Wind Power Production Research Articles

Dimensioning and optimization of multi-source offshore renewable energy parks

Multi-source offshore renewable energy parks, consisting of wind, solar and wave converters, can utilize the available offshore area much more efficiently than single source wind parks. They can also produce power with a higher capacity factor than single source parks, utilizing the available grid connection more efficiently. Dimensioning and financial optimization of such parks will be driven by geographic constraints like available area, water depths, shipping lanes, exclusion zones etc., but also by the available resources of wind, solar and wave power. Another major driver in the optimization will be the electricity price. With the raising share of weather dependent renewables in the electricity mix, the electricity price will become more volatile. Therefore, an optimized design process of a multi-source park should also incorporate a pricing mechanism that can produce hourly electricity prices based on actual weather conditions. The paper will present the results of a numerical model that can integrate solar and wave power in a wind park area as well as optimize the export cable capacity. Battery storage can be added to this multi-source park to shift part of the production to hours with higher electricity prices. A case study has been performed for the planned offshore wind park TNW, North of the Dutch Waddeneilanden. Since such a multi-source park will have an expected lifetime of about 30 years, it will even reach the year 2050, in which weather dependent renewable energy will be much more dominant than today, with far stronger electricity price volatility. For the electricity price calculation, assumptions are made for the installed base of solar, wind and wave power in the whole of The Netherlands, as well as the geographic spread of this installed base over the land area and the offshore exclusive economic zone in the North Sea. This installed base is simplified by concentrating it in about ten locations divided over this area. Other necessary assumptions are the future electricity demand pattern, the future capacity of the interconnections with the surrounding countries and the capacity of the flexible load that will be available at that time (electric cars, electric heaters etc.). From literature, the cost of conventional power fueled by natural gas and/or hydrogen is derived which serves as back up power for hours with low wind, solar and wave power production. Based on all these assumptions, a pricing curve is constructed reaching from sub zero at abundant renewable supply to a maximum value at zero renewable supply. With the model, scenarios of future developments in installed wind, solar and wave power, but also in e.g. electric cars, electric heaters and other flexible loads, can be examined and the sensitivity of the optimization of the multi-source park design can be determined. The relevance of wave power can be determined from the average price per MWh that wave power can earn compared to wind and solar.

Optimal multi-market operation of gravity energy storage and wind power producer using a hybrid stochastic/robust optimization

In light of physical limitations, the well-known large-scale pump hydro energy storage was unable to take place in predominantly flat areas. The utilization of innovative gravity energy storage (GES) has increased in the power system in the past few years, with no geographical restrictions and reduced investment costs. According to the paucity of installed GES, a proper formulation considering the physical specifications of gravity energy for market participants in GES has not been sufficiently investigated. In this paper, a new non-linear formulation for the strategic multi-market running of GES and wind suppliers in an uncertain environment characterized by joint robust optimization (RO) and stochastic programming is introduced. Using piecewise linearization, the proposed nonlinear model is then reformulated as a linear version. In the proposed method for market involvement, the bid-offer plans of the GES are obtained in order to participate actively in the DA market. Obtained results show that market conditions significantly impact the profitability of the system, with a 30 % reduction in the day-ahead (DA) price leading to a 32 % reduction in daily profit. Also the risk-based operation of the GES and wind producer is compared with risk-neutral condition, highlighting the importance of considering intraday and real-time markets and risk profiles in optimizing the operation and profitability of the hybrid wind-GES system.

A power storage system planning model for the Wolfe Island wind farm

As one of the alternatives to conventional energy sources, wind power is a fast-growing renewable technology. The intermittent characteristic of wind speed leads to unstable wind power production; therefore, a storage system is typically needed at wind power farms to stabilize the power output. This project aims to develop a power storage system planning model to optimize the power transfer between wind turbines and storage devices on an hourly basis to stabilize power output and maximize annual net earnings. The developed model utilizes the dynamic programming technique, and it is applied to a case study of the Wolfe Island Wind Farm in Ontario, Canada, for the year of 2020. Two scenarios with different power output tolerance considerations (i.e., tolerance ranges based on 6- and 8-hour subperiods) are analyzed to compare the fluctuations of power output and the total net earnings. The optimized number of storage devices is also discussed. The developed model can provide technical support for the design and operation of power storage systems at wind power farms.

