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The mesoscale interaction between sea surface temperature (SST) and wind is a crucial factor influencing oceanic and atmospheric conditions. To investigate the mesoscale coupling characteristics of the Yellow Sea and East China Sea, we applied a locally weighted regression filtering method to extract mesoscale signals from Quik-SCAT wind field data and AMSR-E SST data and found that the mesoscale coupling intensity is stronger in the Yellow Sea during the spring and winter seasons. We calculated the mesoscale coupling coefficient to be approximately 0.009 N·m−2/°C. Subsequently, the Tikhonov regularization method was used to establish a mesoscale empirical coupling model, and the feedback effect of mesoscale coupling on the ocean was studied. The results show that the mesoscale SST–wind field coupling can lead to the enhancement of upwelling in the offshore area of the East China Sea, a decrease in the upper ocean temperature, and an increase in the eddy kinetic energy in the Yellow Sea. Diagnostic analyses suggested that mesoscale coupling-induced variations in horizontal advection and surface heat flux contribute most to the variation in SST. Moreover, the increase in the wind energy input to the eddy is the main factor explaining the increase in the eddy kinetic energy. [ABSTRACT FROM AUTHOR]

As the world shifts towards low-carbon energy, many countries are improving energy development by promoting renewable sources such as solar photovoltaics and wind power. Does the promotion of these sources align with the objectives of energy security development? In other words, will it improve energy security? To this end, we use a provincial panel data set of 30 provinces in China from 2007 to 2019 to explore the potential impact of the high-quality energy development (HED) process on energy security, and conduct a series of robustness tests. The results indicate that HED can effectively promote China's energy security, implying that HED and energy security can achieve a win-win situation. In addition, we conduct the asymmetry analysis to establish the extent to which HED influences energy security under different energy security levels, which provides a blueprint for China's energy development paths. Finally, some policy recommendations are highlighted for improving China's energy development level to a reasonable extent and formulating relevant energy security policies. [ABSTRACT FROM AUTHOR]

The climate commitment to achieving carbon neutrality before 2060 in China has been announced recently. In the context of pursuing carbon neutrality, sharing similar sources as greenhouse gases, aerosol particle and precursor emissions are projected to substantially decrease in China, which can potentially have a great impact on climate. Here, we investigate the effects of future aerosol reductions, because of achieving carbon neutrality, on solar and wind energy in China by using an earth system model. We show that significant reductions in aerosol emissions, particularly in eastern China, lead to increases in the surface downwelling shortwave radiation, surface air temperature and wind speed, which can further enhance the potential of solar and wind energy production. The findings underline that the pursuit of carbon neutrality can yield co‐benefits of not only mitigating climate change and air pollution but also fortifying the stability of renewable energy sources. Plain Language Summary: In response to China's commitment to achieve carbon neutrality by 2060, our study examines the potential impact of reducing aerosol emissions and greenhouse gases on solar and wind energy generation. Employing an earth system model, we observe significant reductions in aerosols, particularly in eastern China, resulting in increases in surface downwelling shortwave radiation, surface air temperature, and wind speed. These changes have promising implications for enhancing solar and wind energy production. Our research underscores the interconnected benefits of pursuing carbon neutrality, encompassing climate change mitigation, air quality improvement, and the enhancement of renewable energy reliability. Key Points: The effects of future aerosol reductions in China due to achieving carbon neutrality on solar and wind energy are investigatedAerosol emission reductions, especially in eastern China, increase surface downwelling shortwave radiation, temperature and wind speedClimate changes resulting from emission reductions under carbon neutrality enhance the potential of solar and wind energy [ABSTRACT FROM AUTHOR]

As China gradually transitions towards a low-carbon energy structure, the proportion of grid-connected new energy sources like wind and solar power continues to increase. To ensure the safe and reliable operation of the power system while meeting the capacity planning for future new energy installations, there is a need for flexible resources with corresponding adjustment capabilities in the power system. In response to this situation, this paper presents an optimization model for the allocation of multiple types of flexible resources that takes into account spatiotemporal response characteristics. Firstly, a flexibility evaluation model is developed based on spatial and temporal response characteristics. Flexibility evaluation indices, such as flexibility average deficit and flexibility coverage index, are constructed. These indices are used for screening nodes with inadequate flexibility in the power system and analyzing the flexibility adequacy at various nodes. Next, the adjustment characteristics of multiple types of flexible resources are analyzed, and a model for their adjustment capabilities is established. Finally, by considering constraints based on time flexibility evaluation indices, a two-stage optimization model for flexible resource allocation is constructed. This model leverages the multiscale matching characteristics between flexibility resources and the fluctuation patterns of new energy sources to guide the allocation of flexible resources at nodes with insufficient flexibility. The effectiveness and applicability of the proposed flexible resource allocation method are validated using the IEEE 9-node system. [ABSTRACT FROM AUTHOR]

In recent years, the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing, but the peak regulation capacity of the power grid in the three north regions of China is limited, resulting in insufficient local wind power consumption capacity. Therefore, this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid's wind power consumption capacity. The objective of the upper model is to minimize the peak-valley difference of the system load, which is mainly to optimize the system load by using the demand response resources, and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid. The lower scheduling model is aimed at maximizing the system operation benefit, and the scheduling model is selected based on the rolling scheduling method. The load-side scheduling model needs to reallocate the absorbed wind power according to the response speed, absorption benefit, and curtailment penalty cost of the two DR dispatching resources. Finally, the measured data of a power grid are simulated by MATLAB, and the results show that: the proposed strategy can improve the power grid's wind power consumption capacity and get a large wind power consumption benefit. [ABSTRACT FROM AUTHOR]

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators. [ABSTRACT FROM AUTHOR]

In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results. [ABSTRACT FROM AUTHOR]

