Wind energy has experienced a notable development during the last decades, driving new challenges for animal communities. Although bird collisions with wind turbines and spatial displacement due to disturbance have been widely described in the literature, other potential impacts remain unclear. In this study, we addressed the effect of turbine noise on the vocal behaviour of a threatened shrub-steppe passerine highly dependent on acoustic communication, the Dupont's lark Chersophilus duponti. Based on directional recordings of 49 calling and singing males exposed to a gradient of turbine noise level (from 15 up to 51 dBA), we tested for differences in signal diversity, redundancy, and complexity, as well as temporal and spectral characteristics of their vocalizations (particularly the characteristic whistle). Our results unveiled that Dupont's lark males varied the vocal structure when subject to turbine noise, by increasing the probability of emitting more complex whistles (with increased number of notes) and shifting the dominant note (emphasizing the longest and higher-pitched note). In addition, males increased duration and minimum frequency of specific notes of the whistle, while repertoire size and signal redundancy remain constant. To our knowledge, this is the first study reporting multiple and complex responses on the vocal repertoire of animals exposed to turbine noise and unveiling a shift of the dominant note in response to anthropogenic noise in general. These findings suggest that the Dupont's lark exhibits some level of phenotypic plasticity, which might enable the species to cope with noisy environments, although the vocal adjustments observed might have associated costs or alter the functionality of the signal. Future wind energy projects must include fine-scale noise assessments to quantify the consequences of chronic noise exposure.

Ontario is Canada’s provincial leader in wind energy, with over 4000 MW of installed capacity supplying approximately five percent of the province’s electricity demand. Wind energy is now one of the fastest-growing sources of renewable power in Canada and many other countries. However, its possible negative impact on population health, as a new source of environmental noise, has raised concerns for people living in proximity to wind turbines (WTs). The aims of this study were to assess the effect of individual differences and annoyance on the self-reported general health and health-related quality of life (QOL) of nearby residents, using a pre- and post-exposure design. Prospective cohort data were collected before and after WT operations, from the individuals (n = 43) in Ontario, Canada. General health and QOL metrics were measured using standard scales, such as SF12, life satisfaction scales developed by Diener (SWLS) and the Canadian Community Health Survey (CCHS-SWL). The mean values for the Mental Component Score of SF12 (p = 0.002), SWLS (p < 0.001), and CCHS-SWL (p = 0.044) significantly worsened after WT operation for those participants who had a negative attitude to WTs, who voiced concerns about property devaluation, and/or who reported being visually or noise annoyed.

The North Atlantic right whale (Eubalaena glacialis) faces increasing pressure from commercial shipping traffic and proposed marine renewable energy developments. Drawing upon the successful Stellwagen Bank National Marine Sanctuary model, we propose a multi-stakeholder marine spatial planning process that considers both appropriate positioning of offshore wind farms and redefining commercial shipping lanes relative to whale migration routes: placement of wind turbines within certain right whale habitats may prove beneficial for the species. To that end, it may be advisable to initially relocate the shipping lanes for the benefit of the whales prior to selecting wind energy areas. The optimal end-state is the commercial viability of renewable energy, as well as a safe shipping infrastructure, with minimal risk of collision and exposure to shipping noise for the whales. This opportunity to manage impacts on right whales could serve as a model for other problematic interactions between marine life and commercial activities.

Human activities at sea are still increasing. As biodiversity is a central topic in the management of our seas, it is important to understand how diversity responds to different disturbances related with physical impacts. We investigated the effects of three impacts, i.e. sand extraction, dredge disposal and offshore wind energy exploitation, on the soft-bottom macrobenthic assemblages in the Belgian part of the North Sea. We found similar diversity-disturbance responses, mainly related to the fact that different impacts caused similar environmental changes. We observed a sediment refinement which triggered a shift towards a heterogenic, dynamic (transitional) soft-bottom macrobenthic assemblage, with several species typically associated with muddy sands. This led to a local unexpected biodiversity increase in the impacted area. On a wider regional scale, the ever increasing human impacts might lead to a homogenization of the sediment, resulting in a more uniform, yet less diverse benthic ecosystem.

Aiming at the uncertainty of wind power and the low accuracy of multi-step interval prediction, an ultra-short-term wind power multi-step interval prediction method based on complete ensemble empirical mode decomposition with adaptive noise-fuzzy information granulation (CEEMDAN-FIG) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) is proposed. Firstly, the CEEMDAN is used to decompose the wind power time series into several sub-components to reduce the non-stationary characteristics of the wind power time series. Then, different components are selected for FIG, and the maximum value sequence, average value sequence, minimum value sequence gotten from FIG, and the remaining components without FIG are combined with the wind speed data, wind direction data, and the temperature data. They all are input into the CNN-BiLSTM combined prediction model to obtain the initial wind power prediction interval. The prediction results of the maximum value sequence, the average value sequence, and the minimum value sequence are respectively superimposed on the prediction results of the remaining components to obtain the upper limit, point prediction, and lower limit of the initial prediction interval. Finally, the improved coverage width criterion is used as the objective function to optimize the interval, and the forecast interval of wind power under a given confidence level is generated. Taking the actual operating data of a certain unit of a wind farm as an example, the validity of the proposed model is verified.

