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Abstract

Communities in Newfoundland and Labrador continue to rely heavily on grid electricity, which is often expensive and vulnerable to weather-related disruptions. In this context, hybrid renewable energy systems offer a practical way to improve energy security while lowering emissions. The transition to clean energy is crucial for mitigating climate change, particularly in Canada, where fluctuating temperatures and environmental shifts pose significant challenges. This study evaluates the techno-economic feasibility of a hybrid renewable energy system designed for residential use in Stephenville, Newfoundland and Labrador, integrating wind turbine technology (Enercon E-44), solar technology (Canadian Solar Dymond), and grid electricity. Using HOMER Pro, the system was optimized based on NASA wind data (average speeds: 7.2 m/s in winter, 5.32 m/s in summer). Results show a levelized cost of energy (LCOE) of $0.0356/kWh, a net present cost of $1.56 million, and annual CO₂ reductions of 222,514 kg, with 60.1% renewable penetration. Computational fluid dynamics (CFD) analysis in ANSYS Fluent, focusing on the NACA 63-415 airfoil, confirmed the turbine’s aerodynamic efficiency across seasonal winds. This study highlights hybrid renewable systems as cost-effective, sustainable solutions, aligning with Canada’s net-zero goals while ensuring energy security.

Keywords

ANSYS fluent HOMER pro hybrid energy system solar energy techno-economic feasibility wind energy

Introduction

Hybrid renewable energy systems (HRES) integrate solar photovoltaic (PV) panels and wind turbines (WT) to create a reliable and efficient energy system, as shown in Figure 1. These systems provide flexibility by addressing the natural intermittency of renewable sources, thus improving grid stability and reliability to great lengths. That is the reason the systems find immense usage for on-grid and off-grid applications, substantially reducing the dependence on non-renewable energy sources. At the core of the systems’ design lie advanced control strategies to balance the generation and storage of energy. Therefore, efficiency is enhanced while costs are brought down. This synergy realizes not only a clean energy transition but also combats the critical challenges associated with energy storage and system optimization (Khalil et al., 2021; Uddin et al., 2023).

Figure 1.

General HRES system architecture illustrating integrated components of the system, connecting the renewable energy sources and grid to the loads maximizing system efficiency.

The demand for energy has surged since the Industrial Revolution, driven by rapid advancements in production and manufacturing. This has led to an overreliance on fossil fuels, significantly increasing carbon emissions and contributing to global temperature rise (Cheng et al., 2021; Lindsey and Dahlman, 2023). Additionally, industrial expansion has accelerated deforestation, reducing natural carbon sinks and intensifying climate change (Zaman, 2022). These challenges emphasize the pressing need to transition toward renewable energy systems as sustainable alternatives.

Technologies include innovative energy storage, optimizing hybrid systems, and demand response strategies, for balancing renewable energy sources against energy consumption imbalances and reducing the effects of climate change. Such improved technologies and methods strengthen assurance in supplies and lay the foundation for a secure energy future. In the energy supply industry, advanced technologies are essential. For example, artificial intelligence (AI) makes EMS more efficient and reliable for satisfying complex challenges in classification, forecasting, optimization, and control (Huber and Steininger, 2022; Maghami and Mutambara, 2023). Hybrid systems often encompass technologies like smart grids, energy storage systems, and microgrids, which can either work alone or in alliance with larger networks. Different indices are used to measure the technology’s sustainability, such as carbon footprint, energy efficiency, lifecycle assessment, renewable penetration, sustainability index, and grid reliability (Ghenai et al., 2020).

The expansion of renewable energy is crucial to meet global sustainability goals while mitigating climate change. Leading these efforts for renewable energy deployment are key players, including the United States, China, Japan, and Europe, whereby wind and solar capacity expanded momentum and further developed. These countries had attained remarkable deployment levels in renewable energy by the end of 2020, drawing benchmarks and setting a course for global trends to set measures necessary to curb greenhouse gas emissions and foster a more sustainable energy future (Atawi et al., 2023; Lloret et al., 2022). Nations with abundant natural resources can capitalize on this shift to promote economic stability (Rahman et al., 2024).

Advanced wind turbine designs, for instance, enhance efficiency by optimizing the swept area and tower height, improving energy output while reducing costs (Hirth and Müller, 2016). Experimental and numerical studies on a NACA 0026 airfoil have shown that applying riblets to approximately 5% of the surface can alter flow behavior, delay separation, and influence vortex formation, demonstrating the potential of surface geometry modification for aerodynamic performance enhancement (Harun et al., 2014). Furthermore, offshore wind turbines have less intermittent flow of wind compared to onshore counterparts (Tanvir and Etminan, 2025). In addition, renewable energy systems based on solar panels produce less carbon emissions than systems based on natural gas (Bhutia et al., 2025). However, these technologies must also address environmental concerns, such as noise pollution from offshore wind farms that may disrupt marine life (Dai et al., 2015; Kikuchi, 2010). Similarly, while photovoltaic panels contribute to decarbonization, the hazardous materials they contain require effective recycling solutions to mitigate environmental risks and reduce costs (Artaş et al., 2023).

Despite the benefits of standalone renewable energy systems, their intermittency highlights the need for hybrid renewable energy systems (HRES). By integrating multiple energy sources like solar and wind with battery storage, HRES ensure a stable and continuous power supply. They also strengthen grid stability, particularly during peak demand, and are vital for off-grid applications in remote areas, including military bases and university microgrids (Hassan et al., 2023; Swarnkar et al., 2016). Additionally, hybrid systems are essential in addressing seasonal energy variation, allowing flexibility in meeting demand fluctuations across different geographical locations.

Optimization tools such as HOMER Pro help design efficient HRES by conducting techno-economic analyses to determine the most cost-effective configurations. Studies from Cameroon and rural Oman illustrate the financial and environmental benefits of HRES, where HOMER Pro has been instrumental in assessing different combinations of PV, wind, and battery systems (Barhoumi, 2023; Ngouleu et al., 2023).

Studies by Faisal and Anwer (2023) reinforce the techno-economic advantages of HRES, leveraging gray wolf optimizer (GWO) and HOMER Pro to lower net present cost (NPC) and cost of energy (COE) in rural electrification. Additionally, Salem et al. (2024) evaluate a solar-wind-battery hybrid system for a desalination plant, achieving 100% renewable energy utilization with minimized environmental impact. These findings support the integration of hybrid systems in residential and remote applications, aligning with efforts to enhance sustainability in Stephenville, NL. In addition, an HRES system including solar, wind, and diesel sources illustrates a reduction in carbon emissions and economic advantages (Mahtab et al., 2025).

This study presents an alternative solution to systems that rely solely on fossil fuels. The HRES system combines wind energy, solar energy, and partial reliance on the grid for stability. The system presents a reliable system that relies on abundant resources while offering reliable costs. Onshore wind, from Lazard asset management corporation, as per their latest LCOE reports, yields an LCOE in the range of $0.029–$0.068/kWh, whereas utility-scale solar PV ranges from $0.036 to $0.060 per kWh (Lazard, 2024). The system harnesses the energy of the wind from the Enercon E-44 turbine and the energy of the sun from Canadian Solar Dymond CS6K-285M-FG panels together with some grid power to get a constant, reliable power supply. It is based on the concept of harnessing both wind and solar resources in an effort to reduce dependence on grid power sources that are not renewable, hence contributing toward a more sustainable energy future. Having a system that relies mainly on different renewable energy resources in a small community allows the community not to rely solely on the grid and lower the carbon footprint. Furthermore, the addition of renewable energy systems to the small community of Stephenville provides the community more reliable and independent system with low cost (Table 1).

