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Renewable energy is regarded as an effective method to alleviate environmental pollution and accelerate the green transition in China. This paper systematically investigates the impact of financial inclusion on renewable energy demand by using S-GMM model based on provincial data in the green finance era. This paper also analyzes the asymmetry, heterogeneity, and mediating mechanism in the nexus between financial inclusion and renewable energy demand, and highlights the following findings: (1) Improved financial inclusion can promote renewable energy development in China and present the characteristics of green finance; (2) the impact of financial inclusion on renewable energy demand is asymmetric and heterogeneous; specifically, this impact is much higher in northern China and the regions with higher renewable energy development; and (3) wind and photovoltaic power generation are effective mediators between financial inclusion and renewable energy industry, while the mediation effects of hydropower and nuclear power are not significant. New evidence in this study is presented for the achievement of green transition in China, as well as practical policy implications for improving financial inclusion and increasing renewable energy consumption. For instance, fiscal and tax incentives and green investment subsidies in the renewable energy sector need to be further improved. • This study explores the impact of financial inclusion (FI) on renewable energy demand (RED) in China. • Asymmetry, heterogeneity, and the mediating mechanism are further discussed. • Increased FI can promote RED , which presents the characteristics of green finance. • The impact of FI on RED is asymmetric and heterogeneous. • Wind and photovoltaic power are effective mediators between FI and RED. [ABSTRACT FROM AUTHOR]

Renewable portfolio standard is an effective administrative tool to guide the behavior of electricity producers and impact the renewable energy diffusion. However, currently the renewable energy quota is generally set to be static and the efficiency is limited. In this paper, we proposed a dynamic renewable portfolio standard policy and we aimed to analyze how it will impact the renewable energy diffusion in China. The connections between electricity producers were simulated under both the perfectly competitive market and monopoly market. And a networked evolutionary game approach is applied considering dynamic linking between the electricity producers. The results show that dynamic quota policy is effective in promoting the proportion of wind power generation from 20% to 59.17% and promoting the proportion of solar power generation from 20% to 79.25%. With the sensitivity analysis, we found that renewable energy quota and the on-grid price are the most influential parameters. The proportion will reach 76.47% if quota is 0.7 and 68.1% if on-grid price is 630 Yuan/MWh. And the influence of tradable green certificate prices and generation cost is limited in promoting renewable energy diffusion. This paper offers the Chinese government a potential implementation for the perfection of national renewable energy diffusion. [Display omitted] • Propose a novel dynamic renewable portfolio standard scheme. • Analyze the influences of different kinds of the electricity market. • Investigate parameters affecting the wind power and solar power generation. • Establish dynamic networked evolutionary game models. • Dynamic linking is considered when the connections of players change. [ABSTRACT FROM AUTHOR]

The promotion of wind power is an important strategic measure to promote energy transformation and achieve carbon neutralization in China. Driven by a series of industrial policies, China's wind power industry is still faced with the dilemma of "large quantity yet poor quality". Therefore, the paper considers as research object the wind power industry policies issued by the government of China between 2008 and 2019, and evaluates their impact on the innovation quality of enterprises from the perspective of financing modes. The results show that different categories of industry innovation policies and their combination have an important positive influence on enterprise innovation quality. There are differences in the regulatory effects of different financing modes on wind power industrial policy and enterprise innovation quality: internal financing inhibits the effect of industrial policy on enterprise innovation quality, while external financing promotes the impact of industrial policy on enterprise innovation quality. Further analysis shows that different categories of innovation policies and their combinations have impacts on the innovation quality of both private and state-owned enterprises, and in many regions. Finally, based on the research results, there are recommendations for policy managers and micro enterprises to stimulate the innovation quality of wind power plants. • The paper evaluates wind power industry policies and their combination impact on the innovation quality of enterprises from the perspective of financing modes. • Different categories of wind power industry policies and their combinations have an important positive influence on enterprise innovation quality. • There are conspicuous differences in the regulatory effects of different financing modes on industry policy and enterprise innovation quality. • Different types of industry policies and their combinations have impacts on the innovation quality of both private and state-owned enterprises, and in many regions. • There are differences in the regulatory effects of different financing modes on industry policy and innovation quality of both private and state-owned enterprises, and in many regions. [ABSTRACT FROM AUTHOR]