A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned.

Analysis of turbulence intensity in the megacity of Beijing by High-frequency observations on a 325-m Tower

Turbulent flow over complex urban morphology poses challenges to utilization of distributed wind energy in cities. In this paper, turbulence intensity (TI), which is often used to guide wind turbine design and evaluate wind power production, is analyzed by high-frequency observations on a 325-m tower located in the downtown of Beijing, China. The results show that the characteristics of TI, including the diurnal variation, the statistical distribution, and the relations between TI and wind speeds near the ground are significantly different from these at higher heights. It is also found that the normal turbulence model (NTM) with the parameters recommended by the IEC standard cannot fit the data well. The new NTM parameters are given by the least-square fittings. Besides, a three-parameter model is found to fit the data well and performs slightly better than the NTM with the least-square fitting parameters. This research indicates that the NTM parameters in the IEC standard should be re-evaluated in urban areas, and different turbulence model parameters are recommended for higher and lower hub heights.

Wind power estimation on local scale—A case study of representativeness of reanalysis data and data-driven analysis

IntroductionWith hydropower being the dominant source of renewable energy in Austria and recent years being disproportionally dry, alternative renewable energy sources need to be tapped to compensate for the reduction of fossil fuels and account for dry conditions. This becomes even more important given the current geopolitical situation. Wind power plays an essential role in decarbonizing Austria's electricity system. For local assessments of historic, recent, and future wind conditions, adequate climate data are essential. Reanalysis data, often used for such assessments, have a coarse spatial resolution and could be unable to capture local wind features relevant for wind power modeling. Thus, raw reanalysis data need post-processing, and the results need to be interpreted with care. The purpose of this study is to assess the quality of three reanalysis data sets, such as MERRA-2, ERA5, and COSMO-REA6, for both surface level and hub height wind speed and wind power production at meteorological observation sites and wind farms in flat and mountainous terrain. Furthermore, the study aims at providing a first knowledge baseline toward generating a novel wind speed and wind power atlas at different hub heights for Austria with a spatial resolution of 1 × 1 km and for an experimental region with sub-km resolution. Thus, the study tries to answer (i) the questions if the reanalysis and analysis data can reproduce surface-level wind speed and (ii) if wind power calculations based on these data can be trusted, providing a knowledge base for future wind speed and wind power applications in complex terrain.MethodsFor that purpose, a generalized additive model (GAM) is applied to enable a data-driven gridded surface wind speed analysis as well as extrapolation to hub heights as a first step toward generating a novel wind speed atlas. In addition, to account for errors due to the coarse grid of the re-analysis, the New European Wind Atlas (NEWA) and the Global Wind Atlas (GWA) are used for correction using an hourly correction factor accounting for diurnal variations. For the analysis of wind power, an empirical turbine power curve approach was facilitated and applied to five different wind sites in Austria.Results and discussionThe results showed that for surface-level wind speed, the GAM outperforms the reanalysis data sets across all altitude levels with a mean average error (MAE) of 1.65 m/s for the meteorological sites. It even outperforms the NEWA wind atlas, which has an MAE of 3.78 m/s. For flat regions, the raw reanalysis matches the production data better than NEWA, also for hub height wind speeds, following wind power. For the mountainous areas, a correction of the reanalysis data based on the NEWA climatology, or even the NEWA climatology itself, significantly improved wind power evaluations. Comparisons between modeled wind power time series and real data show mean absolute errors of 8% of the nominal power in flat terrain and 14 or 17% in mountainous terrain.

