Estimation Of Energy Production Research Articles

Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria

This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

A copula post-processing method for wind power projections under climate change

Wind energy plays a pivotal role in the ongoing effort to reduce carbon emissions in the energy sector. With the increasing evidence of climate change, there is a growing concern regarding the planning and operation of wind energy resources. Accurate forecasts are essential to understand the frequency distribution of wind speed data in a given area and, consequently, to estimate energy production. This paper aims to analyze the wind resources under climate change, assess their potential, and create zoning maps for wind energy production in the island of Ireland. For this objective, wind speed data from 31 general circulation models (GCMs) and two climate change scenarios were utilized for both hindcast and forecast periods in 1981–2010 and 2021–2050, respectively. The GCM outputs were first bias-corrected and then post-processed using various (non–)parametric statistical distributions and 3 Copula families. The results indicate an expected decrease in the average wind speed in the region up to ∼ 21 % by 2050, contingent on the climate scenarios under consideration and the target point. Ultimately, this study concludes by presenting wind power density maps specifically to the study region, offering valuable insights for sustainable energy planning.

An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN)

This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

The Relationship between Vertical Kinematic Eddy Heat Flux, Air Temperature and Turbulent Kinetic Energy in Atmospheric Boundary Layer: Baghdad City

Studying the heat flux in the boundary layer and understanding its relationship to the various atmospheric variables reflects positively on understanding the nature of turbulence in this layer and thus understanding the nature of the spread and movement of pollutants and the transmission and distribution of energy in this layer, also, the vertical heat flux is considered a significant influence on the movement of buoyancy and stability in the boundary layer and its effect on the horizontal wind movement, and therefore it is considered one of the important studies indirectly involved in the estimates of wind energy production, pollutant diffusion, and turbulence. This study involved the calculation of the Eddy Heat Flux and turbulent kinetic energy across Baghdad city. Our investigation revealed a correlation between the Eddy Heat Flux, temperature, and Turbulent Kinetic Energy (TKE). The observations have been made for wind speed with three components (u, v, w) and temperature by using a fast-response anemometer. As for the atmospheric pressure data, it was obtained from the automatic weather station located in the department of Atmospheric Sciences at Mustansiriyah University. The range of observations extended through the duration of thirty days for 24 hours from 1st July 2016 to 30th July 2016 every second. The maximum Eddy Heat Flux value was 0.092 J/(m2.s) at 10:00 on 23th July 2016, while the minimum Eddy Heat Flux value was -0.013 J/(m2.s) at 21:00 hour on 25th July 2016, the negative sign refer to a change in the direction of heat transfer,. It was also found that there is a positive relationship between the eddy heat flux and temperatures with a correlation coefficient of 0.93, as well as between the eddy heat flux and the turbulent kinetic energy with a correlation coefficient of 0.8.

Evaluation of offshore wind energy zones within marine spatial planning: A case study in the Spanish Mediterranean Sea

Offshore renewable energy is gaining attention as a vital component in the battle against climate change. This emerging use of the marine environment is poised for rapid growth, presenting new challenges for ocean management. As a result, marine spatial planning is being developed to designate potential zones for this use, however, these processes often lack important considerations for adequate decision-making. This work evaluates high-potential zones for offshore wind energy within marine spatial planning. This approach is illustrated through a case study in the Spanish territorial waters in the Mediterranean. This work considers port facilities, water depth, availability factor of wind turbines, estimated energy production and, last but not least, the impact of the wave climate on maintenance scheduling. In selecting port facilities, the decision between prioritising proximity or well-equipped infrastructure is crucial – particularly in the Levantine-Balearic subdivision. Water depth, which influences the foundation costs, presents a considerable range across the high-potential zones (100–1000 m), potentially decisive for offshore wind siting. Distinctive wave climates occur in the zones within the enclosed Alboran Sea, leading to a higher number of available windows for essential maintenance operations (∼85% of the total). Finally, whereas the availability factor of wind turbines remains consistent across the high-potential zones (∼75%), regions closer to the Gulf of Lyon exhibit substantially greater estimated energy production – up to 1.24 times the energy produced in other zones. Given that energy generation is the paramount objective of offshore wind farms, this aspect has the potential to make a difference. This analysis aims to identify the most favourable locations for advancing offshore wind initiatives in the Spanish territorial waters in the Mediterranean, offering valuable insight to support informed decision-making processes in marine spatial planning.

