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Accurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short‐term forecasting, such as 60‐min predictions. This article introduces a hybrid data‐driven framework that employs an ensemble deep learning model to provide highly precise short‐term wind power predictions. The framework leverages a data‐driven approach to identify the intrinsic components of wind power data, including high‐frequency and low‐frequency components. A convolutional layer‐based feature fusion network is then established to properly extract important information from irrelevant wind energy features. Subsequently, an ensemble of long short‐term memory (LSTM) networks is developed to forecast wind power using the fused features, thereby mitigating the disadvantage of a single prediction model. The numerical experiment is carried out based on two different real‐life datasets. The results demonstrate the effectiveness of the proposed method in forecasting short‐term wind power compared to five benchmarks. [ABSTRACT FROM AUTHOR]

A novel Wasserstein generative adversarial network (WGAN) is proposed for stochastic wind power output scenario generation. Wasserstein distance with gradient penalty adapts to the gradient vanishing problem that is easy to occur in the new energy generation scenario. This model has better robustness and generalization ability than the traditional generative adversarial network. WGAN is optimized to simulate ideal wind power scenarios. The generated data are measured by cumulative distribution function (CDF) and continuously ranked probability score to evaluate the performance of the proposed model. Compared with the probability models, the proposed model is data‐driven, that is, it can simulate wind power scenarios based on historical samples rather than probability hypothesis, and it can independently learn the space‐time correlation of wind power generation in different locations. Experiments show that the CDF curve of data generated by the proposed WGAN is highly coincident with that of real data. [ABSTRACT FROM AUTHOR]

This work investigates the optimal design and operation of an alkaline water electrolysis plant fed by renewable electricity. At the basis of this investigation lies a mathematical model that is developed to capture essential mechanisms regarding gas purity and temperature constraints. This model is optimized for maximized hydrogen production given varying power inputs and design variations. Resulting operational profiles are then weighted with realistic wind power data, allowing for a novel framework in which mechanistic models of different scales are combined to predict annual hydrogen production and online-factors. The highest online-factors are achieved with independent power supplies, which, for degraded plants, results in the largest annual production increase of all design variations (up to 9%). Designs facilitating flexible pressure operation show the largest improvement potential for novel plants, increasing the production rate by up to 4.5%. [Display omitted] • Development of modeling and optimization framework for alkaline electrolysis plant. • Investigation of BoP design on load range and utilization of wind power. • HTO related load limit reduced by flexible pressure or individual power supplies. • Flexible pressure operation increases production most (4.5%) for novel plant. • Individual power supplies increase production most (9%) for degraded plant. [ABSTRACT FROM AUTHOR]

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction. [ABSTRACT FROM AUTHOR]

Maximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a 5×5$$ 5\times 5 $$ wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks. [ABSTRACT FROM AUTHOR]

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

This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method's primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods. [ABSTRACT FROM AUTHOR]

Ethiopia possesses abundant wind resources that have the potential to revolutionize its energy sector by providing reliable and sustainable electricity through wind power. Despite the presence of a few operational wind farms, the country is facing challenges in generating sustainable electricity. The slow progress in wind power development raises concerns, as the impact of declining power generation from existing wind farms on economic development and return on investment remains insufficiently examined. The research paper aims to examine the status, challenges, and opportunities in developing, deploying, and sustaining wind power generation. This was accomplished through qualitative and quantitative analysis using 11 years of power generation data from operational wind farms and field research. Data sources include Ethiopian Electric Power, wind power operation reports, official government reports, field observations and discussions with wind power operators. The study reveals that challenges such as intermittent wind resources, high initial investment costs, inadequate transmission infrastructure, limited skilled workforce, and environmental concerns hinder the realization of wind energy's full potential. The article provides evidence-based recommendations for policymakers and the wider stakeholders to address the challenges and maximize benefits of wind energy in Ethiopia. This contributes to understanding the current situation and necessary actions to harness the country's abundant wind power potential. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation. [ABSTRACT FROM AUTHOR]

