Search

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

Scouring around the fixed foundations of offshore wind turbines (OWTs) remains a pressing concern within the offshore wind industry, significantly jeopardizing OWT safety. Despite previous efforts, the current understanding of this phenomenon remains incomplete, as various factors related to offshore wind farms and OWT operations can exert intricate influences. To further explore this matter, this paper undertakes a new study to further understand the interplay between monopile vibrations, tidal currents, and sea waves, elucidating their combined impact on scour surrounding an OWT's foundation. It was found that, besides pile vibration amplitude and frequency, the pile vibration direction can also notably influence the scour around the monopile. Specifically, the most pronounced scour mostly transpires when the monopile undergoes vibrations along the 135° direction relative to the inflow direction. The mildest scour mostly occurs when the pile vibrates along the 90° direction. Additionally, this study reveals that different types of waves, indicated by the ratio of water depth, H, to wavelength, L, also engender varying scour around the monopile. The influence of waves on scour increases with the decrease in the H/L ratio, implying that OWT foundations are more threatened by shallow water waves than by deep water waves. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative and eco-friendly computational methodology to assess the wind potential of a location with the aid of high-resolution simulations with a mesoscale numerical weather prediction model (WRF), coupled with the statistical "10% sampling condition". The proposed methodology is tested for a location with complex terrain on the Greek island of Crete, where moderate to strong winds prevail for most of the year. The results are promising, indicating that this method has great potential for studying and assessing areas of interest. Adverse effects and challenges associated with wind energy production may be mitigated with methods such as the proposed one. Mitigating such effects should constitute the main focus and priority in research concerning wind energy production. [ABSTRACT FROM AUTHOR]

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks. [ABSTRACT FROM AUTHOR]

Offshore wind farms are great options for addressing the world's energy and climate change challenges, as well as meeting rising energy demand while taking environmental and economic impacts into account. Floating wind turbines, in specific, depict the next horizon in the sustainable renewable energy industry. In this study, a life-cycle cost analysis for floating offshore wind turbines is developed by combining the most recent data and parametric formulas from databases and relevant papers. The cost analysis models focused on cost minimization with special emphasis on Operation and Maintenance Cost (OPEX), Decommissioning Cost (DECOM), and Levelized Cost of Energy (LCOE), which are important factors in wind power economy. Given that floating wind energy is still developing, the presented scenarios should be beneficial in making future decisions. The cost analysis scenarios include on-site and off-site maintenance scenarios for OPEX. In addition, four alternative scenarios for DECOM have been examined: mechanical recycling, mechanical-incineration, incineration processes, and landfill. According to the findings of these scenarios, OPEX varies from 16.89 to 19.93 £/MWh and DECOM between 3.47 and 3.65 £/MWh, whilst the total LCOE varied from 50.67 to 66.73 £/MWh. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme. [ABSTRACT FROM AUTHOR]

Wind prediction has consistently been in the spotlight as a crucial element in achieving efficient wind power generation and reducing operational costs. In recent years, with the rapid advancement of artificial intelligence (AI) technology, its application in the field of wind prediction has made significant strides. Focusing on the process of AI-based wind prediction modeling, this paper provides a comprehensive summary and discussion of key techniques and models in data preprocessing, feature extraction, relationship learning, and parameter optimization. Building upon this, three major challenges are identified in AI-based wind prediction: the uncertainty of wind data, the incompleteness of feature extraction, and the complexity of relationship learning. In response to these challenges, targeted suggestions are proposed for future research directions, aiming to promote the effective application of AI technology in the field of wind prediction and address the crucial issues therein. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

Beyond solar and wind energy, wave energy is gaining great interest due to its very high theoretical potential, although its stochastic nature causes intermittent and fluctuating power production. Energy storage system (ESS) integration to wave energy converter (WEC) plants represents a promising solution to mitigate this issue. To overcome the technological limits of the single storage devices, the hybridization of complementary ESSs represents an effective solution, extending the operating range over different timeframes. This paper analyzes the benefits of Li-ion battery–supercapacitor hybrid ESS integration into a grid-connected WEC, aiming at smoothing the produced power oscillations. The hybridization concept involves coupling a power-intensive technology, such as a supercapacitor devoted to managing fluctuations at higher frequency, with a battery technology exploited to manage power variations over longer timeframes to mitigate degradation issues. In this study, a multi-objective data-driven power management strategy, based on the simultaneous perturbation stochastic approximation (SPSA) algorithm, is implemented to minimize power fluctuations in terms of power ramp (representing the power variation between two consecutive values with a 1 s time step), both at the Point of Common Coupling (PCC) and the Li-ion battery terminals, thanks to the supercapacitor peak-shaving function. The SPSA management strategy, together with a suitable sizing procedure, allows a reduction of more than 70% in the power oscillations at the PCC with respect to those at the WEC terminals, while decreasing battery stress by more than 25% if compared to a non-hybrid ESS consisting of a Li-ion battery. This shows how supercapacitor features can extend battery lifespan when integrated in a hybrid ESS. [ABSTRACT FROM AUTHOR]

