Wind Power Research Articles

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

This study introduces a novel application of modified particle swarm optimization (PSO) for optimizing multi-energy hub systems (EHSs) to enhance efficiency and sustainability. The proposed method leverages PSO to optimize the scheduling of various energy resources, including gas turbines, biomass units, and renewable sources such as solar and wind power. Unlike traditional optimization approaches that rely on genetic algorithm (GA) and complex encoding schemes, the PSO algorithm simplifies the process using real-valued vectors and direct communication within the swarm, which significantly reduces implementation complexity. Key contributions of this work include the development of a tailored PSO algorithm that integrates seamlessly with the multi-objective optimization of EHSs. The algorithm simultaneously targets a reduction in operational costs and carbon emissions, offering a comprehensive solution for energy hub design. The proposed PSO approach has demonstrated a 10.35 % reduction in operating costs and an 85.03 % decrease in CO2 emissions compared to traditional baseline setups. In comparative analysis, the integration of renewable sources using the PSO algorithm resulted in a 77.91 % reduction in total CO2 emissions and an 85.61 % decrease in operating costs, showcasing its effectiveness in advancing both economic and environmental objectives. Furthermore, the study provides a detailed evaluation of various scenarios, revealing that the PSO-optimized EHS configuration achieves a significant reduction in reliance on non-renewable energy sources (RES). For instance, the incorporation of photovoltaics and wind turbines in the EHS setup led to a 46.39 % increase in energy sold to the grid and a 26.82 % decrease in electricity purchased from external sources. These quantitative results underscore the robustness and practical benefits of the proposed PSO method in designing and optimizing energy systems for improved sustainability and cost-effectiveness.

Effects of Offshore Wind Farms: Environmental and Social Perspectives from Uruguay

The installation of offshore wind farms is rising, driven by the goal of changing the global energy matrix. However, many of their possible impacts are still unknown. Increased noise levels, disruptions to food chains, pollution due to traffic, and impacts on fishing communities and tourism are all potential effects to consider. Marine habitats are essential carbon dioxide sinks. Therefore, losing marine biodiversity due to offshore wind farms can be counterproductive in mitigating climate change. Balancing biodiversity conservation, wind potential, and political interests is challenging. Today, Uruguay has significantly decreased the fossil share in its electricity generation, incorporating electricity generation from wind, solar, and biomass energy alongside hydroelectricity. In line with this, the country’s Hydrogen Roadmap highlights green hydrogen as relevant, potentially serving as a fuel for both domestic and export transportation. Combining the country’s strong base of wind energy production experience with its sustainable policy, it plans to implement offshore wind farms to produce green hydrogen, making studies of its impacts crucial. This paper reviews the current social and environmental information on the Uruguayan coastal habitat, analyzes onshore wind farms’ ecological studies, and examines offshore wind farms’ global environmental and social impacts. Finally, it proposes studies for environmental approval of offshore wind farms.

Algorithm for detecting surface defects in wind turbines based on a lightweight YOLO model

Improving wind power generation efficiency and lowering maintenance and operational costs are possible through the early and efficient diagnosis and repair of surface defects in wind turbines. To solve the lightweight deployment difficulty and insufficient accuracy issues of the traditional detection methods, this paper proposes a high-precision PC-EMA block based on YOLOv8 using partial convolution (PConv) combined with an efficient multiscale attention (EMA) channel attention mechanism, which replaces the bottleneck layer of the YOLOv8 backbone network to improve the extraction of target feature information from each layer of the network. In the feature fusion phase, GSConv, which can retain more channel information, is introduced to balance the model’s complexity and accuracy. Finally, by merging two branches and designing the PConv head with a low-latency PConv rather than a regular convolution, we are able to effectively reduce the complexity of the model while maintaining accuracy in the detection head. We use the WIoUv3 as the regression loss for the improved model, which improves the average accuracy by 5.07% and compresses the model size by 32.5% compared to the original YOLOv8 model. Deployed on Jetson Nano, the FPS increased by 11 frames/s after a TensorRT acceleration.

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Machine Learning for Optimising Renewable Energy and Grid Efficiency

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.

