Seasonal turnover in Lepidoptera and Odonata communities in Northeastern Mexican wind farms and their environmental drivers

Assessment of wind resources in two parts of Northeast Brazil with the use of numerical models

Offshore wind energy is experiencing a rising importance for many electricity markets. While the effects of wind energy overall on electricity prices have been thoroughly studied, it remains unknown if offshore wind has a different impact on electricity prices than onshore wind. The aim of this paper is therefore to estimate the effect of offshore wind energy on wholesale electricity prices and how it differs to the impact of onshore wind. For this purpose, we propose three time series models to describe the development of electricity prices in Germany, Western Denmark and Great Britain from 2015–2018. We focus on the impact on the level and volatility of electricity prices using different time series models such as AR-GARCH or ARMA. Following these models, we can identify that onshore and offshore wind power do have a significantly different impact on wholesale electricity prices in the investigated countries. Based on our results, we discuss the implications of our findings for electricity markets and policy makers.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

Wind energy site selection requires robust frameworks that simultaneously address expert uncertainty, objective efficiency screening, and predictive capability beyond sampled locations. This study presents an integrated framework for strategic wind farm site selection in Ethiopia's Amhara Region by combining Fuzzy Analytic Hierarchy Process (FAHP), efficiency analysis, and predictive modelling to overcome the limitations of static GIS approaches. The FAHP stage incorporates expert judgment through fuzzy triangular numbers to weight six variables such as wind speed, slope, elevation, distance to transmission lines, distance to roads, and land-use/land-cover to achieve a consistency ratio of 0.0175 with wind speed emerging as the dominant factor of 0.4211 weights in order to generate a spatial suitability surface and extract candidate high-potential areas. High-potential zones identified by FAHP are then evaluated as decision-making units using input-oriented Data Envelopment Analysis (DEA) models. DEA efficiency screening identifies North Shewa as frontier-efficient zone (CCR = BCC = SBM = 1.0), which contributes 32.25% of regional suitable land. Finally, machine learning (ML) models (Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boost (XGBoost)) are trained on 1.5million sampled pixels from the 31-million-cell feature space to learn generalizable suitability functions. Random Forest achieved optimal performance with RMSE of 0.2981 and R2 of 0.8145 in regression and F1-score of 0.9207 and accuracy of 0.9237 in classification to delineates 1,698km2 of high-priority corridors within North Shewa for immediate wind farm deployment. Independent validation through five different sources that includes 250MW of Debre Birhan under Ethiopian Ministry of Finance private public project pipeline to confirm North Shewa as the highest-potential development corridor. This framework advances wind energy planning by integrating subjective weighting, objective efficiency analysis, and predictive modelling into a unified strategic decision-support system applicable for wind energy planning in data-scarce region.

Improving the accuracy of wind speed statistical analysis and wind energy utilization in the Ningxia Autonomous Region, China

Similar to birds, bats also perform long-distance migration between their breeding and wintering sites. In Northern Europe, migratory bat species are often detected along the coastline of the Baltic Sea particularly during migration seasons in the spring and autumn. In spite of regular monitoring of bat migration at coastal sites, the overall distribution of migratory bats in Northern Europe and variability between sites and seasons are still very poorly known. In this study we used automated bat detectors to compare the activity of migratory bat species between coastal and inland monitoring sites along the west coast of Finland (61.5–61.9°N, 21.3−22.3°E). Our main goal was to test whether the activity of migratory bat species is associated with the coastline or whether these species also occur inland. Of migratory bat species observations, 98.6% were covered by Pipistrellus nathusii, which was detected at all our monitoring sites. The activity of the species decreased rapidly, with increasing distance from the coastline towards inland, indicating a sharp activity gradient along the coastline of the Baltic Sea. Because the activity of P. nathusii occurred in migration season and no similar spatial pattern was detected among sedentary species, our results suggest that the aggregation of P. nathusii at the coastline is related to migration as such rather than regular foraging behavior of this species. Our study has direct implications to the wind power planning in Northern Europe. Based on our study we conclude that the impact of wind power on both migratory (namely P. nathusii) and sedentary bat species (namely Eptesicus nilssonii) should be taken into account in wind power planning and impact mitigation in Northern Europe, especially if new wind farms are located along the coastline of the Baltic Sea. When the turbines are located further inland, more attention in the planning process should however be given to the sedentary bat E. nilssonii.

