Design and Evaluation of a Grid-Connected Distributed Wind Turbine
Introduction
Sustainable and energy-efficient energy supply to end consumers is critical for a country's socioeconomic success. Several countries intend to modernize their energy infrastructure in order to reflect natural resource availability while maintaining grid stability. At the moment, the majority of the world's energy is produced by fossil-fuel-based power plants, which have a severe influence on the environment. The process of turning fossil fuels into electricity directly contributes to ozone layer depletion and acid rain. Aside from these effects, various dangerous compounds have now disrupted the optimal air component ratios essential for healthy breathing. Large-scale renewable-based power generation, as demonstrated in the report on global energy generation, may directly reduce the usage of fossil fuel generators.
The minimum average wind speed necessary for improved efficiency from wind energy generation is roughly 6.5 m/s. However, in West Texas, the average wind speed is roughly 8.5 m/s. These accessible average wind speeds can be used to harvest electricity from larger-scale wind energy while also creating chances for dispersed and/or freestanding wind turbines, particularly in rural towns and ranches. However, freestanding wind turbine technology currently has a lower chance of commercialization and availability than solar and diesel generators. Several recent studies, however, show that greater development of off-grid small-scale wind generation in Texas' coastal regions offers significant economic advantages.
Texas is home to many rural areas and traditional ranches that rely on electric cooperatives for power. Distributed wind systems may be deployed in a variety of sites, including urban areas, giving greater flexibility and avoiding aesthetic and environmental consequences, which can be difficult for large-scale traditional wind farms. These electric cooperatives are exploring incorporating distributed energy resources within their service region, as well as offering incentives to its members to engage in virtual power plant initiatives. High-speed distributed wind energy systems might be created in West Texas areas with a high potential for electricity generation.
Microgrids are frequently characterized as a local energy grid that includes distributed generation, energy storage, and maybe load control capabilities. It works as a separate entity from the usual centralized power grid and may function both linked to and removed from it (island mode). Microgrids are intended to offer dependable and resilient power to essential locations, communities, and infrastructure. They can be made up of a variety of energy sources, including renewable energy resources, fossil-fuel-powered generators, and energy storage devices. Figure 3 depicts a typical microgrid design.
Figure 3 depicts a basic grid-connected microgrid schematic.
Lubbock wind scenarios
Data Examination
Height Calculation
Because the major purpose of the research is to increase the overall resiliency of our electric grid by integrating more dispersed wind turbines to serve houses in Lubbock, TX, the height that we utilized was within the usual limit for a suburban region.
The wind power law states that wind speeds may be determined at any reference location using the following equation:
(1).
where h = Height (Measured), h0 = Height (Reference), v = Wind Velocity, v0 = Wind Velocity at the height of reference, and = Frictional Coefficient. According to Equation (1), the frictional coefficient in a small town or suburban city is = 1/3 = 0.33. The wind speed may be determined using the following method at the suggested height of 100 feet:
𝑣=𝑣0(ℎℎ0)𝛼=11 ms−1 (Yearly average)
Figure 5 depicts a histogram of MESONET site data at a height of 10 meters.
Figure 5 shows the histogram of the wind speed data for Lubbock, Texas.Density of Air
Wind energy production relies heavily on air density, which is affected by temperature fluctuations. The Lubbock area's typical temperature is 28 °C, or 82 °F. Figure 6 contains a list of the air density values as they change with temperature.
Calculation of Maximum Power
where the Betz limit coefficient CP = 0.37 and the swept area (rotor blade) A = πr2. 𝑃𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 = 6 kW (desired) for the low-speed turbine's maximum output power, height (pole)=20 m, blades count = 3 blades, and a 2.5 m radius for the blade swept area. 𝐴=𝜋𝑟2=19.635 m2 will be the blade's swept area as a result.
According to the wind speed in section III.B, which is v = 12 m/s or (18 mph),
and the air density is ρ = 1.165 kg/m3 for the generalized temperature of 30 °C (86 F);
Equation (2) will be used to get the theoretical maximum accessible power for 6000 Watts of output power (desired), and it is defined as follows:
𝑃𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒=12𝜌𝐴𝑣3𝐶𝑃=12×1.165×19.635×123×0.40=6202.46 W≈6 kW
A appropriate turbine blade may be employed, and it will be fairly effective for the anticipated scenario given the available power and wind speed.
Given that the rapid area is 19.635 m, the following efficiency may be attained:
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦, 𝜂=𝑇𝑜𝑡𝑎𝑙 𝑒𝑥𝑡𝑟𝑎𝑐𝑡𝑒𝑑 𝑝𝑜𝑤𝑒𝑟𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑒𝑛𝑒𝑟𝑔𝑦×100=77%
Design for a 6-kW Wind Turbine
There are a few key elements that define the design process of a wind turbine system before construction, regardless of the system's size (small or huge).
The Design of the Blade
Airfoil polar extrapolation is a method for predicting an airfoil's performance under different flying situations based on its lift and drag characteristics. For a particular velocity and Angle Of Attack (AoA), an airfoil polar is a graph that shows the Lift Coefficient (Cl) on the vertical axis and the Drag Coefficient (Cd) on the horizontal axis. The polar is obtained by wind tunnel testing the airfoil using Computational Fluid Dynamics (CFD) simulations done using Qblade software version number 2.0.6.3.
Figure 8 depicts the extrapolated possibilities for Lift Coefficient (Cl), Drag Coefficient (Cd), and Moment Coefficient (Cm) with the specified airfoil and a 360-degree angle of attack.
