Wind Power Production Research Articles

Enhancing Regional Wind Power Forecasting through Advanced Machine-Learning and Feature-Selection Techniques

In this study, an in-depth analysis is presented on forecasting aggregated wind power production at the regional level, using advanced Machine-Learning (ML) techniques and feature-selection methods. The main problem consists of selecting the wind speed measuring points within a large region, as the wind plant locations are assumed to be unknown. For this purpose, the main cities (province capitals) are considered as possible features and four feature-selection methods are explored: Pearson correlation, Spearman correlation, mutual information, and Chi-squared test with Fisher score. The results demonstrate that proper feature selection significantly improves prediction performance, particularly when dealing with high-dimensional data and regional forecasting challenges. Additionally, the performance of five prominent machine-learning models is analyzed: Long Short-Term Memory (LSTM) networks, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Extreme-Learning Machines (ELMs). Through rigorous testing, LSTM is identified as the most effective model for the case study in northern Italy. This study offers valuable insights into optimizing wind power forecasting models and underscores the importance of feature selection in achieving reliable and accurate predictions.

Investments in green hydrogen as a flexibility source for the European power system by 2050: Does it pay off?

The European Union aims to deploy a high share of renewable energy sources in Europe’s power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply–demand balance. Green hydrogen (GH) can increase power systems’ flexibility and decrease renewable energy production’s curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around €30/MWh.

A Multi-Decadal Hourly Coincident Wind and Solar Power Production Dataset for the Contiguous United States

As renewable energy continues its rapid expansion in the Unites States, multi-decadal hourly datasets of electricity production are needed to asses reliability and resource adequacy of power grids. Recent years have seen the release of grid-cell-level simulated meteorological variables, however these are not extended to the power domain, are not developed from a dynamically consistent numerical weather model, and only cover a historical baseline of less than a decade. To fill this gap, this work provides a dataset of 43 years of coincident plant-level wind and solar power production data. The dataset is designed to be aggregated to appropriate scales of interest for bulk system studies such as Balancing Authorities (BAs), states, and nodes of a production cost model. The dataset covers every plant in the contiguous U.S. that is reported in the U.S. Energy Information Administration (EIA) Form 860 as of 2020. When compared with the EIA-923 monthly generation, we find minimal bias (less than 5%). When compared with BA-reported hourly generation, we find low bias in solar (less than 7%), and slight underdispersion in wind. This coincident multi-decadal historical dataset provides a documented and evaluated multi-resource baseline for studies on reliability, resource adequacy, climate change impacts, and characterization of emergent climate threats on renewable resources.

A decision-making model for joint energy and reserve scheduling of wind power producers with local intraday demand response exchange market

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

Estimating the Wind Power by Using K-Nearest Neighbors (KNN) and Python

In recent years the production and distribution of renewable energy has become a popular trend in power generation, and renewable energy sources such as hydro, solar, and wind energy are examples of natural and clean energy sources. Among the renewable energy sources, wind energy source is one of the trending and crucial energy sources that has outstanding electricity power generation. This research investigates an estimation model of wind power to address the real-life issues in renewable energy power generation by estimating an accurate amount of wind power production by applying the machine learning (ML) K-Nearest Neighbors (KNN) technique integrated with Python programming. To achieve the expected outcome for this research, it is expected to collect data from wind energy, wind speed forecasts, and all the relevant information that supports the research in analyzing the technique. There are various meteorological methods to evaluate the performance of wind power generation, but in this research, the methodology is to employ machine learning techniques such as KNN integrated with Python coding to achieve estimated wind energy output. Python is a software program and is diversely used for coding various algorithmic formulas to obtain results. In this research, Python is best suited for investigating the performance of the KNN technique for estimating the most accurate result for wind power generation. Here, the report presents the methods of applying the KNN technique and displaying the outcome on screen by a graph representation. The outcome of the research showed that the KNN is a more reliable technique to make an accurate prediction on wind power generation. A similar approach can be implemented in various renewable energy data to estimate an accurate energy output.

Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

Economic analysis of hydrogen energy systems: A global perspective

In the realm of renewable energy, the integration of wind power and hydrogen energy systems represents a promising avenue towards environmental sustainability. However, the development of cost-effective hydrogen energy storage solutions is crucial to fully realize the potential of hydrogen as a renewable energy source. By combining wind power generation with hydrogen storage, a comprehensive hydrogen energy system can be established. This study aims to devise a physiologically inspired optimization approach for designing a standalone wind power producer that incorporates a hydrogen energy system on a global scale. The optimization process considers both total cost and capacity loss to determine the optimal configuration for the system. The optimal setup for an off-grid solution involves the utilization of eight distinct types of compact horizontal-axis wind turbines. Additionally, a sensitivity analysis is conducted by varying component capital costs to assess their impact on overall cost and load loss. Simulation results indicate that at a 15 % loss, the cost of energy (COE) is $1.3772, while at 0 % loss, it stands at $1.6908. Capital expenses associated with wind turbines and hydrogen storage systems significantly contribute to the overall cost. Consequently, the wind turbine-hydrogen storage system emerges as the most cost-effective and reliable option due to its low cost of energy.

Short-Term Wind Speed Prediction via Sample Entropy: A Hybridisation Approach against Gradient Disappearance and Explosion

High-variant wind speeds cause aberrations in wind power systems and compromise the effective operation of wind farms. A single model cannot capture the inherent wind speed randomness and complexity. In the proposed hybrid strategy, wavelet transform (WT) is used for data decomposition, sample entropy (SampEn) for subseries complexity evaluation, neural network autoregression (NNAR) for deterministic subseries prediction, long short-term memory network (LSTM) for complex subseries prediction, and gradient boosting machine (GBM) for prediction reconciliation. The proposed WT-NNAR-LSTM-GBM approach predicts minutely averaged wind speed data collected at Southern African Universities Radiometric Network (SAURAN) stations: Council for Scientific and Industrial Research (CSIR), Richtersveld (RVD), Venda, and the Namibian University of Science and Technology (NUST). For comparison purposes, in WT-NNAR-LSTM-GBM, LSTM and NNAR are respectively replaced with a k-nearest neighbour (KNN) to form the corresponding hybrids: WT-NNAR-KNN-GBM and WT-KNN-LSTM-GBM. We assessed WT-NNAR-LSTM-GBM’s efficacy against NNAR, LSTM, WT-NNAR-KNN-GBM, and WT-KNN-LSTM-GBM as well as the naïve model. The comparative study found that the WT-NNAR-LSTM-GBM model was the most accurate, sharpest, and robust based on mean absolute error, median absolute deviation, and residual analysis. The study results suggest using short-term forecasts to optimise wind power production, enhance grid operations in real-time, and open the door to further algorithmic enhancements.

Impacts of El Nino on renewable energy industry and fishery and aquaculture industry around the pacific

The effects of climate change on the world economy come into view in recent years, in El Nino is one of the most intricate and solid problems in predicting its occurrence and mitigating its effects. Former studies include its impacts on fishery and aquaculture, agriculture and major crops and the renewable energy market. Possible impacts include reproductive failure of fish, worsening well-being of people in coastal consortiums, decreasing production in maize, especially in warmer regions and decreasing efficiency and quantity of wind power production. Observed regions varied from the South American Coast and Peruvian waters to Malaysia, but neither of them compared the differences between each region as their major part. In this paper, fixed effects and dummy variable regression are used to evaluate El Ninos impact on fishery and renewable energy industries and contrast the differences between Japan, the USA and Australia. Results show that El Nino has a clear restraint on both industries but a stable increase in renewable energy production makes the relationship positive. Among the countries, Japan is the most restrained in both industries while the USA is the least affected. Among the different renewable energy sources, solar energy is promoted while hydro energy is restrained.

China's onshore wind energy potential in the context of climate change

China has great potential for developing renewable energy to achieve its carbon neutrality goals. Orderly development of renewable energy is essential to enhance resource utilization efficiency and ensure safety during the energy system transition process. This study presents a thorough assessment of China's onshore wind power potential by considering the land suitability, the potential and temporal characteristics, and the impacts of climate change. The high-resolution maps combining wind resources with land conditions and climate scenarios are produced to provide insights into system planning, grid integration, and flexibility management. The results show that the capacity potential of onshore wind energy in China is 9.6 TW with an annual generation of 12.6 PWh, and 83 % of total capacity has a cost advantage with the levelized cost lower than the 60 $/MWh threshold. By comprehensively considering geographical, economic, and social criteria, around 8.1 % of the national territorial area is identified as the most suitable area for wind power development, primarily in Inner Mongolia and Xinjiang. The annual electricity generation from these areas can fulfill nearly 69 % of the nation's electricity demand. Future climate change projections indicate a remarkable generalized drop by 18 % in the north and a slight increase by 7 % in the south under the RCP 8.5 scenario. However, significant changes in wind resources are mostly within restricted areas, suggesting that future climate change would like to bring negative but limited impacts on wind power production in China.

