind Energy Conversion System (WECS). It calculates the power output of the WECS based on power versus wind speed characteristics, which is typically provided in a table format stored in an external file. This model considers the impact of changes in air density and the variation in wind speed with height. In essence, this model allows for the simulation of a WECS by considering how the power output varies with wind speed and how it is affected by factors like the air density and changes in wind speed at different heights above the ground. This modelling approach is valuable for understanding and predicting the performance of wind energy systems and optimizing their efficiency under varying conditions. The selected wind turbine has a nominal maximum power rating of 500 W. TRNSYS often employs a model based on the power curve of a wind turbine. The power output (P) of a wind turbine can be calculated using Equation (1) [39]:

$$P_w={\displaystyle \frac{1}{2}}\rho A{v}^3{C}_p$$

(1)

• $\rho$: air density (kg/m³);
• A: the swept area of the turbine blades (m²);
• v: wind speed (m/s);
• Cp: a power coefficient representing the efficiency of the turbine in extracting power from the wind.

Solar Panels: Photovoltaic (PV) panels are central to the system. They convert incident solar irradiation into direct current (DC) electricity. When solar irradiation enters the system, only a fraction of it is converted into electricity, while the remainder is transformed into heat [39]. The PV panels can be installed on the rooftop of the laboratory building. The selected PV panels have a nominal maximum power rating of 550 W. Type 94a is the model used to represent photovoltaic panels in this study.

TRNSYS typically models the electrical output of a PV system based on the photovoltaic array’s characteristics and the incident solar radiation. The electrical power (P) produced by a PV module can be represented by the following equation [39]:

$$P=\eta G A$$

(2)

• η: the conversion efficiency of the PV module;
• G: incident solar radiation on the module (W/m²);
• A: the area of the PV module (m²).

Electrolyzer: An electrolyzer is employed to facilitate water electrolysis, a process in which water is split into hydrogen and oxygen. This process is pivotal for producing green hydrogen. In this research, an alkaline electrolyzer is utilized for hydrogen production. The simulation of the alkaline electrolyzer is accomplished using the Type160a component in TRNSYS. The fundamental relationship governing electrolysis is Faraday’s law of electrolysis, which relates the amount of chemical reaction to the amount of electrical charge passed through the electrolyte. For the electrolysis of water, the reaction is as follows [39]:

• Faraday’s law

$$2H_{2}O \to 2H_2+O_2$$

(3)

The Faraday’s law equation is given by

$$Q=n F$$

(4)

Q: electrical charge (Coulombs);

n: the number of moles of electrons exchanged in the reaction;

F: Faraday’s constant (approximately 96,485 C/mol).

• Cell Voltage

The cell voltage in an electrolysis system can be related to the standard cell potential and overpotentials:

$$V_{cell}=E_{cell}-{\eta }_{activation}-{\eta }_{Ohmic}-{\eta }_{Concentration}$$

(5)

E_cell: the standard cell potential;

ᵑ_activation: the activation overpotential;

ᵑ_Ohmic: the electrical resistance overpotential;

ᵑ_{Concentration}: the concentration overpotential.

These overpotentials depend on factors such as electrode materials, temperature, and electrolyte concentration.

Control Unit: This subroutine serves as a collection of control functions for an electrolyzer within an integrated mini-grid that is connected to a wind turbine, electrolyzer, hydrogen storage, and fuel cell system. Specifically, the electrolyzer in this system is configured to operate in a variable power mode. When the electrolyzer is in the ON state, the Electrolyzer Setpoint Power is determined as the maximum value between the excess power generated by the renewable energy sources connected to the mini-grid and the Idling Power. Conversely, when the electrolyzer is in the OFF state, the Electrolyzer Setpoint Power is set to the Idling Power. This control logic ensures that the electrolyzer operates efficiently based on the available power and the system’s operational status.

Power Conditioning: This is a mathematical model designed for a power conditioning unit, specifically for electrical converters (DC/DC) or inverters (DC/AC or AC/DC). This model is based on empirical efficiency curves that describe the relationship between input power and output power. In the context of this specific instance, it is assumed that the available input power is a known parameter, and the model is used to calculate the corresponding output power. This type of model is valuable for understanding and optimizing the performance of power conditioning units in various electrical systems.