Summer heatwaves, wind production and electricity demand in Southern Europe: climatic conditions and impacts

Electricity demand for cooling and heating is directly related to weather and climate, primarily through ambient temperature. In Southern Europe, the maximum electricity demand for cooling in summer can be more pronounced than in winter, especially during heat wave (HW) episodes. With the growth of renewable technologies in the energy mix, the dependency of the electricity system on the weather is becoming evident not just from the demand side, but also from the energy supply side. From the resources point of view, summer wind presents a minimum on its annual cycle, so a combination of maximum electricity demand can coincide with a minimum of wind power production. This study presents a strong multidisciplinary focus, merging climate, energy and environmental discipline, due to their relevant connections in Southern Europe where important climate change stresses are expected. The combined anomalies of electricity demand and wind production during heat wave episodes are quantified at the country level, taking into account the HW extension. The summer period (1989-2019) of ERA5 reanalysis and E-OBS-21.0e data is used for atmospheric magnitudes and the Copernicus climate change service (C3S) energy dataset for demand. In heat wave events, an increase of 3.5%–10.6% in electricity demand and a decrease up to −30.8% in wind power production is obtained, with variability depending on the country. The greater the extension of the HW, the greater the anomalies. Different weather regimes related to heatwaves also play a role on this range of values. Therefore, the impact of extreme weather events, such as heatwaves, on wind power production in conditions of high electricity demand, should be considered in the energy supply strategy and planning in order to minimize the impact of these events on an electricity system with high penetration of renewables.

Artificial Intelligence Approach for the Control of SPMSG Based Grid Connected WECS

In India, power plant requirement is rapidly increasing as non-conventional sources are in current need. Government financial aid plays important role for generator plant execution because of its tedious procedure. If the utilization of wind power production has to be doubled, then machines, turbines and converters are essential in two numbers. The research work includes parallel connection of two machines using star-delta-star transformer fashion resulting in six-phase permanent synchronous machine. This arrangement is more economical as the number of converters are reduced thereby increasing the efficiency by minimizing the switching losses. Initially rectifier setup is used wherein high frequency AC is converted to low frequency and maximum power point tracking is done with the aid of a converter. It is DC-DC conversion by decoupled DQ strategy. Later, it is again converted back to three phase AC so as to synchronize it to the grid. The system response is analysed by conventional PI controller by reducing the noise in the real & reactive power generation. Further, ANFIS-PI controller is implemented to improve the response of PI controller which is much essential for wind power generation. The MATLAB simulated results of PI controller system response and ANFIS-PI controller is presented in this paper.

Quantifying the impacts of synoptic weather patterns on North Sea wind power production and ramp events under a changing climate

Only a few studies on the overall impact of climate change on offshore wind power production and wind power ramps in the North Sea region have been published. This study focuses on the characteristics of expected wind power production and wind power ramps in the future climate aided by the classification of circulations patterns using a self-organizing map (SOM). A SOM is used to cluster high-resolution CMIP5-CORDEX sea level pressure data into 30 European area weather patterns. These patterns are used to better understand wind power production trends and any potential changes. An increased frequency of occurrence and extended persistence of high pressure systems lasting at least 24 h is projected in the future. Whereas a contrasting reducing tendency for low-pressure systems is estimated. No significant evidence is seen for a change in wind power capacity factor over the North Sea, though tentative evidence is seen for a reduction in wind power ramps. Annual energy production is seen to be dominated by a small number of weather patterns with westerly, south-westerly or north-westerly winds. Future wind power production is projected to become less from westerly winds and more from south-westerly and north-westerly flows. Ramp up events are primarily associated with strong south-westerly winds or weather patterns with a weak pressure gradient. Ramp down events have a stronger association with more north-westerly flow. In a future climate, a reduction in ramp up events associated with weak pressure gradients is projected.

