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This paper presents a novel and effective approach for calculating Levelized Cost of Hydrogen production across the globe, without requiring timely resolved modelling or data. We select characteristic production patterns of photovoltaic and wind turbine power generation based on location qualities indicated by full load hours. These representative patterns are applied worldwide on similar locations to evaluate the proportion of electricity that electrolyzers can use if they have a smaller capacity than the renewable electricity source. Hence, the ratio of capacities is optimized to minimize hydrogen cost. The findings from various worldwide locations reveal that decreasing electrolyzer capacity is economically beneficial across all locations for boosting full load hours and curbing investments, particularly in locations with lower electricity generation. Validation using precise electricity production timeseries in specific locations revealed minimal errors in the methodology, though the quality of the results is impacted by the seasonality index differences between representative and exact location. Levelized cost of hydrogen from wind electricity for different countries depending on the ratio between wind turbine capacity and electrolysis capacity. [Display omitted] [ABSTRACT FROM AUTHOR]

This document explores the challenges and solutions related to the sustainable development of new power systems under dual carbon goals. The integration of renewable energy sources has impacted the stability of power systems, requiring measures such as grid control, optimization, and accurate forecasting of renewable energy generation. The accurate prediction of photovoltaic and wind power is crucial, and deep learning-based models have shown superior forecasting accuracy. Control and optimization strategies, such as voltage feedforward control and adaptive controllers, enhance system stability. Effective control of the power grid also has economic benefits, and planning and scheduling play a significant role in power system reliability and stability. System state management and intelligent question-answering systems contribute to efficient system operation. The advancement of artificial intelligence technology and big data analytics is crucial for enhancing power system stability and efficiency. Overall, these measures are essential for the sustainable development of power systems. [Extracted from the article]

This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8%. [ABSTRACT FROM AUTHOR]

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In this study, a comprehensive analysis is done on a dual rotor wind turbine (DRWT) in co‐rotating arrangement. First, a co‐rotating dual rotor is designed and its performance is analyzed by blade element momentum theory simulation. Later, the designed co‐rotating DRWT is manufactured and then tested in the field by mounting the wind turbine on a truck. Running the truck steadily at different speeds while logging the performance parameters of the wind turbine, the power curve of the DRWT is obtained. Then, using the power curve of the co‐rotating DRWT and the actual wind measurement data from Çatalca Istanbul, the annual electricity generation of the designed turbine is calculated. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to single rotor wind turbines (SRWTs), in accordance with the literature. Analyses show that with DRWT, around 22% higher power generation can be achieved. Additionally, the payback period for DRWT was found to be less than 1 year. The marketing strategy for introducing the product emphasizes identifying and targeting early adopters and influencers within the market. The present techno‐economic analysis states that the co‐rotating DRWTs are more efficient compared to SRWTs, in accordance with the literature. [ABSTRACT FROM AUTHOR]

Deployment of wind and solar electricity technologies is crucial for the energy transition, yet anticipated deployment rates differ widely often underestimating actual deployments. This issue is significant for countries like Aotearoa New Zealand, leading the way in major energy transitions. This study addresses two main issues: the historical underestimation of deployment rates and the need to synthesise various future scenarios to assess their potential impacts on the energy system. To address these problems, an empirical modelling approach based on logistic growth is adopted to create and analyse scenarios for the deployment of solar and wind technologies in New Zealand. We propose two scenarios: a reference growth scenario, which represents prevailing estimations for the deployment of solar and wind from previous forecasts; and a disruptive scenario, which anticipates a more rapid deployment pace. Both scenarios envision phasing out fossil fuels by 2030 but differ in the remaining electricity supply. In the disruptive scenario, the installed electricity generation broadens, in which solar and wind generation could represent 72% of the electricity generation capacity by 2050. Our findings emphasise the transformative potential of rapid wind and solar technologies deployment and highlight the importance of preparing for a disruptive future that challenges conventional expectations. [ABSTRACT FROM AUTHOR]

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio. [ABSTRACT FROM AUTHOR]

