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Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy. [ABSTRACT FROM AUTHOR]

"Carbon peak and neutrality" are an important strategic decision to promote the transformation of China's energy economy and build a community with a shared future for mankind. China is a big energy consumer, and the whole society is facing huge challenges under the goal of carbon peak and neutrality. The realization of the goal of carbon peak and neutrality requires the guidance of correct theoretical methods and scientific deployment. This paper mainly studies the energy consumption scheduling, demand response management, and energy trading problems of microgrid. In view of the shortcomings of the existing energy optimization scheduling methods in the microgrid, a variety of energy resources such as electric energy, natural gas, heat energy, and cold energy are considered into the microgrid model. Based on the noncooperative game and Stackelberg game, a new energy optimal dispatch model is constructed with a variety of game methods such as two-layer game. Maximize the personal benefits of the microgrid while meeting the reliable operation of the system and the electricity demand of users. The three-stage noncooperative game problem is solved based on the reverse bootstrap method, and the closed expression of the optimal strategy in each stage is obtained. The power generation forecasting technology based on big data is studied, and a power forecasting method is proposed, which can effectively guide the energy consumption of the microgrid. The simulation results show the effectiveness of the proposed renewable energy management model based on big data, which verifies that the accurate wind power prediction results are conducive to better theoretical analysis of energy management. [ABSTRACT FROM AUTHOR]

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro-wind-photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long-term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario-generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro-wind-PV complementary operation; finally, the long-term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption. [ABSTRACT FROM AUTHOR]

This review proposes a performance evaluation system of low-carbon economic development based on multiobjective analysis in low-carbon environment. This is a modeling method combining super efficiency and relaxation-based measurement model (super-SBM model), which can effectively measure green innovation efficiency with unexpected outputs and traditional innovation efficiency without unexpected outputs. Using the Malmquist–Luenberger index method to dynamically analyze the efficiency of green innovation, a multiobjective model is obtained, including economic scheduling target considering wind power cost and low-carbon scheduling target considering carbon trading. The efficiency of green innovation considering unexpected output is obviously lower than that of traditional innovation without considering unexpected output. This phenomenon is more pronounced in some areas of central and western China. Technical efficiency improves the innovation level of environmental protection economy in China and the impact of technological progress is greater than that of technical efficiency. In this review, the output super SBM model is used to study the development of China's low-carbon industry, and the correlation between the prediction model and the performance change of low-carbon economic development is analyzed. The Malmquist–Luenberger (ML) index of environmental protection product development efficiency in China is not less than 1. Due to the improvement of the efficiency of scientific and technological development, the combination of the two will eventually lead to the improvement of the development of green products and environmental economic products in China. Combining with the empirical analysis, this paper puts forward some methods to promote the low-carbon economy in the economic zone. [ABSTRACT FROM AUTHOR]