Wind power is a key strategy for mitigating climate change and improving air quality by displacing fossil-fuel-based electricity generation. However, large wind farms can also modify local atmospheric conditions, raising questions about their combined effects on air quality. This study assesses both the indirect benefits from emission reductions and the direct meteorological impacts of wind energy deployment on PM2.5 concentrations in Taiwan. We employed a modeling framework that incorporates the Weather Research and Forecasting and Community Multiscale Air Quality models to simulate scenarios with and without wind farm installations. The approach accounts for emission displacement from fossil fuel power plants and wind turbines-induced effects, including turbulence and momentum drag, represented through a dedicated wind farm parameterization. Model performance is evaluated against surface observations and demonstrates high fidelity in reproducing key meteorological and pollutant variables. Results indicate that wind energy deployment can reduce regional PM2.5 concentrations by up to 15%, primarily due to reduced emissions from displaced fossil fuel generation. At the same time, turbine-induced meteorological changes modulate pollutant dispersion. Enhanced daytime turbulence generally improves near-surface air quality, particularly in southern coastal regions. However, in central Taiwan, weakened nocturnal land breezes under stable autumn conditions lead to modest downwind PM2.5 accumulation, with increases ranging from 5 to 10%. Wind farm size, seasonal circulation patterns, and coastal proximity influence these effects' magnitude and spatial extent. These findings underscore the dual effect of wind energy's influence on atmospheric composition. While emission reductions provide substantial air quality benefits, localized meteorological effects may introduce minor but regionally variable trade-offs. Our study highlights the importance of integrating emission and meteorological considerations into wind energy planning and provides a scientific foundation for policies that maximize environmental co-benefits while minimizing unintended impacts.

This paper proposes a genetic algorithm-based approach to optimize wind farm layouts in Bosnia and Herzegovina, a country with a high potential for wind energy production. The primary objective is to maximize the total power output derived from the wind farm while concurrently minimizing the wake effects resulting from turbine interactions. This balance is pivotal in ensuring the optimal utilization of wind energy resources. In this study, three different wind data scenarios are considered: a single wind direction with overall average velocity, the most prevalent wind direction with overall average velocity, and an all-encompassing wind direction analysis incorporating a weighted objective function. The results of the study suggest that the genetic algorithm is highly effective in identifying optimal solutions across each scenario. This serves as a testament to the algorithm's accuracy, robustness, and applicability in tackling real-world problems, thereby marking a significant step forward in the realm of wind farm optimization. The limitations of this research include the use of a simplified wake model and a fixed turbine type. The implications of this research include the potential for increasing the efficiency and profitability of wind farms in Bosnia and Herzegovina, as well as informing future research on more complex and realistic optimization problems.

The rapid development of wind energy in Japan and the associated high uncertainties and fluctuations in power generation present a big challenge for both wind power generators and electric grids. Accurate and reliable wind power predictions are necessary to optimize the integration of wind power into existing electrical systems. In this study, a hybrid forecasting system of wind power generation was developed by integrating the Kalman filter (KF) with the high resolution Weather Research and Forecasting (WRF) model as well as an empirical formula of wind power output (power curve). The system has been validated with observations including wind speed and power output over a six-month period for 15 turbine sites at a wind farm in Awaji-island, Japan. The results show that the tuned WRF model is able to provide hub-height wind speed prediction for the target area with reliability to some extent. The predicted wind field can be substantially improved by the Kalman filter as a post-processing procedure. The 15-turbine averaged improvements of mean error, root mean square error, and correlation coefficient are 97%, 22%, and 10%, respectively. Meanwhile, the Kalman filter also demonstrates a promising capability of reducing the uncertainties in the power curve model. Systematic validations regarding both wind speed and power output were carried out against the observations for the target wind farm, which show that the hybrid power forecasting system presented in this paper can be an effective and practical tool for short-term predictions of wind speed and power output in Japan area.

When discussing the connection of wind energy and climate change, normally, the potential of wind energy to reduce green house gas emissions is emphasised. Hence, effects of wind energy on climate change are analysed. However, what about the other direction? What is the impact of climate change on wind energy? Recently, the effect of a reversal in global terrestrial stilling,that is, an increase in global wind speeds in the last decade, on the wind energy production has been analysed. Certainly, knowledge about potential changes in energy production is essential to plan future energy supply. Nonetheless, at least similarly important is the effect on loads acting on wind turbines. Increasing loads due to higher wind speeds might reduce wind turbine lifetimes and yield higher costs. Moreover, especially for already existing turbines, it might even affect the structural reliability. Since the impact of climate change on wind turbine loads is largely unknown, it is studied in this work in more detail. For this purpose, different existing models for predicted changes in wind speed and air temperature and their uncertainties are used to forecast the environmental conditions an exemplary offshore wind turbine is exposed to. Subsequently, for this turbine, the lifetime fatigue damages are calculated for different prediction models. It is shown that the expected changes in lifetime fatigue damages are present but relatively small compared to other uncertainties in the fatigue damage calculation.