These components work in harmony with the TRNSYS software to simulate the overall performance of the hydrogen energy system throughout the year, considering the dynamic nature of energy production and consumption.

3. Results and Discussion

In this section, the performance of the hydrogen system is thoroughly evaluated through energy analysis, providing a comprehensive understanding of how the system operates. To achieve this, a dynamic simulation of the system was carried out using TRNSYS software, and it covered various hours and months throughout the year. The simulation was conducted with a time step of 1 h, and the time scale used spanned around a year, running from 1 January to 31 August 2023. This extensive simulation allows for a detailed assessment of the system’s behaviour over different seasons and under varying conditions. The weather data in this context pertain to the geographical location of Oshawa City, within the Markham zone, which is situated in Ontario, Canada. These data are crucial for understanding the climatic conditions and environmental parameters specific to this region, and they play a significant role in simulations and analyses related to energy systems and environmental modelling in this area.

The simulation was conducted using two distinct power sources: the first one is the wind turbine, and the second is the PV (photovoltaic) panel. Each of these power sources will be described in detail in the next section, including their characteristics with the respective data input and output, to provide a comprehensive understanding of their performance and contribution to the energy system. This detailed description will help analyze the operation and efficiency of the wind turbine and PV panel within the simulation. The price estimation of the hydrogen and electric energy can be conducted using the capacity factor ratio. The capacity factor (CF) is a unitless ratio of the actual electrical energy output over a given period to the theoretical maximum electrical energy output over that period [40]:

$$\mathrm{CF}={\displaystyle \frac{\mathrm{O}\mathrm{u}\mathrm{t}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{H}\mathrm{o}\mathrm{u}\mathrm{r} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{t}\mathrm{h}\mathrm{e} \mathrm{T}\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{e}}{\mathrm{M}\mathrm{A}\mathrm{x}.\mathrm{T}\mathrm{u}\mathrm{r}\mathrm{b}\mathrm{i}\mathrm{n}\mathrm{e} \mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{a}\mathrm{v}\mathrm{a}\mathrm{i}\mathrm{l}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}\times 365\times 24}}$$

(6)

Figure 4 illustrates the connection between electricity prices, capacity factors, and the resulting levelized hydrogen cost, which is calculated using the H₂ model for different capital costs related to LTE (low-temperature electrolysis) [41].

3.1. Turbine Power Source

3.1.1. Simulation Parameters

The simulation was executed using TRANSYS software to model the power generated by the wind turbine. The power generated by the wind turbine is subsequently utilized in the process of electrolysis to produce hydrogen. The specifications for both wind turbines and electrolysis can be shown in

Table 4. Turbine specifications.

No.	Specification	Value	Description
1	Rotor_Ht	2	Rotor centre height, metres
2	Rotor_Di	1	Rotor diameter, metres
3	Sensr_Ht	6	Sensor height for data pairs given below, metres (often rotor centre height)
4	Sher_Exp	0.16	Power-law exponent for vertical wind profile
5	Turb_Int	0.1	Turbulence intensity valid for this curve
6	Air_Dens	1.225	Power curve air density, kg/m3
7	Pwr_Ratd	318	Rated power of turbine, W
8	Spd_Ratd	9	Rated wind speed, m/s
9	Nominal turbine efficiency	0.35

and

Table 5. Electrolysis specifications.

No.	Specification	Value	Description
1	Unit Area	0.066	Total area of unit (m2)
2	N cells	2
3	IELY	4	Current through single electrolyzer stack (A)
4	PELY	1	Electrolyzer pressure (atm)
5	TROOM	20	Room temperature (°C)
6	TELY	50	Temperature of electrolyzer (°C)

. The primary input for this simulation is weather data, specifically the relationship between wind speed and hours. The source of these weather data is the official website of the Government of Canada [40], indicating that the data have been obtained from a reputable and authoritative source, ensuring the accuracy and reliability of the input data used for the simulation. The simulation was carried out for the conventional green system for hydrogen generation, focusing on the first stage, which involves the alkaline electrolysis system. This initial stage is a key component of the hydrogen generation process, and the simulation aims to analyze and evaluate its performance within the broader system.