Public subsidies for offshore wind power will be eliminated in 2022. At present, offshore wind power still depends on subsidies. In this new situation, it is necessary to study the influence on the stakeholders and how the offshore wind power industry achieves healthy and sustainable development. Firstly, this paper qualitatively analyzes the impact of the cancellation of state subsidies on offshore wind turbine manufacturers and developers and finds that if there is no subsidy to relay offshore wind power, the development of offshore wind power will stagnate, and the wind turbine manufacturers will face great challenges. Then, by constructing the subsidy demand calculation model and the transmission and distribution price cross subsidy calculation model according to China's electricity price-related policies, the results show that the local subsidy is basically feasible to continue the national subsidy, and the burden is decreasing year by year, so the relay subsidy should be continued. Based on the cloud model, an evaluation model for the development of offshore wind power benefits from the cancellation of national subsidies is constructed to quantify the impact of the cancellation of overseas subsidies on local governments and power grid enterprises. According to the example analysis, under the background of the cancellation of national subsidies, Guangdong has the best development of offshore wind power benefits, which verifies the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Since the installed capacity of wind power is greatly affected by the subsidy policy, this paper predicts the installed wind power capacity in China under different policy scenarios. Firstly, a new two-parameter variable-weight buffer operator is proposed to quantify the impact of the policy shock, whose optimal parameters are obtained by the genetic algorithm, and combined with the grey GM(1,1) model to predict the installed capacity of wind power in China under the cessation of subsidy policy. Then, the GM(1,1) model with optimized background value is used to predict the installed capacity under continued subsidy. Finally, two policy subsidy strategy models are constructed based on the forecast data to simulate the future trend of wind power installed capacity under different subsidy policies and explore the strength of wind power subsidies in China during the "14th Five-Year Plan" period (2021–2025). The results show that both the GM(1,1) model based on the two-parameter variable-weight buffer operator and the GM(1,1) model with optimized background value have high fitting accuracy, with errors of 0.15% and 4.93%, respectively. Furthermore, in the case of the central government's subsidy cancellation, the local government should take over the central government's subsidy policy and adjust the subsidy intensity to 0.57–1 times of that during the "13th Five-Year Plan" period (2015–2020) to achieve the national planning target. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy and with the increasing awareness of environmental protection, clean energy will become the ultimate solution of energy in the near future. In order to meet the future demands of clean energy, it is important to accurately predict the consumption of clean energy. This paper is based on the total consumption data of clean energy in China from 2000 to 2019, including natural gas, hydropower, nuclear power, and wind power consumption. The combined model is used to predict the clean energy consumption and achieves the optimal prediction with minimum variance. The results show that compared to the GM(1,1) and ARIMA model, the combined forecasting model has lower relative error when fitting and predicting the consumption demand of clean energy. It is observed from the prediction results that the clean energy consumption would have a rapid growth tendency, and the growth rate will be about 8.6% in the next five years. The consumption would be about 1.7 billion tons standard coal in the year of 2024. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient peak shaving capacity of the power system after large-scale renewable energy such as wind power and photovoltaics is connected to the grid, a new energy-pumped storage combined system low-carbon economic operation strategy is proposed. First, the energy storage characteristics of pumped storage are studied; wind power and photovoltaic output are shifted in time and space; and a new energy-pumped storage combined system model with photovoltaics, wind power, and pumped storage as the main body is established. Second, the carbon trading mechanism was introduced, and the reward and punishment ladder carbon trading cost model was established. Then, via the new energy-pumped storage combined system, the upper optimization target with the smallest investment cost and the lower optimization target with the largest system benefit are established, and a two-layer optimization model solution method based on linearly decreasing inertia weight particle swarm optimization and sequential quadratic programming algorithm is proposed. Finally, via the operating data of a pumped storage power station on an island in northern China, the new energy-pumped storage combined system low-carbon economic operation strategy was simulated and verified. The simulation results show that the low-carbon economic operation strategy of the new energy-pumped storage combined system proposed in this paper can effectively suppress load fluctuations, reduce the impact of wind and wind anti-peak regulation characteristics on the power grid, increase the utilization rate of renewable energy, and improve system economy. [ABSTRACT FROM AUTHOR]

China currently boasts the largest installed capacity of wind power; however, its output is unstable and highly dependent on weather variability. Despite this, the influence of extreme weather events on wind energy production at the interprovincial scale in China has not been fully characterized. This study aims at investigating the daily variations and regional differences in wind power output during heat wave (HW) and cold wave (CW) days in six regions of China. In addition, the study projects the monthly changes in HW and CW days in the coming decades by utilizing a stacking ensemble machine learning method. The projections are under a real-world warming scenario that incorporates current and long-term actions or policies. The findings of the study reveal that, for most regions, the daily cumulative wind power generation on HW days is close to that on normal days; however, there is a lower output during the daytime and a higher output at night. Furthermore, the number of HW days is projected to increase by 2.3 to 21.8 days during the periods of 2031–2040, 2041–2050, and 2051–2060 in these regions. By comparison, the daily cumulative wind power generation increases significantly on CW days, and the monthly distribution of CW days is expected to undergo notable changes in the future. These findings provide valuable insights into wind resource planning and operation under extreme weather conditions in China. [ABSTRACT FROM AUTHOR]