The development of renewable energy and the construction of a comprehensive energy system with multiple complementary energy sources have gradually become the main direction of China's energy development. As the penetration rate of renewable energy increases, the intermittent and fluctuating output of wind and solar power has a more significant impact on the system. This article conducts research on the optimization configuration of integrated energy system (IES) considering photovoltaic output uncertainty under a ladder carbon trading mechanism. Firstly, a two-stage distributed robust optimization (DRO) configuration model for integrated energy system is established. In detail, a deterministic model aimed at minimizing investment costs is given in the first stage and an uncertainty model aimed at minimizing operating costs in the probability distribution of the worst scenario is built in the second stage. Then, a data-driven distributed robust optimization method is adopted to deal with the uncertainty of photovoltaic output using MATLAB software (R2020A). Finally, the column and constraint generation (C&CG) algorithm is used to solve the problem, and the optimal investment capacity and cost results of the integrated energy system considering demand response under a ladder carbon trading mechanism are obtained. Through analysis, the proposed method achieves a 5.54% reduction in carbon emission costs while maintaining nearly unchanged investment costs, thus balancing economic and environmental benefits. Additionally, the CCG algorithm can effectively improve computational efficiency and guarantee the optimality of the solution. [ABSTRACT FROM AUTHOR]

With the continuous advancement of China's "peak carbon dioxide emissions and Carbon Neutrality" process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM model. Secondly, Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model. Finally, the correlation between wind power prediction errors and Numerical Weather Prediction (NWP) data is introduced as incremental updates to the initial wind power prediction model. During the incremental learning process, multiple error performance indicators are used to measure the overall model performance, thereby enabling incremental updates of wind power models. Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points, indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate. The accuracy and precision of wind power generation prediction are effectively improved through the method. [ABSTRACT FROM AUTHOR]

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low- carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world's leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa. [ABSTRACT FROM AUTHOR]

Aiming at the "net-zero carbon" target, a higher proportion of variable renewable energies (VREs) has been integrated into power grids, and pumped storage plants (PSPs) are crucial for guaranteeing the safe and stable operation of hybrid energy systems. As secondary frequency regulation (SFR) is related to the economic operation and the quality of auxiliary services provided by PSPs, it is critical to clarify its performance and compensation. Therefore, the corresponding quantitative evaluations of the SFR of pumped storage units (PSUs) are carried out in this paper. First, the performance of SFR is quantified based on the fuzzy analytic hierarchy process (FAHP). Considering the current situation in China's electricity market, the average compensation of the PSP with each MW is determined to be 9.336 CNY per day, and a method for calculating the compensation of SFR is constructed. Then, taking a real Chinese PSP as an example, based on different wind power deviation signals, the quantitative evaluation of the compensation of SFR is conducted in accordance with the fixed-speed pumped storage units (FSPSUs) and the variable-speed pumped storage units (VSPSUs) in power priority and speed priority modes. The results show that the compensation of FSPSUs and VSPSUs (power priority mode and speed priority mode) is 10,900 CNY per day; 54,400 CNY per day; and 17,300 CNY per day. This paper could contribute to clarifying the compensation of SFR and providing technical support for the development of the auxiliary service market. [ABSTRACT FROM AUTHOR]

The temporal variation of wind power is primarily influenced by wind speed, exhibiting high levels of randomness and fluctuation. The accuracy of short-term wind power forecasts is greatly affected by the quality of Numerical Weather Prediction (NWP) data. However, the prediction error of NWP is common, and posing challenges to the precision of wind power prediction. To address this issue, the paper proposes a NWP wind speed error correction model based on Residual Network-Gated Recurrent Unit (ResNet-GRU). The model corrects the forecasted wind speeds at different heights to provide reliable data foundation for subsequent predictions. Furthermore, in order to overcome the difficulty of selecting network parameters for the combined prediction model, we integrate the Kepler Optimization Algorithm (KOA) intelligent algorithm to achieve optimal parameter selection for the model. We propose a Convolutional Neural Network-Long and Short-Term Memory Network (CNN-LSTM) based on Attention Mechanism for short-term wind power prediction. Finally, the proposed methods are validated using data from a wind farm in northwest China, demonstrating their effectiveness in improving prediction accuracy and their practical value in engineering applications. [ABSTRACT FROM AUTHOR]

With the increasingly prominent issues of climate change and energy shortages, large-scale development of renewable and clean energy has become a common choice for countries around the world. Among them, onshore wind power plays an important role in the global energy transformation process due to its mature technology, safety, and efficiency advantages. However, because wind energy resources have the characteristics of low energy density, instability, and spatial dispersion, large-scale development of onshore wind power requires a large amount of land resources to support. To ensure the land resource demand for wind power development under the goal of 'carbon neutrality,' this paper conducted a quantitative study on the land occupation of onshore wind power in China. Firstly, based on the criterion of exclusivity, this paper reclassified the types of land occupation for wind farms from a spatio-temporal perspective. Secondly, based on the sample parameter method and the alpha shape algorithm, this paper calculated the land area of existing onshore wind farms in China categorically. Finally, considering the development trend of wind power technology, this paper systematically simulated and predicted the wind power land demand in different development scenarios under the 'carbon neutrality' goal. The results indicated that: (1) Based on the principle of exclusivity, the total land use area of wind farms could be divided into two parts in the spatial dimension: the direct impact area and the indirect impact area. The direct impact area could be divided into two parts in the temporal dimension: permanent land use and temporary land use. (2) In 2022, the land occupied by the wind turbine foundation, permanent land use, temporary land use, and direct impact area of China's onshore wind farms reached 48.28 km², 352.08 km², 1 234.86 km², and 1 638.81 km², respectively. (3) In 2022, the indirect impact area and total land use area of China's onshore wind farms reached 9.81x104 km ² and 9.99x104 km², respectively. The total land use area was almost equivalent to the total urban land use area of China. (4) Compared with the scenario of current technical level, under the scenario of technological progress and wind turbine upgradation, the various types of land use demand for wind power in China will have significantly decreased by 2060, and land use demand will have reached its peak by around 2050. The large-scale upgradation of wind turbines has great potential for saving land resources. Therefore, perfecting the land use standards of wind power construction, improving the land use efficiency of wind power development, and promoting the construction of offshore wind power are important paths for coordinating the sustainable use of wind energy resources and land resources. [ABSTRACT FROM AUTHOR]