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

Galloping-based piezoelectric energy harvesting systems are being used to supply renewable electricity for low-power wireless sensor network nodes. In this paper, a W-shaped bluff body is proposed as the core component of a piezoelectric wind energy harvester. Experiments and simulations have shown that the W-shaped bluff body can improve harvesting efficiency at low wind speeds. For the W-shaped structure, the finite element simulation results indicate that the structure can help improve the aerodynamic performance to obtain high aerodynamic force. The experimental results demonstrate that compared with the traditional bluff bodies, the piezoelectric wind energy harvester with the W-shaped bluff body (WEHW) can generate higher output voltages and has a lower cut-in speed. When the length L is 30 mm and the rear groove angle β is 30°, the W-shaped structure can induce the best harvesting performance. When an external load resistance of 820 KΩ is connected and the wind speed is 5 m/s, the WEHW generates an average output power of 0.28 mW. [ABSTRACT FROM AUTHOR]

This paper performs a comprehensive analysis of the wind energy potential of onshore regions in Greece with emphasis on quantifying the volume risk and the spatial covariance structure. Optimization techniques are employed to derive efficient wind capacity allocation plans (also known as generation portfolios) incorporating different yield aspirations. The generation profile of minimum variance and other optimal portfolios along the efficient frontier are subject to rigorous evaluation using a fusion of descriptive and statistical methods. In particular, principal component analysis is employed to estimate factor models and investigate the spatiotemporal properties of wind power generation, providing valuable insights into the persistence of volume risk. The overarching goal of the study is to employ a set of statistical and mathematical programming tools guiding investors, aggregators and policy makers in their selection of wind energy generating assets. The findings of this research challenge the effectiveness of current policies and industry practices, offering a new perspective on wind energy harvesting with a focus on the management of volume risk. [ABSTRACT FROM AUTHOR]

Demand response (DR) can improve the accommodation of renewable energy and further affect the distributed generation (DG) allocation strategy. In this context, this paper proposes a stochastic optimal allocation model of DG, considering DR. Firstly, to address the uncertainty of wind and solar power outputs, a large number of scenarios of wind and solar power are generated based on the scenario method, which are then clustered into 10 typical scenarios by the k-means method. Secondly, with the goal of maximizing the total cost, the DR cost and corresponding constraints are introduced. Then, the stochastic planning model for DG is established, where the planning level aims to minimize the investment cost while the operation level minimizes the total operation expectation cost. For the non-linear term in the DR cost and power flow constraint, the Taylor expansion method and second-order conic relaxation method are both adopted to transform the original mixed-integer non-linear model to the mixed-integer second-order conic planning model. Finally, the whole planning model for DG is solved by CPLEX 12.6.0. The results show that DR can reduce the total cost and improve the accommodation of renewable energy in the DG planning process, which should be paid more attention to in the DG planning model. [ABSTRACT FROM AUTHOR]

This paper focuses on the design of the wings used in airborne wind energy systems. At the moment, two different designs are being developed: soft wings and rigid wings. This paper aimed to establish which of the two alternative design choices has the highest chance of dominance and which factors affect that. We treated this problem as a battle for a dominant design, of which the outcome can be explained by factors for technology dominance. The objective was to find weights for the factors for technology dominance for this specific case. This was accomplished by applying the best worst method (BWM). The results are based on literature research and interviews with experts from different backgrounds. It was found that the factors of technological superiority, learning orientation and flexibility are the most important for this case. In addition, it appeared that both designs still have a chance to win the battle. [ABSTRACT FROM AUTHOR]

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon. [ABSTRACT FROM AUTHOR]

Wind energy research plays a vital role in the possibility of the success story of wind energy as one of the most promising sustainable energy sources. This continuous process has been achieved from the era of small wind turbines to the current Multi-WM standard and beyond. In this editorial paper, the progress and future outlook of wind energy research in two main aspects are discussed. The first aspect is in the area of wind turbine design and computations which covers engineering modeling and high-fidelity approaches. The second part of the paper discusses the usage of data-driven approaches in wind energy research. The paper compiles and presents the key findings of several recent studies in these two areas of research. The discussion of the paper is focused on the technical aspects of wind energy modeling. The main aim is to provide an overview about the direction of current research and its importance to meet future expectations. [ABSTRACT FROM AUTHOR]

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous development of new energy, how to efficiently use wind energy has received more and more market attention. Due to cost advantages, the development of small wind turbines is accelerating. Among them, the design and research of the airfoil design and research of the lift vertical axis wind turbine has matured, but because of the aerodynamic characteristics of the lift airfoil structure, it is impossible to start itself at low wind speed, resulting in the waste of low wind speed energy. Although the drag wind turbine has good self-starting performance, the wind energy utilization efficiency in the high-speed state is inefficient. Each has its own unique shortcomings, which directly affects the marketization of small wind turbines. In order to solve these problems, this paper presents a drag-lift hybrid type wind turbine structure based on an NACA0018 symmetrical airfoil, which can adaptively change the blade shape. This design can keep the blade in the drag shape under static and low speed conditions, and adaptively change the lift shape with the increase of speed. In addition, through the research method of CFD numerical simulation combined with physical experiments, the proposed wind turbine design is studied and analyzed from multiple angles. At the same time, the "6DOF + dynamic grid" setting is used to study the influence of the opening angle factor of the drag-lift hybrid blade on the self-starting performance, and the study shows that the design of the drag-lift hybrid blade proposed in this paper has a higher self-starting torque and lower starting wind speed than the traditional lifting blade, and it is observed that the drag-lift hybrid blade has the best self-starting performance when the opening angle of the blade is 80°. At the same time, the problem of switching the blade morphology of the drag-lift hybrid blade is also analyzed, along with how to use the spring to control all this adaptively. In order to better analyze the advantages of the drag-lift hybrid design proposed in this paper, a wind tunnel test was also carried out using the physical model, and the relationship between the leaf tip speed ratio and the wind energy utilization rate was obtained, which intuitively showed the improvement of the wind energy utilization rate of the drag-lift hybrid design compared with the traditional lift blade. [ABSTRACT FROM AUTHOR]