A Study on Energy Production and Consumption Based on Seasonal ARIMA and REAO

In the face of the global energy challenge, this study delves into the intricate landscape of energy dynamics within the rapidly evolving global economy and technological sphere. Recognizing energy as a pivotal driver of societal progress, we present a comprehensive evaluation and optimization model for China's energy sources, emphasizing economic, cost, electric energy, and environmental considerations. Employing time series models, specifically ARIMA and gray scale forecasting, we meticulously analyze wind and solar power generation, as well as overall power consumption. The results showcase nuclear power and hydropower as significant contributors to the energy landscape. Leveraging a linear programming model, we simulate and optimize the future energy development structure, considering constraints such as cost, energy, capacity, and carbon emissions. Our findings reveal the optimal quota contributions of thermal power, hydro power, nuclear power, wind power, and solar power as 13.7%, 14.2%, 17.6%, 54.5%, and 0%. Building upon these insights, we propose strategic recommendations for sustainable electric energy development. Emphasizing wind and solar energy's potential, we advocate for continued interest and increased support for technological advancements. Additionally, we call for the establishment of comprehensive power and energy development plans, integrating economic and environmental goals. To address imperfections in China's electricity tariff system, we recommend ongoing reforms aligned with market dynamics and environmental considerations.

Using stakeholder knowledge to co-produce research priorities for a raptor species vulnerable to impacts of wind energy facilities

The global energy transition necessitates a rapid increase in infrastructure in developing countries, including for wind energy facilities (WEFs). Concerns arise regarding the negative impacts of WEFs on biodiversity, especially on birds of prey or raptors and other volant animals. In Africa, South Africa has the largest installed wind energy capacity and also hosts several raptor species of conservation importance. One such raptor is the regionally endemic Jackal Buzzard Buteo rufofuscus (family Accipitridae), which has its core population in South Africa. Despite frequent fatalities from collisions with wind turbine blades, this species is understudied. To address this gap, we conducted interviews with key stakeholders in the wind energy sector, including avifaunal specialists, wind energy developers, government representatives, and conservation nongovernmental organisations. The goal was to begin a cooperative process to identify crucial research gaps and to establish a future research agenda for Jackal Buzzards in the context of wind energy development. Stakeholders expressed confidence in current environmental impact assessment (EIA) techniques but highlighted a concern for direct collisions as a significant threat to Jackal Buzzards. The consensus was that WEFs are more likely to cause local rather than national population-level impacts. Although stakeholders generally shared similar opinions, minor differences emerged between groups. Movement ecology studies were identified as a high priority for future research on this species. By collaboratively formulating research priorities, this study aims to improve the relevance of future investigations, fostering stakeholder buy-in for potential policy and EIA practice recommendations.

Analysis of Wind Power Fluctuation in Wind Turbine Wakes Using Scale-Adaptive Large Eddy Simulation

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models

Renewable energy sources, such as solar, hydro, wind, and geothermal energy, have emerged as key alternatives to fossil fuels in combating climate change and addressing energy security concerns in the USA and ad worldwide. Strategic use of this renewable resource is important not only for carbon emission reduction and improvement of environmental sustainability but also for maintaining future energy supplies. At the same time, such transition raises thorough assessments of environmental and socio-economic impacts. Machine learning (ML) models offer a powerful tool for predicting and analyzing such impacts, allowing for more efficient decision-making and long-term planning. These models are supposed to analyze patterns in energy production, land use, and emissions to make a more dynamic and predictive understanding of how renewable energy adoption influences CO2 levels. The principal aim of this research project was to develop and curate machine learning algorithms for predicting CO2 emissions based on renewable energy data, using the knowledge to better understand how solar, wind, hydro, and geothermal energy systems affect environmental outcomes. The predictive models developed in this research would serve as useful tools for the policymakers and major stakeholders in decision-making on investments in energy infrastructure and characterization of regulatory frameworks. These datasets for this research project were retrieved from several prominent institutions, including governmental agencies, international organizations such as the International Energy Agency-IEA and the World Bank, satellite data repositories, and USA environmental monitoring agencies. For this research project, 3 machine learning algorithms in the experiment were used, namely Logistic Regression, XG-Boost, and Random Forest. Amongst these three, the linear regression model gave the best performance, as it had the least MSE; indicating that its predictive capability was impressive. The comparative analysis of renewable energy projects in Germany, China, and California underlines that effective policy-making plays a very decisive role in the transition toward sustainable energy.