3.1.2. Simulation Results

Figure 5 illustrates the performance of the turbine at various wind speeds, including the cut-off power of 1000 W. It provides a visual representation of how the turbine’s power output varies with changes in wind speed, and it highlights the point at which the turbine’s power generation is cut off or limited. This information is valuable for understanding the turbine’s operational characteristics and its efficiency in different wind conditions. Figure 6 displays the weather data collected for Oshawa City, situated within the Markham zone, on an hourly basis. These data include information on wind speed at different times throughout the day. Analyzing these data can provide insights into the local climate and weather conditions, which are essential for various applications, including energy system simulations and environmental modelling.

Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15 depict the monthly power consumption from the wind turbine and the monthly hydrogen flow rate output from the electrolysis process. These figures provide a visual representation of how the wind turbine’s power generation varies from month to month and how it correlates with the hydrogen production rate via electrolysis. During the winter months, from January to April, the wind power output peaks at an average of 850 W, achieving a maximum hydrogen flow rate of 0.18 m³/h. In contrast, during the spring and summer months, from May to September, the wind power output reaches a maximum of 210 W, with a hydrogen flow rate of 0.05 m³/h. By analyzing these figures, researchers and stakeholders can gain insights into the seasonal variations in renewable energy production and hydrogen generation. This information is valuable for understanding the system’s performance, assessing the impact of weather and climate on energy production, and optimizing the hydrogen production process.

Figure 16 illustrates the monthly power output or extraction from the wind turbine and its input to the electrolysis process in comparison to the hydrogen generated as a power rate. This figure provides a comprehensive overview of how the wind turbine’s power production, when used in the electrolysis process for hydrogen generation, aligns with the resulting monthly hydrogen production rate.

Figure 17 presents the electrolysis efficiency, and this efficiency is calculated using Equations (2)–(4). The figure provides a visual representation of how efficiently the electrolysis process converts electrical energy into hydrogen. Electrolysis efficiency is a crucial parameter, as it reflects the effectiveness of the hydrogen generation process. By assessing the efficiency over time, researchers can identify trends and variations in the system’s performance. This information is valuable for optimizing the hydrogen production process and improving the overall system efficiency.

$$\mathrm{E}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{y}\mathrm{s}\mathrm{i}\mathrm{s} \mathrm{E}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y} \%={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t} \mathrm{P}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{t}\mathrm{o} \mathrm{E}\mathrm{l}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{y}\mathrm{s}\mathrm{i}\mathrm{s}}{\mathrm{H}\mathrm{y}\mathrm{d}\mathrm{r}\mathrm{o}\mathrm{g}\mathrm{e}\mathrm{n} \mathrm{p}\mathrm{o}\mathrm{w}\mathrm{e}\mathrm{r} \mathrm{g}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}\mathrm{d}}}$$

(7)

H₂ mass (kg) = flow rate × density = flow rate × 0.08375

(8)

H₂ (kWh) = H₂ mass × 33.6

(9)

The cost of producing 1 kg of hydrogen (kg H₂) can be determined based on the capacity ratio (CR) and the hydrogen energy output, as well as the power input. This cost calculation is summarized in

Table 6. Electrical data of PV panel at STC.

Rated Maximum Power (Pmax) [W]	525	530	535	540	545
Open-Circuit Voltage (Voc) [V]	49.15	49.3	49.45	49.6	49.75
Maximum Power Voltage (Vmp) [V]	41.15	41.31	41.47	41.64	41.8
Short-Circuit Current (Isc) [A]	13.65	13.72	13.79	13.86	13.93
Maximum Power Current (Imp) [A]	12.76	12.83	12.9	12.97	13.04
Module Efficiency [%]	20.3	20.5	20.7	20.9	21.1
Power Tolerance	0~+5 W
Temperature Coefficient of Isc (α_Isc)	0.046%/°C
Temperature Coefficient of Voc (β_Voc)	−0.277%/°C
Temperature Coefficient of Pmax (γ_Pmp)	−0.351%/°C
STC	Irradiance 1000 W/m2, cell temperature 25 °C, AM1.5G
Cell Maximum Efficiency	Mono 21.3%

and Figure 4. The table presents a breakdown of the cost of hydrogen production under different conditions or scenarios, reflecting the economic implications of the hydrogen generation process. Analyzing Table 4 can provide insights into the cost-effectiveness of producing hydrogen using specific methods or under various operational conditions. It allows researchers and stakeholders to assess the economic viability of hydrogen production and make informed decisions regarding the most cost-efficient approaches.