Currently, data-driven deep learning models are widely applied in the field of wind power prediction. However, when historical data are insufficient, deep learning models struggle to exhibit satisfactory predictive performance. In order to overcome the issue of limited training data for new wind farms, this study proposes a novel transfer learning strategy to address the challenge of less-sample learning in short-term wind power prediction. The research is conducted in two stages. In the pre-training stage, the TimesNet-GRU prediction model is established using data from a source wind farm. Parallel TimesNet modules are employed to extract multi-period features from various input feature sequences, followed by the extraction of long- and short-term features from the time series through gate recurrent unit (GRU). In the transfer learning stage, an effective transfer strategy is designed to freeze and retrain certain parameters of the TimesNet-GRU, thereby constructing a prediction model for the target wind farm. To validate the effectiveness of this approach, the results from testing with actual data from five wind farms in northwest China demonstrate that the proposed method exhibits significant advantages over models without transfer learning as explored in this study. [ABSTRACT FROM AUTHOR]

Offshore wind speed in China plays a key role in affecting air–sea interactions, coastal tides, and wind energy, but its changes in a warming climate and the associated causes remain unclear. Based on the ERA5 reanalysis and the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, this study evaluates the past and future variations of wind speed at 10 m (WS10) over China's offshore seas in summer and winter. The results show that the CMIP6 multi-model mean performs well in simulating the climatological patterns (1981–2010) of WS10 for both seasons. The trends and leading variabilities in WS10 are also reasonably reproduced in the South China Sea (SCS). In the northern SCS, WS10 has strengthened during both seasons in the recent decades. In contrast, in the East China Sea (ECS), WS10 has increased (decreased) during summer (winter). Further attribution analysis suggests that the forcing of greenhouse gasses (aerosols) may make WS10 stronger (weaker) in the two seas and for both seasons, while natural variability tends to slow down (speed up) WS10 in the SCS and ECS during summer (winter). In addition, according to the CMIP6 model projections under various warming scenarios, WS10 is likely to increase over both the northern SCS and the ECS in summer, while WS10 will increase over the northern SCS but decrease over the ECS in winter. Differences in the projected WS10 changes in the ECS during summer and winter are attributed to the projected intensification (weakening) of the East Asian summer (winter) monsoon circulation. [ABSTRACT FROM AUTHOR]

Energy distribution justice is of primary concern within the energy justice framework and it is crucial to increase public acceptance of offshore wind energy and further advance its development. The rapid development of offshore wind energy in China has inevitably impacted the livelihoods of coastal vulnerable groups (CVGs) engaged in fisheries and tourism in the coastal zone. While current policies often compensate for livelihood losses through cash payments, the fiscal strain caused by COVID-19 renders this approach unsustainable. Consequently, this research pioneers the exploration of Chinese tourist groups' landscape preferences towards offshore wind farms (OWFs). This study proposes a new approach to enhance OWF landscapes for tourism development, thereby balancing the distribution of costs and benefits between CVGs and tourists. The research focuses on Beihai City in the Beibu Gulf Economic Region, utilizing a combination of Q-methodology and choice experiments that incorporates cut-offs. Answers to eighty Q-methodology questionnaires and 1324 choice experiment questionnaires are obtained. The findings indicate that this region can achieve energy distribution justice by compensating for the livelihood losses of CVGs through tourism. Contrary to traditional assumptions about wind farm noise preferences, Chinese tourists prefer proximity to OWFs, as an appropriate coastal acoustics landscape can enhance their tourism experience. In light of these findings, this paper presents policy recommendations towards energy distribution justice. [ABSTRACT FROM AUTHOR]

In China's mountainous coastal terrain, storms can badly damage low-rise buildings. At present, it is not clear how the relative position of buildings and mountains affects the surface of low-rise buildings. The study compared these results with the wind pressure distribution without the surrounding environment. The distribution of wind pressure in different hillside landforms is examined through a wind tunnel experiment, which is also compared with the distribution in an open environment. The study examined the fluctuating coefficient as the distance between the building and the hillside changed, specifically for wind blowing at a 0° angle. The investigation examined the power spectrum and wind pressure probability distribution while considering the proximity of the building to an adjacent hill. The findings indicated that as the distance between the slope and the mountain increases, the fluctuating wind pressure coefficient continues to increase, and the contour lines of the wind pressure distribution are relatively denser compared to where there is a mountain. The maximum value of the fluctuating wind pressure coefficient is 0.22, which appears at the windward roof. The roof's wind pressure coefficient fluctuated and gradually increased until it reached its peak, unaffected by the surroundings. The wind pressure on the leeward side exhibited Gaussian characteristics in its probability distribution. [ABSTRACT FROM AUTHOR]

The renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [ABSTRACT FROM AUTHOR]

The Chinese government has strongly acknowledged the strategic significance of offshore wind power in hydrogen production and has introduced many relevant policies for promoting the offshore wind power industry development. This study explains the evolution of policies regarding Chinese offshore wind power industry since 1995, with evolutionary characteristics such as emphasis on government role change, focus on problem orientation, prioritizing industrial upgrading, and close link to national energy strategy. However, there are still challenges in the formulation and implementation of policies, including the lack of scientific industrial construction planning, insufficient incentives for technological innovation, imperfect support for industrial integration, and inadequate environmental regulation. Moreover, this study also proposes prospects for the improvement and implementation of offshore wind power industry policies in China. The results of the study are intended to provide insights for decision makers in the clean energy industry and a useful reference for future hydrogen energy development. • This study offers a panorama of China's offshore wind power industry policy. • China's offshore wind power industry policy has distinct evolution characteristics. • This study analyzes the prospects of China's offshore wind power industry policy. • Results provide reference for the offshore wind power hydrogen production policy. [ABSTRACT FROM AUTHOR]

