Experimental study of grouting protection against local scouring of monopile foundations for offshore wind turbines

In this study, the multi-level factorial analysis approach is employed to characterize the major impact factors on the performances of different data assimilation schemes. Four data assimilation methods, including EnKF and PF methods, and two integrated data assimilation methods are adopted for real-time hydrological prediction through a conceptual rainfall-runoff model in a catchment of Jing River. Different uncertainty scenarios for model inputs and outputs, as well as streamflow observations are tested through the multilevel factorial analysis to track the dominant impacts factors on the performances of data assimilation approaches. The multi-level factorial results suggest that, for different data assimilation schemes, the impacts from stochastic perturbations in model inputs, outputs and streamflow observations are different and some of them may be statistically insignificant. But the impact for one factor is generally dependent upon the others and scenarios with extreme stochastic perturbations (low or high) may more likely result in a good performance for all data assimilation schemes.

Characterizing Impact Factors on the Performance of Data Assimilation for Hydroclimatic Predictions through Multilevel Factorial Analysis

China is likely to lead global offshore wind power development, in the hope of transforming the coal-based electricity system and reducing greenhouse gas emissions. However, the potential of power generation and emissions mitigation is largely unknown, and the contribution of offshore wind utilization to regional carbon neutrality needs to be further clarified. Here, we reveal that offshore wind energy resources are abundant in China, with an estimated power generation potential of about 17.5 PWh, more than doubling the current power consumption nationwide. Although current utilization of offshore wind energy in China accounts for 21% of global overall capacity, the total share is still limited, supplying just 0.4% of national electricity needs (2019). With the increasing use of offshore wind, by 2050, the planned installation along China coast would be nearly five times as much as current (2019) global capacity, or 25 times of current national offshore wind power generation. The total CO2 emissions reduction in 2050 due to the decrease in coal use is projected to be 294.3 Tg CO2-eq yr–1, equivalent to 20% of current emissions from coal-fired power in the coastal region. The size of reduced emissions is higher than current CO2 emissions in about 90% of countries. Our results highlight the important role of offshore wind power in upgrading the energy system and achieving carbon neutrality. Future studies are encouraged to further explore technological, economic and institutional challenges facing offshore wind energy deployment and low-carbon energy system development.

Offshore wind power in China: A potential solution to electricity transformation and carbon neutrality

Dynamic adaptive spatio-temporal graph neural network for multi-node offshore wind speed forecasting

Assessing benefits and costs of expanded green hydrogen production to facilitate fossil fuel exit in a net-zero transition

Offshore wind can power Canada

Stochastic Rainwater Harvesting System Modeling Under Random Rainfall Features and Variable Water Demands

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Offshore wind can power Canada

Stochastic Rainwater Harvesting System Modeling Under Random Rainfall Features and Variable Water Demands