3.2. PV Panel Power Source

Simulation Parameters

The simulation was conducted using TRNSYS software to model and simulate the power generated from the PV (photovoltaic) panel. This generated power is then used in the electrolysis process to produce hydrogen. The primary input for this simulation is solar irradiance data, which is typically provided as solar irradiance values over hours. The solar irradiance data used in this simulation are sourced from the official website of the Government of Canada [38]. It is important to note that this simulation is focused on a conventional green system for hydrogen generation, specifically using an alkaline electrolysis system as the initial stage. The simulation aims to assess the performance and efficiency of using solar energy, in this case, PV panels, for hydrogen production through electrolysis. It enables an analysis of the hydrogen production process using renewable energy sources and offers insights into the feasibility and effectiveness of this approach.

Table 7. Summarized results of the simulation and cost analysis for the wind turbine power source.

Month	Input Power kWh	Hydrogen Energy kWh	Electrolysis Efficiency	CF	kg H2	USD/kg H2
January	197.480	155.566	79%	7%	3.733575	0.75
February	142.614	117.076	82%	5%	2.8098125	1.75
March	377.391	301.081	80%	14%	7.22595	1
April	539.370	376.352	70%	19%	9.0324375	0.55
May	119.729	103.710	87%	4%	2.48905	2.5
June	105.254	75.270	72%	4%	1.8064875	2.5
July	157.917	129.638	82%	6%	3.1113125	1.5
August	157.917	129.638	82%	6%	3.1113125	1.5

presents the specifications of the PV (photovoltaic) panel that was used in the TRNSYS simulation. These specifications include important details about the PV panel, such as its electrical characteristics, efficiency, and other relevant parameters. Figure 18 shows the I–V (current–voltage) curves for the selected PV panel. These curves represent the relationship between the electrical current generated by the PV panel and the voltage across its terminals under various operating conditions. Both the specifications in Table 5 and the I–V curves in Figure 18 are critical for accurately modelling and simulating the behaviour of the PV panel within the TRNSYS software. They allow researchers to assess the PV panel’s performance and its contribution to the overall system, particularly in the context of hydrogen production through electrolysis.

Figure 19 provides a visual representation of the total solar irradiance on an hourly basis from January to September based on available data. This graph shows the variation in solar irradiance throughout these months. Analyzing this figure can offer insights into the seasonal changes in solar energy availability. Solar irradiance is influenced by factors such as daylight hours, weather conditions, and the angle of the sun, and these data are essential for understanding the potential for solar energy generation in different months. This can be valuable for optimizing the operation of solar panels and, in this context, their role in hydrogen production through electrolysis.

Figure 20, Figure 21, Figure 22, Figure 23, Figure 24, Figure 25, Figure 26 and Figure 27 depict the monthly power consumption from the PV panel and the monthly output of the hydrogen flow rate as a representation of how PV power generation varies from month to month and how it correlates with the hydrogen production rate via electrolysis. During the period from January to August, the PV power output peaks at an average of 60 W, achieving a maximum hydrogen flow rate of 0.0126 m³/h. By analyzing these figures, researchers and stakeholders can gain insights into the seasonal variations in renewable energy production and hydrogen generation. This information is valuable for understanding the system’s performance, assessing the impact of weather and climate on energy production, and optimizing the hydrogen production process.

Figure 28 illustrates the monthly power output or extraction from the PV panel and its input to the electrolysis process in comparison to the hydrogen generated as a power rate. This figure provides a comprehensive overview of how the wind turbine’s power production, when used in the electrolysis process for hydrogen generation, aligns with the resulting monthly hydrogen production rate.

Figure 29 presents the electrolysis efficiency, and this efficiency is calculated using Equations (2)–(4). The figure provides a visual representation of how efficiently the electrolysis process converts electrical energy into hydrogen. Electrolysis efficiency is a crucial parameter, as it reflects the effectiveness of the hydrogen generation process. By assessing the efficiency over time, researchers can identify trends and variations in the system’s performance. This information is valuable for optimizing the hydrogen production process and improving the overall system efficiency.