Innovating Through Public–Private Partnership

The article titled Innovating through Public-Private Partnership, explores the potential of public-private partnerships (PPP) as a means to foster innovation, economic stabilization, and sustainable community development. It highlights how the collaboration between public administration authorities and private or non-governmental sectors can address pressing societal challenges, including global warming, healthcare modernization, and technological advancements. The research outlines the essential components of PPPs, including financial models, management frameworks, and project evaluation criteria, with a particular focus on innovation-driven initiatives. Various stages of establishing a successful PPP are examined, from project conceptualization and partner selection to implementation and performance monitoring. Furthermore, the paper delves into the importance of funding methods and risk management, demonstrating how a well-structured PPP can combine public and private resources to deliver public goods effectively. The article also provides insights into sector-specific innovation strategies, such as digitalization and renewable energy projects. The case for renewable energy production through PPPs, involving solar and wind energy, is highlighted as a sustainable development model that strengthens collaboration between public and private sectors. By analyzing a five-year history of PPP projects in Europe and providing forecasts for the future, the paper underscores the growing relevance of PPPs in achieving long-term goals for economic and infrastructural development. The study concludes with recommendations for fostering a conducive environment for PPPs, such as legislative reform, the creation of national databases, and investment in education and training to enhance the success and innovation potential of PPPs.

Assessment of merits and demerits of perpendicular and slanted photovoltaic/thermal facades

This study compares the thermal and electrical energy production potential and wind load assessment of perpendicular versus slanted photovoltaic (PV) facades. The goal is to identify the optimal facade orientation for societal adoption based on energy efficiency and wind load considerations. Using Hybrid RANS/LES models, the aerodynamic performance and energy yield of both orientations are analyzed under varying angles and environmental conditions. The findings show that while the slanted façade is better designed to capture solar irradiance, it produces 4 to 8 % less energy than the perpendicular façade and yields approximately 2 % less exergy efficiency due to wind regime. However, the slanted design reduces wind load by 9.8 %, enhancing structural stability. The maximum thermal energy output of 217 kW is recorded from the perpendicular façade at a wind speed of 1 m/s, while the slanted façade generates a peak electrical power of 68 kW at 8 m/s wind speed. Overall, the perpendicular façade underscores more efficient in equal weather conditions, making it a sustainable choice for PV installation. These results provide valuable insights for architects, engineers, and policymakers in optimizing PV facade design for urban environments.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Site Selection Decision-Making for Offshore Wind-to-Hydrogen Production Bases Based on the Two-Dimensional Linguistic Cloud Model

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

Constructing High-Performance and Versatile Liquid-Solid Triboelectric Nanogenerator with Inflatable Columnar Units

Abstract The use of water resources for energy generation has become increasingly prevalent, encompassing the conversion of kinetic energy from streams, tides, and waves into renewable electrical power. Water energy sources offer numerous benefits, including widespread availability, stability, and the absence of carbon dioxide and other greenhouse gas emissions, making them a clean and environmentally friendly form of energy. In this work, we develop a droplet-based liquid-solid triboelectric nanogenerator (LS-TENG) using sophisticatedly designed inflatable columnar structures with inner and outer dual-electrode. This device can be utilized to harvest both the internal droplet-rolling mechanical energy and the external droplet-falling mechanical energy, capable of assembling into various structures for versatile applications. The design incorporates a combined structure of both internal and external TENG to optimize output performance via multiple energy harvesting strategies. The internal structure features a dual-electrode columnar-shaped LS-TENG, designed to harvest fluid kinetic energy from water droplet flowing. By leveraging the back-and-forth motion of a small amount of water within the air column, mechanical energy can be collected, achieving a maximum mass power density of 9.02 W/Kg and an energy conversion efficiency of 10.358%. The external component is a droplet-based LS-TENG, which utilizes a double-layer capacitor switch effect elucidated with an equivalent circuit model. Remarkably, without the need for pre-charging, a single droplet can generate over 140 V of high voltage, achieving a maximum power density of 7.35 W·m² and an energy conversion efficiency of 22.058%. The combined LS-TENG with sophisticated inflatable columnar structure can simultaneously collect multiple types of energy in high efficacy, exhibiting great significance in potential applications such as TENG aeration rings, inflatable lifejacket, wind energy harvesting, gas-column TENG tents, and green houses.

