Coastal to deep sea wind energy resource refined evaluation method and system

Abstract

The invention discloses a coastal to deep sea wind energy resource refined evaluation method and system, and relates to the technical field of renewable energy sources in the climate resource development and utilization field, and the method comprises the steps: constructing a Fujian province national meteorological observation station, an ocean buoy station, reanalysis data, an offshore anemometer tower and other multi-source gradient anemometer databases; the hourly 1 * 1km high temporal-spatial resolution 0-300m gradient wind energy resource fine evaluation method based on WRF numerical mode simulation of an optimization design scheme is developed; and researching and developing WTGS exceeding probabilities and thresholds of different levels of wind speeds during the influence of the tropical cyclones, determining available wind speed, early warning wind speed and defense wind speed regions for wind resource development, and drawing a risk zoning map for development and utilization of wind farms from coastal to deep sea. Meanwhile, an offshore wind energy resource refined evaluation system is researched and developed, wind energy resource refined evaluation and developable potential estimation at different heights from the coastal sea to the deep sea are achieved, and wind energy resource development and utilization risk level zoning at any grid point in different sea areas is achieved.

Description

Technical Field

[0001] This invention relates to the field of renewable energy technology in the development and utilization of climate resources, specifically to a refined assessment method and system for wind energy resources from coastal to deep-sea areas. Background Technology

[0002] Accurate assessment of offshore wind energy resources is crucial for their orderly and efficient development and forms a vital foundation for large-scale offshore wind power development. Currently, offshore wind energy resource assessment primarily relies on mathematical and statistical methods such as wind speed variation and wind power density, utilizing conventional observation data from meteorological stations, wind towers, buoy stations, satellite remote sensing, and reanalysis data. However, conventional meteorological observation methods often fail to acquire large-scale, long-term ocean wind field information, especially in the deep sea, due to the sparse deployment of meteorological stations in many areas and limitations in observation periods (such as wind tower data). Furthermore, the limited observation periods (such as wind tower data) prevent the acquisition of large-scale, long-term ocean wind field information, particularly in the deep sea. In addition, satellite radiation signals from the ocean surface are often interfered with by signals from land, leading to missing or distorted satellite wind field data for nearshore areas. Although reanalysis data assimilates data from multiple sources, including observational data, satellite data, and numerical weather prediction products, apart from ERA5 which provides wind field data at a height of 100m near the ground, the reanalysis wind field data of other products (such as NCEP, CLDAS, etc.) only reflect a height of 10m.

[0003] Located on China's southeast coast, Fujian Province is rich in offshore wind resources due to the funnel effect of the Taiwan Strait, making it an important sea area for offshore wind power development in my country. With advancements in offshore wind turbine construction technology, wind turbines have reached heights of 100-250 meters, and construction is continuously expanding into deeper waters. To meet the current and future development needs of offshore wind power in Fujian Province, it is urgent to ascertain the wind resource conditions at greater heights and in wider areas of the nearshore waters.

[0004] Therefore, this invention requires the design of a refined assessment method and system for wind energy resources from coastal areas to deep-sea areas to solve the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide a refined assessment method and system for wind energy resources from coastal areas to deep-sea areas in order to solve the above-mentioned problems, thus resolving the issues mentioned in the background art.