Abstract. With the current expansion of wind power as a renewable energy source, wind turbines increasingly extract kinetic energy from the atmosphere, thus impacting its energy resource. Here, we present a simple, physics-based model (the Kinetic Energy Budget of the Atmosphere; KEBA) to estimate wind energy resource potentials that explicitly account for this removal effect. The model is based on the regional kinetic energy budget of the atmospheric boundary layer that encloses the wind farms of a region. This budget is shaped by horizontal and vertical influx of kinetic energy from upwind regions and the free atmosphere above, as well as the energy removal by the turbines, dissipative losses due to surface friction and wakes, and downwind outflux. These terms can be formulated in a simple yet physical way, yielding analytic expressions for how wind speeds and energy yields are reduced with increasing deployment of wind turbines within a region. We show that KEBA estimates compare very well to the modelling results of a previously published study in which wind farms of different sizes and in different regions were simulated interactively with the Weather Research and Forecasting (WRF) atmospheric model. Compared to a reference case without the effect of reduced wind speeds, yields can drop by more than 50 % at scales greater than 100 km, depending on turbine spacing and the wind conditions of the region. KEBA is able to reproduce these reductions in energy yield compared to the simulated climatological means in WRF (n=36 simulations; r2=0.82). The kinetic energy flux diagnostics of KEBA show that this reduction occurs because the total yield of the simulated wind farms approaches a similar magnitude as the influx of kinetic energy. Additionally, KEBA estimates the slowing of the region's wind speeds, the associated reduction in electricity yields, and how both are due to the depletion of the horizontal influx of kinetic energy by the wind farms. This limits typical large-scale wind energy potentials to less than 1 W m−2 of surface area for wind farms with downwind lengths of more than 100 km, although this limit may be higher in windy regions. This reduction with downwind length makes these yields consistent with climate-model-based idealized simulations of large-scale wind energy resource potentials. We conclude that KEBA is a transparent and informative modelling approach to advance the scientific understanding of wind energy limits and can be used to estimate regional wind energy resource potentials that account for the depletion of wind speeds.

With the scale of grid-connected wind farms increasing, accurate forecast of ultra-short-term wind speed and wind power is very important to the stable operation of power systems. This paper presents a dynamic selective neural network ensemble (DSNNE) forecast method, which makes use of K nearest neighbor algorithm to collect the generalization errors of certain different BP neural networks and RBF neural networks into a performance matrix and then the neural networks with low local generalization errors are dynamically selected and locally dynamic averaging is applied to the neural networks in order to conduct the final results of the ensemble. Then this method is applied to realize the wind speed and power ultra-short-term advance forecast, taking the wind speed and wind turbine power output from a wind farm in China as the original data. The research results show that DSNNE improves the generalization ability of the neural network system and the prediction accuracy of wind power and wind speed significantly. It proves the validity and effectiveness of the DSNNE with controlling the biggest mean relative error of 2 minutes ahead wind power and wind speed forecast as low as 25% and 16% respectively.

Most studies on the effects of wind energy on animals have focused on avian and bat activity, habitat use, and mortality, whereas very few have been published on terrestrial, non-volant wildlife. In this paper, we studied the utilization of functioning wind farm areas by four terrestrial animals common to agricultural landscapes: European roe deer, European hare, red fox, and the common pheasant. Firstly, we expected that the studied animals do not avoid areas close to turbines and utilize the whole area of functioning wind farms with a frequency similar to the control areas. Secondly, we expected that there is no relation between the turbine proximity and the number of tracks of these animals. The study was conducted over two winter seasons using the snow-tracking method along 100 m linear transects. In total, 583 transects were recorded. Wind farm operations may affect terrestrial animals both in wind farm interiors and in a 700-m buffer zone around the edge of turbines. The reactions of animals were species specific. Herbivorous mammals (roe deer and European hare) avoided wind farm interiors and proximity to turbines. The common pheasant showed a positive reaction to wind turbine proximity. The red fox had the most neutral response to wind turbines. Although this species visited wind farm interiors less often than the control area, there was no relation between fox track density and turbine proximity. Greater weight should be given to the effects of wind farms on non-flying wildlife than at present. Investors and regulatory authorities should always consider the likely impacts of wind farms during environmental impact assessments and try to reduce these negative effects.

Capsule Red Kite populations in Sardinia are very small but show a strong spatial overlap with current and planned wind energy infrastructure, highlighting a conservation concern. Aims To assess the status of the Red Kite population in Sardinia and evaluate the potential conflict between its distribution and the development of wind energy. Methods Breeding populations were surveyed through road transects, while wintering numbers were assessed with coordinated roost counts. Spatial overlap with operational and planned wind farms was analysed using distribution data from breeding and wintering areas. Results The Sardinian population comprises 10–13 breeding pairs and up to 87 individuals in winter. A total of 188 operational turbines currently occur within the breeding range and 417 within the wintering range. Planned developments include an additional 265 turbines in the breeding area and 440 in the wintering area, indicating substantial overlap with key sites for Red Kite. Conclusions The large degree of overlap between areas utilized by Red Kites and wind farms raises concerns over collision risk and habitat displacement. Integrating updated distribution data into wind energy planning is essential to reconcile renewable energy goals with raptor conservation. Transboundary cooperation is needed to ensure the long-term conservation of Mediterranean island populations.