The world's key renewable power markets are generally challenged by wind and solar power curtailment. Research on the influencing factors of curtailment improvement can provide a reference for the further penetration of renewable energy (RE) into the power system. Based on the logarithmic mean Divisia index (LMDI) method, this paper analyzes the factors influencing the wind and solar power consumption rate in China from 2015 to 2018, and four typical provinces are discussed in detail. By decomposing the consumption rate into six factors (transmission structure, generation proportion, power self-sufficiency, resource development, resource utilization, and power consumption), and focusing on the transmission structure factors, this paper further clarifies the contributions of local use, ultra-high-voltage (UHV) transmission, and conventional transmission to the improvement of curtailment. The results reveal the following. (1) At the national level, the power mixing effect and the resource development effect have played major roles in the change of the consumption rate, but the influences of these two effects have been decreasing. (2) The UHV power lines provide a robust resource for the inter-provincial deployment of renewable power (such as the RE power transfer in Inner Mongolia and Gansu), but the role of UHV transmission has not yet been fully utilized. (3) The impact of the electricity consumption effect on renewable power consumption is increasing year by year, but the local consumption capacity remains insufficient (both at the national level and in the four typical provinces analyzed), so it is necessary to further increase the share of renewable electricity in UHV power transmission and the local consumption capacity. (4) The expanding development of wind and solar power may be the reason for the curtailment in Shaanxi, and a reasonable renewable power development plan must be further formulated. [ABSTRACT FROM AUTHOR]

Accurate and credible operation data sets of wind and solar power stations are the basis of many research works. However, such data sets often contain abnormal data due to failure, maintenance, energy curtailment, etc. The existing identification methods fail to consider the operating characteristics of power stations and the forms of abnormal data, resulting in low identification ability. Therefore, an adaptive identification method of abnormal data (AIMAD) in the wind and solar power stations is proposed in this paper, including the bidirectional one-sided quartile method and double DBSCAN method to deal with unevenly distributed abnormal data; the improved K-means clustering method based on the distance between the cluster center and benchmark power curve to process the abnormal data that are densely accumulated and closely connected with normal data in the power scatter diagram. The proposed method can adjust adaptively according to the forms of abnormal data to realize accurate identification and has strong robustness for power stations. The operation data of 30 wind farms and 8 solar plants in China are taken as examples to verify the effectiveness and superiority of the proposed method. • BOQ and d-DBSCAN are proposed to deal with unevenly distributed abnormal data. • Improved K-means method is proposed for identifying stacked abnormal data. • AIMAD is proposed to realize accurate identification of abnormal data. • Operation data in 38 stations are used to verify the effectiveness of AIMAD. [ABSTRACT FROM AUTHOR]

Wind power curve describes the relationship between wind speed and output power of wind turbine. In this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The active shape model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. An energy space is constructed based on class uncertainty map generated from dual feature images and eventually the optimal segmentation boundary is determined by an optimization process. Comparative experiments are conducted over the real-world datasets consisted of 37 wind turbines from two wind farms located in West and East China respectively. The accuracy reaches as high as 0.95, the precision rate P is 0.98, recall rate R is 0.99 and F1-score is 0.96, which demonstrates the superior performance of the proposed method. Besides, the reproducibility and sensitivity analyses are also carried out to assess the algorithm's performance. • A novel image-based abnormal WPC data cleaning algorithm is presented in this paper. • A multi-scale image space is built to determine the optimal scale for WPC data binarization. • Dual feature images are constructed to generate the class uncertainty map. • The Active Shape Model is used to model WPC shapes and filter abnormal data. • Accuracy, reproducibility and sensitive analysis are conducted on real-world datasets. [ABSTRACT FROM AUTHOR]