[0006] To address the above problems, the present invention provides a technical solution: A refined assessment method for wind energy resources from coastal to deep-sea areas includes the following specific steps: S1. Construct a multi-source gradient wind measurement database of the Fujian Provincial National Meteorological Observatory, coastal automatic weather stations, marine buoy stations, reanalysis data, and offshore wind measurement towers. Integrate the existing wind energy resource assessment methods and the numerical model WRF simulation technology to construct a refined assessment method for coastal to deep-sea wind energy resources based on numerical model WRF simulation. S2. Select a set of high-resolution reanalysis data as the initial field, select USGS geographic information base data and gtopo elevation data in the WRF model, and use multiple schemes covering cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long and short wave radiation to carry out numerical simulation. S3. Using the wind measurement data from the multi-source wind tower database constructed in S1, the effects of different model schemes were verified and the optimal combination of model schemes was selected. Numerical simulation was carried out using three-dimensional linear interpolation and two-dimensional spatial downscaling methods to develop the wind resource characteristics of any height layer between 10-300m from the coastal area to the deep sea area of ​​Fujian Province. This filled the gap in the lack of long-term series measured wind data at near-surface height in multiple areas of the nearshore and deep-sea areas of Fujian Province. S4. The sea area of ​​Fujian Province is divided into northern, central and southern sea areas. Daily maximum wind speed data from national meteorological observation stations, coastal island regional automatic weather stations and marine buoy stations are used to select tropical cyclones that have historically affected different sea areas of Fujian. Long-term maximum wind speed sequences from national meteorological observation stations and more than 250 regional automatic weather stations and marine buoy stations are constructed during the period of tropical cyclone influence. Since the regional automatic weather stations only have wind measurement data from 2009, correlation analysis is used to determine the long-term representative stations of each region's automatic weather stations. Then, ratio correction, correlation analysis and other methods are used to construct the maximum wind speed sequences during the period of tropical cyclone influence from more than 250 regional automatic weather stations and marine buoy stations from the coast to the deep sea. The two correction methods are fully compared and analyzed to determine the optimal correction method and construct the optimal long-term daily maximum wind speed sequences from regional automatic weather stations and marine buoy stations. S5. Calculate the WTGS exceedance probability and threshold of wind speeds at different levels during the influence of tropical cyclones, determine the areas of wind speeds available for wind resource development, warning wind speeds, and defense wind speeds, and draw a risk zoning map of wind farm development and utilization from the coast to the deep sea.

[0007] In a preferred embodiment of the present invention, the reanalysis data in step S2 includes 6-hourly data of the ERA5 ground field and upper-air field at a resolution of 0.25°×0.25°.

[0008] In a preferred embodiment of the present invention, the hourly standard for the height-level spatiotemporal distance in step S1 is 1km × 1km.

[0009] In a preferred embodiment of the present invention, the wind energy resource assessment method in step S1 includes the following specific steps: S101. Based on wind observation data from a 100m anemometer tower in the coastal area of ​​Fujian Province, collect and control the quality of data on 2-minute average wind speed and direction, 10-minute average wind speed and direction, maximum wind speed and direction, and extreme wind speed and direction. S102. Based on wind resource assessment methods, calculate parameters such as turbulence intensity and gust coefficient; This invention also provides a refined assessment system for wind energy resources from coastal to deep-sea areas. The system includes a multi-source fusion data analysis module for the spatiotemporal distribution characteristics of wind energy resources, a offshore wind energy resource monitoring module, a numerical model simulation assessment result module for offshore wind energy resources, a module for calculating design parameters for meteorological disaster risk assessment of wind energy resources, a module for drawing offshore wind energy resource distribution maps, a control port for existing assessment methods, and a human-interactive management terminal. The control port for existing assessment methods and the human-interactive management terminal are bidirectionally connected. The output of the offshore wind energy resource distribution map drawing module is connected to the input of the offshore wind energy resource monitoring module. The output of the offshore wind energy resource monitoring module is connected to the input of the module for calculating design parameters for meteorological disaster risk assessment of wind energy resources. The output of the module for calculating design parameters for meteorological disaster risk assessment of wind energy resources is connected to the input of the human-interactive management terminal.

[0010] In a preferred embodiment of the present invention, the offshore wind energy resource distribution map drawing module includes an offshore wind energy resource distribution map data acquisition unit, a numerical simulation unit, and a coastal related data storage unit. The output end of the coastal related data storage unit is communicatively connected to the input end of the offshore wind energy resource distribution map data acquisition unit, and the output end of the offshore wind energy resource distribution map data acquisition unit is communicatively connected to the input end of the numerical simulation unit. The offshore wind energy resource distribution map data acquisition unit is used to collect, integrate and process multi-source heterogeneous data. Based on the raw data, it automatically calculates key indicators such as annual average wind speed, wind power density, wind direction frequency rose diagram, turbulence intensity, and wind shear index for any time period, divides the sea area into high-resolution grid units, assigns wind energy parameter values ​​to each unit, and forms a visual distribution map. The numerical simulation unit is used to perform numerical simulations based on multiple schemes related to wind elements, including cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long and short wave radiation, in the numerical model WRF. The numerical simulation is performed using three-dimensional linear interpolation and two-dimensional spatial downscaling methods to obtain a spatiotemporal resolution wind field dataset with arbitrary height between 10-300m near the sea surface in coastal and deep-sea areas of Fujian Province. The coastal relevant data storage unit is used to record and continuously update the relevant coastal city data that needs to be evaluated, and to provide a search window to provide the corresponding coastal relevant data for the searched city.