Table 1.

Literature review summary by key points.

References	Discipline	Optimization tool	Energy sources	Storage type	Application context
This study	HRES for residential use	HOMER Pro and ANSYS Fluent	Wind, solar, and grid	Battery	Residential power applications
Rahman et al. (2024)	Renewable energy adoption benefits	—	Wind, solar, and hydro	—	Economic stability and emissions
Salem et al. (2024)	HRES for desalination plants	HOMER Pro	Solar and wind	Battery	Water treatment facilities
Artaş et al. (2023)	PV panel recycling and sustainability	—	Solar	—	End-of-life solar panel disposal
Barhoumi (2023)	Hydrogen refueling station optimization	HOMER Pro	Solar and wind	Hydrogen	Hydrogen economy, Oman
Eltayeb et al. (2023)	Solar-wind hybrid tree	—	Solar and wind	—	Urban renewable solutions
Faisal and Anwer (2023)	Cost optimization in HRES	HOMER Pro and GWO	Solar and wind	Battery	Rural electrification
Hassan et al. (2023)	HRES integration and grid stability	HOMER Pro	Solar and wind	Battery	Microgrids and military bases
Lindsey and Dahlman (2023)	Climate change and temperature rise	—	Fossil fuels	—	Global temperature trends
Maghami and Mutambara (2023)	AI and advanced storage in HRES	AI models	Solar and wind	Solid-state batteries	Smart energy management
Ngouleu et al. (2023)	HRES techno-economic evaluation	HOMER Pro	PV, wind, and diesel	Battery and fuel cell	Cameroon energy sector
Zaman (2022)	Deforestation and carbon sink reduction	—	Biomass impact	—	Environmental conservation
Cheng et al. (2021)	Energy demand and fossil fuel dependency	—	Fossil fuels	—	Global industrial expansion
Hirth and Müller (2016)	Wind turbine efficiency	—	Wind	—	Wind energy optimization
Swarnkar et al. (2016)	HRES in institutional settings	HOMER Pro	Solar and wind	Battery	Universities and research centers
Dai et al. (2015)	Offshore wind environmental impact	—	Wind	—	Marine noise pollution
Kikuchi (2010)	Offshore wind environmental impact	—	Wind	—	Marine noise pollution

Problem description

Methodology

As shown in Figure 2, the methodology starts by setting the study objectives and understanding where current research stands, then NASA Prediction of Worldwide Energy Resource (POWER) database is used to gather historical solar and wind data specific to Stephenville. This database provides long-term monthly averages of global horizontal irradiance (GHI) and wind speed at different heights, ensuring accurate and site-specific input data for the simulations. However, while the NASA POWER database provides reliable long-term modeled data, it does not fully capture microclimatic or terrain-specific variations that would be observed through on-site meteorological measurements. As such, a small margin of error may exist, which should be considered when interpreting site-specific performance results. Residential load profiles for Stephenville are developed based on local consumption patterns, considering daily and seasonal variations to reflect realistic demand scenarios. The hybrid system includes wind turbines (Enercon E-44), solar panels (Canadian Solar Dymond CS6K-285M-FG), and grid power. If the wind turbine and PV models meet the specified criteria, the wind turbine and PV models are selected for HOMER Pro and ANSYS simulations. Each component is defined with its respective technical and economic parameters, such as rated capacity, efficiency, capital costs, and operational costs. Furthermore, the collected solar and wind data from NASA’s POWER database is input into HOMER to simulate the renewable energy generation potential. The load profile data is also entered to model the energy demand accurately. HOMER runs simulations to evaluate different system configurations and operational strategies. It considers various factors such as energy generation, storage requirements, and grid interactions to identify the optimized system size and component mix. The software calculates the LCOE, total NPC, and operating costs for each scenario. This analysis helps identify the most economical and feasible system configuration. In addition, HOMER is used to estimate the emissions of CO₂, sulfur dioxide (SO₂), and nitrogen oxides (NO_x) for each configuration. This enables a comparison of the environmental benefits of the hybrid system against conventional fossil fuel-based systems.

Figure 2.

Flowchart describing the steps of this study.

Mathematical models

The following mathematical model has been used to scale and calculate the residential electric load profile required to be met, in the proposed energy system model.

E_{Served} = E_{AC} + E_{DC} + E_{Def} + E_{Grid sales}

(1)

where E_Served is the total amount of electricity energy required to be served, E_AC is the AC primary load to be served, E_DC is the DC primary load to be served, E_Def is the electrical deferrable load served, and E_{Grid sales} is the electric energy sold to the grid.

HOMER Pro will then use this mathematical model to simulate and calculate the renewable fraction and levelized cost of energy accordingly. In every time step iteration, HOMER Pro determines the renewable penetration using the following mathematical model:

P_{Ren} = \frac{P_{Ren, S}}{L_{Served, S}}

(2)

where P_Ren,s is the total renewable electrical power output in this specific time step, P_Ren,S is the total renewable electrical power output in this specific time step, and L_Served,S is the total electric load served in this specific time step.

Regarding the LCOE, the following equation is used to determine the value.

LCOE = \frac{C_{Annual} - (C_{Boiler} \times H_{Served})}{E_{Served}}

(3)

where C_Annual is the total annualized cost of the system, C_Boiler is the boiler marginal cost of the system, H_Served is the total thermal load served, and E_Served is the total electric load served.

Finally, the following mathematical model was used by HOMER Pro to calculate the overall operating cost of the system.

C_{Operating} = C_{ann, tot} - C_{ann, cap}

(4)

where C_Operating is the total operating cost of the proposed energy system model, C_ann,tot is the total annualized cost, and C_ann,cap is the total annualized capital cost in $/yr.

Physical modeling of energy system

In the model shown in Figure 3, a hybrid energy system is designed to meet an electric load demand of 4,931.51 kWh/day with a peak load of 915.61 kW. The system integrates various energy sources and a battery storage solution. On the AC side, the grid supplies electricity directly to the electric load. Additionally, an Enercon E-44 wind turbine (marked as E-44) also generates AC power, which is converted to DC using a converter. On the DC side, the system includes a CS6K-285M-FG solar panel, which generates power from solar energy, and a 1 kWh lithium-ion battery for energy storage. The generated DC power from both the solar panel and the converted wind energy supports the electric load, maintaining a consistent and reliable power supply.

Figure 3.

Overall system architecture modeled by HOMER Pro ensuring constant flow of electrical energy from renewable and non-renewable sources.