The transfer of energy-intensive projects to areas abundant in clean energy resources holds promise for optimizing the energy structure. After obtaining specific information on transferred projects of listed energy-intensive firms through search or phone contact, this paper innovatively constructs a three-dimensional panel containing 1,859,220 observations to empirically examine the attraction of clean energy to energy-intensive projects under the carbon intensity constraint policy (CICP). Our findings reveal that: (1) the CICP promotes the transfer of gray energy-intensive projects, leading to the transfer structure trending towards graying. (2) Furthermore, we observe a positive correlation between the proportion of thermal power in transfer-out areas and the outflow of gray energy-intensive projects. Conversely, areas with higher wind power and photovoltaics attract a greater inflow of gray energy-intensive projects. (3) Notably, clean energy predominantly attracts the inflow of gray projects rather than green projects. Our findings provide micro evidence and valuable insights into the transfer of energy-intensive industries, informing the design and implementation of future environmental policies for policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

Wind energy assessment at multi-time scale is essential for the effective utilization of offshore wind resources. Conventionally, energy assessment factors are estimated based on parametric models under the assumption of wind speed stationarity. However, given the subjection of seasonal variability and long-term trends, the hypothesis is violated, especially in the context of climate change. In this paper, to advance the conventional approach, wind speeds were modelled using the decomposition-based method to consider the non-stationarity, and a novel framework for the estimation of energy factors at multi-time scale was designed. Using long-term ERA5 data, wind energy potential across the sea area along Chinese continental coastline was investigated. It is demonstrated that the established non-stationary models are superior to commonly used stationary models, allowing the estimation of energy assessment factors at arbitrary time scales after one-time parametrization of parametric distributions. Results confirmed that wind energy exhibits apparently temporal and spatial variability. The difference between annual averaged wind power density at different nodes exceeds 1000 W/m2. Moreover, significant long-term trends of wind energy characteristics were detected. The increase can be up to 16 W/m2/year in Taiwan Strait, emphasizing that an adequate consideration of the non-stationarity of wind speed in future research is required. [ABSTRACT FROM AUTHOR]

The issue of large-scale retirements of renewable energy modules has attracted considerable attention from governments and the public worldwide. Using the Chinese wind and photovoltaic industries as examples, this paper, for the first time, employs a real options approach to explore the optimal time for energy firms to retire their power plants and to predict the future trend of renewable energy wastage from a bottom-up perspective. The results show that the optimal retirement time for the four subdivided categories ranges from 16 to 23 years. The later the investments are made, the earlier the decommissioning time comes and the lower the project returns. Furthermore, wind projects have been found to generate more revenue than PV projects for the same size, and the gap continues to widen. Offshore wind technology always has the longest optimal service life (18.62 years in 2022) and the greatest project returns (10.84 million CNY in 2022). Annual utilization hours, the renewable electricity price and the waste price can accelerate the retirement of plants and generate more revenue for energy companies, while the initial investment cost has a completely opposite effect. The first wave of PV retirements will occur in 2035 at 305.82 GW, while onshore wind will be in 2036 at 530.77 GW and offshore wind will not explode until 2040 at 65.84 GW. An assessment of potential policies suggests that for every 100 CNY increase in incentive strength, the retirement wave arrives 1 year earlier. In contrast, for every 100 CNY increase in the strength of penalties, the retirement wave is delayed by 1 year. In addition, we verify that the size of the plants does not affect the forecast of the retirement trend, indicating that the assumptions about installed capacity do not cause bias. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and intermittence of wind power, and the geographical mismatch between the wind power bases and the load demand, the problem of wind curtailment is becoming increasingly serious in China. To promote the consumption of wind power, this paper studies the short-term operation of a wind farm-pumped storage hydropower plant (WF-PSHP) hybrid system which transmits power to multiple cross-regional power grids through ultra-high-voltage (UHV) transmission lines. In this study, the forecasted error distribution of wind power is fitted using kernel density estimation. An optimization model that considers the peak shaving demands of receiving-end power grids (REPGs) is then established to determine the daily operation of the WF-PSHP system. The hydraulic constraints of the PSHP and individual unit operation constraints are also taken into account to make the model more accurate. To improve the solving efficiency, the original stochastic model is transformed into a deterministic mixed-integer linear programming (MILP) problem through several deterministic conversion and linearization techniques. The real-world case studies demonstrate that the integrated transmission of the WF and PSHP can reduce the peak-valley difference of the REPGs while absorbing large-scale wind power on different typical days in summer and winter. [ABSTRACT FROM AUTHOR]