The cost of producing 1 kg of hydrogen (kg H₂) can be determined based on the capacity ratio (CR) and the hydrogen energy output, as well as the power input. This cost calculation is summarized in

Table 8. Summarized results of the simulation and cost analysis for PV.

Month	Input Power kWh	Hydrogen Energy Conventional System kWh	Electrolysis Efficiency of Conventional System	CF	kg H2	USD/kg H2
January	20.363	12.629	62%	2%	0.38	3
February	21.498	13.333	62%	4%	0.40	2.5
March	25.917	16.074	62%	4%	0.48	2.5
April	24.2	15.037	62%	5%	0.45	1.75
May	30.455	19.518	64%	6%	0.58	1.5
June	30.455	18.888	62%	6%	0.56	1.5
July	28.663	18.888	66%	4%	0.56	2.5
August	25.081	16.666	66%	5%	0.50	1.75

and Figure 4. The table presents a breakdown of the cost of hydrogen production under different conditions or scenarios, reflecting the economic implications of the hydrogen generation process. Analyzing Table 6 can provide insights into the cost-effectiveness of producing hydrogen using specific methods or under various operational conditions. It allows researchers and stakeholders to assess the economic viability of hydrogen production and make informed decisions regarding the most cost-efficient approaches.

The simulation results clearly demonstrate significant differences in both the quantity and cost of hydrogen generation between the two power sources, the wind turbine and the PV panel.

In Figure 30, it is evident that the wind turbine has a much more substantial effect on hydrogen production compared to the PV panel throughout the entire year. This indicates that the wind turbine is a more reliable and efficient power source for hydrogen generation in the given location.

Figure 31, which analyzes the hydrogen cost based on the capacity ratio (CR), reinforces the idea that using the wind turbine as the power source is the most cost-effective option. The cost of hydrogen production is notably lower when the wind turbine is employed, and it is close to half the cost when the PV panel is used for most months of the year.

In summary, the data suggest that in the Markham zone, the most effective and cost-efficient power source for hydrogen production through alkaline electrolysis is the wind turbine. This is attributed to the consistent availability and stability of wind energy throughout the year, in contrast to the variability of solar radiation. Additionally, the higher efficiency of the wind turbine compared to the PV panel contributes to its cost-effectiveness in hydrogen generation. These findings provide valuable insights for selecting the optimal power source for hydrogen production in this specific location.

4. Conclusions

This study conducted a dynamic simulation of a hydrogen system using TRNSYS software to evaluate the use of a solar panel and a wind turbine for electricity generation and an electrolyzer for hydrogen production. The analysis aimed to understand how various design parameters affect the system’s performance. The results indicate that, considering the climate conditions in the Markham zone, Toronto, the renewable integrated system can effectively provide electricity and meet the load demand throughout the year. However, it is observed that on cold days when solar radiation is limited, wind turbines prove to be the most effective and efficient power source. This makes them a valuable resource for meeting the load demand for most of the year. The summary of the research findings can be outlined as follows:

• The analysis indicates that the system consistently supplies enough energy to meet the laboratory’s load demand throughout the year, potentially enabling net-zero energy operations in the Markham zone, Toronto, Canada.
• The wind turbine exhibits an average power consumption of 224.70 kWh, generating 173.541 kWh of hydrogen with an average efficiency of 79%.
• The PV panel, on the other hand, has an average power consumption of 25.829 kWh, producing 16.38 kWh of hydrogen with an average efficiency of 63%.
• When using the wind turbine as the power source, the maximum quantity of hydrogen generated is 9.03 kg, while it is only 0.58 kg with the PV panel power source. Additionally, the minimum cost per kilogram of hydrogen (kg H₂) is USD 0.55 with the wind turbine power source, compared to USD 1.5/kg H₂ with the PV panel power source.

These findings highlight the advantages of using wind turbines as the primary power source for hydrogen production in this specific location, emphasizing their effectiveness and cost-efficiency, especially in comparison to solar panels. This information is essential for making informed decisions regarding the design and implementation of hydrogen systems in similar climates and regions.

Future research will focus on developing hydrogen production using the alkaline method through innovative technology patented by the authors, aiming to address existing gaps and enable scalability at the industrial level.