Wind Production Research Articles

A system dynamics model of offshore wind farm degradation: Enabling operation and maintenance planning under foreseen asset management impacts

This paper presents a system dynamics model to for the degradation of offshore wind farms (OWFs) optimise O&M planning and asset management, taking into account the effects of weather-related delays. The model predicts the future states of wind turbines, repairs, downtime expenses, failures, and production losses. Simulation results show the optimal scenario for effective intervention and the adaptability of the proposed model in controlling wind farm assets. By considering various maintenance planning approaches, the model identifies the factors affecting maintenance and minimises the impact of human response on O&M activities in OWFs.

Spectroscopy in the Middle Corona: Probing the Kinetic Processes in the Nascent Solar Wind and Solar Energetic Particle Production

Spectroscopy in the Middle Corona: Probing the Kinetic Processes in the Nascent Solar Wind and Solar Energetic Particle Production

Validation activities of Aeolus wind products on the southeastern Iberian Peninsula

Abstract. In 2018, the European Space Agency launched the first Doppler wind lidar system into space, providing wind observation profiles from the lower stratosphere down to the surface in two different channels based on the scene classification: cloudy or clear. A statistical validation campaign of Aeolus wind products has been performed with a ground-based Doppler lidar system and radiosondes at the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS)–Andalusian Global ObseRvatory of the Atmosphere (AGORA) facility in Granada (Spain). The validation activities with the automatic ground-based lidar system lasted from July 2019 to the orbit shift of June 2021. Aeolus data from two different processing baselines (10 and 11) were validated with 30 min averages of coincident ground-based lidar measurements, using a 100 km horizontal spatial collocation criterion. This resulted in 109 collocations and a mean observation distance from the ground-based lidar system of ∼50 km. The comparison did not raise any significant over- or underestimation of Aeolus horizontal line-of-sight wind speed during that period for the Rayleigh-clear and Mie-cloudy configurations. However, the ground-based lidar measurements were limited to the lower 3.5 km of the atmosphere and, consequently, the obtained results. Multiple analyses were performed varying the criteria of maximum distance and the average period for the ground-based lidar measurements in order to confirm the reliability of the criteria considered. A separate study was performed with Aeolus products after the orbit shift (baseline 12) with different collocation criteria (mean observation distance of ∼75 km, to the station), from July 2021 to May 2022. A set of seven radiosondes were launched with the aim of increasing their coincidence in space and time with the satellite overpass (∼30 min before). The radiosondes could provide full vertical coverage of Aeolus profiles (from the surface up to ∼20 km above sea level), and the comparison did not yield any significant over- or underestimation of the Rayleigh-clear wind speed, while the Mie-cloudy wind speed was significantly overestimated. Multiple analyses were performed in order to test how the spatiotemporal collocation of the radiosonde affected the results. Radiosondes not ideally collocated were proven to still be useful for comparison with the Rayleigh-clear observations but not with the Mie-cloudy observations.

How do North American weather regimes drive wind energy at the sub-seasonal to seasonal timescales?

There has been an increasing need for forecasting power generation at the subseasonal to seasonal (S2S) timescales to support the operation, management, and planning of the wind-energy system. At the S2S timescales, atmospheric variability is largely related to recurrent and persistent weather patterns, referred to as weather regimes (WRs). In this study, we identify four WRs that influence wind resources over North America using a universal two-stage procedure approach. These WRs are responsible for large-scale wind and power production anomalies over the CONUS at the S2S timescales. The WR-based reconstruction explains up to 40% of the monthly variance of power production over the western United States, and the explanatory power of WRs generally increases with the increase of timescales. The identified relationship between WRs and power production reveals the potential and limitations of the regional WR-based wind resource assessment over different regions of the CONUS across multiple timescales.

Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China

Comprehensive knowledge about wind energy potential is critical for decision-making to achieve carbon neutrality and shape future energy pathways. Wind turbine technology advances (e.g., higher hub-heights, larger rotor diameters and rated power) can better support wind energy harvesting and alter wind energy potential. This study established an integrated model to evaluate the impact of wind turbine technology advances on onshore wind energy potentials under different wind resource conditions by using China as a case study. We found that technological advances of wind turbine can significantly impact wind energy potential. Compared with land-based 1.5-MW turbines, the onshore wind energy potential using newer generation turbines (land-based 2.5-MW turbines) would increase by 43% to 14.8 PWh (i.e., 2.0 times the electricity consumption of China in 2020). Importantly, advances in wind turbine technology can significantly increase capacity factor in poor wind resource regions, and thus enable economically viable generation in more areas. This can significantly increase wind energy potential in poor wind resource regions with large electrical load (e.g., Central, South and East China). Moreover, in contrast to the wind production of 0.46 PWh in 2020, China's wind energy potential is projected to hit 20.1 PWh by 2030 under the assumption of developed turbine technology in line with the trends of the past decade. The corresponding potential CO2 emission reduction would range from 12.6 Gt in 2020 to 21.7 Gt in 2030. These values are 1.3 and 2.2 times higher than China's CO2 emissions in 2020, respectively. To enhance wind energy contribution toward China's carbon neutrality goal, we recommend that the conversion of wind turbines to be accelerated, especially in poor wind resource regions with large electrical load.

Parameterizing building effects on airflows within the urban canopy layer for high‐resolution models using a nudging approach

AbstractIn this study, a new multilayer urban canopy parameterization for high‐resolution (∼1 km) atmospheric models using the nudging approach to represent the impacts of urban canopies on airflow is presented. In our parameterization, a nudging term is added to the momentum equations and a source term to the turbulent kinetic energy equation to account for building effects. The challenge of this parameterization lies in defining appropriate values for the nudging coefficient and the weighting function used to reflect canopy effects. Values of both are derived and the parameterization developed is implemented and tested for idealized cases in the Mesoscale Transport and Stream model (METRAS). Comparison data are taken from obstacle‐resolving microscale model results. Results show that the parameterization using the nudging approach can simulate aerodynamic effects induced within the canopy by obstacles well, in terms of reduction of wind speeds and production of additional turbulent kinetic energy. Thus, models with existing nudging can use this approach as an efficient and effective method to parameterize dynamic urban canopy effects.

Using Deep Learning for Energy Modeling in Intelligent Structures with Decentralized Power Generation

In this research, we propose new methods to enhance the construction, designs, also hybrid renewable energy plant maintenance by incorporating battery energy storage systems. Our investigates focused by the three key aspects of renewable electricity energy monitoring: size optimization, improved combination forecasts of wind and solar energy production using deep reinforcement learning, and an advanced battery life forecasting model employing Hidden Markov Models and network analysis to ensure equilibrium throughout the battery’s lifespan, with the utilization of the ZSoft Fuzzy method. To address the renewable energy sources of dynamic nature connected with grid, we consider a load requirements significance and select a reliable operational mode accordingly. Additionally, we apply Z-Soft Ridge Regression (ZS-Fuzzy) analysis to investigate if matching the battery capacity with the system would enhance its service life. By resolving architectural challenges, we determine that an optimal mix of renewable energy sources, with the PV plant contributing 37.1% of the total, offers the best solution. Moreover, our findings indicate that a BESS capacity of 12.9% relative to the HREP capacity satisfies the design criteria effectively.

Wholesale price dynamics in the evolving Texas power grid

Our study conducts an innovative analysis of the power grid from a network perspective, shedding light on crucial aspects of the wholesale electricity market in Texas. By employing a power system engineering model, we construct a transmission network representation, presenting a novel contribution to the economics literature. Through our analysis of the network matrix, we delve into the characteristics of the Texas power grid, specifically focusing on the structural changes resulting from the Competitive Renewable Energy Zone (CREZ) project and their impacts on nodal prices. The empirical analysis provides additional evidence that ignoring changes in the transmission grid leads to biased estimates of various coefficients. This includes the spatial dependence coefficient, as well as the coefficients associated with control variables such as wind production and load. These empirical findings underscore the significance and distinctive value of network analysis in studying power grids.