Usage of Statistical Techniques to Monitor the Performance of Wind Turbines

Calculating the yearly energy output, which keeps the balance between both the generation and consumption of electricity, is made easier with the use of wind energy production estimates for grid interfaces. Effective wind speed forecasting is crucial for achieving this goal. In this research, linear statistical models of prediction Generalized Autoregressive Score (GAS), GAS model with exogenous variable x (GASX), and Autoregressive Integrated Moving Average (ARIMA) are the models used to estimate wind speed accurately. Additionally, the modeling of non-linear time-series data has been done using the Non-Linear GASX (NLGASX) statistical predictive modeling method. Additionally, the REctified Linear Unit (RELU), Softmax, Hyperbolic Tangent (TANH), and Sigmoid modeling approaches are used to optimize the suggested NLGASX model. In comparison to existing models, the suggested optimized NLGASX approach performs significantly better. In order to anticipate wind power, the wind power curve modeling additionally takes wind speed as an input. The estimated wind power has been used to determine the yearly energy

Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms

Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.

Design and Analysis of a Hybrid Stand-Alone Microgrid

This research article presents a comprehensive investigation into the design, optimization, and performance analysis of a hybrid stand-alone microgrid for an industrial facility in Iraq at coordinates 36.51 and 43.99. The system consists of photovoltaic (PV) modules, inverters, a battery energy storage system (BESS), a generator, and AC loads. Leveraging the capabilities of PVsyst version 7.3.1, HOMER Pro version 3.14.2 and SAM version 2022.11.21 software tools, this study assesses the feasibility and functionality of the hybrid stand-alone microgrid. In this study, PVsyst software is used for detailed designing and analysis of a PV plant, and the PVsyst design file is then used in HOMER Pro software to optimize and design the proposed hybrid stand-alone microgrid, and for detailed performance analysis SAM software is employed. This paper also investigates the impact of ground clearance and ground albedo on the annual generation of bifacial PV modules at various tilt angles. Key findings include a promising normalized production rate of 4.53 kWh/kWp/day with a performance ratio of 0.815 and annual energy production estimates of 84.31 MWh (P50), 79.57 MWh (P90), and 78.24 MWh (P95) for monofacial PV modules, highlighting the system’s potential for renewable energy generation. Notably, this research demonstrates the hybrid stand-alone microgrid’s capacity to significantly reduce CO2 emissions, saving approximately 1811.6 tons over a 30-year period, thus contributing to sustainability and environmental conservation goals. Additionally, this study reveals operational challenges during the winter months, necessitating generator support to meet load demands. The successful installation and experimental validation of the hybrid stand-alone microgrid underscore its practical viability and its role in advancing clean energy solutions. This research provides valuable insights into hybrid stand-alone microgrid design, emphasizing its importance in ensuring reliable power supply and environmental stewardship.

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

On-Site Pilot-Scale Microalgae Cultivation Using Industrial Wastewater for Bioenergy Production: A Case Study towards Circular Bioeconomy.

This case study assesses the valorization of industrial wastewater streams for bioenergy generation in an industrial munition facility. On-site pilot-scale demonstrations were performed to investigate the feasibility of algal growth in the target wastewater on a larger outdoor scale. An exploratory field study followed by an optimized one were carried out using two 1000 L open raceway ponds deployed within a greenhouse at an industrial munition facility. An online system allowed for constant monitoring of operational parameters such as temperature, pH, light intensity, and dissolved oxygen within the ponds. The original algal seed evolved into an open-air resilient consortium of green microalgae and cyanobacteria that were identified and characterized successfully. Weekly measurements of the level of nutrients in pond liquors were performed along with the determination of the algal biomass to quantitatively evaluate growth yields. After harvesting algae from the ponds, the biomass was concentrated and evaluated for oil content and biochemical methane potential (BMP) to provide an estimate of the algae-based energy production. Additionally, the correlation among biomass, culturing conditions, oil content, and BMP was evaluated. The higher average areal biomass productivity achieved during the summer months was 23.9 ± 0.9 g/m2d, with a BMP of 350 scc/gVS. An oil content of 22 wt.% was observed during operation under low nitrogen loads. Furthermore, a technoeconomic analysis and life cycle assessment demonstrated the viability of the proposed wastewater valorization scenario and aided in optimizing process performance towards further scale-up.