[0011] In a preferred embodiment of the present invention, the offshore wind energy resource monitoring module includes an offshore wind energy resource monitoring unit, an offshore wind energy resource assessment unit, and a developable potential estimation unit. The output end of the offshore wind energy resource monitoring unit is communicatively connected to the input end of the offshore wind energy resource assessment unit. Both the offshore wind energy resource monitoring unit and the offshore wind energy resource assessment unit are bidirectionally communicatively connected to the developable potential estimation unit. The offshore wind energy resource monitoring unit is used to provide accurate offshore wind energy resource data through real-time monitoring and data acquisition to support the dynamic assessment of wind energy resources, optimize wind farm site selection, and predict wind energy production capacity. The offshore wind energy resource assessment unit is used to assess wind energy resources using existing and real-time data. By analyzing historical data and real-time monitoring data, it estimates the wind energy potential of a specific area, thereby providing a scientific basis for the planning, site selection and design of wind power projects. The exploitable potential estimation unit is used to estimate the exploitable potential of wind energy resources using existing data and real-time data.

[0012] In a preferred embodiment of the present invention, the wind energy resource meteorological disaster risk assessment design parameter calculation module includes a parameter calculation unit, an auxiliary algorithm database, and a parameter preset unit. The output end of the parameter preset unit is communicatively connected to the input end of the auxiliary algorithm database, and the output end of the auxiliary algorithm database is communicatively connected to the input end of the parameter calculation unit. The parameter calculation unit is used to calculate various wind parameters such as turbulence intensity and wind shear index, providing important scientific and technological support for the rational development of offshore wind power in Fujian Province and the long-term planning and layout of offshore wind farms. The auxiliary algorithm database is used to obtain and analyze wind speed, wind direction and meteorological data, and further support key tasks such as wind farm site selection, power generation prediction and resource assessment. The algorithm includes a statistical model calculation, which establishes a probability distribution model of wind speed through statistical analysis of historical wind energy data; The neural network algorithm can help to perform pattern recognition and prediction of wind energy resources, model complex nonlinear data, and identify the implicit patterns of wind energy resources. Remote sensing and geographic information system algorithms, through remote sensing technology and GIS systems, combined with satellite imagery, meteorological data and terrain features, provide spatial data analysis required for wind energy resource assessment; A time series analysis algorithm is used to predict the dynamic changes of wind energy resources by using historical wind speed data to predict future wind speed and wind energy output. Big data analytics algorithms integrate large amounts of data on wind speed, meteorology, ocean, and environment through big data analytics technology, and use data mining techniques to discover patterns in wind energy resources; The parameter preset unit is used to preset multiple sets of calculation parameters. The corresponding parameters can be added or deleted according to the needs of each evaluation. The parameters include wind speed, wind energy density, wind direction, turbulence intensity, wind shear index, air pressure, ocean waves, air density, ocean current, sea surface temperature and humidity, and seasonal wind energy changes.

[0013] In a preferred embodiment of the present invention, the control port of the existing evaluation method includes an existing evaluation technology synchronization update unit, a historical record backup storage unit, and a visualization data comparison unit. The output of the evaluation technology synchronization update unit is communicatively connected to the input of the historical record backup storage unit, and the output of the historical record backup storage unit is communicatively connected to the input of the visualization data comparison unit. The existing assessment technology synchronization and update unit is used to record existing publicly available offshore wind energy resource assessment methods and update the continuously improving processes in real time. The historical record backup storage unit is used to back up and store the data of the offshore wind energy resource assessment method for each update, including recording the backup time, the difference from the previous backup, and the backup location; The visualization data comparison unit is used to visualize the required offshore wind energy resource data through multiple sets of tables, and to mark the key cities to be searched.

