Search

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 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]

Hydrogen production from wind power and energy storage from wind power are considered as effective measures to overcome the problem associated with wind curtailment. However, their further development is clearly constrained by the incurred costs. Hydrogen production from offshore wind power (HPFW), hydrogen production from onshore wind power (HPNW), and underground pumped hydro energy storage from wind power (UPHESW) are considered in this paper. This paper employs a life cycle cost analysis to calculate and compare the levelized costs of hydrogen production and energy storage in China. A sensitivity analysis is conducted to evaluate the influences of different factors on levelized costs. The results show that the levelized cost of HPFW is 1.44 times that of HPNW. The levelized cost of UPHESW is lower than that of traditional pumped hydro energy storage with the same installed capacity and also lower than that of UPHESW with the smaller installed capacity. The economic competitiveness between UPHESW and HPNW depends on the installed capacity of UPHESW. If it exceeds 453 MW, UPHESW is more economical. If it remains below 453 MW, HPNW is more economical. [ABSTRACT FROM AUTHOR]

With the development of the economy and society, the problems of resources and the environment are becoming more and more prominent. As an environmentally friendly and clean energy, wind energy is developing rapidly with the support of relevant policies and technologies. At the same time, the development of the wind power industry is also faced with some problems, as the "abandoning wind" phenomenon of wind farms is still relatively common; the continuous decline of wind turbine prices and the sharp rise in operation and maintenance costs have further reduced the profit space of wind power manufacturers. The fundamental reason for the series of difficulties in the development of the wind power industry is the imbalance between the various links in the wind power industry chain. There is an urgent need to evaluate the performance of the wind power industry chain to improve the weak links. In this paper, by using the three-stage data envelopment analysis (DEA) model, the input-oriented BCC model is used in the first stage, and the stochastic Frontier analysis method is used to analyze the input relaxation variables in the second stage to eliminate two exogenous factors. In the third stage, the adjusted input variables are recalculated in the first stage, and the chain performance value of the wind power industry excluding environmental factors and random errors is obtained in the performance evaluation. This paper takes China's wind power industry as an example and makes an overall analysis of the upstream, middle, and downstream of the wind power industry chain from three aspects: technology performance, scale performance, and pure technology performance. The results show that under the support of policy and economy, the technical performance of the upper and lower reaches of China's wind power industry chain is higher, while the performance of the wind power industry chain is underestimated by environmental factors. The three-stage DEA model can eliminate the influence of environmental and random errors, which makes the evaluation of the wind power industry chain more objective. In addition, according to the evaluation results, the optimization direction of each structure of the industrial chain is put forward. [ABSTRACT FROM AUTHOR]

As the installed capacity of renewable energy continues to increase, the proportion of China's renewable energy power generation has increased year by year, and carbon emissions have been effectively reduced. However, there is still a big gap between this and China's promised carbon emission reduction target. It is important to explore the coordination and optimization of power supply structure with the goal of carbon emission reduction. In the power market environment, there are conflicts of interest among wind power companies, thermal power companies, and energy storage equipment. Therefore, it is necessary to comprehensively consider their economic and environmental benefits to formulate subsidy policies. In order to solve the above problems, this paper studies the key issues of smart grid operation model, input-output efficiency, market game behavior, and policy effect simulation based on production simulation, game theory, statistics, and other theories. Finally, Matlab and Gridview were used to calculate the model results, and policy recommendations on wind power subsidies, thermal power carbon tax, and thermal power feed-in tariff were put forward. To achieve the goal of carbon emission reduction, renewable energy generation should be encouraged, and the proportion of installed capacity of wind and solar energy in the power structure should be increased. The most effective way is that the government should actively guide the subsidy mechanism into the electricity trading market. Based on the calculation results of the RTS-25 system in this article, the following policy recommendations can be put forward: within the government's financial capacity, at least 1 yuan per kilowatt-hour of wind power subsidy is provided, and the maximum of thermal power feed-in tariff is set at 0.7 yuan, and the higher the wind power subsidy and the lower the feed-in tariff of thermal power, the higher the carbon emission reduction. At the same time, a thermal power carbon tax of about 0.36 yuan per kilowatt-hour should be set. The results of this study can provide suggestions for consuming renewable energy and formulating competition rules in power market transactions. This article can be further studied. The scope of subsidy prices can be narrowed, and more precise subsidy prices that balance the benefits of various systems can be determined on the basis of this research. [ABSTRACT FROM AUTHOR]