The first electrical energy input source into the system is the grid. This hybrid energy system, which combines grid power, wind energy (Enercon E-44), and solar power (CS6K-285M-FG), is designed to meet a daily demand of 4,931.51 kWh with a peak demand of 915.61 kW. The system leverages multiple energy sources to ensure reliability and cost-effectiveness. During times when renewable energy generation (from wind and solar) exceeds the load demand, the excess electricity can be sold back to the grid at $0.060 per kWh, generating revenue. This sell-back value was chosen for modeling simplicity and does not reflect any specific policy in Newfoundland and Labrador. The province currently uses a net-metering system, where exported energy is credited at the retail rate of approximately $0.13/kWh, but not reimbursed as cash (NL Hydro, 2024). In this study, a grid purchase rate of $0.015/kWh and a sell-back rate of $0.060/kWh were applied during the physical modeling stage to represent industrial-scale or wholesale conditions typical for large users in Newfoundland and Labrador, rather than residential retail tariffs. These modeled values reflect an optimistic or policy-supported scenario designed to explore cost flexibility and grid interaction sensitivity under favorable industrial conditions. Applying current retail rates ($0.12–$0.14/kWh) would increase overall costs but would not alter the comparative feasibility or performance trends of the proposed hybrid energy system.

The second electrical energy input is the wind turbine. The wind turbine used is the Enercon E-44. Its rated capacity is 900 kW. The hub height is set for the wind turbine at 55 m. The associated costs include the capital cost, which is 1,300,000 USD. This figure reflects market prices for similar turbine classes (Enercon, 2022; Wind Europe, 2022). The replacement cost is 20% – 30% of capital with external factors. This range aligns with typical maintenance cycles for major turbine components (IRENA, 2023). Furthermore, the operation and maintenance costs are 30,800 USD/year. This estimate is supported by manufacturer service contracts and recent studies (Wind Europe, 2022). The third electrical energy input is the solar PV panel. The solar panel type used is the flat plate manufactured by Canadian Solar. Its rated capacity is 285 W. The temperature coefficient is −0.41% per °C. Its operating temperature is 45°C. The efficiency of the solar panel is 17.33%. The associated costs include the capital cost, which is $1,000. This includes not only the panel itself but also installation, wiring, inverter, and other balance-of-system components (Canadian Solar, 2023; NREL, 2023). The replacement cost is $300, while the operation and maintenance costs are $20/year. A discount rate of 7% was selected based on values commonly used in Canadian renewable energy studies (IRENA, 2023; NREL, 2023). It reflects a middle ground between public-sector and investor-led project assumptions.

To test the robustness of the results, a sensitivity analysis was carried out in HOMER Pro. Table 2 below outlines the ranges used for several financial inputs, such as the discount rate, capital costs, and grid pricing. The analysis showed that changes in wind turbine cost, discount rate, and grid sell-back price had the most noticeable effect on total system cost and payback period.

Table 2.

Sensitivity analysis of key economic parameters used in the hybrid system model. The table presents tested value ranges and their estimated impact on NPC and LCOE as simulated in HOMER Pro.

Parameter	Base value	Tested range	Effect on NPC/LCOE	Impact level
Grid purchase	$0.015/kWh	$0.01–$0.03/kWh	NPC increases to about 12% when cost doubles	Moderate
Grid sell	$0.060/kWh	$0.05–$0.09/kWh	Higher sell-back prices improve ROI by about 18%	High
Discount rate	7%	5%–10%	LCOE increases by 20% when discount rate is raised to 10%	High
Turbine cost	$1,300,000	±20%	Higher wind turbine costs impact NPC significantly	High
Panel cost	$1,000	$800–$1,200	Minimal impact due to smaller price share	Moderate
Battery cost	$400/kWh	$300–$600/kWh	Moderate increase in payback period	Moderate to high

Regarding the electrical load, the average household in Newfoundland and Labrador consumes about 18,000 kWh of electricity per year (NL Hydro, 2024). This equates to approximately 1,500 kWh per month or 50 kWh per day per household. The total energy consumption for the 100 households is approximately 1,800,000 kWh/year. In addition, the average load for the 100 households is 208.33 kW, while the peak load for the households is approximately 718.38 kW. The scaled annual average figure displays the annual consumption for the 100 households as 1,800,000 kWh/year, which means that the daily average consumption is around 4,931.51 kWh/day. Hence, the proposed system design will be optimized and modeled to meet the demand of 4,931.51 kWh/day.

The wind turbine power curve in Figure 4 illustrates how the power output (kW) of the turbine increases with wind speed (m/s). The power curve is obtained from the manual of the wind turbine manufacturer based on the turbine’s performance at different wind speeds. As wind speed rises, power output grows rapidly until reaching a maximum level, after which it stabilizes, indicating the turbine’s capacity limit. For the chosen location, where the average wind speed is 6.6 m/s, the turbine operates efficiently within the optimal range, producing a steady and reliable power output suitable for meeting the energy demands of the hybrid system.

Figure 4.

Power curve of E-44 wind turbine illustrating efficient power output at wind speed range around 6.6 m/s, the average wind speed in Stephenville, NL.

To better understand the wind resource availability at the project site, monthly average wind speeds and corresponding daily usable hours were estimated. Usable hours refer to periods when wind speeds exceed the turbine’s cut-in threshold of 3.5 m/s, allowing the Enercon E-44 to produce power. Figure 5 summarizes this information for Stephenville, Newfoundland, based on data modeled from global atmospheric datasets (NASA Langley Research Center, 2024).

Figure 5.

(a) Monthly average wind speeds (m/s) and (b) estimated daily usable wind hours (i.e., when wind speed ≥3.5 m/s), for Stephenville, Newfoundland, which ensures efficient operation for model’s wind turbine operation.

Results and discussion

Collecting data

The region of Stephenville requires an efficient wind turbine to design a viable hybrid grid-based energy system that can meet all residential load demands in Stephenville, NL. The computational fluid dynamics (CFD) work aims to test the aerodynamic performance of an airfoil that looks like the Enercon E-44 airfoil. This will ensure that the chosen wind turbine model can be used reliably in the designed hybrid energy system. The analysis here targets obtaining the lift and drag coefficients, C_l and C_d, respectively, at several angles of attack (AoA) where the relative wind meets an airfoil. The angle of attack is the angle between the airfoil’s chord line and the relative wind’s direction, which in this study ranges from 0° to 16° due to the varying oncoming wind conditions in the chosen location throughout the year.

ANSYS Fluent is used in this study because it is a suitable tool for boundary layer effects, which is very important in analyzing aerodynamic performance. The inlet velocity boundary conditions defined here are based on seasonal averages, while the outlet was set at zero-gauge pressure to simulate open-field conditions. Every value set in the ANSYS simulation must be aligned with the atmospheric condition in Newfoundland and input into the simulation to make the results as realistic and reliable as possible.

Since seasonal variations directly affect the performance of blades, Figure 6 shows 3 years of wind speed data, which is the latest available data on the NASA Power website for Stephenville. The graphics shown in Figure 6 were produced using a custom Python script created in-house specifically for this study. The script was written to have full control over data handling and graphing, thus ensuring accuracy and consistency in the graphical representation of the simulation results.

Figure 6.

Seasonal wind speed statistics for Stephenville from NASA POWER plotted with our in-house Python script: (a) 2020, (b) 2021, and (c) 2022. Winter averages ≈7.2 m/s, spring/fall ≈6.5 m/s, and summer ≈5.3 m/s; these values feed the CFD boundary conditions and the HOMER Pro resource model.