Unreliable resources in conventional power systems have created new challenges for the users of these systems. These sources of production, including wind and solar sources, with a significant share in the power generation of many power systems, cause problems in the safe and stable operation of power systems due to uncertainty. Therefore, it is very important to provide a method or model for safe and stable operation of power systems. The main innovation of this paper is to present a new SCUC model to maximize the flexibility of the system along with reducing the operating cost in the form of a security constrained multi-level robust optimization problem in the presence of wind uncertainty sources. Also, a set of demand response programs and rapid response hybrid cycle units are used as sources of flexibility. Accordingly, the tolerable range of uncertainty and system slope reservation is developed with a low-cost combination and with the commitment to flexible covering resources. The desired optimization problem has been solved with the help of the NCCG algorithm and MILP method. The efficiency and reliability of this proposed model have been simulated and evaluated in IEEE 6-bus and 118-bus standard power systems, and the simulation results confirm the technical and economic efficiency of this model. [ABSTRACT FROM AUTHOR]

Background: Renewable energies are key to reduce CO2 emissions and other environmental impacts of fossil-fueled electricity generation. However, renewable energy systems can also cause negative environmental effects. In this paper, we analyze the potential environmental trade-offs associated with different spatio-technical (de)centralization options for a renewable electricity system. For this purpose, we first review the potential environmental life cycle impacts of key technologies for renewable electricity systems. Subsequently, we develop a framework identifying which factors determine actual environmental effects of renewable electricity systems. We apply the framework to four basic spatio-technical (de)centralization options for the future Germany electricity system. Results: Our analysis shows that all (de)centralization options are associated with potential environmental trade-offs. We find that the (de)centralization of the system is a relevant factor determining these trade-offs. For instance, the two more centralized options considered have lower environmental impacts related to PV, whereas the two more decentralized options have lower environmental impacts related to grid infrastructure. However, we also find that the trade-offs depend on the specific way (de)centralization is pursued. For instance, only in one of the two considered more decentralized development options, there is a potential environmental trade-off between higher impacts related to battery storage and lower impacts related to offshore wind power. Conclusions: Our analysis reveals that the spatio-technical (de)centralization of a renewable electricity system plays a role for its environmental trade-offs while further factors like the institutional and stakeholder management in place also shape the environmental trade-offs. Policy makers should acknowledge the identified potential environmental trade-offs and their influencing factors when making policies favoring certain spatio-technical (de)centralization options. Highlights: Review of potential environmental impacts of key renewable technologies Framework on determinants of actual environmental effects of electricity systems Review of electricity system (de)centralization scenarios for Germany Application of framework to four spatio-technical (de)centralization options Environmental trade-off analysis for the four (de)centralization options [ABSTRACT FROM AUTHOR]

Forecasting future wind electricity generation requires diverse methods and tools to estimate potential output at specific locations. This study utilizes historical meteorological data and simple models at particular sites, along with past electricity production records from selected wind farms. The interannual and decadal oscillations of wind energy production at the grid level are computed. The large‐scale, long‐term energy storage needed to achieve dispatchable electricity, addressing generation variability is assessed. For the continental United States, the estimated storage requirement is approximately 1300 GWh per GW of installed capacity. The inclusion of solar power generation and the round‐trip efficiency of energy storage positively or negatively impact this estimation. [ABSTRACT FROM AUTHOR]

Previous research has indicated that wind power plants can be designed to have less‐variable power generation, thereby mitigating the drop in economic value that typically occurs at high wind power penetration rates. This study investigates the competitiveness of adapted turbine design and the interplay with other flexibility measures, such as batteries and hydrogen storage, for managing variations. The analysis covers seven turbine designs for onshore and offshore wind generation, with different specific power ratings and hub heights. Various flexibility measures (batteries, hydrogen storage and transmission expansion) are included in the optimization of investment and dispatch of the electricity system of northern Europe. Three driving forces for turbine design selection are identified: (1) lowest cost of electricity generation; (2) annual wind production per land area and (3) improved generation profile of wind power. The results show that in regions with good wind resources and limited availability of variation management, it is cost‐efficient to reduce the variability of wind power production by adapting the turbine design. This remains the case when variation management is available in the form of batteries, hydrogen storage and transmission system expansion. Moreover, it is more cost‐effective to improve variability by changing the specific power rating rather than the turbine hub height. [ABSTRACT FROM AUTHOR]

The modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind-based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30-year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects. [ABSTRACT FROM AUTHOR]

The proposed study analyzes the efficiency of the European energy mix of electricity generation technologies from two perspectives: environmental and economic. The context is that of European energy dependence and a technology mix conditioned by the import of fossil fuels. The impact is centered, among other elements, on the leakage of national income and the emission of polluting gases. The aim is to determine the participation that each type of power plant in each country should present in order to minimize the total environmental impact. In order to solve this problem, a double optimization is proposed through the use of two methodologies: one based on a multi-criteria decision-making method (MCDM) model with which to evaluate the environmental performance of each power plant, and a second optimization based on a quadratic model of portfolios modern portfolio theory (MPT), with which to evaluate the efficiency of the portfolio of technologies from the cost/risk binomial. The results confirm that an environmentally efficient portfolio leads to higher levels of economic risk-taking, with a slight increase in the level of assumed cost. Nevertheless, from the results obtained, it is possible to say that hydro (with a share between 11 and 13%) and wind (37–44% mix participation) technologies resulted to be preferred options both environmentally and in terms of minimum risk efficiency. Nuclear power generation stands out as one of the main baseload technologies with shares between 25 and 35% in environmental and cost/risk efficiency. As main findings, Hydro, supercritical lignite, solar PV and wind are identified as preferential technologies to be present in both minimum risk and minimum cost efficient portfolios. In case European Union pursues for minimizing the cost of electricity production, the shares of supercritical lignite, nuclear, solar PV and wind energy go up to the maximum allowed limits. The novelty lies in the application of both methodologies with which to complement the analysis and design efficient portfolios of energy technologies from environmental and economic points of view. One possible future approach would be to assess (with the MPT model) new environmentally optimal portfolios obtained through the application of other MCDM techniques. [ABSTRACT FROM AUTHOR]

Recent reports indicate that the development of electricity generation using wind turbines will continue to grow. Despite the long service life of wind turbine blades, their technological life comes to an end at a certain point. Currently, there is no industrial method for recycling them, and the proposed solutions need to consider a complete and comprehensive approach to this material. In many countries, these blades are stored in special landfills and await proposals for rational recycling. It has been proposed that this recyclable yet still troublesome raw material be used in building sheathing boards. Sheathing boards used in the construction industry have a relatively long lifecycle. Three types of polymer chips and two resins, i.e., PF and MUF, were used in the study. The boards' quality was assessed per the standards specified for particle boards. The resulting boards were characterized by strengths above 20 N/mm2 and an elastic modulus close to 4000 N/mm2. Slightly better results were obtained with the MUF resin. [ABSTRACT FROM AUTHOR]

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 Mt CO 2 , eq /year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P 75 and P 90 are 10.08% and 19.22%, respectively, lower than the energy value in P 50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas. [ABSTRACT FROM AUTHOR]

The world depends heavily on electrical energy for accessing technologies. For the generation of electricity, technology can utilize renewable energy sources like solar energy and wind energy. Solar photovoltaic (PV) systems occupy space among consumers due to their feasibility, flexibility, cost, and simple implementation procedures. The solar PV system experiences many factors causing power loss like partial shading, hotspots, and diode failure. In this work, a new static PV array configuration, named Renzoku puzzle patternbased array configuration, is proposed. This proposed configuration technique was designed to overcome the drawbacks of the previously proposed array configurations in terms of power generation, fewer mismatch losses, a high shade-dispersion rate, and consistent performance under any level of partial shading. The proposed array configuration has been validated using both simulation and hardware. The simulation is carried out in a 9 x 9 PV array in MATLAB/Simulink®. The performance analysis, results, and corresponding characteristic curves are presented in this manuscript. [ABSTRACT FROM AUTHOR]