The rapid development of the wind industry is accompanied by increasing environmental impacts. Currently, there is a lack of research on the impacts of offshore wind farm (OWF) on tropical cyclone (TC) intensity, including the mechanisms involved. This research is carried out by using a coupled and an uncoupled numerical model to investigate the impact of OWF on an autumn TC in the northeastern South China Sea. The results show that the wind speed deficit caused by OWF leads to an increase in surface pressure on the inflow side. This causes the surface pressure in the TC periphery to increase by advection, even if the TC is some distance away from the OWF. The increase in pressure gradient from the periphery to the TC center enhances the TC secondary circulation, thereby intensifying the TC. When the TC enters the OWF, the above mechanisms weaken and the ocean dominates the TC intensification. This is because the reduction in wind speed caused by the OWF results in a weaker sea surface current velocity, which weakens the flow of upstream cold water into the OWF, warming the sea surface temperature (SST) within the OWF. This implies that the horizontal gradient of the local SST is an important factor to be considered in the development of OWF. Sensitivity experiments indicate that OWF can also intensify other types of TC, and that higher cut‐out wind speeds lead to stronger intensification effects. These results also provide a new perspective on TC intensity forecasts. Plain Language Summary: The rapid development of the offshore wind industry has significant impacts on the surrounding environment. There is limited research on the impact of offshore wind farms on the tropical cyclone (TC) intensity. This study indicates that wind farms in the northern South China Sea intensify an autumn TC that makes landfall in China. Wind farms intensify the TC by increasing the surface pressure in the TC periphery when the TC is far from the wind farm. When the TC is close to the wind farm, it is the warming sea water within the wind farm that intensifies the TC. Tropical cyclone can also be slightly intensified when the number of turbines is small. These results help to understand the impact of offshore wind farms on TCs and to improve TC intensity forecasts. Key Points: The tropical cyclone (TC) intensifies before approaching offshore wind farms due to increased peripheral pressure caused by wind farmsThis intensification within wind farms is mainly due to the reduced transport of cold water upstream associated with the wind speed deficitHigher cut‐out wind speeds can lead to a more pronounced intensification of the TC before it enters wind farms [ABSTRACT FROM AUTHOR]

This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs. [ABSTRACT FROM AUTHOR]

The cascaded H‐bridge multi‐level converter is the main topology of high‐voltage and high‐capacity converter in renewable energy power generation, energy storage system and reactive power compensation equipment. The parameter design of cascaded H‐bridge multi‐level converters directly determines the material cost and performance. Taking the output current distortion rate and the ripple voltage of the capacitor of the cascaded H‐bridge multi‐level converter as the constraint conditions, this paper proposes an overall parameter optimal design method of the cascaded H‐bridge multi‐level converter based on the optimization of the total material cost. Finally, the effectiveness of the proposed parameter design method is verified by the simulation result. [ABSTRACT FROM AUTHOR]

This paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants' output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method. [ABSTRACT FROM AUTHOR]

Bringing floating offshore wind turbines (FOWTs) to a real industrial maturity and reducing the levelized cost of floating wind energy are key to significantly increasing the penetration of renewables in the energy mix of Mediterranean countries, especially if in combination with suitable energy storage systems, such as those involving green hydrogen production. The present study analyses techno‐economic aspects of some of the technologies related to FOWTs and hydrogen production by means of offshore‐generated energy, aiming to evaluate the potential of a floating wind farm integrated with a power‐to‐gas energy storage system in a specific installation site near the Sardinian shores. In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. Proposed results obtained with the statistic and the time‐dependent approach show values ranging between 3.79 and 5.47€/kg, respectively. These outcomes are thought to provide an interesting comparison between different fidelity approaches and realistic reference values for the levelized cost of hydrogen by floating wind in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%. [ABSTRACT FROM AUTHOR]

This article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts. [ABSTRACT FROM AUTHOR]

This study explores the optimal balance between grid‐forming (GFM) and grid‐following (GFL) converter capacities within power stations to ensure stable operations. The investigation introduces a novel, generic modelling approach for analysing multiple converter systems in the wind and photovoltaic power plants. The method aims to elucidate the dynamic characteristics of the converters in power plants, particularly focusing on the continuity and existence of the equilibrium manifolds and their impact on system stability. Findings reveal a pronounced difference in the recovery capabilities of GFM and GFL following synchronization losses, highlighting an asymmetry in their abilities. Specifically, GFL converters exhibit more effectiveness in reinstating synchrony after synchronization losses caused by GFM. Conversely, GFM demonstrates a lesser capacity to recover from synchronization losses induced by GFL. Furthermore, analysis indicates that when the capacity ratio of GFL to the system's short‐circuit capacity significantly exceeds that of GFM (exceeding a 1:5 ratio), the system experiences an absence of a stable equilibrium point, thereby affecting the synchronization stability of GFM. These conclusions have been validated through joint controller hardware‐in‐the‐loop testing. [ABSTRACT FROM AUTHOR]