The inlet velocities used in aerodynamic analysis of the blade are almost between 5 m/s and 7 m/s for angles of attack varying from 0° to 16°. The average wind speed for seasons is considered for the CFD study, which is 7.2 m/s for winter in Newfoundland and Labrador for 3 years from 2020. Also, the average velocities calculated for other seasons are 6.52 m/s, 5.32 m/s, and 6.49 m/s for spring, summer, and fall, respectively. Given the numerical equality of fall and spring wind speeds, we perform the simulation only once to represent both seasons.

To get the most similar and accurate results, we selected the NACA 63-415 airfoil of the 63 series for simulation purposes, as the design specifications of blades used in commercial turbines are often proprietary, along with their respective NACA codes. Given that NACA 63-415 is used as a representative profile, there can be some discrepancies in the accuracy of results when applied to the specifications of the Enercon E-44 wind turbine airfoil. However, simulation using NACA 63-415 airfoil provides approximate and appropriate results given that the Enercon E-44 is not a small-sized turbine. These results can later be scaled to the appropriate specifications. The NACA 63 series is a family of airfoil designs optimized to yield a high lift-to-drag ratio (L/D), making them suitable for turbulent flow conditions. In the case of high Reynolds number conditions, which are dominated by turbulent flows, the NACA 63-415 airfoil has better performance. Its aerodynamic efficiency enables stable operation over moderate to high wind speed ranges, making it a highly appropriate choice for wind turbine blades. Its ability to maintain a high lift-to-drag ratio over a wide range of angles of attack also makes it highly versatile in adapting to different wind conditions. Also, the aerodynamic characteristic of the system reduces energy losses caused by turbulence and increases the operating efficiency of wind turbines in realistic settings (Chaudhary and Nayak, 2015; Erkan et al., 2020).

Reynolds number (Re) values calculated for different wind speeds and seasons, show that the airflow around the airfoil is turbulent, as all of them are greater than 10⁶. This backs up the choice of the $k - ω$ shear stress transport (SST) turbulence model, which is very good at showing how boundary layers move and how flows separate, which makes sure that predictions of aerodynamic forces like lift and drag are accurate. The equation below which establishes whether the flow is laminar or turbulent defines Re where L is the characteristic length, normally the chord length of the airfoil:

R e = \frac{ρ U L}{μ}

(5)

Governing equations for numerical analysis

The main equations for analyzing two-dimensional, steady, and incompressible flow around an airfoil are the continuity and momentum equations, also known as the Navier-Stokes equations. u and v represent the velocity components of the flow in the x and y directions, respectively. The continuity equation for incompressible flow in two dimensions is as follows:

\frac{\partial u}{\partial x} + \frac{\partial v}{\partial y} = 0

(6)

For a two-dimensional, steady, incompressible flow, the Navier-Stokes momentum equations in the x and y directions are as follows:

ρ (u \frac{\partial u}{\partial x} + v \frac{\partial u}{\partial y}) = - \frac{\partial p}{\partial x} + μ (\frac{\partial^{2} u}{\partial x^{2}} + \frac{\partial^{2} u}{\partial y^{2}})

(7)

ρ (u \frac{\partial v}{\partial x} + v \frac{\partial v}{\partial y}) = - \frac{\partial p}{\partial x} + μ (\frac{\partial^{2} v}{\partial x^{2}} + \frac{\partial^{2} v}{\partial y^{2}})

(8)

where ρ is the fluid density, p is the fluid pressure, and μ is the dynamic viscosity of the fluid. Numerical methods, such as CFD, typically solve these equations, comprehensively describing fluid flow behavior around an airfoil to obtain accurate results.

In the current simulation, the reference area for calculating the lift and drag coefficients, C_l and C_d, respectively, is defined as the chord length of the airfoil as the simulation is carried out in two dimensions for a unit span length. Expressions for these aerodynamic coefficients are provided below.

C_{l} = \frac{L}{\frac{1}{2} ρ U^{2} A}

(9)

C_{d} = \frac{D}{\frac{1}{2} ρ U^{2} A}

(10)

These governing equations, boundary conditions, turbulence models, and coefficients constitute the foundation of a complete aerodynamic analysis of an airfoil in steady, incompressible, two-dimensional flow. By solving these governing equations with ANSYS Fluent, one can gain detailed knowledge of the flow field, including velocity distribution, pressure, and aerodynamic forces on the airfoil.

CFD model and boundary condition

The NACA 63-415 points have been extracted from the AirfoilTools website in the form of an Excel file, and then converted into a text file to be ready for ANSYS Fluent simulations. In Figure 7, it can be seen that the almost symmetrical profile of this blade, with a chord length of 10000 mm in the ANSYS simulation space.

Figure 7.

Schematic of the NACA 63-415 airfoil used as a representative E-44-class blade section in the 2D CFD. The computational chord is 10,000 mm and simulations are run at Re = $3 \times 10^{6}$ .

Figure 8 shows that the C-type mesh around the airfoil has high refinement near the leading and trailing edges to accurately capture the flow gradients. The maximum aspect ratio of 90 is located in the boundary layer region, where more refinement is needed for the proper resolution of velocity gradients. The average aspect ratio 5.7572 over the mesh indicates a well-balanced grid with controlled element stretching. This structured meshing strategy decreases numerical diffusion but preserves computational efficiency and accuracy in aerodynamic performance analysis.

Figure 8.

C-type structured mesh with near-wall refinement and leading/trailing-edge clustering. The production grid (Case II) has 261,618 elements (average aspect ratio 5.76, maximum 90); grid-independence differences are <0.5% in C_l and <0.1% in C_d.

Mesh independence study

A mesh independence analysis is conducted to evaluate the effect of grid resolution on the aerodynamic performance parameters, namely, the lift and drag coefficients, during winter conditions at an angle of attack equal to zero degrees. Table 3 shows four different grid resolutions ranging from 60,161 to 4,201,616 elements are considered, and the results show that an increased number of elements significantly brought down the relative error for both coefficients. The relative error dropped from 0.5% in Case II to 0.017% in Case IV, and it was also reduced from 0.1% to 0%, thus further verifying that Case IV converged with negligible error and achieved mesh independence. However, the computational time increased with finer mesh refinement; to be more precise, Case I (60,161 elements) took about 10 minutes, Case II (261,618 elements) required 30 minutes, and the last two cases exceeded an extra hour. While Case IV showed the highest accuracy, its related computational cost made it less suitable for the current study. Therefore, Case II, with 261,618 elements, was the most beneficial setup, reaching a balanced compromise between sufficient accuracy and an acceptable computational time, ultimately making it the most suitable choice for the conducted aerodynamic analyses.

Table 3.

Grid-independence study for the NACA 63-415 CFD at AoA = 0° in winter (Re = $3 \times 10^{6}$ ). C_l and C_d converge with mesh refinement; Case II (261,618 elements) was selected as the production grid since errors were ≤0.5% in C_l and ≤0.1% in C_d with acceptable runtime.