A coupled model of global energy production and ERoEI applied to photovoltaic and wind, an estimation of net production

Photovoltaic (PV) and wind energy production modelling essentially follows bottom-up approaches, based on physical considerations. These models focus mostly on the calculation of Energy Return on Energy Invested (ERoEI), while some also present calculation of energy payback time or energy cannibalism, to evaluate the ability of these technologies to handle energy transition. Besides, sigmoïd or S-curve function are often used to fit the production of these industries over time, based on economical considerations, rather than physical ones. This paper aims at coupling net production and ERoEI, using energy conservation and ERoEI definition, to develop a model that could reproduce a S-curve function and allow to investigate the net energy delivered by PV and wind industries. This approach allows to explain the inconsistency which sometimes exists between bottom-up and top-down studies on PV ERoEI calculation. It also explains why the classic relationship between net and gross energy, that is a ratio equal to (ERoEI−1)/ERoEI, does not apply to low carbon energy industries before they reach a steady-state or a quasi-static evolution. As an example, net PV production is still close to zero whereas wind production began to produce a net substantial energy around 2007–2010. It finally allows to investigate how fast the energy transition can physically be to see if a risk of energy disruption exists during that transition, due to fossil fuel depletion.

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.

Evaluation of Blended Wind Products and Their Implications for Offshore Wind Power Estimation

The Cross-Calibrated Multi-Platform (CCMP) wind analysis is a satellite-based blended wind product produced using a two-dimensional variational method. The current version available publicly is Version 2 (CCMP2.0), which includes buoy winds in addition to satellite winds. Version 3 of the product (CCMP3.0) is being produced with several improvements in analysis algorithms, without including buoy winds. Here, we compare CCMP3.0 with a special version of CCMP2.0 that did not include buoy winds, so both versions are independent of buoy measurements. We evaluate them using wind data from buoys around the coasts of the United States and discuss the implications for the wind power industry and offshore wind farms. CCMP2.0 uses ERA-Interim 10 m winds as the background to fill observational gaps. CCMP3.0 uses ERA5 10 m neutral winds as the background. Because ERA5 winds are biased towards lower values at higher wind conditions, CCMP3.0 corrected this bias by matching ERA5 wind speeds with satellite scatterometer wind speeds using a histogram matching method. Our evaluation indicates that CCMP3.0 has better agreement with the independent buoy winds, primarily for higher winds (>10 m/s). This is reflected by the higher correlation and lower root-mean-squared differences of CCMP3.0 versus buoy winds, especially for higher wind conditions. For the U.S. coastal region (within 200 km), the mean wind speed of CCMP3.0 is enhanced by 1–2%, and the wind speed standard deviation is enhanced by around 3–5%. These changes in wind speed and its standard deviation from CCMP2.0 to CCMP3.0 cause an 8–12% increase in wind power density. The wind power density along the U.S. coastal region is also correlated with various climate indices depending on locations, providing a useful approach for predicting wind power on subseasonal to interannual timescales.

A Synthesis Method to Generate Hourly Electricity Production Time-series of Wind Plants in Peru for Long-term Expansion Planning

Plenty of works have treated the system expansion planning problem in the presence of intermittent renewable energy resources like wind. However, most of those proposals have been approached from scenarios of plenty of data, which is not the rule in developing countries, where principal investment actors have recently switched their focus. In contrast to operation problems where existing literature can be successfully applied since it requires short-term historical time-series gathered from the same studied plants, proposals for planning problems are almost impossible to apply because of a lack of information and measurement about renewable resources in places where no renewable plants have been previously installed. In order to fill this information gap, this paper presents a novel methodology to synthesize wind production time-series on an hourly time scale, taking as inputs aggregate data such as monthly wind speed average values and Weibull annual parameters. The methodology comprises four steps, from data gathering to calculating electrical power produced by a wind farm. Three application tests are performed for different places in India, Chile, and Peru to validate the proposed methodology. The results show that the methodology successfully synthesizes time-series of output power, correctly achieves persistence characteristics, and slightly over or underestimates the produced wind energy, having a discrepancy of 6.2% in the yearly total.