As inverter‐based resources like wind turbines increase, grid inertia and stability decrease. Optimal placement and control of energy storage systems can stablise low‐inertia grids. This paper investigates how optimal battery energy storage systems (BESS) enhance stability in low‐inertia grids after sudden generation loss. The sitting, sizing and control of BESS are determined simultaneously in each genetic algorithm (GA) population, then voltage and frequency stability is evaluated based on the network simulation. This continues until the optimal solution is found. A network based on Kundur's four‐machine system is modelled for the first study and two of the four synchronous generators (SGs) have been replaced with wind farms. Then, the production of the third SG has been decreased by 13%. According to the results, addition of wind farms causes the frequency drop below 49.6 Hz for more than 5 min, indicating instability. It is also demonstrated that with optimal control parameters and placement, a 60 MW BESS can alleviate the voltage and frequency fluctuations, leading to enhanced stability. This method has also been tested on the IEEE 39‐bus network, where the installation of a BESS with a capacity of 9 MVA could restore the frequency stability. [ABSTRACT FROM AUTHOR]

The increase in wind power‐based microgrids emphasizes the importance of addressing stability challenges during low‐voltage ride‐through (LVRT) events in weak AC grid‐connected doubly fed induction generator systems. Compliance with grid standards, notably LVRT capabilities, is critical as wind power plants integrate increasingly into power systems, raising concerns about generation loss and post‐fault oscillations in microgrids. Previously, researchers have utilized techniques like fuzzy logic, ant colony, and genetic algorithms for static synchronous compensator (STATCOM) tuning to enhance microgrid stability during fault scenarios. This study uses the grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and a novel hybrid GOA‐PSO. On the main grid, the power system is subject to both symmetrical and asymmetrical faults. The proposed novel technique aims to improve LVRT, minimize generation loss during faults, and reduce after‐fault oscillations by optimizing reactive power flow between the point of common coupling and the microgrid while adhering to the LVRT grid code. MATLAB/Simulink is utilized to evaluate the LVRT performance of a 16 MW DFIG‐based microgrid operating in grid‐connected mode. The performance of the GOA‐PSO‐tuned STATCOM is evaluated by comparing it with conventional, PSO, and GOA‐tuned STATCOM in three fault scenarios. The comparison shows that GOA‐PSO‐tuned STATCOM improves grid stability and reliability. [ABSTRACT FROM AUTHOR]

Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP. [ABSTRACT FROM AUTHOR]

Simulating wind turbine blade icing quickly is important for wind farms to issue early warnings and effectively deal with the adverse effects of cold weather. However, current numerical simulation methods suffer from high computational costs and lack straightforward acceleration techniques for practical ice prediction. Here, we developed a fast and simple blade icing simulation method via an integrated physical simulation and statistical analysis method. This method consists of two steps: firstly, numerical simulation with CFD, and secondly, table look-up calculations. Over 10,000 sets of wind turbine blade icing simulations based on FENSAP-ICE and an NACA64-A17 wing were conducted to develop this method and analyze the influences of environmental factors on blade icing. The results show that ice thickness generally increases with an increase in wind speed, a decrease in temperature, and an increase in liquid water content (LWC), but there is a nonlinear relationship between them. For example, ice thickness has a linear relationship with the LWC within a certain range but hardly changes with a LWC beyond that range. The validation results show that the fast simulation method established in this paper has good consistency with the original numerical simulation method. It can greatly improve the computational efficiency of icing simulations while retaining the accuracy of numerical simulations. It takes less than 1 s to complete over 1000 sets of icing simulations, which offers potential for the fast prediction of wind turbine blade icing in the future. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of scheduling optimization in regional integrated energy systems, characterized by the coupling of multiple physical energy streams (electricity, heat, and cooling) and the participation of various stakeholders. To tackle this, a novel multi-load and multi-type integrated demand response model is proposed, which fully accounts for the heterogeneous characteristics of energy demands in different campus environments. A leader–follower two-layer game equilibrium model is introduced, where the system operator acts as the leader, and campus load aggregators, energy storage plants, and wind farm operators serve as followers. The layer employs an enhanced particle swarm optimization (PSO) algorithm to iteratively adjust energy sales prices and response compensation unit prices, influencing the user response plan through the demand response model. In the lower layer, the charging and discharging schedules of energy storage plants, wind farm energy supply, and outputs of energy conversion devices are optimized to guide system operation. The novelty of this approach lies in the integration of a game-theoretic framework with advanced optimization techniques to balance the interests of all participants and enhance system coordination. A case study is conducted to evaluate the effectiveness of the proposed strategy, demonstrating significant economic benefits. The results show that the model encourages stakeholders to invest in energy infrastructure and actively participate in coordinated dispatch, leading to improved overall system efficiency and comprehensive revenue enhancement for the multi-agent energy system. [ABSTRACT FROM AUTHOR]