The increasing generation of renewable energy and its concurrent technological innovation are driving the transformations of the energy sector, but little effort has been made at the regional level to specify the heterogeneous impacts of different types of renewable energy development on multi-dimensional energy security. Therefore, this paper establishes a four-dimensional energy security assessment system based on the current energy market situation and economic context of China. Renewable energy generation and technological innovation were simultaneously used as the core proxies for renewable energy development. Then, the panel quantile regression method was used to analyze the heterogeneity of the impact of solar, hydro, and wind energy on energy security, covering 30 provinces in China from 2006 to 2019. The empirical results show that renewable energy power generation generally promotes energy security, but it cannot be effectively applicable to all levels of energy security. Renewable energy technology innovation has surprisingly hindered energy security. These results can serve as a policy and practical guide, suggesting that provincial governments in China should implement energy security and renewable energy policies in line with local characteristics. • The redefined provincial energy security presents obvious spatial heterogeneity. • Panel quantile regression was applied to balanced provincial panel data. • Hydropower contributes only to medium and low levels of energy security. • Wind and solar power contribute differently to all levels of energy security. • Renewable energy technological innovation impedes energy security. [ABSTRACT FROM AUTHOR]

Wind speed accurate forecasting plays an important role in preserving the stability of offshore wind power. Most of current predictions are based on a single wind node. It is difficult for this methods to capture wind high dimensional features and latent spatio-temporal dependencies. In this paper, we propose a general graph optimization neural network specifically for multi-node offshore wind speed prediction named spatio-temporal correlation graph neural network. The proposed model firstly employs graph convolution, which performs Laplace transforms instead of the traditional convolution, to capture the potential spatial dependencies from the nodes' relationship and historical time series better. The channel-wise attention makes the original concentrated weights within a region constructed by adjacency matrix disperse to all input nodes, which distinguishes the nodes' contributions and generates high dimensional spatial features. Long short-term memory is applied to extract temporal correlation from the high dimensional spatial features. The model has ability to excavate the full potential of spatial-temporal dependencies from multiple wind nodes to the utmost. Experiments select 120 wind nodes in the China sea for prediction. The results show that the proposed model can be very competitive with state-of-the-art methods and holds great performance on multi-node and multi-step wind speed forecasting. • A graph optimization model is proposed for multi-node offshore wind speed prediction. • The GCN module can capture multiple wind nodes relationships' spatial correlation. • Channel-wise attention distinguishes the nodes' contributions to spatial dimensions. • The parallel LSTMs is specifically adopted to further integrate time dependencies. • The proposed STGN can track actual wind speed tendencies and maintain stability. [ABSTRACT FROM AUTHOR]