To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R2 is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy. [ABSTRACT FROM AUTHOR]

This paper publishes the experimental performance results of a novel small dual−stage wind turbine at different speeds under wind tunnel testing. The results confirm its superiority at low wind speeds <7 m/s, which is consistent with natural wind conditions in most countries around the world. At lower wind speeds, the efficiency of the small, dual−stage wind turbine is higher than that of a conventional single−stage turbine. At wind speeds of <6 m/s, the novel dual−stage wind turbine always produces approximately 2.5 times more power than a single−stage wind turbine; the power coefficient is greater than 0.4. Thanks to this feature, the novel small, dual−stage wind turbine is effective in practical applications. For windy areas in Vietnam, the power generated over the same amount of time by the novel dual−stage wind turbine can be more than double that of a single−stage turbine. Next, a standard CFD model is established and validated with the above experimental measurements. This method ensures the objectivity and authenticity of the computational model. This standard CFD model is applicable to a horizontal double−stage wind turbine with any configuration and blade profile. The URANS simulation method, which uses sliding meshes and the k−omega SST turbulence model, was implemented with rotational domains of the minimum thicknesses of rotational domains (in this paper, 0.09Rfront and 0.06Rrear, accordingly). This method predicts higher mechanical work for the turbine than the actual value by an interval of <10% in the same speed range as in the experiment. This is an acceptable deviation. [ABSTRACT FROM AUTHOR]

Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is employed to reconstruct the wind power series subsequences to reduce the model's complexity. Second, the long short-term memory (LSTM) network is optimized, the BiLSTM neural network prediction method is used to predict each reconstruction component, and the results of the different component predictions are superimposed to acquire the total prediction results. Finally, MC is used to correct the model's total prediction results to increase the accuracy of the predictions. Experimental validation with measured data from wind farms in a region of Xinjiang, and computational results demonstrate that the proposed model can better fit wind power data than other prediction models and has greater prediction accuracy and generalizability for enhancing wind power prediction performance. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive review on the employment of wide bandgap (WBG) semiconductor power devices in wind energy conversion systems (WECSs). Silicon-carbide- (SiC) and gallium-nitride (GaN)-based power devices are highlighted and studied in this review, focusing on their application in the wind energy system. This is due to their premium characteristics such as the operation at high switching frequency, which can reduce the switching losses, and the capability to operate at high temperatures compared with silicon (Si)-based devices. These advantages promote the replacement of the conventional Si-based devices with the WBG semiconductor devices in the new modular converter topologies due to the persistent demand for a more-efficient power converter topology with lower losses and smaller sizes. The main objective of this paper was to provide a comprehensive overview of the WBG power devices commercially available on the market and employed in the modular converter topologies for renewable energy systems. The paper also provides a comparison between the WBG power technologies and the traditional ones based on the Si devices. The paper starts from the conventional modular power converter topology circuits, and then, it discusses the opportunities for integrating the SiC and WBG devices in the modular power converters to improve and enhance the system's performance. [ABSTRACT FROM AUTHOR]

The open-winding permanent magnet synchronous machines (OW-PMSMs) have recently been gaining more attention because of their fault-tolerant capability and power quality comparable to a 3-level converter-driven system. This paper reviews the common configurations of OW-PMSM when used as a generator, highlighting its shortcomings and benefits. The OW-PMSM with a common DC bus was found to be a promising direct-drive generator solution for wind energy conversion (WEC) systems considering fault tolerance, DC bus utilization, and power quality when appropriate control algorithms are in place. The presence of the zero-sequence current is the key disadvantage of the common DC bus configuration. The review highlights the algorithms that have been proposed to suppress the zero-sequence current of the OW-PMSM under healthy and various fault conditions, especially the open-circuit fault of semiconductor switch. Shutting down remotely located wind turbines because of faults, until they can be repaired, may not make economic sense. The OW-PMSM can offer the opportunity, to run a WEC system even under fault conditions albeit with low output power. This paper will assess the literature gaps in the existing control techniques that prevent the extension via a comprehensive review. [ABSTRACT FROM AUTHOR]