Renewable electricity sources are now widely used worldwide. Currently, the most common sources are those that use energy contained in biomass, water, sun, and wind. When connected to a medium-voltage grid, individual wind power plants must meet specific conditions to maintain electricity quality. This article presents field study results on the impact of switching operations (turning the power plant on and off) at a 2 MW Vestas V90 wind turbine on the voltage parameters at the connection point of a farm located 450 m from the source. The analysis showed that the wind turbine under study significantly affects customers' voltage near the source, causing it to increase by approximately 2.5%. Sudden cessation of generation during the afternoon peak causes a 3% voltage fluctuation, potentially affecting equipment sensitive to rapid voltage changes. [ABSTRACT FROM AUTHOR]

In this study, we investigate a yaw control strategy in a two-turbine wind farm with 3.5 MW turbines, aiming to optimize power management. The wind farm is equipped with a nacelle-mounted multi-plane LiDAR system for wind speed measurements. Using an analytical model and integrating LiDAR and SCADA data, we estimate wake effects and power output. Our results show a 2% power gain achieved through optimal yaw control over a year-long assessment. The wind predominantly blows from the southwest, perpendicular to the turbine alignment. The optimal yaw and power gain depend on wind conditions, with higher turbulence intensity and wind speed leading to reduced gains. The power gain follows a bell curve across the range of wind inflow angles, peaking at 1.7% with a corresponding optimal yaw of 17 degrees at an inflow angle of 12 degrees. Further experiments are recommended to refine the estimates and enhance the performance of wind farms through optimized yaw control strategies, ultimately contributing to the advancement of sustainable energy generation. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the Wind Farm Layout Optimization/expansion problem. This problem is formulated here as a Multi-Objective Optimization Problem considering the total power output and net efficiency of Wind Farms as objectives along with specific constraints. Once formulated, this problem needs to be solved efficiently. For that, a new approach based on a combination of five Multi-Objective Optimization algorithms, which is named the Parallel Collaborative Multi-Objective Optimization Algorithm, is developed and implemented. This technique is checked on seven test cases; for each case, the goal is to find a set of optimal solutions called the Pareto Front, which can be exploited later. The acquired solutions were compared with other approaches and the proposed approach was found to be the better one. Finally, this work concludes that the proposed approach gives, in a single run, a set of optimal solutions from which a designer/planner can select the best layout of a designed Wind Farm using expertise and applying technical and economic constraints. [ABSTRACT FROM AUTHOR]

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed methodology and implemented product in this study serves as a proof of concept that could be replicated by WMO members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for model input selection based on large-scale indicators leveraging artificial intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction in the wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead. [ABSTRACT FROM AUTHOR]

With the increase of wind power generation, the safety and economy of power system operations are greatly influenced by the intermittency and fluctuation of wind power. To take the advantage of the complementary characteristics between different energy storage devices, a Hybrid Energy Storage System (HESS) consisting of Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) can alleviate the uncertainty of wind power. This article has proposed a coordinated control strategy through group consensus algorithm based on Model Predictive Control (MPC) for Hybrid Energy Storage Array (HESA) to smooth wind power fluctuations. To allocate power commands to the FESS and BESS, the fluctuation of wind power output is extracted with different frequency domain characteristics as instructions by Empirical Mode Decomposition (EMD) technology. Moreover, a group consensus algorithm based on MPC is proposed to complete the adaptive power allocation of energy storage units. Eventually, the actual wind farm data is used for the simulation to verify the effect of control strategy proposed in this paper. It can be seen that the developed group consensus algorithm based on MPC can cope with different frequency power commands, avoid overcharging and discharging of energy storage media, and smooth wind power effectively. [ABSTRACT FROM AUTHOR]

The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances. [ABSTRACT FROM AUTHOR]