To achieve a high penetration of renewable energy integration, an effective solution is to explore the interdependence between numerical weather prediction (NWP) data and historical wind power to improve prediction accuracy. This paper proposes a novel combined approach for wind power prediction. The characteristics of NWP and historical wind power data are extracted by using the feature extraction technique, the predictor is designed based on extreme learning machine (ELM) and least squares support vector machine (LSSVM) model, and then key parameters of the prediction models are optimized by improving cuckoo search (ICS) to obtain a reliable value, which is defined as the pre-combined prediction value (PPA). To obtain a reliable result, a variance strategy is developed to allocate the weights of the pre-combined prediction model to obtain the final predicted values. Four seasons dataset collected from regional wind farms in China is utilized as a benchmark experiment to evaluate the effectiveness of the proposed approach. The results of comprehensive numerical cases with different seasons show that the proposed approach, which considers multiple-error metrics, including error metrics, accuracy rate, qualification rate, and improvement percentages, achieves higher accuracy than other benchmark prediction models. • A novel combined framework is proposed to predict short-term wind power. •Linear and nonlinear feature extraction strategies are applied to analyze the character of NWP data. •A variance combined strategy is used to allocate the optimal weight of the primary combined model. •The wind power prediction error analysis is conducted by multiple-error performance metrics. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind and solar power in China, the consumption of these intermittent renewable energies is severely restricted by the capacity of the transmission channel, which leads to massive renewable energy curtailment. Therefore, it would be beneficial to use limited transmission channels to absorb as much renewable energy as possible. In this paper, we propose a chance constraint-based multistage nested hydro-wind-solar coordinated optimal scheduling model to aid peak shaving while ensuring maximum power generation. First, multistage partitioned section chance constraints are introduced to mitigate the power congestion of the transmission network. Then, based on the principle of using hydropower to complement the uncertainty of wind and solar power, the compensation chance constraints are considered. To quantify the uncertainty of wind and solar power, their prediction errors are analyzed using the Gaussian mixture model. Finally, the model is recast and linearized into a mixed integer linear programming model. A case study of a hydro-wind-solar base in Southwest China demonstrates that the proposed model can effectively leverage the regulation ability of hydropower to coordinate multiple power sources with the restrictions of multistage transmission sections, effectively alleviating the congestion of transmission channels and reducing the curtailment of renewable energy. [ABSTRACT FROM AUTHOR]

Since the early stage of wind power development, the Chinese government has attached great importance to the nurturing of the domestic wind power industry and the establishment of a domestic supply chain. This paper examines the trajectory of China's policy pathway by linking the evolution of policy packages with the changing operation states of wind equipment manufacturers (mainly upstream and midstream). It is found that the government selectively combined quantity-oriented policies, quality-oriented, and cost-oriented policies at different historical stages and that a gradualist approach was adopted by strengthening quality and cost regulations in a piecemeal fashion. Though this gradualist approach might risk regulatory gaps, business disputes, and financial stress along the supply chain, it also provides some buffer time for manufacturers to adapt to new quality standards and cost expectations through technological improvement, optimization of production scale, and supply-chain management. The subtle balance between the strengths and weaknesses of this gradualist approach has been centered around the government's capacity to constantly assess the conditions and reactively respond to the needs of domestic enterprises. An in-depth analysis of these dynamic relations helps to better comprehend China's industrial policy pathway and appreciate both its past achievements and future challenges. [ABSTRACT FROM AUTHOR]

To achieve the carbon-neutral goal set for 2060, China has promoted renewable energy development for more than two decades. Meanwhile, China's electricity market is undergoing a new round of reform by introducing more market-oriented mechanisms and changing the market structure. This paper investigates the impact of three electric utility market structures experimented with in China on promoting renewable energy through a simulation model. The three market structures are vertical monopoly, weak monopoly (separation of power generators and grids), and monopolistic competition (Cournot-Nash competition). The model is calibrated with the actual data of the regional utility network in Beijing, Tianjin, and Tangshan areas. The simulation results show that the electricity market structure plays a vital role in renewable electricity, and the competition among utilities tends to benefit renewable energy development (wind power here). The study's conclusions are helpful to our understanding of further reforms in the electric utility industry and the long-term clean energy target in China. [ABSTRACT FROM AUTHOR]