In recent years, the vigorous development of the wind power industry has become an important measure in the transformation of energy structure in China. However, the overall low technical efficiency of wind farms has severely hindered wind power industrial development. It is of great practical significance to evaluate the technical efficiency (TE) of wind power in China and analyze its main factors. Most of the existing literature on the assessment of the TE of wind power in China focuses only on large listed companies and applies the traditional data envelopment analysis (DEA) while ignoring its potential shortcomings for TE estimation. Based on panel data, this paper used stochastic frontier analysis (SFA) to construct an analytical model for assessing the TE and influencing factors of Chinese wind farms and compared the results with those from DEA to verify the robustness. The empirical results showed that the TE of Chinese wind farms was generally low. The age of a wind farm and its power consumption have a negative impact on its technical efficiency, while the utilization of power generation equipment has a positive impact on its technical efficiency. Enhancing the technological innovation capabilities of wind power companies, speeding up the construction of supporting infrastructure and solving structural problems of wind power supply and demand are important measures for the wind power industry to improve the overall TE and promote industrial development in China. [ABSTRACT FROM AUTHOR]

Monthly unit commitment (UC) is a decision‐making process that determines the optimal generation scheduling of mid and long‐term power system, often operating as a subproblem within monthly electricity market operations and long‐term maintenance scheduling optimization in China. Dealing with transmission constraints in monthly security‐constrained unit commitment (SCUC) is considered to be challenging, but its simplification can be achieved through eliminating inactive security constraints. Additionally, the uncertainty associated with wind power needs to be given due consideration in monthly scheduling. Here, a three‐step watchlist construction strategy is proposed to facilitate the rapid screening of overloaded power flow constraints in monthly unit commitment, including three lists (i.e. list of risk, list of concern, and list of interest) under different conservatism. Importantly, the shift factor method is utilized to screen the potential overloaded constraints arising from the redispatch process for hedging the effects of large wind uncertainties. Results obtained from the testing of IEEE‐118 system indicates that more than 99% of active power flow constraints could be successfully screened out when wind power fluctuation is at 20%. As such, the proposed scheme provides an effective method to consider uncertainty associated with large‐scale wind power generation integrated into the power grid. [ABSTRACT FROM AUTHOR]

Wind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China. Plain Language Summary: China has set an ambitious goal of achieving carbon neutrality by 2060, which requires an accelerated energy transition toward wind power and photovoltaic. However, the sources of wind and solar energy are sensitive to weather and climate, and future potential of wind and solar energy in China under a warming climate remains uncertain, posing a significant concern for the decision‐makers. Based on high‐resolution multiple regional climate models (RCMs) projections, we found that the annual average of wind power density (WPD) and photovoltaic power potential (PVP) are expected to decrease in the future by approximately −11.67% to −1.7% and −4.6% to −1.12%, respectively. There are notable uncertainties among the multi‐RCM projections, and the internal variability is the main source of uncertainty for most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. Our results highlight the advantages of reducing greenhouse gas emissions to enhance and stabilize renewable energy sources in China. Key Points: The annual average wind and solar energy in China are expected to decrease in the future based on a multi‐RCM ensembleThe internal variability plays a dominated role in the uncertainty of WPD and PVP projections in most sub‐regionsThe uncertainty in WPD and PVP projections over the TP is driven primarily by the different GCMs and emission scenarios, respectively [ABSTRACT FROM AUTHOR]

This study presents a bibliometric review focusing on the utilization of water electrolysis as a means of generating hydrogen as an energy carrier. The analysis includes research conducted over the past decade, covering from 2014 to 2023 (till August). Several key aspects are highlighted, including publication trends, the leading country in research output, journal rankings, and a citation analysis of papers related to water electrolysis. The findings reveal a notable increase in publication trends, with China emerging as the leading contributor in this research area. Furthermore, the International Journal of Hydrogen Energy is identified as the highest-ranked journal in terms of both publication number and citation impact. Additionally, the top ten most cited research articles and review papers are investigated to determine their influence within the field. To gain a deeper understanding of water electrolysis methods, the three primary approaches: alkaline water electrolysis (AWE), proton exchange membrane (PEM) electrolysis, and solid oxide electrolysis (SOE) is examined. The analysis emphasizes that PEM electrolysis is the most prevalent method for hydrogen generation, particularly when integrated with renewable energy sources such as solar and wind power due to its rapid response to electrical input fluctuation. Finally, the challenges and future directions of water electrolysis in hydrogen production are highlighted, including an exploration of economic and environmental considerations at large scale, offering insights into the path forward for advancing this technology sustainably. • A comprehensive bibliometric analysis of water electrolysis for hydrogen production. • Publication trends, journal rankings, and citation analysis are highlighted. • China is identified as the leading contributor in water electrolysis publications. • International Journal of Hydrogen Energy: the highest-ranked journal in this field. • Specific electrolysis methods, including AWE, PEM, and SOE are examined. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms. [ABSTRACT FROM AUTHOR]