Our paper focuses on assessing the role of state funding in supporting wind energy projects with a focus on economic efficiency and risk assessment. In particular, we analyze the new program aimed at supporting Russian renewable energy (RE) projects envisaged for the period from 2024–2035 that involves a reduction in investments in such projects and the introduction of large fines for non-compliance with regulatory requirements for localization and export. These strict rules imposed by the regulatory authorities, as well as the withdrawal by foreign manufacturers of equipment for renewable energy from the domestic energy market, put into doubt the economic feasibility of the participation of sector players in state-supported programs. Our paper assesses the economic justification for the practicality of the Russian energy market to implement renewable energy projects under the influence of negative environmental factors and the reduction of state support programs. We employ a case study of wind energy projects carried out in 2018–2020 as a part of the first sector support program. Our methodology is based on the calculations of the classical indicators of economic efficiency of projects (NPV, IRR, and DPP). Our own approach reveals that these indicators are supplemented by taking into account the cost of specific political, environmental, and economic risks of wind energy projects. Our results reveal that, at the moment, Russian wind energy projects in various scenarios retain a sufficient margin of financial strength and are able to withstand a reduction in the amount of financial support from the state. Our findings allow the formulation of some practical recommendations for reducing the share of governmental support for wind energy projects on the local energy market as a measure of cutting costs and increasing overall economic efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the instability of the electricity generation of renewable energy sources (RESs), specifically Digital Twins of RESs. The first part deals with the analysis of RES electricity generation around the world and Ukraine. The following chapter describes features of functioning power grids in modern conditions in Ukraine and ways to ensure the balance reliability in the power system for conditions of high-grade RES integration. The rapid increase in electricity generation RESs causes control problems of distributed power supply in the power grid. A mathematical model of the parameter controls in normal mode electric power systems for conditions with high integration of RESs is proposed in the second part. The study investigates components of the optimality criterion at the control of normal mode parameters of the electric power system with RESs. In general, digital transformation helps decarbonize the energy supply, decrease dependency on fossil fuels, and integrate renewables into power systems. A model Digital Twin (DT) of a photovoltaic system, or an exact 3D visualization, analyzing the accumulator system depending on load and generation, are presented. The problems of Digital Twin are very widely discussed, but many papers and studies are general without any practical implementations. The main part of this paper focuses on research and deals with daily electricity generation from different kinds of RESs, namely mini-hydropower stations, photovoltaic power stations, and wind power stations. Measured data of electricity generation from photovoltaic power plants, wind power plants, and mini-hydropower plants and obtained meteorological factors were used for the calculation of Spearman's, Kendall's, and Pearson's correlation rank coefficients. The main contribution of this research is to determine the main metrological factors for each kind of studied RES. In the future, it will help to decide the task of forecasting power generation more presciently. Additionally, the presented model of DT RESs allows the installation and operation of grids with higher efficiency, because it can help to predict all influences, from shading up to the optimization of the battery storage system. [ABSTRACT FROM AUTHOR]

The overexploitation of non-renewable fossil resources has led to dangerous warming of our planet due to greenhouse gas emissions. The main reason for this problem is the increase in global energy demand. The rising prices of oil and gas have pushed governments around the world to turn to renewable energy, especially solar and wind power. For this reason, the present paper aimed to focus on photovoltaic and wind energy systems. However, exploitation of these two sources individually is not always easy because of their intermittent and irregular characters. Therefore, the obvious solution is the hybridisation of these two sources, which, when used alongside other systems such as batteries, increases the reliability, availability, and efficiency of these renewable sources. The main objective of this paper is to give an overview of different configurations of hybrid solar and wind energy conversion systems. First, the behaviour of each system, as well as their mathematical models, characteristics, and existing topologies, is presented. Then, the control strategies, optimal configurations, and sizing techniques, as well as different energy management strategies, of these hybrid PV–wind systems are presented. [ABSTRACT FROM AUTHOR]

In this paper, balancing electricity production using renewable energy such as wind power, PV cells, hydropower, and CHP (combined heat and power) with biomass is carried out in relation to electricity consumption in primarily one major region in Sweden, SE-3, which contains 75% of the country's population. The time perspective is hours and days. Statistics with respect to power production and consumption are analyzed and used as input for power-balance calculations. How long periods are with low or high production, as well as the energy for charge and discharge that is needed to maintain a generally constant power production, is analyzed. One conclusion is that if the difference in production were to be completely covered with battery capacity it would be expensive, but if a large part of the difference were met by a shifting load it would be possible to cover the rest with battery storage in an economical way. To enhance the economy with battery storage, second-life batteries are proposed to reduce the capital cost in particular. Batteries are compared to hydrogen as an energy carrier. The efficiency of a battery system is higher than that of hydrogen plus fuel cells, but in general much fewer precious materials are needed with an H2/fuel-cell system than with batteries. The paper discusses how to make the energy system more robust and resilient. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide a new perspective on the conditions for the multiple dimensions of concerns and to understand why some wind power projects run smoothly, while others lead to unexpected outcomes due to local resistance. The paper considers three Danish wind farm developments with different planning difficulties, from "smooth" to intensified resistance and conflicts between local communities and the planning authority. To better understand the different planning pathways, we use a research framework where social acceptance is viewed from a conflict management theory perspective, with a focus on the uniqueness and different dimensions of conflicts. The paper discusses how the framing of the planning system considers specific types of tangible concerns, while more intangible concerns are neglected, and how this leads to escalating conflicts. The paper is concluded with several policy recommendations on how to avoid conflicts and how authorities and politicians should reflect on their own agency in provoking conflicts, which can be avoided by proactively involving affected communities at early stages. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors. [ABSTRACT FROM AUTHOR]

