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A convenient and effective method based on vector analysis was proposed in this paper to quantitatively describe the pattern of the wind speed distribution curve. This method can accurately describe the steepness of the single‐peak curve by using two parameters of concentration degree (CD) and concentration period (CP). Through the analysis of wind speed data in China over the past 40 years, this paper found that regional wind resources with larger CD were poorer, while regional wind resources with larger CP were richer. In addition, there were obvious cubic relationships between the CP and the average wind power density. These two parameters could reflect the richness of wind energy and realize the comparisons of wind resources across time and scales. In comparison with conventional approaches, this method is simpler and avoids fitting process, which has broad promotion prospects in the field of power grids. [ABSTRACT FROM AUTHOR]

Wind power generation does not emit greenhouse gases or pollutants, but there are some carbon emissions from the manufacturing, transportation, operation, and waste disposal of wind turbines. Directly driven permanent magnet and doubly fed asynchronous wind turbines currently have the largest market share in China, but few Chinese studies have compared their differences in carbon reduction potential. This paper uses life cycle assessment (LCA) to quantitatively analyze the full life cycle carbon emissions of the two wind turbines to determine which type of wind turbine has greater carbon reduction potential, obtaining the following results. (1) The full life cycle greenhouse gas emissions of 2.5 MW directly driven permanent magnet and doubly fed asynchronous wind turbines are 8.48 and 10.43 g CO2/kWh, respectively. The direct‐driven permanent magnet wind turbine is superior in terms of carbon reduction. (2) The stage with the greatest impact and the greatest difference between the two wind turbines in the full life cycle is the production stage, during which the carbon emissions of the directly driven permanent magnet and doubly fed asynchronous wind turbines are 1.045 × 106 and 1.210 × 106 kg, respectively. (3) According to sensitivity analysis, proper waste disposal and transportation can reduce carbon emissions from wind turbines. These research findings can be used to help achieve carbon peaking and neutrality goals, as well as the technological development of wind power enterprises. [ABSTRACT FROM AUTHOR]

In recent years, China has promoted many new energy projects in order to meet the growing demand for electricity. Therefore, China's offshore wind power installed capacity has grown rapidly. China has a long coastline and abundant offshore wind energy resources. Offshore wind power is an important area for the development of renewable energy, which can promote wind power technology advancement and energy structure adjustment. Therefore, conducting effective research and forecast on the cumulative installed capacity of China's offshore wind power will help the government to rationally deploy and reduce the risk of investment in offshore wind power. In order to accurately predict the future prospects of offshore wind power in China, this paper firstly constructed a set of influencing factors and used gray correlation analysis to screen the main influencing factors. Then, this paper proposed a novel forecasting model named e‐VMD‐PCA‐RELM. The algorithm is based on the traditional RELM (robust extreme learning machine) algorithm, which effectively processes the noise information through the PCA (principal component analysis) algorithm, and extracted the feature elements of the RELM hidden layer to reduce the information redundancy. At the same time, the e‐VMD (variational mode decomposition optimized by entropy) algorithm is used to decompose the original time series to obtain multiple components. By comparing with the other forecasting algorithms, it is proved that the proposed forecasting model has strong generalization ability and has achieved good prediction result. Finally, the e‐VMD‐PCA‐RELM model is used to predict the scale of offshore wind farms in China from 2019 to 2035. We find that the cumulative installed capacity of China's offshore wind power will exceed 60 GW in 2035, and the installed capacity will increase year by year. In 2030, there will be a large increase, with a relative growth rate of 20%. [ABSTRACT FROM AUTHOR]

The combined heat and power system (CHPS) is one important type of the integrated energy system in Northern China. Compared with the independently operated power system, the combined heat and power system (CHPS) has a great potential to improve the utilization of wind energy in virtue of the energy storage of the heating system and power‐to‐heat (P2H) devices. This paper studies the optimal operation of the CHPS equipped with the electric boiler (EB) and heat pump (HP). Comprehensive factors are considered, including P2H conversion, the thermal inertia of district heating network (DHN) and buildings, and adjustable heat loads. An equivalent model of the heating system is developed to express the direct mathematical relations between the heat source and heat loads. Redundant details of the DHN are simplified to reduce the complexity of the optimization problem. Furthermore, a self‐repair method incorporated with the real‐coded genetic algorithm (RCGA) is proposed to efficiently handle the nonlinear constraints. The effectiveness of the solving method is verified in case studies. The impacts of the EB and the HP on the CHPS are compared in details. The results show that the HP offers better performance than the EB on coal saving and improving wind power integration. [ABSTRACT FROM AUTHOR]