[0014] In a preferred embodiment of the present invention, the human interaction management terminal includes a mobile control terminal, a remote control terminal, an access unit, and a human interaction management unit. The mobile control terminal and the remote control terminal are both bidirectionally connected to the human interaction management unit, and the access unit is bidirectionally connected to the human interaction management unit. The mobile control terminal is used for manual handheld devices to work while moving, which improves the convenience of controlling the device; The remote control terminal is used to issue corresponding operation instructions through real-time monitoring by remote staff, and to switch operation modes according to the needs of the operation. The access unit is used to summarize the solutions after the completion of this refined assessment, and connect them to the assessment system for the next city assessment or the assessment of data from different times in the same city. The human interaction management unit is used to verify the human's identity, start and stop the evaluation system, and continuously optimize the interactive interface.

[0015] The beneficial effects of this invention are as follows: By setting up an evaluation system, this invention has formed a high spatiotemporal resolution wind field dataset for coastal and deep-sea areas of Fujian Province with an arbitrary height layer of 10-300m, a spatial resolution of 1km×1km, and a temporal resolution of 1h; it has drawn hourly 1km×1km, 10-300m arbitrary height fine-grained wind resource evaluation maps, filling the gap in wind field data for coastal and deep-sea areas of the Taiwan Strait; and it has developed a fine-grained evaluation system for offshore wind resources in Fujian Province, realizing fine-grained evaluation and prediction of exploitable potential of offshore wind energy resources at different heights based on multi-source meteorological data. It is currently in operational use.

[0016] This invention utilizes numerical simulations based on multiple schemes in the WRF numerical model, encompassing cloud microphysics, near-surface layer, boundary layer, cumulus convection, and longwave and shortwave radiation, all related to wind elements. Employing three-dimensional linear interpolation and two-dimensional spatial downscaling methods, the simulations yielded a spatiotemporal resolution wind field dataset at arbitrary heights (10-300m) near the near-sea surface in coastal and deep-sea areas of Fujian Province. Based on model products obtained through this method, along with multi-source meteorological data from weather stations and reanalysis data, a refined assessment system for offshore wind resources in Fujian Province was developed. This system enables a detailed assessment of offshore wind energy resources and development potential in Fujian Province, including the automatic mapping of offshore wind energy resource distribution maps. This method and system enables real-time monitoring and forecasting of offshore wind energy resources, as well as the calculation of various wind parameters such as turbulence intensity and wind shear index. It provides crucial technological support for the rational development of offshore wind power and the long-term planning and layout of offshore wind farms in Fujian Province. Currently, this method and system have been applied in meteorological services such as offshore wind energy resource development and utilization, and tropical cyclone risk assessment for offshore wind farms in the province. It manages, visualizes, and stores refined assessment data and corresponding analysis results of coastal to deep-sea wind energy resources, facilitating refined assessment and management of coastal to deep-sea wind energy resources through internet cloud control, and improving the intelligence level of refined assessment and management of coastal to deep-sea wind energy resources. Attached image description: For ease of explanation, the present invention will be described in detail below with reference to specific embodiments and accompanying drawings.