Climate, wind energy, and CO[formula omitted] emissions from energy production in Denmark

The dynamic relation between CO2 emissions and wind energy in Denmark is analyzed using a fractional cointegration approach, extended to accommodate covariates. The impact of climate and forces of demand on the potential of wind power production for emissions abatement is investigated. Emissions decline as temperature increases. Wind power production increases with precipitation and North Atlantic oscillations. Aggregate output matters for emissions in a manner consistent with an environmental Kuznets curve (EKC). Accounting for a seasonal trend, our main estimate of marginal CO2 emissions avoided (MEA) per MWh of wind energy produced is 0.16 tonnes, based on impulse responses. This estimate of the abatement potential of wind power is lower than values reported in the literature, but statistically significant, and robust to including climate and EKC variables. MEA is reduced by about one third by treating electricity prices as endogenous, and by one quarter by including emissions from combustion of biomass. However, formal exogeneity tests indicate that the main MEA estimate is not inflated due to left-out general equilibrium effects. Without covariates, estimated MEA is 0.07, and insignificant.

Correction: Analysis of the Effect of Different Time Resolutions Between Day-Ahead and Real-Time Markets on Offer Strategies of Wind Power Producers

Correction: Analysis of the Effect of Different Time Resolutions Between Day-Ahead and Real-Time Markets on Offer Strategies of Wind Power Producers

Wind energy potential and its current status in India

Currently, India’s population is growing at a rate comparable to other countries, which means we need to provide more energy. Most of the world’s energy is generated by coal-fired thermal power plants, despite the fact that this method results in a significant increase in the number of pollutants released into the atmosphere. Wind power is a kind of environment-friendly, pollution-free green energy that comes from renewable sources. India is one of the top five producers of wind power in the world. This article throws light upon the current status of wind energy in India, as well as its potential and regulations governing wind energy. India has a significant untapped potential for wind power generation, and this article details that potential as well as wind power generation in various states of India. When it comes to the production of wind power in India, Tamil Nadu is the most productive states.

Derivation and verification of three-dimensional wake model of multiple wind turbines based on super-Gaussian function

Wind power production is strongly affected by wake, and wake analysis is particularly important for wind farm layout optimization. The purpose of this study is to establish a multi-dimensional analysis wake model (3DJSG-M model) which can describe the wake region of multiple wind turbines perfectly to analyze the three-dimensional spatial distribution characteristics of the wake of multiple wind turbines. Firstly, considering the wind speed difference between wind shear inflow and uniform inflow, a three-dimensional wake model based on super-Gaussian function (3DJSG model) is derived by using the flow conservation theorem; Secondly, the influence of wind shear is introduced into the energy deficit theory, and the 3DJSG-M model is derived; Finally, wind field experiments are carried out with two high-precision LIDARs to verify the accuracy of 3DJSG model and 3DJSG-M model respectively. The results show that 3DJSG model and 3DJSG-M model fit well with the experimental data and can better predict the three-dimensional distribution characteristics of the whole wake region after a single wind turbine and multiple wind turbines. This study can provide optimization strategies for wind farm layout, reduce energy costs, and improve wind farm benefits.

Using flexible energy system interactions amongst industry, district heating, and the power sector to increase renewable energy penetration

Sweden’s goal of 100% renewable electricity generation by 2040 requires investments in intermittent electricity production (e.g. wind power). However, increasing the share of intermittent electricity production presents challenges, including reduced flexibility of electricity production. A strategy for overcoming this issue is developing flexibility in electricity consumption. This study analyses the potential for using flexible industrial processes, heat pumps (HP), and combined heat and power (CHP) plants in Swedish district heating systems to increase the share of wind power capacity without compromising grid stability. The simulation tool EnergyPLAN was used to assess the potential contribution of these strategies. The analysis includes a range of annual wind power production between 45 and 60 TWh. The required electricity imports and critical excess electricity (that can neither be used nor exported due to transmission line limitations) were used to evaluate the system’s stability. Managing the operation of CHP plants, HPs, and industrial processes in a flexible way is challenging, but these strategies may still play a decisive role in increasing the share of renewable electricity production and reducing demand peaks in cities. Whilst HP regulation is better at reducing excess electricity production at lower wind power capacities (from 32 to 15% for the analysed interval of wind power production), CHP regulation becomes more relevant when wind power capacity increases (from 14 to 39%). Like HP regulation, flexibility in electricity demand in industrial processes had a greater percentage contribution at lower wind power capacities. Combining HP, CHP regulation, and flexible electricity demands in industry can reduce excess electricity production by 68–80% and electricity imports by 14–26%. Wind power contributing to grid stabilisation reduces excess electricity production but does not reduce electricity imports.