Figure 8: Extrapolated coefficients of lift (Cl), drag (Cd), and moment (Cm).
A wind turbine blade's chord is the distance between the leading edge (the front of the blade) and the trailing edge (the back of the blade). The chord length is a critical geometric element in blade design since it influences the aerodynamic performance and structural efficiency of the blade. Longer chords produce more lift and decrease drag, but they also necessitate more material, making the blade heavier and more costly. The distance between the upper and bottom surfaces of a wind turbine blade is measured in millimeters.
The stiffness and strength of the blade, as well as its aerodynamic efficiency, are affected by blade thickness. Thicker blades are often stronger, but they also drag more because the boundary layer of air surrounding the blade is thicker. Wind turbine blade twist refers to the change in angle of attack throughout the length of the blade. Blade twist is a significant design feature that influences the performance and load distribution of the blade. To ensure that the blade maintains a relatively consistent angle of attack along its length, the twist is often set to be larger towards the root of the blade and decrease toward the tip.
Figure 10: Turbine blade design with chord and twist parameters.
Alternator with a Permanent Magnet
THHN and TFFN are two typical forms of electrical wire utilized in PMA building. THHN is an abbreviation for Thermoplastic High Heat-Resistant Nylon-Coated, which is a single conductor electrical wire. It is built to endure extreme temperatures and is rated for 600 volts and 90 °C. Tinned Copper Stranded Flexible Fixture wire, on the other hand, is abbreviated as TFFN. THHN wire is similar to TFFN wire, but it is more flexible and is typically used in situations where the wire must bend or be readily handled. TFFN wire is similarly rated for 600 volts and 90 degrees Celsius and is constructed of tinned copper.
where 𝑆𝑅𝑎𝑡𝑒𝑑, f, and N are the number of poles, the frequency, and the rating, respectively.
Additionally, a larger magnetic field is needed to make the generator more compact while yet producing the maximum amount of energy. This method was obtained from the equation below:
where 𝜙𝑔= Magnetic Flux, 𝐵𝑎𝑣 = Flux Density (Average), D = Stator Diameter (Inner), and L = Stator Length.
Figure 11 depicts the 3D design of the permanent magnet alternator, and Table 1 lists the specific planned parameters for the desired output.
Analysis of the Results
Small wind turbines, often called as distributed wind turbines, can provide various economic and resilience advantages to communities [48]. One of the most significant benefits is the cost savings generated by generating their own power; communities may lessen their reliance on the grid and, as a result, cut their energy costs. This outcome can be especially useful for isolated or rural towns, where energy costs are generally higher; dispersed wind turbines can supply communities with a sustainable energy source that is not reliant on the bigger grid. This result has the potential to improve energy security and minimize the likelihood of power disruptions.
Power Generation
Figure 12: The load profile needed for the examination of the financial and resilience benefits.
For commercially accessible battery and system converter size consideration, a thorough demand study was undertaken, including monthly peak demand identification and load factor; the peak monthly load demands are displayed in Figure 13.
Resiliency Analysis
Time series analysis may be used to assess the resilience of a power grid. Time series analysis entails statistical modeling and analysis of data gathered over time in order to find patterns, trends, and anomalies. Figure 15 depicts the results of a time series study used to calculate the resilience period of the proposed system. The suggested system has a maximum resilience time of 16 hours.
Although the dispersed wind turbine provided maximum power during the outage, it was unable to generate electricity on two occasions owing to a lack of adequate wind speed. Figure 17 depicts the renewable penetrated power generated by the dispersed horizontal axis wind turbine.
Figure 17 shows the dispersed wind turbine's power output during a blackout.
Economic Benefits
Some economic data are necessary in order to compute the techno-economic analysis of the proposed DER-based system [50]. The project duration, nominal discount rate, inflation rate, and emission penalty for the project site. Given the economic data needed for this study, a variety of economic formulae are explored, as given in Equations (5)-(8), where C stands for cost.
Total Annual Cost,
where the operation and maintenance cost of a system is viewed as the cost connected in operating and maintaining that system. The entire operation and maintenance cost of the system is the sum of the expenses of each system component, and the yearly cost of buying electricity from the grid less any money made by selling power to the grid is the grid's operation and maintenance cost.
The Cost of Energy
where i stands for the Discount Rate and N for the number of years.
All-inclusive Net Present Value
Table 3 presents a thorough techno-economic comparison of the two scenarios.
Table 3. System comparisons including extensive techno-economic comparisons.
The suggested system will increase wind production capacity by 6.0 kW, cut operating expenses to $1387/year, and have an IRR of 3.82%. Its simple payback period is 15.4 years. Figure 18 displays the cumulative cash flows for both DER grid-connected to the grid and grid alone.
Figure 18. Cumulative cash flow throughout the duration of the project
Environmental Advantages
Small wind turbines create no greenhouse gas emissions or other pollutants, making them a clean and sustainable energy source. For both grid-only and grid-tied 6-kilowatt distributed horizontal axis wind turbines with a 100-kilowatt daily community load, a thorough emission study was performed to assess the advantages. The average carbon emission is 380 g per kWh, but the average emissions of sulfur dioxide and nitrogen dioxide are just 2 g and 1 g per kWh, respectively. Table 4 provides thorough comparisons between GHG components taking the emission rate into account.
Table 4 compares emission analysis results.
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