Solar power plant on the rooftop of the Diponegoro University Rectorate: a technical and economic study

Diponegoro University's Rectorate building uses electricity from the National Electricity Company with a S2 social subscription type of 105 kVA. The designed solar power plant has a capacity of 25 kW, or 25% of the installed electrical capacity. This research aims to compare the solar panels and inverter configurations that will be used in solar power plants. Moreover, this study aims to find out which configuration will provide the best results and the biggest savings. Technical analysis is carried out with the photovoltaic system (PVSyst) software to calculate the energy produced by solar panels, inverter losses, and other results. On the other hand, economic analysis is carried out with RetScreen software to calculate net present value (NPV), benefit cost ratio (BCR), and payback period (PP). Based on the PVSyst simulation results, the estimated energy production for each variant is 39,684 kWh; 39,633 kWh; 39,507 kWh; and 39,446 kWh. The first variant has the biggest performance ratio value of 84.1%. Based on the Retscreen calculation result, the third variant has an NPV value of $22,698, a BCR value of 2, and a PP of 8.7 years, which has the best result and the highest advantages.

Solar power plant on the rooftop of the Diponegoro University Rectorate: a technical and economic study

Diponegoro University's Rectorate building uses electricity from the National Electricity Company with a S2 social subscription type of 105 kVA. The designed solar power plant has a capacity of 25 kW, or 25% of the installed electrical capacity. This research aims to compare the solar panels and inverter configurations that will be used in solar power plants. Moreover, this study aims to find out which configuration will provide the best results and the biggest savings. Technical analysis is carried out with the photovoltaic system (PVSyst) software to calculate the energy produced by solar panels, inverter losses, and other results. On the other hand, economic analysis is carried out with RetScreen software to calculate net present value (NPV), benefit cost ratio (BCR), and payback period (PP). Based on the PVSyst simulation results, the estimated energy production for each variant is 39,684 kWh; 39,633 kWh; 39,507 kWh; and 39,446 kWh. The first variant has the biggest performance ratio value of 84.1%. Based on the Retscreen calculation result, the third variant has an NPV value of $22,698, a BCR value of 2, and a PP of 8.7 years, which has the best result and the highest advantages.

Evaluating the impact of wildfire smoke on solar photovoltaic production

There are growing needs to understand how extreme weather events impact the electrical grid. Renewable energy sources such as solar photovoltaics are expanding in use to help sustainably meet electricity demands. Wildfires and, notably, the widespread smoke resulting from them, are one such extreme event that can impair the performance of solar photovoltaics. However, isolating the impact that smoke has on photovoltaic energy production, separate from ambient conditions, can be difficult. In this work, we seek to understand and quantify the impacts of wildfire smoke on solar photovoltaic production within the Western United States. Our analysis focuses on the construction of a random forest regression model to predict overall solar photovoltaic production. The model is used to separate and quantify the impacts of wildfire smoke in particular. To do so, we fuse historical weather, solar photovoltaic energy production, and PM2.5 particulate matter (primary smoke pollutant) data to train and test our model. The additional weather data allows us to capture interactions between wildfire smoke and other ambient conditions, as well as to create a more powerful predictive model capable of better quantifying the impacts of wildfire smoke on its own. We find that solar PV energy production decreases 8.3% on average during high smoke days at PV sites as compared to similar conditions without smoke present. This work allows us to improve our understanding of the potential impact on photovoltaic-based energy production estimates due to wildfire events and can help inform grid and operational planning as solar photovoltaic penetration levels continue to grow.