A new primary frequency controller in power grids undergoing massive wind power penetration is the focus of this paper. The inescapable problem in a largely wind penetrated power grid is to ensure the maintenance of its frequency in the nominal band prescribed by the power system operator (PSO). However, with the massive arrival of wind farms with conventional control schemes, the operation of maintaining and restoring the frequency to the regulatory regimes remains very complicated. In order to overcome the above problem, this paper proposes a new strategy for primary frequency control in power grids using model predictive control (MPC) for a multi-cluster doubly-fed induction generator (DFIG)-based wind farm (WF), with a main objective of reducing the frequency nadir (FN), eliminating the second frequency dip (SFD), and providing the optimal support during wind speed variations. In this approach, a rolling prediction and optimization control strategy is developed based on the dynamic power system model to ideally predict the additional power to be provided. Moreover, in order to avoid second frequency dips, the wind turbines (WTs) are not allocated to extract additional power from the grid during the frequency event, the rotor speeds are not recovered to the maximum power point tracking (MPPT) operating points during the primary frequency control. The performance of the proposed controller was evaluated using a two-zone electrical system in MATLAB/Simulink®. The obtained results disclose that the frequency nadir is enhanced with more than 6.1% compared to the conventional schemes. In addition, the frequency response settling time has been improved with more than 10.51 s. [ABSTRACT FROM AUTHOR]

In hybrid energy systems, the intermittent and fluctuating nature of new energy sources poses major challenges for the regulation and control of power systems. To mitigate these challenges, energy storage devices have gained attention for their ability to rapidly charge and discharge. Collaborating with wind power (WP), energy storage (ES) can participate in the frequency control of regional power grids. This approach has garnered extensive interest from scholars worldwide. This paper proposes a two-region load frequency control model that accounts for thermal power, hydropower, ES, and WP. To address complex, nonlinear optimization problems, the dingo optimization algorithm (DOA) is employed to quickly obtain optimal power dispatching commands under different power disturbances. The DOA algorithm's effectiveness is verified through the simulation of the two-region model. Furthermore, to further validate the proposed method's optimization effect, the DOA algorithm's optimization results are compared with those of the genetic algorithm (GA) and proportion method (PROP). Simulation results show that the optimization effect of DOA is more significant than the other methods. [ABSTRACT FROM AUTHOR]

The social acceptability of wind farms has been researched for several decades now, with the first research tracing back to the 1980s. This paper aims to deliver a literature review within the structural framework proposed by the paper of variables influencing the acceptability of wind farms. The large amount of research published on the social acceptability of wind farms requires an effort to identify and categorise variables to deliver a holistic understanding of opposition and support to wind energy. We classify the variables into three main categories: first, 'psychological variables', including perceived benefits and costs, emotions, and attitudes; second, 'contextual variables', including community energy schemes and media influence; and finally, third, 'personal resources', including income or wealth, place of residence, and relevant knowledge in relation to the wind farm. In agreement with other scholars, we argue that NIMBYism (not in my backyard) is an outdated and simplistic explanation for opposition to wind farms. Instead, we provide a theoretical framework to explain the acceptability of wind energy and show how these variables might influence both acceptance and opposition. [ABSTRACT FROM AUTHOR]

In order to change the centralized operation framework of the active distribution network with electric heating loads (EHLs), a distributed optimization method is proposed for the coordinated operation of the active distribution network with EHLs. Firstly, considering the thermal delay effect and heat loss of the thermal system, a centralized optimization operation model for active distribution networks with EHLs is established. Then, based on the centralized optimization operation model, it is rephrased as a standard sharing problem, and a distributed optimization operation model for the EHL active distribution network is established based on the alternating direction multiplier method (ADMM) solution. In the process of solving ADMM, dynamic step correction was further considered. By updating the steps during the iteration process, the number of iterations was reduced, and the convergence and computational efficiency of ADMM were improved. Finally, the effectiveness of the distributed coordinated operation method proposed in this paper was simulated and verified by constructing an IEEE33 distribution system. The results showed that the proposed distributed coordinated operation method has strong robustness to the randomness of the number of distributed units and parameters, and EHLs participating in coordinated operation can expand the consumption space of wind power and photovoltaic power, and improve the economic efficiency of system operation. [ABSTRACT FROM AUTHOR]