[0017] Figure 1 This is a flowchart of the overall process of the refined assessment method and system for wind energy resources from coastal to deep-sea areas of the present invention. Figure 2 This invention provides a topological map of the multi-year average wind speed at any height within 10m in the coastal to deep-sea areas (including the Taiwan Strait) of Fujian Province, based on the refined assessment method and system for wind energy resources from the coast to the deep sea. Figure 3 This invention provides a topological map of the multi-year average wind speed at any height between 80m and the coastline to deep-sea areas (including the Taiwan Strait) of Fujian Province, based on the refined assessment method and system for wind energy resources from coastal to deep-sea areas. Figure 4This invention provides a topological map of the multi-year average wind speed at any height within 100m in the coastal to deep-sea areas (including the Taiwan Strait) of Fujian Province, based on the refined assessment method and system for wind energy resources from the coast to the deep sea. Figure 5 This invention provides a topological map of the multi-year average wind speed at any height between 150m and the coastal and deep-sea areas (including the Taiwan Strait) of Fujian Province, based on the refined assessment method and system for wind energy resources from the coast to the deep sea. Figure 6 This invention provides a topological map of the multi-year average wind speed at any height between 200m and the coastline to deep-sea areas (including the Taiwan Strait) in Fujian Province, based on the refined assessment method and system for wind energy resources from coastal to deep-sea areas. Figure 7 This invention provides a topological map of the multi-year average wind speed at any height between 300m and the coastline and deep-sea areas (including the Taiwan Strait) of Fujian Province, based on the refined assessment method and system for wind energy resources from coastal to deep-sea areas. Detailed implementation method: like Figures 1-7 As shown, the embodiments of the present invention adopt the following technical solutions: A refined assessment method for wind energy resources from coastal to deep-sea areas includes the following specific steps: S1. Construct a multi-source gradient wind measurement database from the Fujian Provincial National Meteorological Observatory, coastal automatic weather stations, marine buoy stations, reanalysis data, and offshore wind measurement towers. Based on the above scheme, integrate the numerical model WRF simulation technology (Weather Research and Forecasting). The model (weather forecasting and research model) constructs a wind energy resource assessment method based on existing technologies. Inputting relevant coastal data from Fujian Province, it generates a spatiotemporal resolution wind field dataset with arbitrary heights (10-300m) from the nearshore surface of Fujian's coastal and deep-sea areas. This dataset provides wind resource assessment results at any height from the nearshore surface of Fujian's coastal and deep-sea areas, effectively filling the gap in measured wind data for the corresponding hub heights of offshore wind turbines in Fujian Province (100-250m). This provides important scientific basis and technical support for future wind power site selection, planning, and offshore wind engineering construction in Fujian's coastal and especially deep-sea areas. The WRF model is based on the fundamental theories of meteorology and atmospheric dynamics, using numerical methods to solve atmospheric motion equations, such as changes in wind field, temperature, and humidity. It predicts future weather conditions through grid partitioning and iterative calculations. The hourly standard for height-level spatiotemporal data is 1km × 1km. The wind energy resource assessment method includes the following specific steps: S101. Based on wind observation data from a 100m anemometer tower in the coastal area of ​​Fujian Province, collect and control the quality of data on 2-minute average wind speed and direction, 10-minute average wind speed and direction, maximum wind speed and direction, and extreme wind speed and direction. S102. Based on wind resource assessment methods, calculate parameters such as turbulence intensity and gust coefficient; S2. Select a set of high-resolution reanalysis data as the initial field. Select USGS (United States Geological Survey) geographic information base data and gtopo (Global Topography) elevation data in the WRF model. Use multiple schemes covering cloud microphysics, near-surface layer, boundary layer, cumulus convection, and longwave and shortwave radiation for numerical simulation. The reanalysis data includes 6-hourly data of ERA5 (ECMWF Fifth Generation Global Atmospheric Reanalysis) surface field and upper-air field at a resolution of 0.25°×0.25°. S3. Using the wind measurement data from the multi-source wind tower database constructed in S1, the effects of different model schemes were verified and the optimal combination of model schemes was selected. The spatiotemporal wind resource characteristics at any height between 10-300m above the near-sea surface in coastal and deep-sea areas of Fujian Province were obtained by using three-dimensional linear interpolation and two-dimensional spatial downscaling methods. This filled the gap in the lack of long-term series measured wind data at near-surface height in multiple areas along the near-shore and deep-sea areas of Fujian Province.

[0018] S4. The sea area of ​​Fujian Province is divided into northern, central, and southern sea areas. Daily maximum wind speed data from national meteorological observation stations, coastal island regional automatic weather stations, and marine buoy stations are used. Tropical cyclones that have historically affected different sea areas of Fujian are selected to construct long-term maximum wind speed sequences from national meteorological observation stations and more than 250 regional automatic weather stations and marine buoy stations during the influence of tropical cyclones. Since the regional automatic weather stations only have wind measurement data from 2009, correlation analysis is used to determine the long-term representative stations of each region's automatic weather stations. Then, ratio correction, correlation analysis, and other methods are used to construct the maximum wind speed sequences during the influence of tropical cyclones from more than 250 regional automatic weather stations and marine buoy stations from the coast to the deep sea. The two correction methods are fully compared and analyzed to determine the optimal correction method and construct the optimal long-term daily maximum wind speed sequences from regional automatic weather stations and marine buoy stations.