Summary: Due to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively. [ABSTRACT FROM AUTHOR]

With the increasing scarcity of global energy, the rapid development of science and technology, and the growing demand for environmental protection, wind energy is receiving increasing attention as the cleanest source of energy. Due to its pollution-free nature and widespread availability, it has become a preferred source of electricity generation in many countries. However, wind speed prediction plays a vital role in wind power generation. Traditional prediction models, due to randomness and uncertainty, often produce unstable and inaccurate results, leading to power and economic losses. Therefore, this study proposes a hybrid prediction system based on an information processing strategy and a multi-objective optimization algorithm. By preprocessing the data and optimizing the combination of five individual models, the singularity of a single model is overcome, a Pareto-optimal solution is obtained, and accurate and stable prediction results are provided. To verify the effectiveness of the proposed combined model in predicting wind speed, various experiments on a wind speed series were conducted based on a wind power station located in Penglai, China. The results show that the combined model proposed in this study has better prediction performance than conventional models. [ABSTRACT FROM AUTHOR]

As electricity systems become increasingly dominated by variable inverter-based generation (such as wind and solar photovoltaics (PV)), additional sources of variability appear, while the share of dispatchable thermal power plants decreases. Maintaining a stable grid frequency requires new sources of inertia to slow the frequency changes, as well as new sources of reserve power to counter the imbalances. This work uses a linear optimization modeling approach to analyze the reserve market of the electricity system during the transition to a carbon-free system with high shares of variable electricity generation. The modeling is performed for three regional contexts and with varying degrees of available flexibility technologies. As for the reserve supply, batteries, electrolyzers, electric boilers and heat pumps for district heating, curtailed wind and solar power, and hydropower and thermal power plants are all included. The results indicate that of these suppliers of reserves, the introduction of grid-scale batteries into the system drastically reduces the reserve market size. Only early in the transition is revenue from the reserve market greater than 5% of the total revenue for any technology. While the demand for reserves increases as the share of solar PV and wind power increases, the modeling reveals that access to flexible loads, storage units, and emulated inertia from wind power also increases. Depending on the choice of flexibility measures available, demand-side participation could play a major role in minimizing the cost of grid stability in the future. [ABSTRACT FROM AUTHOR]

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model. [ABSTRACT FROM AUTHOR]

To achieve sustainable development, the energy transition from lignite burning to renewable energy resources for electric power generation is essential for Greece. Wind and solar energy have emerged as significant sources in this transition. Surprisingly, numerous studies have examined the potential for onshore wind based on land eligibility, while few studies on open-field photovoltaic (PV) installations have been conducted. Therefore, based on the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD-RES), along with insights from previous relevant studies, this work conducts a land eligibility analysis of onshore wind and open-field PV installations in Greece using the software Geospatial Land Availability for Energy Systems (GLAES 1.2.1) and ArcGIS 10.2. Additionally, through an in-depth exploration of wind and solar PV energy potential in decommissioned lignite mines integrated with wind power density (WPD) and global horizontal irradiation (GHI) maps, this study compares the suitability of wind versus solar as energy sources for the decarbonization of Greece. Overall, despite the greater spatial eligibility for onshore wind turbines compared to open-field PV power plants, the relatively lower wind energy potential and operational limitations of wind turbines lead to the study's conclusion that solar energy (PV) is more suitable for the decarbonization of Greece. [ABSTRACT FROM AUTHOR]

With their constant increase in size, wind turbines are reaching unprecedented heights. Therefore, at these heights, they are influenced by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions becomes more and more likely. In this paper, the presence of the turbulent–non-turbulent interface (TNTI) in the atmosphere is investigated. Three different on- and offshore locations are investigated. Our fractal scaling analysis leads to typical values known from ideal laboratory and numerical studies. The height distribution of the probability of the TNTI is determined and shows a frequent occurrence at the height of the rotor of future multi-megawatt turbines. The indicated universality of the fractality of the TNTI allows the use of simplified models in laboratory and numerical investigations. [ABSTRACT FROM AUTHOR]