The Development of Renewable Energy in Mineral Resource Clusters—The Case of the Siberian Federal District

This article presents a study of the specific features and development factors of renewable energy in macro-regions that combine extractive and industrial clusters with rural, sparsely populated areas. While in some countries—leaders in energy transition (the EU, China)—the growth of investments in solar, wind, and hydropower production is taking place systematically within the framework of an increasing number of national and international strategies and programs, in the Russian Federation there is a lack of renewable generation capacity. Particular difficulties are experienced in regions that, on the one hand, have a developed fuel and raw material complex and, therefore, fuel generation (which makes a significant contribution to global greenhouse gas emissions), and, on the other hand, many rural, sparsely populated areas that are in need of new distributed generation. The aim of the study is to analyze the factors of transition to renewable energy in mineral-resource regions, such as the Siberian Federal District (Siberia), as well as to identify measures to enhance their effectivity. The article shows the place of Siberia in the national energy system and considers the factors related to its energy transition in detail, e.g., the saturation with main powerlines, the control of air pollution caused by fuel energy facilities, its provision with fuel resources, investments in renewable energy, and ways of improving the health and well-being of the local population. Attention is drawn to the challenges and obstacles related to the development of renewable energy in the Siberian Federal District, associated, on the one hand, with Russia’s lagging behind other countries in the dynamics of energy transition, and on the other hand, with external and internal technological and investment restrictions.

Production cost modeling for permanent magnet synchronous machines for electric vehicles

A cost model for the estimation of production costs of permanent magnet synchronous machines (PMSM) is presented, which allows to alter design choices such as wire technology, winding layout, cooling system, materials and more. With the goal to make results reproducible by others, the methods are explained in detail and used data and assumptions are given. The developed model helps to understand the interaction between the design of PMSM, manufacturing methods and the resulting costs. With it, different PMSM technologies and materials can be evaluated regarding its influence on the production costs, which is a perquisite to find the best compromise between performance and costs. Production volume is shown to be the most decisive factor for the resulting production costs. Between minimum and maximum assumed volumes, an average cost per unit reduction of 67% could be observed. Furthermore, the results imply that the winding production is responsible for the greatest part of the overall costs, followed by the rotor assembly (including rare earth magnets). When using the model to compare different wire types, it can be stated that up to a production volume of roughly 150,000 units/year, hairpin wires are more expensive to produce. Above this volume, hairpin windings will get cheaper than round wire windings due to its higher grade of automation of the production process. Through the conducted investigations and the presented results, it is demonstrated that the cost model can serve to evaluate technologies with regards to costs in the early development stage. This way a more holistic assessment of technologies for PMSM is possible, helping to find the ideal compromises between costs and performance and to increase the attractiveness of sustainable mobility.

Assessment of Wind Energy based on Optimal Weibull Parameters Estimation using Bald Eagle Search Algorithm: Case Studies from Egypt