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational. [ABSTRACT FROM AUTHOR]

The transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty. [ABSTRACT FROM AUTHOR]

Accurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting. [ABSTRACT FROM AUTHOR]

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures. [ABSTRACT FROM AUTHOR]

Fonseca, M.; McMahon, A.; Erickson, R.; Kelly, C.; Tiggelaar, J., II, and Graham, B., 2024. Effects of the Block Island Wind Farm on benthic and epifaunal communities. Journal of Coastal Research, 40(6), 1037–1054. Charlotte (North Carolina), ISSN 0749-0208. This study reports on monitoring surveys conducted at three of the five commercially operating turbines in U.S. waters off Block Island, Rhode Island, U.S.A., with an emphasis on the final, fourth year of a Bureau of Ocean Energy Management sampling program. The monitoring focused on changes to sediments and infaunal and epifauna species abundance, richness, and diversity caused by the presence of the turbine structure. As anticipated, based on a comparison with other study results, far-field changes in benthic conditions were not evident. Clear changes to the seabed sediments and faunal composition manifested only in the immediate footprint of the turbine foundations. Aside from a localized and sustained shift in particle size, little evidence of a temporally or spatially progressive pattern (as a function of distance away from the turbines) of change in seabed physical and biological composition, or on the turbine structures themselves, was found. The lack of a systematic pattern of influence suggests that many of the intra- and interannual differences may be attributed to natural fluctuations, especially the epifauna on the turbine structures. Notably, the faunal dynamics suggest a community in constant flux and, as seen in other studies, lacking a trend toward the formation of a climax community, which is characterized by stable faunal composition. For these dynamic communities, future sampling may consider using a fixed station, repeated measures approach, as has been done in similarly dynamic, intertidal communities to manage these scales of habitat variability. [ABSTRACT FROM AUTHOR]

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes. [ABSTRACT FROM AUTHOR]

The aim of this article is to analyse the global environmental impact of wind farms, i.e., the effects on human health and the local ecosystem. Compared to conventional energy sources, wind turbines emit significantly fewer greenhouse gases, which helps to mitigate global warming. During the life cycle of a wind farm, 86% of CO2 emissions are generated by the extraction of raw materials and the manufacture of wind turbine components. The water consumption of wind farms is extremely low. In the operational phase, it is 4 L/MWh, and in the life cycle, one water footprint is only 670 L/MWh. However, wind farms occupy a relatively large total area of 0.345 ± 0.224 km2/MW of installed capacity on average. For this reason, wind farms will occupy more than 10% of the land area in some EU countries by 2030. The impact of wind farms on human health is mainly reflected in noise and shadow flicker, which can cause insomnia, headaches and various other problems. Ice flying off the rotor blades is not mentioned as a problem. On a positive note, the use of wind turbines instead of conventionally operated power plants helps to reduce the emission of particulate matter 2.5 microns or less in diameter (PM 2.5), which are a major problem for human health. In addition, the non-carcinogenic toxicity potential of wind turbines for humans over the entire life cycle is one of the lowest for energy plants. Wind farms can have a relatively large impact on the ecological system and biodiversity. The destruction of animal migration routes and habitats, the death of birds and bats in collisions with wind farms and the negative effects of wind farm noise on wildlife are examples of these impacts. The installation of a wind turbine at sea generates a lot of noise, which can have a significant impact on some marine animals. For this reason, planners should include noise mitigation measures when selecting the site for the future wind farm. The end of a wind turbine's service life is not a major environmental issue. Most components of a wind turbine can be easily recycled and the biggest challenge is the rotor blades due to the composite materials used. [ABSTRACT FROM AUTHOR]

This study presents an integrated methodology for evaluating operations and maintenance (O&M) costs for floating offshore wind turbines (FOWTs), incorporating vessel motion dynamics. By combining UWiSE, a discrete-event simulation tool, with SafeTrans, a voyage simulation software, vessel motion effects during offshore operations are modeled. The approach is demonstrated in a case study at two wind farm sites, Marram Wind and Celtic Sea C. Three major component replacement (MCR) strategies were assessed: Tow-to-Port (T2P), Floating-to-Floating (FTF), and Self-Hoisting Crane (SHC). The T2P strategy yielded the highest O&M costs—94 kEUR/MW/year at Marram Wind and 97 kEUR/MW/year at Celtic Sea C—due to the extended MCR durations (90–180 days), leading to lower availability (90–94%). In contrast, the FTF and SHC strategies offered significantly lower costs and downtime. The SHC strategy was most cost-effective, reducing costs by up to 64% while achieving 97–98% availability. The integrated approach was found to be either more restrictive or more permissive depending on the specific sea states influencing the motion responses. This variability highlights the critical role of motion-based dynamics in promoting safe and efficient O&M practices, particularly for advancing FOWT operations. [ABSTRACT FROM AUTHOR]