Over the past few years, the offshore wind sector has been subject to renewed yet growing interest from the industry and from the research sphere, with a particular focus on a recently developed concept, the floating offshore wind (FOW). Because of its novelty, floating research material is found in limited quantity. This paper focuses on the layout optimization of a floating offshore wind farm (FOWF) considering multiple parameters and engineering constraints, combining floating-specific parameters together with economic indicators. Today's common wind farm layout optimization codes do not take into account either floating-specific technical parameters (anchors, mooring lines, inter-array cables (IACs), etc.) or non-technical parameters (operational expenditure, OPEX; capital expenditure, CAPEX; and other techno-economic project parameters). In this paper, a multi-parametric objective function is used in the optimization of the layout of a FOWF, combining the annual energy production (AEP) together with the costs that depend on the layout. The mooring system and the collection system including the inter-array cables and the offshore substation are identified as layout-dependent and therefore modeled in the optimization loop. Using ScotWind site 10 as a study case, it was found with the predefined technical and economic assumptions that the profit was increased by EUR 34.5 million compared to a grid-based layout. The main drivers were identified to be the AEP, followed by the anchors and the availability associated with the failures of inter-array cables. [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]

It is noted that the traditional direct filed-oriented control (DFOC) is widely used in the field of electric power generation from wind due to its fast response dynamic, ease of implementation and simplicity, but this strategy is characterized by the presence of large ripples at the level of both active and reactive powers. This work presents a new algorithm for DFOC strategy of an asynchronous generator (AG) in a wind power (WP) system, which is based on the use of a new nonlinear controller called fractional-order synergetic control–fractional-order proportional-integral (FOSC–FOPI) controller, where the proposed technique parameters are calculated using the particle swarm optimization (PSO) strategy. It has been observed that the DFOC–FOSC–FOPI–PSO strategy is robust and works well in case of changing generator parameters. Three tests were performed to study the behavior of the designed technique under different working conditions, where the behavior of the DFOC–FOSC–FOPI–PSO strategy was compared with the behavior of the traditional DFOC technique in terms of power ripple ratio, overshoot, steady-state error, response time, tracking reference, and current quality. The simulation of the designed technique based on the FOSC–FOPI–PSO strategy of the AG–WP system is carried out using Matlab software, where the simulation results showed that the suggested technique is better than the classical technique (with PI controller) in terms of improving response time of active power (33.33%) and reactive power (10%) in second test, reduction of the steady-state error of reactive power (96.95%) and active power (97.14) in first test, minimization of harmonic distortion of current (96.57%) in third test and significant minimization of ripples of active power (99.67%, 44.69%, and 98.95%) and reactive power (99.25%, 53.65%, and 70.50%) in the three tests. The effectiveness of the DFOC–FOSC–FOPI–PSO strategy is very high, so it can be a reliable solution for controlling various generators. [ABSTRACT FROM AUTHOR]

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

The power equations of crosswind Ground-Gen and Fly-Gen airborne wind energy systems (AWESs) flying in circular trajectories are refined to include the contribution from the aerodynamic wake, modeled with vortex methods. This reveals the effect of changing the turning radius, the wing geometry and the aerodynamic coefficients on aerodynamic performances and power production. A novel power coefficient is defined by normalizing the aerodynamic power with the wind power passing through a disk with a radius equal to the AWES wingspan, enabling the comparison of different designs for a given wingspan. The aspect ratio which maximizes this power coefficient is finite, and its analytical expression for an infinite turning radius is derived. By considering the optimal wing aspect ratio, the maximum power coefficient is found, and its analytical expression for an infinite turning radius is derived. Ground-Gen and Fly-Gen AWESs, with the same idealized characteristics, are compared in terms of power production, and later three AWESs from the literature are analyzed. With this modeling framework, Ground-Gen systems are found to have a lower power potential than Fly-Gen AWESs with the same geometry because the reel-out velocity makes Ground-Gen AWESs fly closer to their own wake. [ABSTRACT FROM AUTHOR]