Integration of Wind Energy and Geological Hydrogen Storage in the Bakken Formation, North Dakota: Assessing the Potential of Depleted Reservoirs for Hydrogen Storage

Integration of Wind Energy and Geological Hydrogen Storage in the Bakken Formation, North Dakota: Assessing the Potential of Depleted Reservoirs for Hydrogen Storage

Design and economic analysis of energy storage optimization allocation for campus microgrid

In the twenty-first century, the "double carbon" plan is advancing steadily. Energy saving and emission reduction is one of the key points of the plan. A power grid independent park, if energy storage equipment can be configured, it can greatly improve the park microgrid power utilization, in order to achieve the purpose of energy saving. Therefore, research on the optimization of the proportion of power grid and microgrid in the park and the optimization of the configuration of energy storage devices can greatly improve the energy utilization rate, so as to achieve the significance of environmental protection. After collecting and sorting the daily load, daily wind power generation and 12-month daily wind power generation data of the three parks A, B and C, and processing the data, a linear function model is established. After using linear programming, operations research and genetic algorithm, the research results are obtained when the three parks are combined and equipped with 50kW/100kWh energy storage devices.

The Temporal-Political Dimensions of Green Colonialism through Wind Power Development at Fosen, Norway

The Temporal-Political Dimensions of Green Colonialism through Wind Power Development at Fosen, Norway

Electromagnetic performance analysis of a novel radial compound differential field-modulated magnetic gears

Purpose The purpose of this study is to propose a new magnetic gearing device and the proposed transmission model can be applied in the field of wind power and wave energy generation gearboxes. Design/methodology/approach A novel radial differential field-modulated two-stage magnetic gear is introduced, using a unique radial differential linkage to integrate two single-stage magnetic gears. This design incorporates a magnetic isolation ring to enhance transmission efficiency by minimizing magnetic interference and preventing power circulation. The two-stage modulating ring rotor operates synchronously via a connecting bridge, ensuring system stability and efficiency. This configuration not only boosts the gear ratio but also maintains a compact structure, improving power density and efficiency. Leveraging the magnetic field modulation and differential transmission, a finite element model of this gear is developed and its electromagnetic performance is analyzed. Findings The torque density of the new radial differential field-modulated two-stage magnetic gear has increased by 44.97% compared to the traditional tandem two-stage magnetic gear. It achieves a high transmission ratio of 64 and maintains comparable power density, indicating strong torque transfer capabilities suitable for low-speed, high-torque applications. During steady-state operation, the torque pulsation difference between the two stages is minimal, ensuring stable working torque. Social implications This research not only propels the forefront of magnetic gear technology through heightened efficiency and streamlined design but also bears profound societal significance in fostering sustainable energy paradigms. By facilitating superior energy conversion efficiencies in wind turbines and wave energy converters, it plays a pivotal role in mitigating carbon emissions and accelerating the global pivot towards cleaner, renewable energy landscapes. Originality/value A magnetic gear transmission device is proposed, which can achieve high power density and large transmission ratio at the same time, and this study provides a useful reference for the design optimization of new high-performance multistage magnetic gears.

A method for configuring hybrid electrolyzers based on joint wind and photovoltaic power generation modeling using copula functions

Considering the specific wind and photovoltaic power characteristics of a certain region, this study investigates the optimal ratio of Alkaline Electrolysis Cells (AEL) to Proton Exchange Membrane (PEM) electrolyzers in a hybrid electrolysis system for hydrogen production. A flexible model for configuring the hybrid electrolysis system is proposed, based on a copula function for joint wind and solar power modeling. This model generates wind and photovoltaic power generation scenarios using the copula function, incorporating a selection mechanism to ensure that the output scenarios are more representative of the actual data characteristics of wind and photovoltaic power output. Consequently, considering both the fluctuation and amplitude, the wind and photovoltaic power data are decomposed using the Ensemble empirical mode decomposition method. The decomposed components are then allocated to the two types of electrolyzers. Furthermore, the optimal configuration of the hybrid electrolysis system is determined by minimizing the costs associated with wasted power, electricity purchases, and other expenses. Finally, a case study of a 100 MW wind farm and a 50 MW photovoltaic power station in Northwest China is presented, concluding that the optimal configuration ratio of AEL to PEM electrolyzers is 2:1. In a Matlab/Simulink platform, the performance metrics of the hybrid electrolysis system were validated. It was found that the hydrogen production rate of the hybrid electrolyzer is comparable to that of the PEM electrolyzer, but with a lower required cost. Additionally, the hydrogen production rate and volume of the optimal configuration for the hybrid electrolyzer determined by the model proposed in this paper are higher than those obtained through the optimization algorithm's optimal configuration.

A study of short-term wind power segmentation forecasting method considering weather on ramp segments

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research.