In this work, a computer Simulink model of a wind power plant has been developed, which uses a magnetic gearbox instead of a mechanical gearbox and also contains a synchronous permanent magnet generator. A separate Simulink model of a magnetic gearbox built on the basis of a modulated magnetic field in the air gap was developed, which allows to study the stability of its operation both in steady-state and transient modes. Calculations of various dynamic modes of the wind power plant’s operation were carried out, based on the developed model, such as the starting mode, an instantaneous increase in the wind speed acting on the wind turbine, and an increase in the load of the electric generator. According to the results of the calculations, it is shown that in transient modes, when short-term overloads occur, both rotors of the magnetic gearbox can fall out of synchronous motion for a certain period of time and then, depending on the parameters of the gearbox (as well as its other elements), the electromechanical system either reaches a certain operating steady-state mode or loses the ability to transfer mechanical power from the wind turbine to the generator. It has been shown that the use of a more powerful magnetic gearbox, with an increased value of the maximum magnetic torque, allows of a more overload-resistant operation of both: a gearbox and the wind plant as a whole. References 9, figures 10. [ABSTRACT FROM AUTHOR]

Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice. [ABSTRACT FROM AUTHOR]

Recent studies have revealed the large potential of vertical‐axis wind turbines (VAWTs) for high‐energy‐density wind farms due to their favorable wake recovery characteristics. The present study provides an experimental demonstration and proof‐of‐concept for the wake recovery mechanism of the novel X‐Rotor VAWT. The phase‐locked flowfield is measured at several streamwise locations along the X‐Rotor's wake using stereoscopic particle image velocimetry (PIV) with fixed‐pitch offsets applied to the blades. The streamwise vortex system of the upper half of the X‐Rotor is first hypothesized and then experimentally verified. The induced wake deformations of the vortex systems are discussed in comparison with previous studies concerning traditional H‐type VAWTs. The results suggest that positive blade pitch is more favorable for accelerated wake recovery due to the dominant tip‐vortex generated on the upwind windward quadrant of the cycle. Utilizing theoretical blade load variations along the span explains distinct unsteady flow features in the near wake generated at select quadrants of the rotor rotation, shedding light on the potential of the two pitch schemes. [ABSTRACT FROM AUTHOR]

This paper presents a graph‐based dynamic yaw model to predict the dynamic response of the hub‐height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time‐ and space‐dependent estimates of the wind farm inflow based on real‐time data from the wind farm. The model is validated both statically and dynamically using large‐eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output. [ABSTRACT FROM AUTHOR]

Satellite synthetic aperture radar (SAR) provides ocean surface wind fields at 10 m above sea level. The objective is to investigate the capability of SAR satellite StriX observations for mapping offshore wind farm wakes. The focus is on the conditions under which an apparent wind speed‐up is generated, measured in 48% of the 67 images available. The results compare well to Sentinel‐1 observations, showing a 34% wind speed‐up rate during several years based on 1171 images. Three wind speed‐up cases have been studied in detail using the mesoscale Weather, Research, and Forecasting (WRF) model with two wind farm parameterizations. At 10 m above sea level, the SAR‐based observations and WRF model compare for most cases, though only when turbulent kinetic energy (TKE) is included in the wind farm parameterization. The TKE mixes higher momentum downward in a stable atmosphere, causing surface wind speed‐up near the surface. [ABSTRACT FROM AUTHOR]

Wind farms experience significant power losses due to wake interactions between turbines. Research shows that wake steering can alleviate these losses by redirecting the flow through the farm. However, dynamic closed‐loop implementations of wake steering are rarely presented. We present a model‐free closed‐loop control method using reinforcement learning methodology known as policy gradients in combination with recursive least squares to perform real‐time wake steering in a wind farm. We present dynamic simulations of a four‐turbine wind farm row using HAWC2Farm, implementing the reinforcement learning control method for various inflow conditions and controller configurations. By controlling the three most upstream turbines, mean power gains of 11.6±3.0% and 1.4±0.5% (95% confidence interval) are observed in partial wake and full wake conditions respectively at 7.5% turbulence intensity. The study helps to bridge the gap between theoretical wind farm control and real‐world wind farm systems. [ABSTRACT FROM AUTHOR]