As the wind speed is intermittent and unpredictable, statistical distribution approaches have been used to describe wind dates. The Weibull distribution with two parameters is thought to be the most accurate way for modeling wind data. This study seeks wind energy assessment via searching for optimal parameter estimation of the Weibull distribution. For this target, several analytical and heuristic methods are investigated. The analytical methods such as maximum likelihood method, moment method, energy pattern factor method (EPFM), and empirical method (EM) are used to find these optimal parameters. Also, these parameters are obtained by four heuristic optimization algorithms called particle swarm, crow search, aquila optimizer, and bald eagle search optimizers. The simulation results of analytical and heuristics are assessed together to identify the best probability density function (PDF) of wind data. In addition, these competitive models are submitted to find the most appropriate model to represent wind energy production. In all methods, the error between actual and estimated wind energy density is computed as the target fitness function. The simulation tests are carried out based on per year real data that are collected from Zafaranah and Shark El-Ouinate sites in Egypt. Also, different indicators of fitness properties are assessed such as the root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and wind production deviation (WPD). The simulation results declare that the proposed bald eagle search optimization algorithm offers greater accuracy than other analytical and heuristic algorithms in estimating the Weibull parameters. Besides, statistical analysis of the compared methods demonstrates the high stability of the BES algorithm. Moreover, the BES algorithm presents the fastest convergence compared to the others. Furthermore, different models are analyzed to deduce the nonlinear relationship between the wind output power and the regarding speed where the error of wind energy density between actual and estimated is greatly minimized using the cubic model at least values of statistical indicators.

Power Variability of Wind and Solar Production Portfolio in the Republic of Croatia

In this work, we analysed multi-annual data set of wind and solar production portfolio with different power frequency approaches and averaging methods in order to characterize power production variability at different temporal scales. All of the methods have their advantages depending on their scale and purpose, but also some shortcomings that limit their use. For the purposes of this work, we selected the method of explicit derivation as the most appropriate for the fast power change frequency analysis and characterization. 
 The variability of power production from wind and solar power plants in Croatia is strongly present on an hourly, daily and seasonal level, while on an annual level the variability is much less pronounced. Since current electricity production and consumption must remain in balance to maintain the stability of the power network, this variability of production can pose significant challenges for the inclusion of large amounts of wind and solar energy in the power system of the Republic of Croatia.
 A particular challenge for the power system in terms of production variability is the fast change in power. Fast power change affects the quality of production forecasting and consequently causes higher imbalance costs. The impact on the management and balancing of the power system is particularly challenging.

Wind resource assessment and influence of atmospheric stability on wind farm design using Computational Fluid Dynamics in the Andes Mountains, Ecuador

In recent years, research has revealed that hydropower designs in Ecuador have not adequately considered sensitivity to climate change. In addition, variations in rainfall patterns cause a drought from July to October. Therefore, it would decrease the flow of the rivers that feed the dams for hydroelectric generation, which would result in a significant reduction of its generating capacity.To prevent this inconvenience from occurring, it is necessary to promote the use of wind energy to diversify the Ecuadorian energy matrix. This matrix is made up of 70% hydropower and 0.26% wind power. Moreover, the Ecuadorian Andes has significant untapped wind potential due to their complex topography. Currently, there are no detailed studies on wind potential or wind energy prediction in the Andes. As a result, wind resource characterization is necessary. In order to address the challenges mentioned above, promote wind energy development, assess wind potential, and diminish the need for thermal generators. This article describes a methodology for wind resource characterization over complex terrain using measured data from two meteorological towers installed in the mountainous zone of study and wind characteristics modeling through the Ansys Computational Fluid Dynamics (CFD) software for the positioning of 11 Goldwind 70/1500 kW wind turbines to optimize the Annual Energy Production (AEP) of a hypothetical wind farm.The study’s main findings indicate that the wind passing between two volcanoes has a high wind potential. This potential is dependent on meteorological variables, orography, and the accelerating effect of wind speed. These favorable conditions make it possible to install a wind farm in this area with 11 high-power wind turbines. Furthermore, the most suitable turbulence model for wind farm design using Ansys Fluent CFD is the K-epsilon which can model with high precision the profile of wind characteristics over a simulated mountain under the influence of atmospheric stability on the state of the air mass to assist in the decision-making process when selecting the best wind farm locations over complex terrain in the Ecuadorian Andes.