Green hydrogen produced from renewable sources such as wind and photovoltaic (PV) power is expected to be pivotal in China's carbon neutrality target by 2060. This study assessed the potential production, levelized costs of hydrogen (LCOH), and the cost structure in diverse mainland Chinese provinces from 2020 to 2060. It considered various combinations of electrolysis technologies, specifically alkaline electrolysis (AE) and proton exchange membrane (PEM), in conjunction with green electricity sources. The analysis considers the technological learning effects of wind power, PV power, AE, and PEM. This study's primary conclusions and policy recommendations are as follows: (1) PV power would be the predominant energy for green hydrogen production in nearly all of mainland China, providing a potential 2.25–28 642.19 kt/yr hydrogen production in different provinces. (2) AE exhibits cost (with LCOH around 3.18–8.74 USD/kg) competitiveness than PEM (with LCOH around 3.33–10.24 USD/kg) for hydrogen production. Thus, policymakers are advised to focus on the PV power combined with the AE pathway for large-scale hydrogen production. PEM is suggested to be mainly used in cases with high power fluctuations and end devices. (3) The provinces (especially Inner Mongolia, Xinjiang, and Gansu Province) in the Northwest of China show the greatest potential (about 74.35%) and have the lowest LCOH (with around 3.18–4.78 USD/kg). However, these provinces are quite distant from existing energy demand hubs. Thus, decision-makers are advised to focus on developing long-distance transmission/transportation infrastructure for either green electricity or green hydrogen. [ABSTRACT FROM AUTHOR]

With the increasing application of wind energy, reliable wind speed prediction has become imperative. However, prior studies predominantly concentrated on single-model predictions, disregarding the inherent uncertainty in wind speed. This oversight resulted in inadequate deterministic and probabilistic forecasting outcomes across varying scenarios. To make up for these shortcomings, a novel forecasting system combining a data preprocessing technique, a sub-model selection method, and a modified multi-objective integrate optimization strategy is designed in this paper. According to the data obtained from China's wind farm, the forecasting efficiency of this system is verified from multiple perspectives. The findings show that the system takes advantage of each model to boost the precision and stability of point prediction successfully. Furthermore, it achieves higher interval coverage and narrower interval width under distinct confidence levels. These results highlight the system's potential as a reliable technical support for efficient dispatching of the entire power system. [ABSTRACT FROM AUTHOR]

As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of 'excellent' regions covers 83855 km2 and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km2) are classified as 'excellent'. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision. [ABSTRACT FROM AUTHOR]

Open offshore areas boast strong physical self‐purification capacity and abundant non‐fossil energy resources, such as wind, waves, and solar energy. Consequently, the global community anticipates nearshore aquaculture to transition towards offshore to help increase production, alleviate eutrophication, and reduce greenhouse gas emissions. To date, China has constructed over 40 sets of deeper‐offshore aquaculture (DOA) infrastructures, encompassing various types of pens, cages and closed containment systems. Although DOA holds vast potential to address food security and aquaculture sustainability in China, its current development trajectory struggles to meet those goals and primarily achieves profitability by focusing on high‐value species or products. For DOA to realize its potential, innovative production systems must tackle three key contradictions: enterprise profitability versus product affordability, clean energy‐based products versus carbon‐intensive products, and automated operation versus re‐employment of coastal fish farmers. Resolving these contradictions requires the development of a large‐scale, anti‐typhoon offshore enclosure that integrates mariculture with other industries, such as wind farming, food processing, and tourism. This approach will foster a sustainable balance between profitability, environmental impact, and employment opportunities in the sector. [ABSTRACT FROM AUTHOR]

This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences with thorough data cleaning, filtering, and smoothing. Subsequently, the refined K-means algorithm, augmented by the Pearson correlation coefficient and a greedy algorithm, clusters the wind power curves. The optimal number of clusters is ascertained through the silhouette coefficient. The final stage employs particle swarm and whale optimization algorithms for the extraction of quintessential wind power output curves, essential for flexibility planning in power systems. This methodology is validated through a case study involving wind power output data from a new energy-rich provincial power grid in North China, spanning from 1 January 2019, to 31 December 2022. The resultant curves proficiently mirror wind power fluctuations, thereby laying a foundational framework for power system flexibility planning analysis. [ABSTRACT FROM AUTHOR]

The wind speed varies randomly over a wide range, causing the output wind power to fluctuate in large amplitude. An isobaric adiabatic compressed air energy storage system using a cascade of phase-change materials (CPCM-IA-CAES) is proposed to cope with the problem of large fluctuations in wind farm output power. When the input power is lower than the minimum energy storage power of the compressor, the gradient phase-change thermal energy storage is utilized to broaden the operating range of the system. Second, the system design method and operation rules are elaborated. The storage/release characteristic curve is obtained by constructing the system components and the overall variable operating condition model. A matching system scheme is designed according to the characteristics of a wind farm in a port in China. The case study shows that the wind farm configured with the CPCM-IA-CAES system reduces the wind abandonment rate by 5.7%, recovers 4,644.46 kW h of wind power abandonment, and improves the storage power index by 16.67% compared with that of IA-CAES. Meanwhile, the system efficiency is increased from 65.96% to 74.68%, and the energy storage density is increased from 8.69 to 9.89 kW h m-3. [ABSTRACT FROM AUTHOR]

In China, wind power producers will participate in the spot market as strategic producers. They should submit offering prices and forecasted production to the independent system operator. Intraprovincial and interprovincial green certificate trading, as a mechanism to promote the development of wind power, is advanced in parallel with the spot market. Studying the bidding strategies of wind power producers in the spot market, especially with the introduction of intraprovincial and interprovincial green certificate trading, has great practical significance for the stable operation of wind power producers and the construction of a renewable energy-friendly electricity market. This paper proposes a bi-level stochastic model to simulate the bidding strategy of a wind power producer in the spot market. The upper level problem of the model describes the decision-making process of the wind power producer, and the lower level problems include the clearing process of the spot market and the TGC market, in which the spot market includes the day-ahead market and the balance market, and the TGC market includes the intraprovincial and interprovincial TGC markets. Then, using Karush–Kuhn–Tucker (KKT) conditions, the strong duality theorem, and the big M method, the bi-level model is recast into a mixed integer linear (MIL) model convenient to be solved. Finally, an example is given to analyze the wind power producer's optimal bidding strategy under different electricity market environments and different wind power penetration levels. [ABSTRACT FROM AUTHOR]