Mesh (grid)	Number of elements	C_l (error %)	C_d (error %)
Case I	60,161	0.29266 (−)	0.014487 (−)
Case II	261,618	0.29111 (0.5)	0.014471 (0.1)
Case III	360,161	0.29460 (1.1)	0.014702 (1.5)
Case IV	4,201,616	0.29465 (0.017)	0.014702 (0)

Numerical setup

In order to analyze the airfoil under actual operating conditions, simulations were run to replicate various wind velocities and attack angles the blade would be subjected to in a year, in accordance with the seasonal variation of winds in Stephenville. All simulations were run in ANSYS Fluent configured for steady, incompressible flow using a pressure-based solver. Air density was assumed constant in order to simplify calculations but still retain the required accuracy for most wind turbine applications, based on the low-Mach, near-incompressible nature of the site-specific wind conditions. The boundary conditions were modeled to simulate an open field around the airfoil with the wind velocity as the input variable, taken from NASA POWER seasonal averages for Stephenville, where winter averages 7.2 m/s, spring 6.52 m/s, summer 5.32 m/s, and fall 6.49 m/s, and set a zero-pressure outlet for the airflow. The $k - ω$ SST turbulence model was used due to its capability to handle boundary layer separation. These settings ensure proper calculation of forces (lift and drag), making evaluating airfoil performance through simulation realistic and directly supporting the accuracy of the wind energy component used in the hybrid system’s techno-economic model.

The simulations were conducted at different wind speeds and attack angles to keep aerodynamic performance in sync with the seasons. We tracked the convergence for all the simulations until residuals stabilized and converged aerodynamic force coefficients to stable values. After convergence, output values were lift and drag coefficients (C_l, C_d) and velocity components (v_x, v_y) for all the cases.

We compared the CFD outcome with the same profile’s experimental lift and drag coefficient measurements to verify simulated aerodynamic performance replicates NACA 63-415 performance trends reported in the literature. These findings justify the appropriateness of the E-44-class blade for the Stephenville wind regime and further raise confidence in the contribution of the wind subsystem to the hybrid energy system.

Validation of the numerical model

In order to verify the results we compared ANSYS CFD outputs with published experimental data for the NACA 63-415 airfoil. The reference data gives important insight into how the lift coefficient C_l and drag coefficient C_d behave over a range of angles of attack. Figure 9 compares the C_l and C_d values from the CFD simulation of the present study in various seasons at Re = $3 \times 10^{6}$ and experimental data at Re = $5 \times 10^{6} .$ Drag shows larger discrepancies at high angles of attack; differences between solid and dashed reference curves indicate small-scale flow effects and modeling sensitivities that are only partially captured. The reference curves, therefore, serve as baselines for assessing the CFD trends.

Figure 9.

Seasonal C_l and C_d from the present CFD (Re = $3 \times 10^{6}$ ) compared with experiments (Re = $5 \times 10^{6}$ ). C_l peaks near 12°, $C_{d}$ stays low to ≈8° and rises rapidly beyond 12°, supporting an 8–12° AoA operating window.

The reference data in Figure 9 shows a steady rise in the lift coefficient as AoA increases, reaching a maximum near 12°. Similarly, ANSYS simulations show consistent trends across seasons. Figure 9 shows a monotonic rise of C_l with angle of attack up to approximately 12°, followed by a decline due to stall onset. This behavior is consistent across seasons and aligns with the experimental curves of Abbott and Von Doenhoff (1959) and Erkan et al. (2020). This tendency is driven by the creation of increased circulation around the airfoil, consistent with the Kutta-Joukowski theorem, where increasing the angle of attack raises circulation and lift until stall onset. Figure 9(a) shows that C_l increases steadily from 0.29111 at 0° AoA to 1.2116 at 12° AoA, then declines to 1.1353 at 16° AoA. Together with the efficiency maximum at 8° in Figure 11, this defines an effective operating angle of attack window of approximately 8–12° for the blade under Stephenville’s wind regime, supporting control setpoints and the wind-yield assumptions used in the hybrid system model. At higher angles (beyond 12°), separation intensifies over the suction surface, consistent with the rapid C_d increase at high AoA in Figure 9. Likewise, in fall and spring, Figure 9(b), C_l rises from 0.35719 at 0° to 1.1967 at 12° then falls to 1.1194 at 16°, following a similar upward-to-downward trend. Summer Figure 9(c) C_l increases from 0.28288 at 0° to 1.1672 at 12°, then falls to 1.0878 at 16°. Seasonal differences in peak C_l values, highest in winter at 1.2116, followed by fall/spring at 1.1967 and lowest in summer at 1.1672, reflect changes in air density and viscosity that shift Reynolds number and boundary layer behavior. Although minor discrepancies at higher AoAs might reflect changes in turbulence modeling or boundary conditions, such trends are well within experimental results of Erkan et al. (2020) and Abbott and Von Doenhoff (1959) and hence prove that the numerical model indeed simulates the physics of lift creation. These seasonal variations emphasize differences in aerodynamic lift performance across various months, underscoring the blade design adaptability to changing winds in favor of the running robustness of an E-44-class blade for Stephenville conditions.

A smooth increase with the start from low angles of attack (0° to 8°), the drag coefficient plots shown in Figure 9(a)–(c) show a low C_d region for AoA (0° to 8°), with an abrupt peak after 12°. Drag is comprised of pressure drag due to flow separation and skin friction drag due to viscous shear forces within the boundary layer, showing skin friction predominance over pressure drag with increasing separation. The drag coefficient is minimum at low angles of attack as the flow remains attached, so skin friction takes over. As AoA increases, the adverse pressure gradient becomes stronger and leads to the separation of flow and an increase in pressure drag, which drives the sharp C_d growth beyond about 12°. Reference data in Figure 9 indicates that the drag coefficient is very low as long as the angles of attack are low and increases as the angle of attack approaches 16°. In the present study, during winter, as shown in Figure 9(a), the drag coefficient starts at 0.014471 at an angle of attack of 0° and increases up to 0.093376 by 16°. In summer, as depicted in Figure 9(c), the drag coefficient starts at 0.015546 and reaches 0.10187 at 16°. For fall and spring, Figure 9(b) illustrates a change in the drag coefficient from 0.017756 to 0.096167 at 16°. Higher C_d values in summer compared to winter and fall/spring at higher AoAs indicate stronger separation or turbulence, consistent with lower density and higher temperature impacts on viscosity and Reynolds number. These C_d trends reinforce an upper bound near 12° and inform pitch-control limits in the wind component, protecting aerodynamic efficiency.

With higher wind speeds and denser air that enhances lift generation, Figure 9(a) shows that the blade achieves peak aerodynamic efficiency in winter, as indicated by the highest C_l (1.2116 at 12° AoA) and modest C_d increase. In summer, lower wind speeds and perhaps warmer, less dense air cause a lower peak C_l (1.1672 at 12° AoA) and a steeper C_d rise, indicating reduced efficiency due to increased flow separation. Fall and spring show moderate performance levels; performance remains close to winter with minor deviations close to winter trends with minimal deviations due to the transitional nature of the atmosphere. Agreement with Abbott and Von Doenhoff (1959), Bak et al. (2000), and Erkan et al. (2020) further validates the aerodynamic performance across seasons. The uniformly positive drag and lifting coefficients of the blade at all angles of attack and at all seasons demonstrate that it is effective in pulling energy from the wind and, therefore, well qualified to employ under Stephenville, Newfoundland’s varying winds. Collectively, Figure 9 establishes a consistent aerodynamic envelope across seasons, with peak lift near 12° and rapidly increasing drag beyond that threshold. We operationalize this evidence as an 8–12° angle of attack window for the E-44-class blade and use it to bound pitch-control setpoints and to validate the wind yield assumed in the hybrid techno-economic model, which strengthens confidence in the reported LCOE and NPC.

