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With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. [ABSTRACT FROM AUTHOR]

Converting wind energy into ammonia (WtA) has been recognized as a promising pathway to produce “green” ammonia compared with traditional coal-based technologies. As the key part of WtA, Power-to-Ammonia (PtA) has great potential to facilitate the usage of wind generation. This paper proposes a co-planning approach for regional wind resources-based ammonia industry and the electric network (EN). To this end, PtA is first modeled as a flexible power load of power systems with spatial and temporal constraints on hydrogen supply chains (HSC). Then a novel co-planning model of WtA and EN is established to optimize the WtA configuration and the EN expansion. An alternating direction method of multipliers (ADMM) based algorithm is introduced to effectively solve this model. Real data of Inner Mongolia Province in China is adopted to verify the effectiveness and significance of the proposed approach. It is shown that the siting and operation flexibility of PtA with HSC can reduce the expansion burden of EN. The co-planning of WtA and EN can significantly enhance wind power utilization and reduce total investment costs. Furthermore, feasibility analysis on WtA in comparison with coal-to-ammonia (CtA) and ultra-high voltage transmission (UHV) provides helpful guidelines for the realization of WtA. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Topp-Leone Lindley (TLL) distribution by using Topp-Leone (TL) family. Mathematical properties of the TLL distribution are studied. Estimation of the unknown parameters is derived by the methods of maximum likelihood, least squares and maximum product spacings. The performance of the estimation methods is evaluated by means of Monte-Carlo simulation. In the application part of the research, we have used a long term measured wind speed data from ten stations in Inner Mongolia of China. The suitability of the TLL distribution and other six distributions (inverse Gaussian, Birnbaum-Saunders, power Lindley, weighted Lindley, Weibull, inverse Weibull) used to fit for wind speed data is evaluated based on root mean square error, coefficient of determination, the log-likelihood, Akaike information criterion (AIC), Bayesian information criterion (BIC), Kolmogorov-Smirnov (K–S) statistic and power density error. The results substantiate that TLL distribution is widely applicable at all selected stations. TLL distribution outperforms the others at five stations, ranks the second at three stations and classifies as the third in remaining two stations. Alternatively, Weighted Lindley distribution is the next best distribution for analyzing wind speed data in selected stations. It is stronger than others at three stations while ranking the second at five stations and fourth in two stations. Although this finding questions the accuracy of Weibull distribution in modeling wind speed data, it provides superior estimation of wind power density for the ten stations. • The study solved a knowledge gap that existing wind speed distributions perform poorly in wind energy application. • A novel distribution, called the Topp-Leone Lindley (TLL), is proposed to fit the Inner Mongolia wind speed data. • The performance of the TLL distribution is compared with six commonly-used distributions for the considered wind data. • Wind characteristics are analyzed for wind energy potentials at ten sites in Inner Mongolia Autonomous Region, China. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of how to effectively smooth the short-term tie-line power fluctuations for grid-connected industrial microgrids (IMGs) from demand side. A wide-area closed-loop control architecture is proposed by directly and continuously modulating load power consumption, e.g., the aluminum electrolysis load. To optimally design the load smoothing controllers, an output regulation approach combined with LQ optimal control is developed. In particular, dominant frequencies of wind power fluctuations are analyzed and incorporated into the exogenous system that models time-varying disturbances. Simulations are conducted for a real industrial microgrid in the Inner Mongolia, China, to validate the practical feasibility and effectiveness of the proposed smoothing control strategy. It is demonstrated that the demand side control based on output regulation is able to achieve not only zero steady error in rejecting the time-varying wind power disturbance but also guarantee the satisfactory dynamic performance of modulated loads for the grid-connected IMG. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