The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models. [ABSTRACT FROM AUTHOR]

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large‐eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross‐verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large‐scale meteorological models as boundary conditions. [ABSTRACT FROM AUTHOR]

The maintenance and upkeep costs of wind farms and their internal wind turbines have been increasing annually. Therefore, a systematic evaluation of their operating status is of great importance in guiding reductions in maintenance and upkeep costs. In this aspect, this article proposes a three-level service quality index system of "key component–wind turbine–wind farm" based on the fuzzy comprehensive evaluation method. Firstly, raw data on the wind farm are preprocessed to avoid the impact of abnormal data on the evaluation results. Then, the data types are classified and the degradation degree of each indicator is calculated. Based on the entropy weight method, the weight of each indicator is weighted and summed to obtain the overall membership degree. Finally, the overall health level is determined according to the "maximum membership degree", which is the evaluation result. This article conducts an evaluation experiment based on the actual operating data of Gansu Huadian Nanqiu Wind Farm. The example shows that the proposed strategy can systematically evaluate the health level of wind farms and predict the future trends of health status changes. The research results can provide reference for the reasonable arrangement of unit scheduling, operation, and maintenance plans in wind farms. [ABSTRACT FROM AUTHOR]

To mitigate the impact of wind power volatility on power system scheduling, this paper adopts the wind-storage combined unit to improve the dispatchability of wind energy. And a three-level optimal scheduling and power allocation strategy is proposed for the system containing the wind-storage combined unit. The strategy takes smoothing power output as the main objectives. The first level is the wind-storage joint scheduling, and the second and third levels carry out the unit combination optimization of thermal power and the power allocation of wind power cluster (WPC), respectively, according to the scheduling power of WPC and ESS obtained from the first level. This can ensure the stability, economy and environmental friendliness of the whole power system. Based on the roles of peak shaving-valley filling and fluctuation smoothing of the energy storage system (ESS), this paper decides the charging and discharging intervals of ESS, so that the energy storage and wind power output can be further coordinated. Considering the prediction error and the output uncertainty of wind power, the planned scheduling output of wind farms (WFs) is first optimized on a long timescale, and then the rolling correction optimization of the scheduling output of WFs is carried out on a short timescale. Finally, the effectiveness of the proposed optimal scheduling and power allocation strategy is verified through case analysis. [ABSTRACT FROM AUTHOR]

As the large-scale development of wind farms (WFs) progresses, the connection of WFs to the regional power grid is evolving from the conventional receiving power grid to the sending power grid with a high proportion of wind power (WP). Due to the randomness of WP output, higher requirements are put forward for the voltage stability of each node of the regional power grid, and various reactive power compensation devices (RPCDs) need to be rationally configured to meet the stable operation requirements of the system. This paper proposes an optimal configuration method for multi-type RPCDs in regional power grids with a high proportion of WP. The RPCDs are located according to the proposed static voltage stability index (VSI) and dynamic VSI based on dynamic voltage drop area, and the optimal configuration model of RPCDs is constructed with the lowest construction cost as the objective function to determine the installed capacity of various RPCDs. Finally, the corresponding regional power grid model for intensive access to WFs is constructed on the simulation platform to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The wind farm layout optimization problem (WFLOP) is a significant problem in the field of renewable energy. In this development of wind farm, solving the WFLOP is a crucial task, which entails placing the turbines in the wind farm in the best locations to reduce wake effects and enhance predicted power generation. In recent decades, the WFLOP problem has been solved mathematically. Meanwhile, the growing load demand led to an increase in the complexity of WFLOP. The results obtained by mathematical methods were not accurate enough to suit complexity of WFLOP. Nowadays, researchers from a variety of fields are developing nature‐inspired algorithms (NIAs) to solve difficult real‐world problems. This study is an attempt to review the most important innovations in the field of NIAs to solve the WFLOP problem. The classification of the reviewed literature is based on different applied approaches and models. In addition to specific proposals, the advantages and disadvantages of certain domains are also discussed. This study provides a future direction to the fellow researchers who are working in the field of WFLOP. [ABSTRACT FROM AUTHOR]