Optimal scheduling of a RES – Electrolyzer aggregator in electricity, hydrogen and green certificates markets

The emerging threat of climate change underscores the necessity for increased investments in renewable energy sources and hydrogen energy to enhance the transition towards a more sustainable and resilient future. This work presents a simple Linear Programming (LP) mathematical framework to ensure the optimal portfolio management and participation of an aggregator owning a Polymer Electrolyte Membrane (PEM) electrolyzer and renewable energy assets in the electricity, hydrogen and green certificates markets. The objective aims to maximize the aggregator's overall profits by optimally managing green certificates issuance and diminishing the curtailment of the renewable energy production. The proposed approach is applied to an illustrative case study, while additionally a number of scenarios are considered to account for fluctuations in electricity price and wind power production. Furthermore, the impact of expanding electrolyzer's power capacity from the aggregator's side is also investigated. The results obtained illustrate the impact of the above-mentioned scenarios on the optimal operation and profitability of the aggregator.

Extreme Weather Events and the Energy Sector in 2021

Abstract In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California specifically experienced heavy droughts and heat-wave conditions, causing the operations of hydropower stations to halt; floods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.

Modeling the effect of wind speed and direction shear on utility‐scale wind turbine power production

AbstractWind speed and direction variations across the rotor affect power production. As utility‐scale turbines extend higher into the atmospheric boundary layer (ABL) with larger rotor diameters and hub heights, they increasingly encounter more complex wind speed and direction variations. We assess three models for power production that account for wind speed and direction shear. Two are based on actuator disc representations, and the third is a blade element representation. We also evaluate the predictions from a standard power curve model that has no knowledge of wind shear. The predictions from each model, driven by wind profile measurements from a profiling LiDAR, are compared to concurrent power measurements from an adjacent utility‐scale wind turbine. In the field measurements of the utility‐scale turbine, discrete combinations of speed and direction shear induce changes in power production of −19% to +34% relative to the turbine power curve for a given hub height wind speed. Positive speed shear generally corresponds to over‐performance and increasing magnitudes of direction shear to greater under‐performance, relative to the power curve. Overall, the blade element model produces both higher correlation and lower error relative to the other models, but its quantitative accuracy depends on induction and controller sub‐models. To further assess the influence of complex, non‐monotonic wind profiles, we also drive the models with best‐fit power law wind speed profiles and linear wind direction profiles. These idealized inputs produce qualitative and quantitative differences in power predictions from each model, demonstrating that time‐varying, non‐monotonic wind shear affects wind power production.

Wind farm power curve characterization under different atmospheric stability regimes

Wind farm power curves are particularly relevant in different applications. A better understanding of wind farm power curves under different atmospheric regimes is sought. In the present work, the atmospheric stability regime was identified by the vertical gradient of the potential temperature. Vertical profile of the wind velocity presented a clearly different pattern during the stable and unstable regimes. Analysis was performed considering 5 years of data from three wind farms, each corresponding to different mesoscale wind. Power curves are presented as a function of atmospheric stability. For each wind farm, we utilized wind velocity and temperature data measurements of a correspondent near the tower location. Clearly different patterns of wind farm power curves were observed as a function of the stability regime, with clear differences in the dependence of mesoscale wind regimes. For the same mean velocity, wind power production is greater for higher values of turbulence intensity TI.

Trade-offs of wind power production: A study on the environmental implications of raw materials mining in the life cycle of wind turbines

The energy demand in Sweden is increasing due to the growing electrification, and to meet this need, the generation must be expanded. Wind power generation is a very attractive renewable power production alternative for Sweden because of the favorable weather conditions.Research looks at how much wind energy may be deemed sustainable. Increased energy demand puts pressure on the government and industry to build more wind farms and, as a result, produce more wind turbines. Raw materials are necessary for wind turbines to provide a secure transition to green energy technologies. To meet these demands, the materials from various countries should efficiently contribute towards the Sustainable Development Goals (SDGs). This paper gathers information about raw resources from diverse nations worldwide. All materials are mapped to the country where they are produced using social science criteria. A few nations are chosen based on a sample approach for further analysis, and the implications of mining operations are investigated. Finally, the direct and indirect effects of the SDGs are considered.Based on the data gathered, recommendations and considerations are given to avoid or mitigate the repercussions of raw materials mining and make wind power generation more socially and environmentally sustainable.