With the gradual maturity of wind power technology, China's wind power generation has grown rapidly over the recent years. However, due to the on-site inconsumable electricity, the phenomenon of large-scale "wind curtailment" occurs in some areas. In this paper, a novel hybrid hydrogen/electricity refueling station is built near a wind farm, and a part of the surplus wind power is used to charge electric trucks, and the other part of the surplus power is used to produce "green hydrogen". According to real-time load changes, different amounts of liquid hydrogen and gas hydrogen can be properly coordinated to provide timely energy supply for hydrogen trucks. For a 400 MW wind farm in the western Inner Mongolia, China, the feasibility of the proposed system has been carried out based on the sensitivity and reliability analysis, the static and dynamic economic modeling, with an entire life cycle analysis. Compared to the conventional technology, the initial investment of the proposed scheme (700.07 M$) decreases by 13.97%, and the dynamic payback period (10.93 years) decreases by 25.87%. During the life cycle of the proposed system, the accumulative NPV reaches 184.63 M$, which increases by 3.14 times compared to the case by conventional wind technology. • A novel hybrid hydrogen/electricity refueling system to charge electric vehicles, and produce "green hydrogen". • A realistic multi-energy study based on a 400 MW wind farm in western Inner Mongolia, China. • Feasibility study with sensitivity and reliability analysis for the proposed system. • Static and dynamic economic modeling on a hybrid hydrogen/electricity refueling station. • The proposed system has technical and economic benefits, but much less carbon emissions. [ABSTRACT FROM AUTHOR]

As a renewable energy, wind power has been improving energy structures and assisting in the maintenance of strategic energy security. Based on provincial level panel data from 2000 to 2018, this paper developed models to evaluate the effects of different policies on newly installed wind power capacity and investigated the market reform influences on wind development in eight spot market pilots. It was found that on a national level, while the pricing and government guideline policies significantly promoted wind power growth, present market reforms were having an opposite effect. The results also suggested that from a resource endowment heterogeneity perspective, there was a significant difference in the wind capacity promotion policy performances. Additionally, of the eight spot market pilots, market designs had led to a significant reduction in new wind power installations in Gansu and Inner Mongolia, and no significant wind power development effects were found in the other pilots. Based on the research results, a discussion on policy use and suggestions on policy improvements are given. • Effectiveness of different representative wind power policies are examined. • The panel models are developed using data that covers 30 provinces over 2000–2018. • Policy effectiveness varies by policy types and wind resource levels. • The current market designs show little impact on wind power development. • Implications on policy use and improvement suggestions are given. [ABSTRACT FROM AUTHOR]

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model's effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. • A fast EV charging station with renewable energies and ESS is proposed. • An EV charging load simulation model based on demand response is built. • An optimization design model for FEVCS-WPE is proposed with two objectives. • A hybrid optimization algorithm combining MOPSO and TOPSIS method is used. [ABSTRACT FROM AUTHOR]

A kernel density estimation (KDE) model for the probability distribution of wind speed (PDWS) is proposed in this paper for application to wind energy assessment (WEA) in China. Four bandwidth selectors, including normal scale (NS), plug in, biased cross-validation, and least-square cross validation, are proposed for the KDE model. Popular parametric distribution models are also introduced for comparisons with the KDE models. Based on five-year day-average wind speed data from 698 nationwide wind stations in China, the performance and robustness of both parametric and KDE models were evaluated comprehensively on the regional scale. Wind power density (WPD) and wind turbine power output (WTPO), which are the priorities in WEA, are subsequently calculated based on the estimated PDWS models. The ranking results of four individual metrics and one comprehensive metric indicate that the four KDE models outperform the parametric models in fitting the PDWS. The KDE-NS model performs the best compared to the other three KDE models. In addition to the KDE models, the generalized gamma and generalized extreme values were considered as better parametric models in fitting the PDWS. KDE models also performed well in WPD estimation, especially the KDE-NS model with a mean absolute percentage error (MAPE) value as low as 2%. Some parametric models, i.e., Johnson SB and Wakeby, which are not outstanding in PDWS fitting, however perform well in WPD estimation, and their MAPE values can be controlled to remain within 3%. This indicates that the result of the PDWS is not completely equivalent to that of WPD estimation. The WPD and WTPO in most of China's interior areas are less than 40 W / m 2 and 1.2 GWh, respectively. In the eastern coastal areas, middle and eastern Inner Mongolia, and some western provinces, the WPD and WTPO are relatively higher, and can reach or exceed 240 W / m 2 and 3.5 GWh, respectively. • Four KDE models with various bandwidth selectors are proposed for fitting PDWS. • Popular parametric models are introduced for comparisons with the KDE models. • Performance of parametric and KDE models are evaluated on the regional scale. • KDE models generally outperform the parametric models in fitting PDWS. • KDE models also performs well in WEA analysis, especially for the KDE-NS. [ABSTRACT FROM AUTHOR]