After the rapid development of China's wind power industry, the lack of core technology and the mismatch between the expansion and development speed have become increasingly prominent. In order to correctly understand the current situation and problems, the whole research process is divided into the following four steps. The first step takes the profit rate of main business from 2017 to 2019 as sample variable to study the smile pattern of the wind power industry value chain. The second step takes the Principal Component Analysis as the research method, and the evaluation index system is constructed, so as to further analyze the value-added driving factors. The third step uses the Data Envelopment Analysis (DEA) and Malmquist model as evaluation methods, and the static and dynamic efficiencies of each value chain link are evaluated, respectively. In step four, the selected companies are effectively clustered with the Cluster Analysis. The results show that the value chain shows a gradual deepening smile, the overall value-added efficiency is not ideal, and there are specific problems in each link. This paper aims to provide reference for enterprises to make targeted improvement on the basis of analyzing the current situation. It also provides reference for the comparison and promotion of other countries. [ABSTRACT FROM AUTHOR]

As a promising renewable solution for sustainable power generation worldwide, wind energy is receiving continuing attention from both industry and the academic community. However, the randomness and intermittency of wind energy will affect the stable operation and stability of the power system and further affect the economic benefits of the power grid. What makes the matter worse is the inevitable coupling between each pair of wind turbines in the large-scale wind farm. Besides, the resolution of prediction is severely limited by the spatial scale of wind farms. These problems bring great difficulties for the control and scheduling of wind farms. To this end, this paper proposes a novel wind speed prediction method for wind farms by borrowing some wisdom from machine learning methods. First, density peak clustering (DPC) is employed to separate the tremendous number of scattered wind turbines into a much significantly reduced number of groups, the wind turbines in each of which are treated as a unity. Based on the priority setting of each indicator in clustering, the data are preprocessed with different weightings. Principal component analysis is utilized to avoid DPC's poor clustering effects in case the dataset is high-dimensional. Finally, by considering simultaneous effects from historical and present data, long short-term memory based deep learning neural networks are trained and used to iteratively predict the potential of the wind energy in each unit for each time slot. The effectiveness of the proposed algorithm is verified by taking an in-service wind farm in China as an example. [ABSTRACT FROM AUTHOR]

Wind power is associated with uncertainties in scheduling and dispatching to the power grid, because the availability of wind speed is unknown in advance, and precise wind power forecasts are crucial to reducing these uncertainties. However, the accuracy of the wind power prediction, which is driven by average wind speed forecasting, is influenced by the spatial variability of the wind field. In this paper, the mechanisms of how wind power prediction errors are caused by the spatial heterogeneity of wind speeds and wind turbine power curves are analyzed. The degree of spatial heterogeneity of wind farms and errors caused by this are defined, and the relationships between various errors are revealed. We choose wind data collected from one observation site in northeast China as a sample; the results indicate that the short-term wind power prediction error is mainly caused by the inhomogeneity of the power curves. Moreover, these forecast errors can vary with season and installed wind farm capacity. [ABSTRACT FROM AUTHOR]

Most of the existing ultra-short-term wind power prediction methods only involve mathematical models and rarely consider spatial correlation factors. As such, the prediction system remains to be further improved. In this paper, in order to accurately predict the wind power of the large-scale wind farm, the idea of spatial-temporal scale transformation is introduced to establish a spatial up-scaling model of ultra-short-term multi-step wind power prediction based on fractal scaling factor transformation. First, the regional division of the large-scale wind farm is carried out. Then, the affine relationship of the local and whole regions is established by using the theory of stretching transformation of fractal. The process of traditional space up-scaling prediction is improved by fractal transformation. Finally, the deductive process is accomplished by the prediction of local regional to whole regional. To verify the proposed approach, two large-scale wind farms in northeast China were selected to predict its wind power. Compared with the traditional wind power prediction approach of the whole wind farm and the prediction approach of traditional spatial up-scaling, this approach can get better prediction accuracy. [ABSTRACT FROM AUTHOR]

Under the increasing pressure of mitigating air pollution, wind power has been considered as the leader of China's energy revolution for a long time. Although China's wind power development enjoys a favorable policy environment, it is still impeded by the problem of wind power curtailment and overreliance on price subsidies. First, this paper built a system dynamics model to simulate China's wind power development during 2012-2032 considering both economic and technical factors. Further, sensitivity analyses and scenario analysis are performed to analyze the variation ranges of simulation results of different factor settings. The analysis results indicate that China's wind power might not develop as expected under the current development pattern. Our model allows evaluating the long-term trend and major constraints of China's wind power under the latest incentive policies, thus helping to provide reference for policy-making institutions. [ABSTRACT FROM AUTHOR]

Due to the increasingly serious environmental problems in China, more and more renewable energy sources, especially wind power, have accessed to the power grid. The unpredictability and the stochastic volatility of wind power has given the power grid a huge challenge. The existing wind farm models may cause the scheduling usually adopt random wind turbine removal, whose effect is often not ideal, and may have the secondary stress on the power grid. In order to solve the problems in the existing models, this paper discusses the dynamic clustering model for the wind farm, where the modeling process is based on the actual wind speed operation data and contains a rolling update process. Through adopting a relatively mature clustering method and the hierarchical clustering method, the precision of the model is obviously improved and the complexity of the model is greatly reduced. The simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]