Integrating wind power generators, whose frequency varies in a wide range due to varying wind speeds, into a grid is a formidable problem. At present, the use of permanent magnet synchronous generators (PMSG) and doubly fed induction generators (DFIG) as wind generators with suitable control is the best possible solution. However, a dual-field excited synchronous generator (DESG), which has two windings on the rotor, can also be used for the same purpose with appropriate control. A new control strategy, which essentially employs the d-axis and q-axis components of the alternator terminal voltage, is suggested here. This strategy essentially results in exciting the two field windings with a slip frequency. This eventually holds the stator frequency constant, irrespective of the rotor speed of the wind generator. The difference between the required frequency and the natural frequency, analogous to the rotor speed of the wind power generator, is the slip frequency. The ring modulator automatically adjusts the slip frequency depending on the actual speed of the generator's rotor. This paper uses the ANSYS MAXWELL 2022 R1 software to design a DESG and uses a ring modulator as the control function generator for feedback with ANSYS TWIN BUILDER 2022 R1. Simulations are carried out using transient–transient co-simulation by combining both of these software tools for cases of both a constant-speed input and of a variable-speed input to the rotor of the machine. Moreover, a mathematical model of the DESG as a wind generator with the proposed controlled strategy is used to perform the stability analysis of a nine-bus three-machine system, and the results are compared with those of conventional wind generators. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources is becoming increasingly widespread around the world due to various factors, the most relevant of which is the high environmental friendliness of these types of energy resources. However, the large-scale involvement of green energy leads to the creation of distributed energy networks that combine several different generation methods, each of which has its own specific features, and as a result, the data collection and processing necessary to optimize the operation of such energy systems become more relevant. Development of new technologies for the more optimal use of RES is one of the main tasks of modern research in the field of energy, where an important place is assigned to the use of technologies based on artificial intelligence, allowing researchers to significantly increase the efficiency of the use of all types of RES within energy systems. This paper proposes to consider the methodology of application of modern approaches to the assessment of the amount of energy obtained from renewable energy sources based on artificial intelligence technologies, approaches used for data processing and for optimization of the control processes for operating energy systems with the integration of renewable energy sources. The relevance of the work lies in the formation of a general approach applied to the evaluation of renewable energy sources such as solar and wind energy based on the use of artificial intelligence technologies. As a verification of the approach considered by the authors, a number of models for predicting the amount of solar power generation using photovoltaic panels have been implemented, for which modern machine-learning methods have been used. As a result of testing for quality and accuracy, the best results were obtained using a hybrid forecasting model, which combines the joint use of a random forest model applied at the stage of the normalization of the input data, exponential smoothing model, and LSTM model. [ABSTRACT FROM AUTHOR]

The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively. [ABSTRACT FROM AUTHOR]

Offshore wind energy is becoming an increasingly important element in the decarbonisation of energy systems. This study aims to estimate the technical potential for offshore wind energy in the Polish exclusive economic zone (EZZ) of the Baltic Sea. Such estimates are necessary to plan the expansion of energy infrastructure. This paper proposes a multi-criteria analysis for the estimation of offshore wind potential and site selection using QGIS software (QGIS Desktop 3.28.6). The criteria include wind velocity, water depth, distance from nature conservation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, and distance from the coastline. The best sites are identified through the Analytic Hierarchy Process (AHP). The results show that the total suitable marine area of 16,218.67 square kilometres can be divided into three parts, in which the highly suitable area for offshore wind farms accounts for 17.55%, the moderately suitable area accounts for 48.56%, and the marginally suitable area accounts for 10.26%. Two different wind turbine placement configurations are being considered, in which wind turbines with a nominal capacity of 15 MW are laid out: 2.5 km and 2 km apart from each other. The corresponding overall electrical capacities only in highly suitable areas equal ca. 9 GW and 15 GW, respectively. If this area is extended to also include the moderately suitable parts, these values increase to ca. 31 GW and 50 GW. The results indicate that the technical potential of offshore wind in the Polish EEZ is significant and should be taken into account when designing the future electricity generation mix. [ABSTRACT FROM AUTHOR]

The share of wind energy in the energy mix is continuously increasing. However, a very important issue associated with its generation is the high failure rate of wind turbines. This situation particularly concerns large wind turbines, which are expensive and have a lower tolerance for system damage caused by various failures and faults. Vulnerable components include sensors, electronic control units, electrical systems, hydraulic systems, generators, gearboxes, rotor blades, and so on. As a result, significant emphasis is placed on improving the reliability, availability, and productivity of wind turbines. It is extremely important to detect and identify abnormalities as early as possible and predict potential failures and damages and the remaining useful life of components. One way to ensure turbine efficiency is to plan and implement preventive repairs. This work shows a semi-Markov model of a preventive maintenance system based on Enercon E82-2 wind turbines. The system's performance quality is evaluated based on profit over time and an asymptotic availability coefficient. The developed model establishes formulas describing the efficiency functions and formulates the conditions for the existence of extremes (maxima) of these functions. Computational examples provided at the end of the paper illustrate the obtained research results. A preventive maintenance model is developed that can be applied to wind turbine hazard prevention (determining optimal times for wind turbine preventive maintenance). [ABSTRACT FROM AUTHOR]

Faults in wind turbine rotating components contribute significantly to malfunctions and downtime. A prevalent strategy to reduce the Cost of Energy (CoE) in wind energy production focuses on minimizing maintenance expenses associated with these turbine components. An accurate Remaining Useful Life (RUL) diagnosis of these components is crucial for maintenance planning, ensuring uninterrupted energy quality and cost-efficiency. This paper introduces a refined method for RUL prediction of wind turbine rotating components using a Health Index (HI) derived from vibration signals. Performing HI construction by extracting all features from the vibration signal and selecting the best features to build HIs using on Principal Component Analysis (PCA) and some abnormal areas that deviate from the bearing damage trend can be eliminated. After constructing a HI use the similarity model and degradation models to predict RUL. Research results show that this degradation method can provide a reliable means to predict the RUL of wind turbine rotating components based on vibration signals. More importantly, predicting RUL in this way can significantly reduce operating and maintenance costs by providing wind turbine rotating operators with sufficient advance notice to plan repairs or replacements before any component failure occurs. [ABSTRACT FROM AUTHOR]