Optimal sizing of hybrid wind-photovoltaic plants: A factorial analysis

This research attempts to determine the optimal size (in terms of profitability) of a photovoltaic (PV) plant that is going to be added to an existing wind installation. The analysis carried out is based on a real sample of 62 wind facilities located in 25 Spanish provinces in 2021. Given the hourly energy generated throughout the year by the wind facility and its grid capacity, the optimal power of the PV plant in the hybrid facility will be the one that allows maximising the net present value of the investment, i.e., the one that allows to better adjust (in economic terms) the PV production to the characteristics of the wind farm. For our empirical analysis we need data on the day-ahead energy market price, on the grid capacity and hourly production (in the day-ahead energy market) of the 62 wind farms analysed, and on the hourly PV production in the Spanish provinces where those wind farms are located. In average terms, to maximise the Net Present Value (NPV) per € invested, the optimal PV power to be added to an existing wind farm should be 8% of the wind power already installed. The averages of the financial indicators for optimal PV sizing are promising (discount rate of 7%): NPV per € invested is 1.89, NPV is M€ 23.5, discounted payback is 4.85 years, and the internal rate of return index is 25.6%. To conclude our empirical analysis, we estimate a multilevel regression model for the hourly wind production. The regression model shows, among other results, that one more MW of wind power translates into an increase in wind generation of 0.31 MWh. Our findings will help the design of hybrid plants without neglecting the economic aspect.

Future perspectives for wind and solar electricity production under high-resolution climate change scenarios

The impact of climate change on the availability and variability of wind and solar resources for renewable electricity production was assessed under the recent Shared Socioeconomic Pathways scenarios. To do this, the Weather Research and Forecasting model was applied to perform very high-resolution climate simulations (≈1 km2) for one present scenario and two future scenarios (SSP2-4.5 and SSP5-8.5). The modelled meteorological variables were then used to calculate the potential future changes in wind power and solar photovoltaic power for electricity production for each climate change scenario when compared to the present. Results show a strong increase in wind speed during Winter of +45% and a −45% decrease during Summer, with future seasonal production differences for wind power varying ≈100 kWh for both scenarios. For solar radiation, the highest differences are found during the Winter (+30% to +45% increase) with solar photovoltaic generation changes varying from −10 kWh to +20 kWh, depending on the scenario and season. The seasonal changes in wind speed and solar radiation suggest a loss of seasonality, which could be a threat to the stability of future renewable electricity production in the region, making efforts towards decarbonisation more difficult. This study highlights the importance of a comprehensive approach in the assessment of the electricity production system since seasonal variability and intermittency of some renewable energy resources could potentially be covered by others.

A correct perspective for hydrogen from engineered diagenesis

Wind and solar photovoltaic electricity production have already reached very low levels of levelized cost of energy (LCOE). Electrolyzers have already reached high efficiencies which are further improving, while costs are dramatically reducing. They are commercial products. Green hydrogen (H2) is the product of excess wind and solar electricity, specifically electricity that will be otherwise wasted, without the huge energy storage needed presently almost completely missing. By growing the installed capacity of wind and solar power plants, there will be a non-dispatchable production by wind and solar more often in excess, but sometimes also in defect, of the grid demand, in presence of limited energy storage. H2 is one of the key energy storage technologies needed to ensure grid stability. Production of H2 above what is needed to stabilize the grid significantly helps in applications such as land, and sea but especially air transport where the storage of energy onboard in a fuel is preferable to the storage of energy as electricity into a battery. The engineered diagenesis for H2 is unlikely better than green hH2. Apart from being a nice idea to be proven workable, with a technology readiness level (TRL) presently of zero, and thus impossible to be objectively compared with commercial products, the engineered diagenesis for H2, even if possible, also does not help with non-dispatchable renewable energy production. The concept may also have negative environmental aspects similar to fracking which have not been considered yet, and also bear huge economic costs in addition to environmental. Here we review the pros and cons of this novel technology, which once proven workable, which is not the case yet, should be considered as a possible way to complement rather than replace green H2 production.