Optimization of a wind farm layout to mitigate the wind power intermittency

A multi-objective optimization method utilizing genetic algorithms is developed to optimize wind farm layout design with the dual objectives of enhancing the production of wind power and reducing hour-level intermittency in the generated power. This method introduces a novel metric for annual wind power intermittency based on the transition probability of wind conditions (i.e., speed and direction). The present multi-objective optimization method ensures that Pareto optimal layouts are evenly distributed according to multiple cost function values. To mitigate wind power intermittency, the spatial distribution of wake fields is strategically manipulated to counteract hourly fluctuations in wind conditions. In wind conditions favoring high power generation, it is found that increasing the overlap between wakes and turbines helps to minimize disparities in generated power compared to other wind conditions.

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Economic development and optimal allocation of land use in ecological emigration area in China

Due to factors such as climate, environment, and resource scarcity, a large number of ecological immigration activities have been carried out in many regions of the world, and a large amount of land has been abandoned in emigration areas, which makes it difficult to reflect the economic value of the land in the process of land ecological restoration. Many scholars mainly focus on the socio-economic development of the immigration area, while the ecological economic value of the land in the emigration area is neglected. The Chinese government has organised and implemented six large-scale resettlement projects in Ningxia to alleviate poverty and bring prosperity to areas where the soil and water are not well nourished and people are struggling. Thus, this study observed Guyuan City, Haiyuan County, and Tongxin County in central and southern Ningxia and conducted a systematic and comprehensive analysis of land resources, natural resources, the ecological environment, and social economy in the emigration area. Based on the accurate identification of the land scale of the emigration area, evaluating industrial suitability in the ecological emigration area, this study intended to strike a balance between ecological and economic development. The results showed that the emigration area is 8.0794 million mus, and the main types of land use are grassland, arable land, and forest land; the industrial development directions include large-scale livestock farming, drought-tolerant fruit farming, cold-resistant vegetable farming, modern forestry, specialised understory and photovoltaic and wind power production. The results provide policy inspiration for sustainable land use in ecological emigration areas, and propose practical ways for social capital and financial institutions to participate in the land development and promote ecological and economic value balance of emigration areas in the world.

Variability and trends of near-surface wind speed over the Tibetan Plateau: The role played by the westerly and Asian monsoon

Near-surface wind speed exerts profound impacts on many environmental issues, while the long-term (≥60 years) trend and multidecadal variability in the wind speed and its underlying causes in global high-elevation and mountainous areas (e.g., Tibetan Plateau) remain largely unknown. Here, by examining homogenized wind speed data from 104 meteorological stations over the Tibetan Plateau for 1961–2020 and ERA5 reanalysis datasets, we investigated the variability and long-term trend in the near-surface wind speed and revealed the role played by the westerly and Asian monsoon. The results show that the homogenized annual wind speed displays a decreasing trend (−0.091 m s−1 per decade, p < 0.05), with the strongest in spring (−0.131 m s−1 per decade, p < 0.05), and the weakest in autumn (−0.071 m s−1 per decade, p < 0.05). There is a distinct multidecadal variability of wind speed, which manifested in an prominent increase in 1961–1970, a sustained decrease in 1970–2002, and a consistent increase in 2002–2020. The observed decadal variations are likely linked to large-scale atmospheric circulation, and the correlation analysis unveiled a more important role of westerly and East Asian winter monsoon in modulating near-surface wind changes over the Tibetan Plateau. The potential physical processes associated with westerly and Asian monsoon changes are in concordance with wind speed change, in terms of overall weakened horizontal air flow (i.e., geostrophic wind speed), declined vertical thermal and dynamic momentum transfer (i.e., atmospheric stratification thermal instability and vertical wind shear), and varied Tibetan Plateau vortices. This indicates that to varying degrees these processes may have contributed to the changes in near-surface wind speed over the Tibetan Plateau. This study has implications for wind power production and soil wind erosion prevention in the Tibetan Plateau.