As flexible resources, cascaded hydropower stations can regulate the fluctuations caused by wind and photovoltaic power. Constructing pumped-storage units between two upstream and downstream reservoirs is an effective method to further expand the capacity of flexible resources. This method transforms cascaded hydropower stations into a cascaded pumped-hydro-energy storage system. In this paper, a flexibility reformation planning model of cascaded hydropower stations retrofitted with pumped-storage units under a hybrid system composed of thermal, wind, and photovoltaic power is established with the aim of investigating the optimal capacity of pumped-storage units. First, a generative adversarial network and a density peak clustering algorithm are utilized to generate typical scenarios to deal with the seasonal fluctuation of renewable energy generation, natural water inflow, and loads. Then, a full-scenario optimization method is proposed to optimize the operation costs of multiple scenarios considering the variable-speed operation characteristics of pumped storage and to obtain a scheme with better comprehensive economy. Meanwhile, the proposed model is retransformed into a mixed-integer linear programming problem to simplify the solution. Case studies in Sichuan province are used to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout n years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the n + 1 year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the n + 1 year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the n + 1 year is calculated deterministically from the n year. The results are compared with the measured data, and the former are more accurate. [ABSTRACT FROM AUTHOR]

The use of renewable energies contributes to the goal of mitigating climate change by 2030. One of the fastest-growing renewable energy sources in recent years is wind power. Large wind generation systems have drawbacks that can be minimized using small wind systems and DC microgrids (DC-µGs). A wind system requires a control system to function correctly in different regions of its operating range. However, real-time analysis of a physical wind system may not be feasible. An alternative to counteract this disadvantage is using real-time hardware in the loop (HIL) simulation. This article describes the implementation of an HIL platform in an NI myRIO 1900 to evaluate the performance of control algorithms in a small wind system (SWS) that serves as a distributed generator for a DC-µG. In the case of an SWS, its implementation implies nonlinear behaviors and, therefore, nonlinear equations, and this paper shows a way to do it by distributing the computational work, using a high-level description language, and achieving good accuracy and latency with a student-oriented development kit. The platform reproduces, with an integration time of 10 µs, the response of the SWS composed of a 3.5 kW turbine with a fixed blade pitch angle and no gear transmission, a permanent magnet synchronous generator (PMSG), and a three-phase full-bridge AC/DC electronic power converter. The platform accuracy was validated by comparing its results against a software simulation. The compared variables were the PMSG currents in dq directions, the turbine's angular speed, and the DC bus's voltage. These comparisons showed mean absolute errors of 0.04 A, 1.9 A, 0.7 rad/s, and 9.5 V, respectively. The platform proved useful for validating the control algorithm, exhibiting the expected results in comparison with a lab-scale prototype using the same well-known control strategy. Using a well-known control strategy provides a solid reference to validate the platform. [ABSTRACT FROM AUTHOR]

The wind abandonment phenomenon of cogeneration units in regional integrated energy systems (RIES) under the operation mode of "heat for electricity" and the improvement in the operation efficiency of the energy storage system under a low-temperature environment are problems that need to be solved urgently. To this end, a regional integrated energy system optimization scheduling method based on fine energy storage and wind power consumption is proposed in the paper. First, a fine energy storage model more adapted to a low-temperature environment is established on the power side to accurately simulate the actual working state of the energy storage components and quantify the uncertainty of the wind power output using the conditional value-at-risk (CVaR) theory. Then, a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference in the heat and power loads, it is realized to promote the system's consumption of wind power without increasing the transmission power of the contact line. Finally, the example is solved on the MATLAB platform with the objective of minimizing the total cost of the RIES optimal dispatch. The simulation results show that the proposed model is not only more adaptable to a low-temperature environment compared with the traditional model but also reduces the overall cost of the system by 2.58% while realizing the complete consumption of wind power. This innovative study provides a feasible and efficient solution to improve the performance of integrated energy systems, especially the operation capability in extreme environments. [ABSTRACT FROM AUTHOR]

The European Union's climate policy aims to reduce greenhouse gas emissions by 55% by 2030 and to achieve climate neutrality by 2050. One of the instruments for achieving these climate goals is the development of offshore wind energy. Unfortunately, Poland, as one of the few European Union countries with access to the sea, does not have offshore wind farms yet. The purpose of this article is to determine the importance of offshore wind energy for the development of Poland based on the example of two sea regions: the West Pomeranian and Pomeranian Voivodeships. This article uses the input–output method to determine the economic effects of offshore wind power. The region's share in the supply chain was determined based on the location of the offshore wind energy sector. A comparative analysis with the Saint-Brieuc offshore wind farm in France made it possible to show the differences between the studied locations. The supply chain share of the regions surveyed was 2.28% and 6.00% in the CAPEX phase and 5.98% and 8.23% in the OPEX phase. The annual average global value in the CAPEX phase at the country level was EUR 2793 million, and at the regional level, EUR 243 million and EUR 663 million. In the OPEX phase, the corresponding values are EUR 2106 million, EUR 223 million and EUR 663 million. The average annual employment in the CAPEX phase at the national level amounted to 26,323 jobs and at the regional level, 1953 and 5804. In the OPEX phase, employment amounted to 4790, 558 and 751 jobs, respectively. On the other hand, the average annual value added in the CAPEX phase at the national level was EUR 1221 million, and at the regional level, it was EUR 106 million and EUR 290 million. In the OPEX phase, it was EUR 920 million, EUR 97 million and EUR 239 million, respectively. While not all of the findings are conclusive, in general, the domestic offshore wind industry has weaker economic linkages and lower wage levels than the location adopted for comparison. It uses more labour-intensive economic sectors with lower OPEX value added. The results of the analyses presented in this paper are of crucial importance not only for Poland, as their advantage is the possibility to present, from an economic point of view, the profitability of this type of investment in general. [ABSTRACT FROM AUTHOR]