In order to explore the feasibility of a renewable hybrid energy system in highway tunnels, a scenario-coupled construction method for a highway tunnel renewable hybrid energy system is proposed. Based on this method, a tunnel on a highway in southern China serves as an example, and a renewable hybrid energy system for the highway tunnel is constructed. Using HOMER, the hybrid energy system is simulated, optimized, and analyzed, studying the characteristics of the operation of the highway tunnel hybrid renewable energy system in an off- grid mode. The optimal configuration for the hybrid energy system is proposed. The research results indicate the feasibility of constructing a highway tunnel renewable hybrid energy system by utilizing natural resources within the road area (solar energy, wind energy). The hybrid renewable energy system for this tunnel can provide 1112392 kWh of electrical energy annually, with the lowest energy cost being $0.17/kW. Furthermore, the hybrid energy system can reduce greenhouse gas emissions, contributing to the sustainable development of the environment. [ABSTRACT FROM AUTHOR]

In wind power prediction, the input probability distributions in the different sub-periods are shifted owing to the strong randomness of the input features, such as wind speed and direction. This may violate the assumption for machine learning that the training and test data meet the condition of being independent and identically distributed, resulting in an insufficient generalization ability of the prediction model that is trained with the training data and applied to unknown test data. To address this problem, this study proposes an adaptive temporal transformer method for short-term wind power forecasting. First, a temporal transformer model with a gate recurrent unit and multi-head attention layers was used to extract the short- and long-term temporal information of the multiple input variables. Then, an adaptive learning strategy consisting of two stages—temporal distribution characterization and temporal distribution matching—was developed to explore the common knowledge hidden in each sub-period. The case results for an actual wind farm in northwest China showed that the proposed method could effectively weaken the adverse effects of the shifts in time series distribution on forecasting and improve the accuracy and generalization of short-term wind power prediction. [ABSTRACT FROM AUTHOR]

With the progress of society, the supply of electricity has become an inevitable choice for the development of all walks of life. The stability, safety, and economic dispatch of the electric energy system directly impact the entire country's economic development, from national defense, military, and even people's daily life, all closely associated with the smooth running of the network. In the aspect of power grid planning and operation, the economic security dispatching problem of power system is a very typical optimization problem. The problem is how to maximize the operating cost of the system under the premise of satisfying the system's load limit and safety and stability. Due to the rapid development of power technology, the research on power system security and economic planning is essential. As an important means to promote China's energy structure adjustment and promote the supply side structural reform, the regional spot electricity market is playing an increasingly important role in building a resource optimization allocation mechanism, improving China's energy resource allocation efficiency, and promoting social and economic development. However, the development of China's regional spot power market is still in its infancy, and the transaction price of the regional spot power market is quite different from that of other countries. It was given these problems, this article discusses the use of particle swarm algorithms for secure and economic scheduling of power systems in the context of securely reading blockchains and distributed data. The research results showed that: The introduction of environmental pollution penalty programs improved the priority dispatch of wind and photovoltaic power when considering environmental penalty programs and backup penalties for wind and photovoltaic power. The setting of the coefficient of the environmental penalty term depended on the designer's emphasis on the priority scheduling of wind power and photovoltaic (PV) power generation (PG), and the standby capacity penalty clause achieved reasonable dispatch and utilization of wind power and PV PG, which reduced the dramatic fluctuations and intermittent volume of the system, and facilitated safe and consistent operation of the network. The research of this paper shows the positive relationship between the blockchain and distributed data security reading algorithm and the clearing method of regional spot power market, and points out a new method for its development. [ABSTRACT FROM AUTHOR]

With the proposal of the carbon peaking and carbon neutrality goals, China's energy structure is gradually shifting from relying on fossil fuels to relying mainly on renewable and clean energy systems. However, clean energy represented by wind and photovoltaic power generation is difficult to operate and consume separately due to output fluctuations and uncertainties. Hydropower stations have good regulation capabilities and can operate in conjunction with wind farms and photovoltaic power stations to alleviate the instability of new energy generation. The southwestern region of China is rich in hydropower resources, and a large number of cascade hydropower stations with regulatory reservoirs have been built. The complementary operation of cascade hydropower with wind power and photovoltaic resources around the watershed, and the full utilization of the regulating capacity of cascade hydropower to bundle and deliver high-quality electricity, is an important means of achieving energy transformation. To quantify the carrying capacity of cascade hydropower stations on wind power and photovoltaic resources, this paper proposes a nested calculation model for medium to long-term optimal scheduling of hydropower to short-term optimal allocation of hydro-wind-photovoltaic power. The short-term scheduling calculation boundary is determined based on the mid-term and long-term optimization scheduling calculation results of hydropower, the scale of wind and photovoltaic power during short-term scheduling is adjusted, and scheduling with the goal of maximizing source load matching, maximizing power generation, and minimizing abandoned power are optimized. Decoupling is carried out by using a hierarchical nested strategy to calculate the scale of wind and photovoltaic resources that can be carried by cascade hydropower stations. Applying this model to the cascade hydropower station group in the Yalong River Basin, the scale of wind and photovoltaic resources carried by the "three reservoirs and seven cascade" cascade hydropower stations in the Yalong River Basin is calculated. The research results show that considering the 2025 planning level year and 5% curtailment rate constraint, the cascade hydropower in the Yalong River basin can carry 1.396 million kW of wind power resources and 10.74 million kW of photovoltaic power resources. The research results have a certain reference value for the optimization of water solar energy complementary power generation systems in the watershed. [ABSTRACT FROM AUTHOR]