[0019] S5. Calculate the exceedance probability and threshold of different levels of wind speed WTGS (Wind Turbine Generator System) during the influence of tropical cyclones, determine the areas of wind speeds available for wind resource development, warning wind speeds, and defense wind speeds, and draw a risk zoning map of wind farm development and utilization from the coast to the deep sea.

[0020] This invention also provides a refined assessment system for wind energy resources from coastal to deep-sea areas. The system includes a multi-source fusion data analysis module for the spatiotemporal distribution characteristics of wind energy resources, a offshore wind energy resource monitoring module, a numerical model simulation assessment result module for offshore wind energy resources, a module for calculating design parameters for meteorological disaster risk assessment of wind energy resources, a module for drawing offshore wind energy resource distribution maps, a control port for existing assessment methods, and a human-interactive management terminal. The control port for existing assessment methods and the human-interactive management terminal are bidirectionally connected. The output of the offshore wind energy resource distribution map drawing module is connected to the input of the offshore wind energy resource monitoring module. The output of the offshore wind energy resource monitoring module is connected to the input of the module for calculating design parameters for meteorological disaster risk assessment of wind energy resources. The output of the module for calculating design parameters for meteorological disaster risk assessment of wind energy resources is connected to the input of the human-interactive management terminal.

[0021] Furthermore, the offshore wind energy resource distribution map drawing module includes an offshore wind energy resource distribution map data acquisition unit, a numerical simulation unit, and a coastal related data storage unit. The output end of the coastal related data storage unit is communicatively connected to the input end of the offshore wind energy resource distribution map data acquisition unit, and the output end of the offshore wind energy resource distribution map data acquisition unit is communicatively connected to the input end of the numerical simulation unit. The offshore wind energy resource distribution map data acquisition unit is used to collect, integrate and process multi-source heterogeneous data. Based on the raw data, it automatically calculates key indicators such as annual average wind speed, wind power density, wind direction frequency rose diagram, turbulence intensity, and wind shear index for any time period, divides the sea area into high-resolution grid units, assigns wind energy parameter values ​​to each unit, and forms a visual distribution map. The numerical simulation unit is used to perform numerical simulations based on multiple schemes related to wind elements, including cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long and short wave radiation, in the numerical model WRF. It uses three-dimensional linear interpolation and two-dimensional spatial downscaling methods to obtain a spatiotemporal resolution wind field dataset with arbitrary height between 10-300m near the near-sea surface in coastal and deep-sea areas of Fujian Province. The coastal relevant data storage unit is used to record the relevant coastal city data that needs to be evaluated, to continuously update it, to provide a search window, and to provide the corresponding coastal relevant data for the searched city. The multi-source fusion data wind energy resource spatiotemporal distribution feature analysis module is integrated into the offshore wind energy resource distribution map drawing module. The multi-source fusion data wind energy resource spatiotemporal distribution feature analysis module is used to provide the necessary algorithmic technical support for resource acquisition and image drawing.

[0022] Furthermore, the offshore wind energy resource monitoring module includes an offshore wind energy resource monitoring unit, an offshore wind energy resource assessment unit, and a developable potential estimation unit. The output end of the offshore wind energy resource monitoring unit is communicatively connected to the input end of the offshore wind energy resource estimation unit. Both the offshore wind energy resource monitoring unit and the offshore wind energy resource assessment unit are bidirectionally communicatively connected to the developable potential estimation unit. The offshore wind energy resource monitoring unit is used to provide accurate offshore wind energy resource data through real-time monitoring and data acquisition to support the dynamic assessment of wind energy resources, optimize wind farm site selection, and predict wind energy production capacity. The offshore wind energy resource assessment unit is used to assess wind energy resources using existing and real-time data. By analyzing historical data and real-time monitoring data, it estimates the wind energy potential of a specific area, thereby providing a scientific basis for the planning, site selection and design of wind power projects. The exploitable potential estimation unit is used to estimate the exploitable potential of wind energy resources using existing data and real-time data.