Figure 10 presents the effect of variation in the angle of attack on the velocity profile around the airfoil for angles of attack from 0° to 16°. At 0°, the flow is very symmetrical above and below the airfoil with zero suction-side acceleration in the u-velocity field. At 4° and 8°, the airflow accelerates above the top surface of the airfoil and decelerates on the bottom surface, consistent with the rise in C_l and the L/D maximum near 8° (Figure 11). At 12° and 16°, the velocity difference between the upper and lower surfaces is more pronounced, and an extended low $u$ region develops near the suction-side trailing edge, evidencing separation; this aligns with the drop in C_l and the rapid C_d growth beyond 12° in Figure 9. Across 0–8°, the acceleration zone over the suction surface expands toward mid-chord while the pressure side deceleration zone contracts, and the increased ∂u/∂y near the leading edge is consistent with a stronger favorable pressure gradient. At 12–16°, the contours show a broad low $u$ region adjacent to the suction surface and a downstream shift of the minimum u, which are canonical markers of an adverse pressure gradient and impending stall. These spatial patterns provide a flow-field explanation for the C_l/C_d trends and justify limiting routine operation to the 8–12° AoA window derived above.

Figure 10.

X-velocity (u) contours at AoA = 0°, 4°, 8°, 12°, and 16°. Cases 0–8° show attached flow with suction-side acceleration; 12–16° show a suction-side low-u pocket indicating separation (drop in C_l, rapid C_d rise).

Figure 11.

Y-velocity (v) contours showing flow turning and circulation changes with AoA. The antisymmetric v-field strengthens up to ≈ 8–12°; at 12–16° strong v-gradients/vortices near the suction-side trailing edge indicate separation and stall onset.

Figure 11 shows Y-velocity (v) contours at AoA = 0°, 4°, 8°, 12°, and 16°; the v-field highlights flow turning and circulation changes with AoA. At 0°, the flow is nearly symmetric with small vertical velocity components on either surface. At 4° and 8°, the flow deflects more over the top (suction) surface and downward over the bottom (pressure) surface; positive v on the suction side and negative v on the pressure side intensify, consistent with higher circulation and C_l. At 12° and 16°, strong v-gradients and vortical structures appear near the suction-side trailing edge, indicating separation consistent with stall onset. At these angles, the v = 0 contour lifts off the surface and a near zero v pocket forms on the suction side, a kinematic signature of separated flow that explains the drop in C_l and the rapid C_d increase reported in Figure 9. These v-field features corroborate separation onset beyond about 12° and support the 8–12° operating AoA window used for pitch-control limits.

Figure 12 presents the variation of the lift-to-drag ratio (C_l/C_d) versus the angle of attack (AoA) for the investigated airfoil and shows C_l/C_d peaking at about 8° across seasons. At moderate AoA, lift rises faster than drag while the flow remains attached, yielding the efficiency peak. As AoA increases from 0° to about 8°, suction-side acceleration and circulation increase, consistent with the rise in C_l. At the same time, drag values are relatively low, consisting mainly of skin friction with negligible pressure drag. The peak in efficiency at 8° represents the optimum balance in which lift generation increases with only small drag penalties before the onset of flow separation. Beyond this angle, the lift-to-drag ratio decreases as separation develops and C_d increases rapidly (see Figure 9). This behavior is consistent with established airfoil performance at intermediate Reynolds numbers. Operationally, the C_l/C_d peak near 8°, together with the C_l peak near 12° and the rapid C_d growth beyond 12° in Figure 9, defines an AoA operating window of approximately 8–12° for the E-44-class blade and informs pitch-control limits and the wind-yield assumptions used in the hybrid techno-economic model.

Figure 12.

Lift-to-drag ratio (C_l/C_d) versus AoA. C_l/C_d peaks at ≈ 8° across seasons; together with the C_l peak near 12° and the C_d surge beyond 12°, this supports an 8–12° AoA operating window and pitch-control limits.

Optimization

Adaptability is essential to maintaining stable energy production in the hybrid system. With the integration of this aerodynamic investigation, we have concluded that the chosen blade, similar to the Enercon E-44 blade, efficiently works for a broad range of wind conditions experienced in Stephenville. This integration is important for realizing the system’s target LCOE while adhering to environmental sustainability. Table 4 explores three different hybrid energy systems, combining Canadian Solar CS6K-285M-FG solar panels, Enercon E-44 wind turbine, grid connection, and battery storage. The results presented in Table 3 were obtained using HOMER Pro’s optimization engine. HOMER Pro simulates different energy system configurations based on input parameters such as available renewable resources (wind speed, solar radiation, and temperature data for the chosen location), load profile, component costs, and operational constraints. These systems were compared based on key factors such as cost-effectiveness LCOE, long-term expenses NPC, annual operating costs, and how much of the energy comes from renewable sources. This analysis helps determine the practicality and affordability of these models for a residential home profile in Stephenville, NL.

Table 4.

Scenario comparison and optimization results.

	CS6K-285m-FG PV panels	E-44 turbine	Grid	Battery	LCOE ($/kWh)	Operating cost ($/yr)	Net present cost (M$)	Renewable fraction (%)
Scenario 1	Yes	Yes	Yes	Yes	0.0356	13,704.0	1.56	60.1
Scenario 2	Yes	Yes	Yes	No	0.0333	30,574.0	2.25	82.8
Scenario 3	No	Yes	Yes	Yes	0.0416	780.56.0	2.74	80.0

Scenario 1 which is a fully integrated hybrid energy systems brings together solar panels, a wind turbine, grid connection, and battery storage. This setup delivers an LCOE of $0.0356/kWh, the lowest NPC at $1.56 million, and a renewable fraction of 60.1%. Battery storage contributes to a big role in improving reliability by ensuring energy availability during grid outages and balancing the variability and fluctuations in renewable energy supply systems. While the renewable fraction is not the highest, this system possesses a good balance between cost and efficiency. It uses a mix of technologies to manage costs while ensuring reliable energy across the entire year, with the grid stepping in to support during seasonal dips in renewable energy productions. The inclusion of battery storage in this scenario is significant as it helps mitigate the fluctuations in energy levels of renewable sources. Batteries help ensure that energy availability is maintained during peak demands or outage services, in order to balance seasonal variabilities. However, there still remains room for improvement in this scenario in terms of costing and achieving better levels of renewable energy penetration during system sizing phase. This scenario shows cost-effectiveness and long-term performance reliability, with a good balance between different energy resources. However, it is still limited to renewable energy generation and could be sized bigger to achieve more energy productions. Incorporating demand side management techniques will provide an added value for energy optimizations and grid dependence levels.