The randomness of the power supply side and the load side of comprehensive energy systems is increasingly prominent. It is very difficult to meet demand through traditional planning methods. To solve this problem, this paper explores the coordinated planning of a power system under uncertain characteristics using the multilinear Monte Carlo method. The uncertain characteristic model and probability density function of the system's power supply side and load side are established. Taking the optimal operating cost and the maximum wind power consumption as the system planning objectives, a system coordination planning scheme is established, and it is solved by multilinear Monte Carlo simulation. The superiority of this method is verified by taking the modified IEEE 39-bus test system as an example. This method can provide a reference for system planning. [ABSTRACT FROM AUTHOR]

Recently, the proportion of renewable energy (RE) sources in a power system has been increasing worldwide to reduce carbon emissions. To effectively accommodate renewable energy, there is a growing need to develop integrated planning strategies for both renewable energy sources and transmission lines in a power system, taking into account the location-specific characteristics of renewable energy. Economically viable and rapid system expansion plans are required to resolve the problem of delayed integration of renewable energy into existing power system, which arises due to inadequate transmission facilities. To address these problems, this paper presents an integrated economic evaluation method that considers the inherently uncertain output characteristics of renewable energy, contingent on location, the costs associated with installing transmission lines, and environmental benefits. The case study is conducted using a modified power system in Korea. In the case study, it is assumed that by integrating solar power generators in a low-demand area and wind power generators in a high-demand area, wind power plants have higher construction costs than solar power generators. However, the transmission costs for wind power plants are lower than those for solar power generators due to the regional characteristics of the power grid in the installation areas because renewable energy in high-demand areas typically requires less additional power infrastructure than in low-demand areas. The results of this study show that the net benefit for the wind power plant is estimated at USD 225.4 M, while the solar power plant yields a net benefit of USD 22.9 M in the example system. These results demonstrate the effectiveness of the proposed method, underscoring its potential to mitigate the challenges faced in the practical implementation of renewable energy projects. [ABSTRACT FROM AUTHOR]

This paper compares the effects of adding solar and wind power to the power system of Colombia's Caribbean region and its connections to the National Interconnected System. A base scenario was simulated in the PowerFactory 2003 software considering the actual data of the power system in 2023, and then, they were compared with the results obtained for 2033, considering the growth of the network and the addition of new power plants based on variable renewable energy. The comparison analyzes the impact of the newly added renewable energy in the power system on the voltage stability and system frequency. The results obtained show that the addition of new variable renewable energy plants generates voltage fluctuations in the lower voltage levels, ranging from 1% to 3.1%, which indicates potential challenges in maintaining the voltage stability. In higher voltages, no significant variations were found. Regarding the system frequency, the transient value tends to increase but is within the regulatory range, with variations of less than 0.2 Hz. [ABSTRACT FROM AUTHOR]

This study introduces a data augmentation technique based on generative adversarial networks (GANs) to improve the accuracy of day-ahead wind power predictions. To address the peculiarities of abrupt weather data, we propose a novel method for detecting mutation rates (MR) and local mutation rates (LMR). By analyzing historical data, we curated datasets that met specific mutation rate criteria. These transformed wind speed datasets were used as training instances, and using GAN-based methodologies, we generated a series of augmented training sets. The enriched dataset was then used to train the wind power prediction model, and the resulting prediction results were meticulously evaluated. Our empirical findings clearly demonstrate a significant improvement in the accuracy of day-ahead wind power prediction due to the proposed data augmentation approach. A comparative analysis with traditional methods showed an approximate 5% increase in monthly average prediction accuracy. This highlights the potential of leveraging mutated wind speed data and GAN-based techniques for data augmentation, leading to improved accuracy and reliability in wind power predictions. In conclusion, this paper presents a robust data augmentation method for wind power prediction, contributing to the potential enhancement of day-ahead prediction accuracy. Future research could explore additional mutation rate detection methods and strategies to further enhance GAN models, thereby amplifying the effectiveness of wind power prediction. [ABSTRACT FROM AUTHOR]

This paper presents a review of the power and torque coefficients of various wind generation systems, which involve the real characteristics of the wind turbine as a function of the generated power. The coefficients are described by mathematical functions that depend on the trip speed ratio and blade pitch angle of the wind turbines. These mathematical functions are based on polynomial, sinusoidal, and exponential equations. Once the mathematical functions have been described, an analysis of the grouped coefficients according to their function is performed with the purpose of considering the variations in the trip speed ratio for all the coefficients based on sinusoidal and exponential functions, and with the variations in the blade pitch angle. This analysis allows us to determine the different coefficients of power and torque used in wind generation systems, with the objective of developing algorithms for searching for the point of maximum power generated and for the active control of wind turbines with variations in the blade pitch angle. [ABSTRACT FROM AUTHOR]