Smoothing electric power production with DFIG-based wind energy conversion technology by employing hybrid controller model

The operations of WPTSs needs to be regulated under both its linear and nonlinear operating behaviors in order to ensure the maximum possible wind power production with lowering costs. This involves developing robust control structures that can stringently handle WPTSs’ sophisticated operation. In efforts to achieve this objective, the previous studies proposed various control strategies that were widely employed for the adjustments of WPTSs’ mechanical components. Herein, this paper pursues a rarely studied electrical-component control approach by implementing IFOC-based MPPT strategy on a RSC of DFIG-based WPTS. The ultimate goal of this study is to maintain the electric power quality by regulating the signal THDs of the system’s rotor alternating current along with mitigating the switching transients. To this end, the performances of conventional PI, and FL-tuned PI (FLPI) controllers, under the system’s both normal voltage rating & low voltage operation—it was assumed to be 10% of normal voltage rating, are set to be evaluated. Furthermore, the overall simulation of a 2 MW power scaled-DFIG system comprising aerodynamic model, electrical system model, control system model, and the controller models was implemented in MATLAB-SIMULINK environment in testing the effectiveness of the proposed approach based on a rated wind speed of 10 m/s. Accordingly, the THD factors of rotor alternating current are resulted to be 9.15% with PI & 8.61% with FLPI under normal voltage operation, and 35.46% with PI & 23.25% with FLPI under low voltage operation. With the recommended baseline of 75%, the FLPI model performance accuracy for the current THD control is found to be 76.75% along with mitigated switching transients, while that of PI is 64.54% without mitigated switching transients in the case of nonlinear operating behavior. Hence, FLPI model has proven to show a superior overall performance.

Towards failed renewable energy communities? Activist attempts to change market conditions in the Danish wind energy market

Denmark is known for its bottom-up, participatory approach to developing renewable energy communities, and yet wind power has become ‘big business’ in Denmark and other countries, a market affair for large, private renewable energy companies. Drawing on the performative perspective on markets, this paper examines a non-governmental organization's (NGO's) attempts to innovate the market for wind power deployment in Denmark, to include renewable energy communities. Based on document analysis as well as qualitative analysis of interviews and observations, conducted from 2015 to 2020, we highlight what has shaped a paradigm shift in wind power development – from a cooperative and community-based approach to one based on market competition. With this as a backdrop, we analyse two ‘episodes’ – the first one took place before the introduction of competition-based tenders, and the second one occurred after it – where the NGO proposes two alternative market devices to reframe the market for wind power production, i.e., an alternative ownership model, and alternative evaluation criteria for winning bids in the tendering scheme. Both attempts failed and are illustrative of how agency is stymied in, and through, the dynamic interactions taking place within the market assemblage. Our findings show how market devices matter, i.e., how opportunities for successful collective action expand or contract depending on how the market is framed. By directing attention to market devices, we emphasize the ways in which politics and political action are made possible, augmenting research on the power and politics of energy transitions.

Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system

The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature.