Monitoring the stress and deformation state of offshore steel pipe piles is very necessary for complex ocean environments. However, challenges still remain in remotely capturing detailed data on offshore projects. Herein, an ultraweak fiber Bragg gratings- (UWFBGs-) based wireless sensor network was established to obtain the high-precision strain along the piles. The No. 3 offshore wind power safety monitoring project in South Shandong Peninsula, China, used such a fiber-optic monitoring system. From January 1 to June 1, 2022, strain profiles of three offshore steel pipe piles were measured in seconds with a 1 m spatial resolution and a 1 με strain resolution, which clearly show sea level and mud-water interfaces. To assess the pile's operational condition, the displacement and rotational angle of the mud surface are further calculated. It is discovered that the horizontal deformation at sea has a positive correlation with the wind, waves, and current, meanwhile decreasing with depth. The UWFBG-wireless sensor network can realize dynamic monitoring and early warning, which is promising for the life cycle monitoring of offshore wind turbine steel pipe piles. [ABSTRACT FROM AUTHOR]

The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Climate reanalysis products have been widely used to overcome the absence of high-quality and long-term observational records for wind energy users. In this study, the applicability of two popular reanalysis datasets (ERA5 and MERRA2) in estimating wind characteristics for four tall tower observatories (TTOs) in South China was assessed. For each TTO, linear and nonlinear downscaling techniques, namely, multiple linear regression (MLR) and an artificial neural network (ANN), respectively, were adopted for the downscaling of the scalar wind speed and the corresponding U/V components. The downscaled wind speed and U/V components were subsequently compared with the TTO observations by correlation coefficient (Pearson's r), the root mean square error (RMSE), the uncertainty analysis (U95), and the reliability analysis (RE). According to the results, ERA5 had a better applicability (higher Pearson's r and RE, but lower RMSE and U95) in estimating TTO wind speed than MERRA2 when using both the MLR and ANN downscaling method. The average Pearson's r, RE, RMSE, and U95 of the downscaled wind from ERA5 by the MLR (ANN) method were 0.66 (0.69), 40.8% (41.8%), 2.20 m/s (2.11 m/s), 0.181 m/s (0.179 m/s), respectively, and 0.60 (0.63), 38.0% (39.7%), 2.32 m/s (2.25 m/s), 0.189 m/s (0.187 m/s), respectively, for MERRA2. The wind components analysis showed that the better performance of ERA5 was attributed to its smaller error in estimating V component than MERRA2. For the wind direction, the two reanalysis datasets did not display distinct differences. Additionally, the misalignment of the wind direction between the reanalysis products and the TTOs was higher for the secondary predominant wind direction (SPWD) than for the predominant wind direction (PWD). Furthermore, we found that the reanalysis U wind had a higher RMSE but a lower RE and Pearson's r than the V wind, which indicates that the misalignment in the wind direction was mainly associated with the deviation in the U component. [ABSTRACT FROM AUTHOR]

China's wind power industry has grown dramatically in recent years as the country's focus on clean energy and renewable energy generation has increased. Mechanical fault diagnosis of wind power transmission is a common wind maintenance method. It has recently become a research hotspot in the field of mechanical fault diagnosis as a method of fault identification based on picture attributes. Time-frequency images, on the other hand, are better for fault analysis and fault diagnosis of wind power transmission machinery than time-domain and frequency-domain images because they contain more information about the operation status of the gear. This work proposes and applies an image feature extraction-based fault diagnostic method to the defect diagnosis of wind-driven mechanical gears. The feature extraction suitable for gear and gear box faults is analyzed, and the improved artificial immune algorithm is used for fault identification. Through collecting normal vibration signals and two kinds of fault vibration signals from the gearbox of wind power transmission in a wind farm and extracting image features on the basis of data processing, the improved algorithm is finally applied for fault analysis. The experimental results show that the fault diagnosis rate of the improved real-value negative selection algorithm is obviously improved and can improve the fault diagnosis rate by 5%. [ABSTRACT FROM AUTHOR]