Scenario 2, which is a hybrid system without battery storage takes a simpler approach, using solar panels, a wind turbine, and grid connection but does not consider battery storage. This setup has the lowest LCOE at $0.0333/kWh and saves $30,574 annually in operating costs. However, its NPC rises to $2.25 million due to greater reliance on grid infrastructure and higher upfront investments in the system. The renewable fraction is higher at 82.8%, as this setup maximizes renewable resources during sunny or windy conditions. However, without battery storage, the system struggles to deal with interruptions in renewable energy supply and may not be as reliable during grid outages. Without the inclusion of batteries, the system remains exposed to interruptions during periods of low energy availability, or scheduled service outages. This scenario is cost-effective, has high renewable fractions, and minimal operating costs. However, it is still associated with poor reliability during grid outages, and high NPC due to grid dependency. These limitations can be resolved through integrating smaller-scale energy storage solutions, such as hybrid inverter systems, to achieve better reliability and cost-efficiency margins. In addition, adoption of smart grid technology-based systems aids in optimizing grid interactions, reducing net present costs over time.

Scenario 3, which is a wind and grid-based system with battery storage, does not consider solar panels, relying only on a wind turbine, grid connection, and battery storage. This setup has the highest LCOE at $0.0416/kWh and modest annual savings of $780.56, with the NPC reaching to $2.74 million, making it the least cost-effective choice. The renewable fraction is an approximately 80%, but not having solar panels limits the system’s ability to capture solar energy during peak production times. While battery storage helps with energy reliability and availability during outages, this system isn’t as efficient or cost-effective as the others due to its lack of diversity in energy sources. This scenario shows reliable energy supply during scheduled outages, and more consistency in wind energy contribution margins. However, the system is still also limited to factors such as more cost, and limited energy diversification in resources.

Hence, systems with battery storage, like Scenarios 1 and 3, offer more resilience by addressing the positive and negative scenarios in renewable energy production, but they come with higher costs. Scenario 2 is a simpler model that keeps costs low and maximizes renewable energy use, but it lacks the flexibility and reliability of battery equipped systems. Combining solar panels and wind turbines, as seen in Scenarios 1 and 2, takes advantage of the strengths of both technologies with solar energy peaking in summer while wind energy contributes steadily across the entire year. Grid connections stabilize energy supply for all scenarios, but relying on the grid adds long-term costs and exposure to changing energy prices. This means that even though Scenario 2 has more renewable energy penetration than Scenario 1, it is exposed to more volatility in terms of energy prices given its increased reliance on the grid. Scenario 1 provides a solution that reduces the economic volatility by adding a battery storage system. The battery storage system stores the power from renewable energy sources when grid prices are low and relies on that stored energy when grid prices are higher. This simple solution provides great economic savings despite it reducing the renewable fraction slightly, which provides a more feasible solution for stakeholders to implement.

Considering all the above scenarios, scenario 1 stands out as the most balanced choice, offering a reliable, flexible, and cost-effective system. By utilizing solar panels, wind turbines, and battery storage, it keeps costs manageable while ensuring consistent energy availability. Although Scenario 2 offers a higher renewable fraction and lower operating costs, it falls short in resilience due to the lack of battery storage. Figure 13 supports this conclusion, showing how Scenario 1 effectively balances energy production from wind, grid, and little solar contribution, ensuring consistent energy for residential needs throughout the year. The solar PV energy contribution is insignificant compared to that of the grid and the wind turbine. As a result, the solar PV energy values are not represented in Figure 13.

Figure 13.

Monthly electric energy production comparison between energy components, which shows how the wind turbine and grid are the main energy contributors to the system.

The performance metrics of the hybrid energy system designed in Stephenville, NL illustrate that the renewable fraction is 60.1%, meaning that 60.1% of the energy generated by the system is powered by renewable energy sources such as wind and solar, in our case. Additionally, the maximum renewable penetration is 103%, indicating that at certain times, the system’s renewable energy output exceeds and outperforms the total energy residential load demand, which allows for the possibility of storing excess energy or feeding it back into the grid, through a battery storage solution, as proposed in the main system architecture design. For the annual energy performance against the grid for the hybrid energy system in Stephenville, NL, the system purchased 961,724 kWh from the grid and sold 609,645 kWh back to the grid, resulting in a net energy purchase of 352,079 kWh. The peak load reached 876 kW, with an annual energy charge of $22,630.70.

The graphs in Figure 14 display the ratio of instantaneous renewable energy output to load and the inverse ratio of non-renewable energy for each hour of the year in Stephenville, NL. Figure 14(a) shows fluctuating and varying renewable energy contributions, with varying intensities throughout the year, highlighting the intermittency of renewable energy sources, which in this case, are solar and wind energy. Figure 14(b) supports this by indicating the reliance on non-renewable sources when renewable output falls short of meeting the necessary electrical load profile. Overall, the data and results provided in the most cost-efficient simulation result, indicates that around 60% of the energy production is from renewable sources, emphasizing the system’s significant renewable energy penetration to meet the required electric load.

Figure 14.

Instantaneous (a) renewable and (b) non-renewable energy penetration outputs divided by load. The graphs illustrate how non-renewable energy from the grid balances the intermittency of renewable energy sources.

Figure 15(a) shows the hourly distribution of energy purchased from the grid, indicating higher purchases during certain periods. On the other hand, Figure 15(b) displays the energy sold to the grid, with notable variability throughout the year, reflecting the variable nature of renewable energy production, which as discussed earlier in this case, is solar and wind energy.

Figure 15.

Distributions of (a) energy purchased from the grid and (b) energy sold to the grid. The graphs highlight the variability of energy sold to the grid compared to the consistency of energy purchased from the grid.

While the proposed energy system still incorporates the use of a grid, it is evident that there is still significant greenhouse gas emission produced. However, when compared with actual greenhouse gas emissions produces from pure hydrocarbons, it is much less. This also indicates the criticality of future research work and development needed to further enhance and improve the technologies needed to mitigate the greenhouse gas emissions. The system’s results conclude that the system minimizes carbon emissions as it produces significantly less CO₂ (222,514 kg/yr) compared to fossil fuel power plants. In addition, the system produces a low number of pollutants into the atmosphere as the system shows zero emissions for carbon monoxide, unburned hydrocarbons, and particulate matter, indicating a cleaner energy production process compared to fossil fuels. Furthermore, the sulfur dioxide and nitrogen oxide emissions are lower than typical fossil fuel plants for the system.

The hybrid energy system proposed in the previous system architecture in Stephenville, NL, demonstrates significant cost-effectiveness and sustainability overall. The system’s total NPC is $1,561,043.00, with an LCOE of $0.03556 per kWh. This LCOE is competitive with current rates for onshore wind energy in North America, which range from $0.029/kWh to $0.068/kWh (Lazard, 2024), and slightly lower than the LCOE for utility-scale solar PV systems, which range from $0.036/kWh to $0.060/kWh (Lazard, 2024). In addition, the system is associated with a relatively low annual operating cost of $13,704.28. Hence, the overall integration of wind and solar power allows the system to leverage renewable energy sources, contributing to environmental sustainability and reducing reliance on fossil fuels in a cost-effective manner.