Offshore wind power integration is a grand challenge due to the volatility, randomness and intermittency This work presents a wind-thermal-electrolysis-battery (WTEB) hybrid energy system designed to integrate large-scale offshore wind energy and reduce curtailment. The system composes of offshore wind farms (OWF), thermal power plants (TPP), electrolysis station, battery bank. The Qingzhou (geographic name) I-VII offshore wind farms (5000 MW) and the Yangxi (geographic name) coal-fired power plant units #5, #6 (2 × 1240 MW) are taken as a case study. The OWF is the indispensable generator and the renewable fraction increases with integration level. The TPP adjusts the load ratio to meet the changing net load. The electrolysis station consumes the excess electricity and the battery bank time-shifts the electricity to reduce the electrolyzer downtime. The recommended wind-thermal (W-T) capacity ratio is 1.452:1 in the studied case, namely 3600 MW OWF. The WTEB system breaks the trade-off relationship between renewable fraction (50.11%) and curtailment rate (1.48%) compared to traditional wind-thermal (WT) bundled system. The WPEB system improves the electricity shortage rate (4.74%), transmission line capacity factor (61.79%), load following precision (91.79%). The internal rate of return (IRR), levelized cost of electricity (LCOE), levelized cost of hydrogen (LCOH) of WTEB-W3600 are 34.70%, 0.5172 ¥/kWh, 29.76 ¥/kg. The WTEB hybrid energy system for a large-scale offshore wind power integration is first proposed and the techno-economic feasibility analysis proves the feasibility and forward-looking of the system. Collaborative capacity optimization of OWF, TPP, electrolysis station, battery can improve the system performance and economy. The wind-photovoltaic-thermal-electrolysis-battery (WPTEB) system co-locating the OWF and floating photovoltaic (PV) is an important development direction. Figure. Schematic of the wind-thermal-electrolysis-battery (WTEB) hybrid energy system in Yangjiang, China. [Display omitted] • WTEB enables a large-scale offshore wind power integration and curtailment reduction. • WTEB improves load following precision, electricity shortage and curtailment rate. • The wind-thermal (W-T) capacity ratio of the WTEB system is 1.452:1. • The electrolyzers and battery bank operate on the curtailed electricity. • The NPV, LCOE, IRR of WTEB-W3600 are 3.13 billion¥, 0.5172 ¥/kWh, 4.70%. [ABSTRACT FROM AUTHOR]

Currently, a standardized design and calculation specification for suction drum foundations has yet to exist in China. The engineering design currently depends mainly on the subjective understanding and engineering experience of the designers, which can be considered somewhat blind and subjective. In this paper, we utilize the offshore wind power project in Yangjiang City, Guangdong Province, as our case study. Building upon domestic and international research results, relevant investigations, design specifications, and engineering applications in related fields, we conduct a systematic study on the design calculation and construction control technology of the suction drum foundation. The document presents the design calculation and inspection of the suction drum foundation. Building on this foundation, we propose a sinking feasibility analysis method and a parameter value method for the suction drum foundation calculation. We also examine the suction drum foundation construction process, examining its control parameters, technology, and standards. Finally, based on the measured data from six four-barrel guided frame platform suction drum foundations that were successfully installed, the proposed design and control method are evaluated, and their effectiveness is verified. The results of this study can provide valuable references for the design and construction of similar suction drum foundation platforms. [ABSTRACT FROM AUTHOR]

Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations. Plain Language Summary: Wind energy is one of the key renewable energy sources and has been widely developed worldwide to mitigate global warming. Extreme wind can damage wind turbines and jeopardize the reliability of wind power generation, disturbing the stability of regional power supplies. Therefore, understanding the spatial and temporal variability of extreme wind speeds at wind turbine hub height under climate change is essential for the development of local wind power. This study examines the spatial and temporal patterns of 100‐m extreme wind speeds in China using empirical downscaling and Bayesian model averaging ensemble methods driven by several global climate models and reanalysis data. The results show that Inner Mongolia, northeastern China, western Tibet, and eastern coastal areas are the areas in China where high extreme wind speeds are most prevalent. This spatial pattern will typically persist in the future, but extreme wind speeds in Tibet, Qinghai, and Sichuan will drop dramatically by the end of the 21st century. Extreme wind speeds are considerably higher in winter and autumn than in the other two seasons. Local governments should take measures to prevent regional energy disparities due to the deterioration of wind turbines because of the anticipated wind extremes under climate change. Key Points: An empirical downscaling Bayesian ensemble projection model is constructed to investigate extreme wind speed variationsExtreme wind speeds in central and southeastern China are projected to increase in future periodsExtreme wind speeds are considerably higher in winter and autumn than in the other two seasons [ABSTRACT FROM AUTHOR]

An extreme torrential rain (ETR) event occurred in Henan Province, China, during 18–21 July 2021. Based on hourly rain-gauge observations and ERA5 reanalysis data, the ETR was studied from the perspective of kinetic energy (K), which can be divided into rotational wind (VR) kinetic energy (KR), divergent wind kinetic energy (KD), and the kinetic energy of the interaction between the divergent and rotational winds (KRD). According to the hourly precipitation intensity variability, the ETR process was divided into an initial stage, a rapid increase stage, and maintenance stage. Results showed that the intensification and maintenance of ETR were closely related to the upper-level K, and most closely related to the upper-level KR, with a correlation coefficient of up to 0.9. In particular, the peak value of hourly rainfall intensity lagged behind the KR by 8 h. Furthermore, diagnosis showed that K transformation from unresolvable to resolvable scales made the ETR increase slowly. The meridional rotational wind (uR) and meridional gradient of the geopotential (φ) jointly determined the conversion of available potential energy (APE) to KR through the barotropic process, which dominated the rapid enhancement of KR and then caused the rapid increase in ETR. The transportation of K by rotational wind consumed KR, and basically offset the KR produced by the barotropic process, which basically kept KR stable at a high value, thus maintaining the ETR. [ABSTRACT FROM AUTHOR]