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems' safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are vital for environmental sustainability. With the depletion and damage of fossil fuels to nature, energy production from clean and inexhaustible RES has become widespread. Wind energy, one of the RES, is a clean energy source that does not emit any harmful waste to the environment. Wind energy is a low-cost energy source that is mostly used for electricity generation. Criteria such as wind speed, turbine structure and the characteristics of the areas where the wind turbines will be located are effective on the amount of energy to be produced. In this study, a comprehensive review of the studies using MCDM methods related to wind energy is made. In the manner of the statistical data obtained from the 97 studies examined, it has been observed that the wind energy investments and the scientific publications made in these countries do not progress linearly with each other. The fact that countries have different wind energy potentials and the difference in the countries' interest in RES is thought to be effective in this regard. While there are articles in the literature in which studies on RES are discussed together with MCDM methods, there is no comprehensive review study in which wind energy and MCDM methods are discussed together. According to our best knowledge, this is the first study to comprehensively evaluate wind energy studies in terms of MCDM methods. With this study, a framework has been presented for subsequent studies on the application of MCDM methods in wind energy. [ABSTRACT FROM AUTHOR]

The paper presents a solution methodology for a dynamic electricity generation scheduling model to meet hourly load demand by combining power from large-wind farms, solar power using photovoltaic (PV) systems, and thermal generating units. Renewable energy sources reduce the coal consumption and hence reduce the pollutants' emissions. Because of uncertain wind velocity and solar radiation, the share of solar and wind units is restricted to certain limits of demand, considering ramping requirements. The solar and wind units start quickly, while the thermal units start slowly. The commitment to renewable energy units is made through a proposed optimistic optimizer to restrict the power generation share. Economic and environmental objectives of thermal units are unified using a price penalty factor, and the hybrid sine–cosine algorithm (HSCA) optimization method solves the problem. A forward approach is applied to handle the dynamic electricity generation scheduling optimization problem. HSCA incorporates local mutation and hill-climbing heuristics to reduce global operating costs and emission pollutants' while allocating generation to the devoted units. To handle the constraints, replacement and heuristic techniques are applied. The proposed method is verified on a system having solar PV units and wind power plants connected to 6, 13, and 40 thermal generating units, respectively. [ABSTRACT FROM AUTHOR]

The aim of this research is to analyse and provide realistic insight that will improve the provisions and investment in wind energy as potential source of energy for electricity generation. The data used for this assessment were obtained from the central database of Nigeria Meteorological Agency Abuja, which includes average wind speed, and direction per day of the selected location, measured at the anemometer height of 10 m, over a period of eleven years (2011-2021). The maximum monthly mean wind speed was found in January and December with a value of 4.9 m/s, and annual extractable energy was found to be 347.97KWh/M2. On average, the annual wind speed through eleven years was found in the range 3.88 - 4.00 m/s. The results revealed that, the wind resource in the study area fall within range and category 3.00 which is considered as marginal or unsuitable for higher wind power development and an excellent for water pumping and small scale electricity generation. [ABSTRACT FROM AUTHOR]

The restructuring of the energy industry is imperative, as New Zealand strives to reduce greenhouse gas emissions. New Zealand has abundant renewable energy resources, and about 85% of current electricity generation is from renewable energy sources. However, in recent years, it appears that a considerable fraction of wind energy has been underutilized. This article reviews the history, current status, and future trends of wind energy development in New Zealand. The main challenges to the current development of wind energy are summarized compared to other countries. The main challenges come from the bi-cultural influence, environmental influence, and economic and social influence due to the variable nature of wind power, it is critical to store and operate power safely and reliably during peak power generation periods. This article compares seven mainstream wind energy storage technologies and analyzes the best solution for wind energy storage in New Zealand. This article analyzes the feasibility of using small-scale household (standard power rating range from 0.004 to 16 kW) wind turbines in New Zealand cities regarding their construction and operation process. The life cycle and the maximum capacity coefficient of such small-scale wind turbines are overviewed via three case studies and later compared with large commercial wind turbines (standard power rating ranges from 1 to 3 MW) in power generation capacity. It has been found that small-scale household wind turbines have notable power generation potential and economic benefits in the long term. [ABSTRACT FROM AUTHOR]