Policies and regulations for renewable energy systems

Policies toward clean energy have been a vital part of Canada’s energy regulations in recent years. Canada’s budget planned 2023 focuses heavily on achieving a net-zero energy sector and reducing carbon emissions (Harland and Dion, 2023). The decrease in carbon emissions is also directly linked to an increase in savings for Canadians as the prices of energy are expected to decrease by 12% in the year 2050 (Harland and Dion, 2023). To combat any electricity price increases that can occur, Canadian provinces, including Newfoundland and Labrador, have policies and regulations to ensure the plan moves swiftly.

One of the most important policies in place is the financial support for provincial and federal clean energy projects. For example, budget plan 2023 includes approximately $26 billion dollars till 2035 in tax through the Clean Electricity Investment Tax Credit program (Government of Canada, 2023; Harland and Dion, 2023). In addition, the Canada Infrastructure Bank will provide $20 billion for the support of clean energy and clean infrastructure, which includes electrical energy generation, transmission, and distribution (Harland and Dion, 2023). Furthermore, the Smart Renewables and Electrification Pathways program will provide another $3 billion supporting the upgrade in electrical grid to support renewable energy projects (Harland and Dion, 2023).

The federal government ensures that all provinces will have advantages from the policies and regulations in place for clean energy projects. Newfoundland and Labrador can obtain $370 million for every gigawatt of power in support of clean energy projects (Harland and Dion, 2023). Such regulations support fossil fuel dependent provinces to shift toward a cleaner energy production. For example, Newfoundland and Labrador’s Minister of Industry, Energy, and Technology provided that energy companies bid for four wind-hydrogen productions. The revenue of these projects is approximately $11.7 billion for the province of Newfoundland and Labrador with a capital spend of approximately $66.3 billion.

The electricity rates are estimated to increase in Canadian provinces. For example, the electricity rate is expected to increase in Newfound and Labrador by 2.7 cents per kWh from 2025 to 205 (Harland and Dion, 2023). However, if the policies and regulations for clean energy were followed, electrical bills are set to decrease despite increase in electrical rates (Harland and Dion, 2023).

In addition, clean energy jobs are expected to increase greatly in Atlantic Canada alone (Careers in Energy, 2024). This increase is substantial to the Canadian economy. In addition, the increase in jobs also supports provincial initiatives toward clean energy projects as shown in Newfoundland and Labrador. The province pushes toward hydrogen production and wind turbine projects to decarbonize its energy sector (Department of IndustryEnergy and Technology, n.d).

Regulations and policy implications are essential in driving the adoption of hybrid renewable energy systems forward. Such systems can be promoted with government incentives, collaborations between public and private sectors, and a supply of regulatory frameworks for mobilizing investments. This has been documented to be in place for all successful cases. However, there are challenges, such as inconsistency and inadequacy of proper policies, integration into the grid, and issues with licenses and standards. This could also be related to balancing environmental and economic considerations, land use, resource assessment, and dealing with high initial costs and uncertainties in return on investment (Hassan et al., 2023).

The regulations imposed by the federal government, in addition to the incentives provided of the province of Newfoundland and Labrador, ranging from environmental goals to economic benefits enable hybrid renewable energy system projects such as the one conducted in this study. The system presented in this study achieves an LCOE of $0.0356/kWh, which is equal to or less than the average costs of onshore wind and solar projects in North America. The system also presents a renewable energy fraction of 60.1%, ensuring minimizing the reliance on grid. The system aligns with the regulatory framework presented by the federal government and the ambitions of the province.

In general, Canada has regulations that require the country’s electrical generation to be at net-zero by the year 2035 and a net-zero emissions plan by the year 2050 according to the Canadian Environmental Protection Act and the Canadian Net-Zero Emissions Accountability Act, respectively (ICLG, 2023). Such regulatory framework creates the initiative for the private sector to work on research and development for renewable energy projects as well. In addition, these acts illustrate the need for renewable energy projects, like wind turbine, solar, or hybrid projects to meet the plans by 2035 and 2050 appropriately.

Conclusion and recommendations

This study highlights the potential of a hybrid renewable energy system to reliably meet residential electricity needs in Stephenville, NL. By combining wind power from the Enercon E-44 turbine, solar energy from Canadian Solar Dymond panels, and grid electricity, the system achieves an LCOE of $0.0356/kWh—putting it on par with or even below the costs of typical onshore wind and solar projects across North America. With a total NPC of $1.56 million and annual operating expenses of $13,704.28, this system not only proves to be cost-effective but also has a meaningful environmental impact, for example, a 222,514 kg/year of CO₂ reduction along with other pollutants like sulfur dioxide and nitrogen oxides. The system demonstrates adaptability to seasonal variations in Stephenville’s energy demand, achieving a renewable fraction of 60.1% and peaking at a maximum renewable penetration of 103%.

An essential part of this study was the aerodynamic analysis of the Enercon E-44 wind turbine blade, which was modeled using CFD in ANSYS Fluent. By simulating local seasonal wind conditions and using the $k - ω$ SST turbulence model, we found the blade achieved strong lift and low drag performance throughout the year, ensuring reliable wind energy generation—even during periods of lower wind speeds. This analysis builds confidence in the system’s adaptability and cost-efficiency for the Stephenville area.

Furthermore, several limitations should be acknowledged. First, the wind blade design relies on proprietary aerodynamic characteristics from Enercon, which restricts public validation of CFD results. Second, while HOMER Pro offers robust optimization capabilities, its performance predictions may not fully capture localized microclimates or terrain effects without detailed on-site meteorological data. Third, current regulatory frameworks, tariffs, and net-metering policies in Newfoundland and Labrador may evolve, potentially impacting grid integration economics. Finally, the system design is based on modeled data rather than real-time operational testing, so practical deployment may reveal site-specific constraints or performance variations. Addressing these factors in future studies would help strengthen the real-world applicability of the proposed solution.

In summary, this study shows that a well-designed hybrid renewable energy system can not only be financially viable and environmentally friendly but also resilient enough to meet local energy demands year-round, contributing to both community resilience and Canada’s vision for a greener future.

Footnotes

ORCID iD

Amin Etminan

Author Contributions

Conceptualization: A.I., N.H., and A.J. Methodology: A.J. Software: A.I. and N.H. Validation: A.I. and N.H. Formal analysis: A.I. and N.H. Investigation: A.E., A.I., N.H., and A.J. Resources: A.I., N.H., and A.J. Data curation: A.E., A.I., N.H., and A.J. Writing—original draft preparation: A.I., N.H., and A.J. Writing—review and editing: A.E., A.I., N.H., and A.J. Supervision: A.E. Project administration: A.E., A.I., A.J., and N.H. Funding acquisition: A.E. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting this article will be made available upon reasonable request.*

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Optimizing hybrid renewable energy systems: Techno-economic feasibility and CFD study for residential load applications in Stephenville,NL

Abstract

Keywords

Introduction

Problem description

Methodology

Mathematical models

Physical modeling of energy system

Results and discussion

Collecting data

Governing equations for numerical analysis

CFD model and boundary condition

Mesh independence study

Numerical setup

Validation of the numerical model

Optimization

Policies and regulations for renewable energy systems

Conclusion and recommendations

Footnotes

ORCID iD

Author Contributions

Funding

Declaration of conflicting interests

Data Availability Statement

References