Assessment Of Wind Resources And Wind Speed ForecastingWithin Onshore And Offshore Areas

Promoting the high-quality development of renewable energy is a key approach to achieving the goals of "peak carbon emissions" and "carbon neutrality." Considering the significant challenges faced by China in terms of its new energy resource reserves and the development of new energy utilization,it is important to systematically advance the construction of offshore wind power bases in the eastern coastal areas,actively promote the distributed development of wind and solar power,and further facilitate the transformation of the energy structure,providing essential support for achieving the "dual carbon" goals.In the "14th Five-Year Plan" for renewable energy development,the focus is on improving the performance of new energy generation integration into the grid,enhancing the accuracy of wind resource forecasting,and improving the precision of wind power forecasting.These efforts aim to enhance the proactive support capability of new energy generation.sustainably consolidate and increase the proportion of renewable energy in the overall energy structure and promote the high-quality development of new energy.The research focuses on the assessment of offshore and adjacent onshore wind farms as the application objects.Starting from different stages and key tasks,it aims to develop accurate,mechanistically sound,and practical modeling and evaluation methods for wind resources in coastal and marine areas,considering the real atmospheric,oceanic,and wave conditions during the design and construction phases.During the operational phase,the study constructs a multi-timescale wind speed prediction model that adapts to the"combined centralized and distributed" format,and designs a corresponding federated forecasting framework to comply with data protection trends.The specific research contents are as follows:(1)A coupled wind farm parameterization scheme and a method for assessing the impact of wake disturbance between wind farms in coastal areas using mesoscale meteorological models are proposed.Taking a certain offshore wind farm in Yancheng,China,and adjacent onshore wind farms as research objects,the difference between the cases with and without offshore wind farms is compared,and the spatial distribution characteristics of the overall wake intensity of offshore wind farms under typical real atmospheric conditions are analyzed.The effectiveness and parameter sensitivity of the evaluation method are validated using measured data.The spatiotemporal characteristics of the disturbance effect of offshore wind farms on the operation of downstream onshore wind farms under typical conditions are quantitatively evaluated,and the relative power loss rules of onshore wind farms are summarized,aiming to improve regional resource utilization and ensure the coordinated and sustainable development of the wind power industry.(2)Based on the mesoscale meteorological model,the atmospheric model is combined with ocean and wave models to construct an air-sea-wave coupling model for assessing wind resources and characteristics in coastal areas.The spatiotemporal characteristics of the disturbance effect of wind farms on land and sea under typical conditions are quantitatively evaluated,accurately simulating the interaction between the atmosphere,ocean,and waves,and their interaction with offshore wind farms.Through this coupling model,a comprehensive analysis of the impact of wind farms in the Yancheng coastal area of Jiangsu Province on wind energy resource characteristics is conducted,further revising the spatial distribution and variation rules of wind farm wakes,realizing the transition from passive observation of wind resource characteristics to active simulation and dynamic extrapolation of regional wind resource intrinsic characteristics and evolution trends,and improving the wind resource assessment capabilities of onshore and offshore areas.(3)A double-layer attention mechanism-based offshore wind speed forecasting model is constructed,which can adaptively select the most relevant forecasting variables and past historical information segment lengths.By constructing a variational Bayesian neural network,the uncertainty of forecast results is quantified throughout the modeling process,helping to reduce risks and optimize decision-making,improve wind speed forecast accuracy,and enhance the active support capacity of offshore wind power.(4)A variational Bayesian inference multi-scale wind speed probabilistic forecasting model based on secure federated learning is constructed.The secure federated learning framework allows data to be stored locally and complete global model training,fully meeting the requirements of data privacy,security,and deployment performance,and opening up new avenues for data sharing.A forecasting neural network modeling method based on prior knowledge and limited data is proposed,which approximates Bayesian variational inference at extremely low cost.The performance of the proposed model is validated on actual datasets at multiple time scales,demonstrating that the approach achieves competitive performance while preserving data privacy and significantly outperforms centralized training models in scenarios with restricted data flow.