As a kind of renewable energy enterprise, wind power enterprise plays an important role in the energy transformation. However, its financing problems are also widespread. The purpose of this paper is to study the financing mode selection in order to improve sustainable development and future benefits of wind power enterprises. First, an evaluation index system is constructed through literature research and expert interviews, and the weight of each index is determined comprehensively by G1 and information entropy methods. On this basis, a financing mode selection model of wind power enterprises is constructed by using the prospect theory and hesitant fuzzy set. Next, this framework is used for case study. Financing patterns are ranked according to the comprehensive prospect value. The results show that financial leasing is the most suitable financing mode. Finally, the applicability and optimization of the model are proved by sensitivity analysis and comparative analysis. [ABSTRACT FROM AUTHOR]

To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China's isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, to analyze battery-swapping station operation and isolated microgrid operation schedules. Considering the needs of potential future communities that might depend on electric vehicle battery-swapping stations, two scenarios were analyzed, and the most effective integer planning method was adopted. The international algorithm software program YALMIP + CPLEX was used to address the problem, and simulation results proved that the proposed model and its solution method could effectively affect the safe operation and scheduling of islanded microgrid battery-swapping stations and reduce the cost of islanded microgrid operation, with significant advantages. [ABSTRACT FROM AUTHOR]

Foreign ownership of U.S. farmland has recently attracted growing interest from the public as well as the federal and state policy makers. Using all reported AFIDA transactions, this article provides a comprehensive analysis on the structure of foreign land ownership in the United States. We find that (1) long-term leasing is the main driver of the increasing foreign interests of U.S. farmland in the past 20 years; (2) a considerable number of foreign transactions are related to wind and solar energy development, especially for entities holding long-term leases; and (3) “adversary” countries like China hold only 1% of all the foreign-owned agricultural land. [ABSTRACT FROM AUTHOR]

Sustainable energy sources, such as wind energy, are pivotal in driving our energy landscape towards a more environmentally conscious and responsible future. Wind power, as an exemplar of clean and renewable energy solutions, adeptly harnesses the kinetic energy of the wind to generate electricity. While wind energy significantly contributes to our sustainability objectives, the quality of the energy it produces is equally essential. A critical challenge in this context is harmonic distortion, which manifests as unwanted fluctuations in the frequency and amplitude of electrical waveforms. Effectively mitigating these distortions within wind energy systems is vital to maintaining the stability and reliability of power grids, guaranteeing that the electricity supplied adheres to high-quality standards. The objective of this study was to conduct a technological prospection focused on the contemporary scenario of genetic algorithm applications in addressing harmonic variations. This investigation unearthed a total of 634 relevant documents. The findings suggest that the utilization of genetic algorithms for enhancing energy quality is a relatively recent but promising field. The State Grid Corp of China emerged as the principal contributor, with ten noteworthy inventors identified. Remarkably, both China and the United States lead in patent filings. The insights gleaned from these documents underscore the potential for further exploration and the synergistic application of these techniques. These collaborative efforts have the potential to yield processes and devices that offer significant economic and environmental advantages for the energy industry, solidifying our commitment to a cleaner and more sustainable energy future. [ABSTRACT FROM AUTHOR]

This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement. [ABSTRACT FROM AUTHOR]

China's coal chemical sector uses coal as both a fuel and feedstock and its increasing greenhouse gas (GHG) emissions are hard to abate by electrification alone. Here we explore the GHG mitigation potential and costs for onsite deployment of green H2 and O2 in China's coal chemical sector, using a life-cycle assessment and techno-economic analyses. We estimate that China's coal chemical production resulted in GHG emissions of 1.1 gigaton CO2 equivalent (GtCO2eq) in 2020, equal to 9% of national emissions. We project GHG emissions from China's coal chemical production in 2030 to be 1.3 GtCO2eq, ~50% of which can be reduced by using solar or wind power-based electrolytic H2 and O2 to replace coal-based H2 and air separation-based O2 at a cost of 10 or 153 Chinese Yuan (CNY)/tCO2eq, respectively. We suggest that provincial regions determine whether to use solar or wind power for water electrolysis based on lowest cost options, which collectively reduce 53% of the 2030 baseline GHG emissions at a cost of 9 CNY/tCO2eq. Inner Mongolia, Shaanxi, Ningxia, and Xinjiang collectively account for 52% of total GHG mitigation with net cost reductions. These regions are well suited for pilot policies to advance demonstration projects. The coal chemical sector uses coal to produce chemicals and emits substantial greenhouse gases, which are hard to abate by electrification alone. Deploying green H2 for China's coal chemical plants can reduce ~50% of emissions at a low cost. [ABSTRACT FROM AUTHOR]

Existing studies have primarily focused on the resources and technologies related to renewable energy. However, it is essential to investigate the influence of economic, social, and governmental actions on the spatiotemporal changes in renewable energy. This paper utilizes spatial autocorrelation analysis, fixed effects modeling, and Geographical and Time Weighted Regression (GTWR) to analyze the phased characteristics and spatial differences of renewable energy electricity, based on time series and spatial layout data of renewable energy power by province and power source type in China. Additionally, it incorporates relevant economic, demographic, and investment data to identify the driving factors behind the spatiotemporal transformations of photovoltaic (PV) and wind power, as well as the main forces promoting sustainable growth. The study reveals the presence of spatial agglomeration in China's renewable energy power generation. Moreover, a positive correlation is observed between investments in environmental protection (EPI), state-owned electric power (SPI), scientific and technological advancements (STI), and renewable energy electricity. Technological progress has reduced the dependence of renewable energy on the natural environment, facilitating its promotion and widespread adoption. These findings provide empirical support and policy guidance for the sustainable and rapid growth of China's renewable energy industry. [ABSTRACT FROM AUTHOR]