Background: The transition towards renewable energy sources has become an imperative step to mitigate climate change, reduce carbon emissions and improve energy security and economic prosperity in a sustainable manner. Maximizing the cost effectiveness of electric power generation is crucial to making renewable energy sources viable and attractive options for clean energy production. The strategic allocation of wind, hydro and solar power systems is essential to achieving this goal. This paper attempts to demonstrate how the cost effectiveness of electrical power system could be maximized through the integration of wind, solar and hydropower systems and comparison at different penetration levels of 0, 25, 50, 75 and 100% on cost effectiveness of electric power generation. The different generator technologies were designed based on their electrical output attributions. Results: The cost of electric generation for the integration of each generator at the various buses were calculated at different penetration level for fair comparison. The results indicate that the minimum money loss for the integration of solar power was $743.90 at bus 4 and at 50% penetration level, the minimum money loss for the integration of wind power was $999.00 at bus 4 and at 25% penetration level while the minimum amount loss for the integration of hydropower was $546.50 at bus 4 and at 75% penetration level. Conclusions: The magnitude to which the integration of the different generator affects the cost effectiveness of power production hinges on the type of generator, the penetration level and the location of the generator in the grid. [ABSTRACT FROM AUTHOR]

The Dutch climate agreement anticipates the large-scale implementation of solar and wind energy systems on land and water. Combining solar and wind farms has the benefit of multiple surface area use, and it also has the advantage of energy generation from both solar and wind energy systems, which is rather complementary in time; thus, a better balance can be found between electricity generation and demand and the load on the electricity grid. In combined solar and wind farms (CSWFs), the turbines will cast shadows on the solar panels. This concerns the static shadow from the construction tower of the turbine as well as the dynamic shadow caused by the rotating blades. This paper reports on the results of millisecond data monitoring of the PV farm of a CSWF in the Netherlands on land. Static and dynamic shadow effects are discussed, as well as their dependency on farm design. It is observed that the dynamic shade of the wind turbine blade causes serious disturbances of the DC inputs of the inverter, resulting in deviation of the maximum power point tracking monitored. The shadow of the wind turbine results in a total energy loss of about 6% for the given period, park configuration, PV modules, inverter type, and setting. [ABSTRACT FROM AUTHOR]

One of the main factors for changes in the structure of the energy mix in Poland is the development of renewable energy sources, in particular wind generation. In 2009–2020, the installed capacity of wind sources in Poland increased more than ninefold. At the same time, new legislation significantly curbed the development of onshore wind farms. Further development of wind energy in Poland will rely largely on offshore wind farms. The current state of development of wind power in Poland allows for analyses of the onshore part of wind energy development in Poland. The paper aims to conduct a detailed analysis of the Polish wind sector from an electric power generation perspective. This article presents a comprehensive discussion of the development of onshore wind generation in Poland. In particular, analyses address the production of electric power from wind. Various time horizons are taken into account, as well as the correlation of wind generation with demand for power in the Polish Power System (PPS). The results of the analysis indicate a high variability of wind generation throughout the month or year. The largest wind generation occurred during the night valley, which makes it difficult to operate the power system. In the winter months, wind generation is much greater than in the summer months. Monthly average values of the capacity factor for onshore wind farms (WFs) vary from 0.14 in August to 0.48 in February. Moreover, the coefficient of determination R2 close to zero shows a lack of correlation between offshore wind power generation and real power demand in the PPS. The studied high variability of wind generation in PPS can be mitigated by the wide use of electricity storage systems. Moreover, the obtained results can be part of a model to describe the energy mix in Poland. [ABSTRACT FROM AUTHOR]