The profitability of electric heating projects in Northern China is poor. A kind of combined market-oriented trading strategy to improve the economy of electric heating projects is put forward. The price game model for direct power purchase, compensation model for carbon emission reduction benefits and load aggregation revenue model are constructed, and the optimal proportion of clean energy and conventional energy consumed under the condition of ensuring heating reliability is analyzed. An electric heating project was analyzed as an example, the results showed that in the direct power purchase transaction, the power price is 0.2125 RMB/kWh, which is 0.0591 RMB/kWh lower than the catalogue tariff. Revenue from carbon trading and auxiliary service transaction is 469,913.87 RMB and 289,218.09 RMB, respectively, which is equivalent to reducing electricity cost of 0.0253 RMB/kWh and 0.0156 RMB/kWh, respectively. The ultimate power cost is 0.1716RMB/kWh, which is 0.1 RMB/kWh lower than the current catalogue tariff. Besides, the more clean energy an electric heating project consumes, the better its economy will be, and in this example, the best proportion of clean energy and conventional energy is 80% and 20%. The research results could help to promote the sustainable development of clean heating in northern China.

Accurate wind speed forecasting (WSF) not only ensures stable power system operation but also contributes to enhancing the competitiveness of wind power companies in the market. In this paper, a hybrid prediction model based on secondary decomposition algorithm (SDA) is proposed for WSF. First, wavelet transform (WT) is used to decompose the wind speed sequence into approximate and detailed components. Second, the obtained detailed components are further decomposed by symplectic geometry mode decomposition (SGMD). Then, the marine predators algorithm-optimized back-propagation neural network (BPNN) is used to predict the new subsequences. The case study was implemented on 4 datasets. The experimental results show that, first, the proposed hybrid model has the highest prediction accuracy and the best robustness among all the compared models in 1–4-step prediction. Second, the proposed hybrid decomposition strategy has significant utility in reducing the difficulty of WSF. After adding SDA, the average improvement levels of MAPE in 1–4-step prediction were 85.64%, 84.93%, 81.08% and 80.67%, respectively. Third, the re-decomposition of the details obtained by WT can improve the prediction accuracy. After the re-decomposition of the details obtained by WT, the proposed WT-SGMD-MPA-BP model leads to the average improvement percentages of 44.44%, 61.69%, 50.56% and 49.28% in RMSE compared with WT-MPA-BP model in various horizons. The proposed model provides valuable reference for WSF. In future work, the performance of the model for other nonlinear sequences is worth exploring.

This study analyzes the relationship between wind energy consumption, coal energy consumption, globalization, economic growth, and carbon emissions. Data from 37 countries for the period 2000–2019 are included in the analysis. To examine the long-term relationship between the variables, the AMG method, which considers the cross-section dependence and slope homogeneity, was used. According to the long-term coefficient estimates of the cointegrated variables, wind energy consumption has a statistically significant and negative effect on carbon emissions in the long run. For example, a 1% increase in wind energy consumption reduces carbon emissions by 0.018%. On the other hand, the variable of globalization has a statistically significant and positive effect on carbon emissions in the long run. A 1% increase in globalization increases carbon emissions by 0.107%. These findings show the importance of wind energy consumption in reducing carbon emissions.

Wind energy is one of the important renewable energy alternatives due to its wide potential and meeting increasing energy demand. However, location selection in wind farms is a complex spatial decision process for decision-makers. This study aimed to determine suitable wind farm locations by combining Fuzzy-Analytical Hierarchical Process (F-AHP) and Maximum Entropy (MaxEnt) methods for Hatay Province, Turkey. Firstly, nine decision criteria for selecting suitable wind farm locations were determined by climate, environmental, social and economic factors. Secondly, the F-AHP and MaxEnt models were implemented and suitable areas were mapped according to five suitability classes. Finally, F-AHP and MaxEnt model results were combined to define and classify priority locations for the wind farm. Study results show that wind speed, air densities and elevation are important criteria for F-AHP, while wind speed, wind power density and distance from power criteria are the most important factors for MaxEnt. Very high and high suitable wind farm locations of Hatay Province cover 21.6% in F-AHP and 29.8% in the MaxEnt model, while very low and low suitable areas cover 48.1% of the study area in both model results. To determine the priority wind farm location, F-AHP and MaxEnt model results were overlapped and reclassified according to the combination of suitability classes. The priority classes show that 62.9% of the study area is unsuitable for the wind farm. However, the limited area was determined as the 1st-priority area (3.2%), 2nd-priority area (4.9%) and 3rd-priority area (6.2%) to locate the wind farm. Consequently, the study methodology enables a more precise evaluation by combining different model results for decision-makers to select the optimum wind farm location selection.

In the last few decades, wind energy has become a significant source of the renewable energy system, and it is essential to use wind energy for generating power and run the wind turbine system (WTs) at a higher level. With the rapid penetration of wind energy in the distributed generation system (DGS) and isolated micro-grid (MG), the WT runs at its optimal energy conversion output. For this, WT has to track or drive at the optimal power point tracking algorithm. However, various publications are available on MPPT algorithms for wind energy system (WES) applications, making a choice on exact trackers for a particular algorithm because each tracker has its advantages and disadvantages. Therefore, our primary goal is to review and evaluate the exact tracking algorithm for WES applications in this manuscript. To introduce the power controller, it is essential to track maximum power despite wind energy results. Besides, many algorithms have been evaluated, and their maximum output is achieved compared to their performance. This research paper will help researchers provide an accurate reference for future recommendations by selecting the best tracking algorithms in WES.