Combining monthly wind and inflow uncertainties in the stochastic dual dynamic programming

AbstractFollowing a global trend, intermittent sources, especially wind, have been experiencing accelerated growth in Brazil—in the last decade, wind power grew 13 times and became the second largest source in the electricity mix (12%), just behind hydropower (60%). Currently, although following regulatory guidelines, the representation of wind power in the long-term operation planning model is done in a simplified way, based on the monthly average of the last five years of aggregated generation, thus demanding improvements. The objective of this work is to describe an approach to be used by the Brazilian power industry to represent the uncertainties of monthly wind power production in the SDDP algorithm applied in the long-term operation planning model, keeping the large-scale stochastic problem still computationally viable, when applied to large interconnected systems, especially with hydroelectric predominance. The proposed methodology comprises statistical clustering of wind regimes and definition of equivalent wind farms; evaluation of monthly transfer functions between wind speed and power production; integrated generation of monthly multivariate synthetic scenarios of inflows and winds, considering associated cross-correlations; and representing monthly wind power in the SDDP algorithm. The application to real configurations of the Brazilian interconnected system, including case studies related to the monthly operation program and the calculation of the maximum amount of energy that can be traded in long-term power purchase agreements, points to its effectiveness and the relevance of modeling the wind uncertainties in the long-term operation planning of large hydro-dominated systems.

Effects of Ramp Rate Limit on Sizing of Energy Storage Systems for PV, Wind and PV–Wind Power Plants

As the share of highly variable photovoltaic (PV) and wind power production increases, there is a growing need to smooth their fast power fluctuations. Some countries have set power ramp rate (RR) limits that the output powers of power plants may not exceed. In this study, the effects of RR limit on the sizing of energy storage systems (ESS) for PV, wind, and PV–wind power plants are examined. These effects have been studied prior for PV power plants. However, for the wind and PV–wind power plants, the effects of the RR limit are studied comprehensively for the first time. In addition, the effects of the size of the power plant are considered. The study is based on climatic measurements carried out with a sampling frequency of 10 Hz for a period of 153 days. The modeling of the PV and wind powers and the simulation of the RR-based control algorithm of the ESS were completed using MATLAB. The results show that as the applied RR limit increased from 1%/min to 20%/min, the required relative energy capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 88%, 89%, and 89%, respectively. The required relative power capacities of the ESSs of the PV, wind, and PV–wind power plants decreased roughly 15%, 12%, and 20%, respectively. The utilization of the ESSs was found to decrease as the applied RR limit increased and as the size of the power plant grew.

Techno-economic investigation of an environmentally friendly small-scale solar tracker-based PV/wind/Battery hybrid system for off-grid rural electrification in the mount bamboutos, Cameroon

The present work demonstrates the techno-economic analysis of an environmentally friendly small-scale PV/Wind/Battery hybrid system for off-grid rural electrification in the city of Mbouda. The major contributions of this work are fourfold: (i) PV/Wind/Battery hybrid system is proposed for the very first time in this locality; (ii) six PV tracking techniques namely: horizontal axis (monthly adjustment), horizontal axis (weekly adjustment), horizontal axis (daily adjustment), horizontal axis (continuous adjustment), vertical axis (continuous adjustment) and dual-axis tracker are implemented in the present hybrid system; (iii) the impact of ambient air temperature on the PV power production is evaluated; (iv) the influence of three different hub heights (17 m, 50 m, 150 m) on the wind turbine power output is investigated. The electric load for the basic needs of a community of 152 consumers (households, hospitals, schools, businesses) such as; lighting, radio, television, phone charger, water heater, electric iron, and laptop, is estimated. HOMER Pro is used for the optimization and sensitivity analysis of the proposed hybrid system. The solutions obtained in the present work showed that compared to the PV power generated by fixed solar panels, the dual-axis solar tracker system was the system that offered the maximum power production (about 7.02%–30.73% more power). In addition, compared to the PV penetration of fixed solar panels, the dual solar tracker also offered the maximum PV penetration (about 7.64%–31.50% more PV penetration). It was also concluded that increasing the hub height (from 17 m to 150 m), significantly increased the wind power generation and penetration. Concerning the economic aspect of the work, it was noticed that the dual-axis solar tracking system presented the lowest levelized cost of energy (LCOE) and net present cost (NPC) when compared with the other aforementioned solar tracking systems. The results also indicated that taking into account the ambient air temperature, impacted the PV and wind power productions. The analysis of emissions allowed us to confirm that the system was environmentally friendly since no greenhouse gas was produced.