A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect

The wind energy utilization has attracted worldwide attention. The wind farm is the most important place to convert wind energy into electric energy. The accurate estimation of annual energy production and its uncertainty involves the wind resource assessment and investment risk management for the wind farm. In order to reasonably analyze the uncertainty, a Monte Carlo simulation method is proposed to obtain the annual energy production and its uncertainty for the wind farm. The method proposed in this paper not only includes the uncertainties associated with Weibull distribution parameters, measure-correlation-prediction method, power law exponent, air density and power curve in previous studies, but also incorporates the wind flow model uncertainty and wake effect. Specifically, the method proposed in this paper extrapolates the wind speed from the meteorological mast to each wind turbine in the wind farm based on the uncertainty of the wind flow model. At the same time, the wind speed reduction on the downstream wind turbines due to the wake effect will be considered, which leads to the annual energy production and uncertainty of the whole wind farm. Also, the effectiveness of the proposed method is verified based on the numerical example. The results show that the average annual energy productions of different wind turbines in the wind farm with flat terrain are close, but there are some slight differences among turbines due to the wake effect with the maximum loss of 3.5%. It is also observed that the simulated uncertainty of annual energy production of concerned wind farm is 9.0%, which is larger than 7.48% by square root of the sum of the squares method where correlations among variables are not considered.

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

Energy aspects of Eucalyptus saligna wood in a seven-year-old plantation

The objective of this study was to characterize the energy parameters of wood and energy production from a seven-year-old experimental plantation of Eucalyptus saligna. For this purpose, information was collected from an experimental planting spaced at 3.0 m x 3.0 m in Chapadão do Sul, MS, Brazil. Values were obtained for the diameter and height of the trees, which were felled and rigorously cubed to obtain the volumes of wood. Wood and plantations were characterized in relation to: proximate chemical, calorific value, wood basic density and annual production of wood and energy. Trees and planting were able to yield satisfactory estimates of energy production in relation to annual wood production. The immediate chemical composition of the wood was 82.85%, 17.07% and 0.08% for volatile materials, fixed carbon and ash, respectively. The evaluated woods can be classified as medium basic density, and can be recommended for use with energy purposes. Therefore, the evaluated E. saligna woods showed results that characterize these materials as suitable for energy use, with potential for the production of energy forests. In this way, they can meet the demands of forest biomass for this purpose.

Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods

We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective.

Assessing the impact of wave model calibration in the uncertainty of wave energy estimation

The accuracy of estimated sea conditions, specifically wave height and peak/average wave periods, affects the estimation of electrical energy production from wave energy converters. This study investigates the uncertainty in wave energy harvesting estimated by the SWAN wave model and determines possible improvements by adjusting the model's tunable parameters. Three different wave energy converters (OEBuoy, WaveBob, and Pontoon) and ten different locations along the Atlantic coast of the Iberian Peninsula are used in the study. The SWAN model is calibrated using the ST6 term package based on both wave height and peak period wave parameters. Different wave hindcast data produced by different model settings are used to estimate the wave energy produced by the wave energy converters at ten buoy locations and compared to wave energy produced estimated based on wave observations. The study finds that the physical settings of the SWAN model have a considerable influence on the uncertainty in the estimation of the power output produced by the device. The best-fitting calibrated model improved the mean energy output value of all locations compared to the SWAN default settings. The study concludes that adjusting the SWAN model parameters can improve the accuracy of the estimation of the energy output.

An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation

The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.

Influence of Atmospheric Stability on Wind Turbine Energy Production: A Case Study of the Coastal Region of Yucatan

Wind energy production mainly depends on atmospheric conditions. The atmospheric stability can be described through different parameters, such as wind shear, turbulence intensity, bulk Richardson number, and the Monin–Obukhov length. Although they are frequently used in micrometeorology and the wind industry, there is no standard comparison method. This study describes the atmospheric stability of a coastal region of Yucatan, Mexico, using these four parameters. They are calculated using six-month data from a meteorological mast and a marine buoy to determine atmospheric stability conditions and compare their results. The unstable atmospheric condition was predominant at the site, with an 80% occurrence during the measurement period, followed by 12% in neutral and 6% in stable conditions. Wind speed estimations were performed for each atmospheric stability scenario, and the variation in the energy produced was derived for each case. Unstable atmospheric conditions deliver up to 8% more power than stable conditions, while neutral conditions deliver up to 9% more energy than stable conditions. Therefore, considering a neutral state may lead to a considerably biased energy production estimation. Finally, an example calculation indicates that atmospheric stability is a crucial parameter in estimating wind energy production more accurately.