[0023] The numerical model simulation and evaluation results module for offshore wind energy resources is integrated into the offshore wind energy resource monitoring module. This module is used to finalize the prediction and estimation results of the offshore wind energy resource evaluation unit during operation and can be processed through manual intervention.

[0024] Furthermore, the wind energy resource meteorological disaster risk assessment design parameter calculation module includes a parameter calculation unit, an auxiliary algorithm database, and a parameter preset unit. The output end of the parameter preset unit is communicatively connected to the input end of the auxiliary algorithm database, and the output end of the auxiliary algorithm database is communicatively connected to the input end of the parameter calculation unit. The parameter calculation unit is used to calculate various wind parameters such as turbulence intensity and wind shear index, providing important scientific and technological support for the rational development of offshore wind power in Fujian Province and the long-term planning and layout of offshore wind farms. The auxiliary algorithm database is used to obtain and analyze wind speed, wind direction and meteorological data, and further support key tasks such as wind farm site selection, power generation prediction and resource assessment. The algorithm includes a statistical model calculation, which establishes a probability distribution model of wind speed through statistical analysis of historical wind energy data; The neural network algorithm can help to perform pattern recognition and prediction of wind energy resources, model complex nonlinear data, and identify the implicit patterns of wind energy resources. Remote sensing and geographic information system algorithms, through remote sensing technology and GIS systems, combined with satellite imagery, meteorological data and terrain features, provide spatial data analysis required for wind energy resource assessment; A time series analysis algorithm is used to predict the dynamic changes of wind energy resources by using historical wind speed data to predict future wind speed and wind energy output. Big data analytics algorithms integrate large amounts of data on wind speed, meteorology, ocean, and environment through big data analytics technology, and use data mining techniques to discover patterns in wind energy resources; The parameter preset unit is used to preset multiple sets of calculation parameters. The corresponding parameters can be added or deleted according to the needs of each evaluation. The parameters include wind speed, wind energy density, wind direction, turbulence intensity, wind shear index, air pressure, ocean waves, air density, ocean current, sea surface temperature and humidity, and seasonal wind energy changes.

[0025] Furthermore, the control port of the existing evaluation method includes an existing evaluation technology synchronization update unit, a historical record backup storage unit, and a visualization data comparison unit. The output of the evaluation technology synchronization update unit is communicatively connected to the input of the historical record backup storage unit, and the output of the historical record backup storage unit is communicatively connected to the input of the visualization data comparison unit. The existing assessment technology synchronization and update unit is used to record existing publicly available offshore wind energy resource assessment methods and update the continuously improving processes in real time. The historical record backup storage unit is used to back up and store the data of the offshore wind energy resource assessment method for each update, including recording the backup time, the difference from the previous backup, and the backup location; The visualization data comparison unit is used to visualize the required offshore wind energy resource data through multiple sets of tables, and to mark the key cities to be searched.

[0026] Furthermore, the human interaction management terminal includes a mobile control terminal, a remote control terminal, an access unit, and a human interaction management unit. The mobile control terminal and the remote control terminal are both bidirectionally connected to the human interaction management unit, and the access unit is bidirectionally connected to the human interaction management unit. The mobile control terminal is used for manual handheld devices to work while moving, which improves the convenience of controlling the device; The remote control terminal is used to issue corresponding operation instructions through real-time monitoring by remote staff, and to switch operation modes according to the needs of the operation. The access unit is used to summarize the solutions after the completion of this refined assessment, and connect them to the assessment system for the next city assessment or the assessment of data from different times in the same city. The human interaction management unit is used to verify the human's identity, start and stop the evaluation system, and continuously optimize the interactive interface.

[0027] Example When the refined assessment method for coastal to deep-sea wind energy resources is applied to Fujian Province: S1. After staff have tested each on-site and remote device and confirmed that they are all in normal working order, the evaluation system will be activated. S2. The mobile control terminal of the assessment system is used by manual handheld devices for working while moving, which improves the convenience of controlling the equipment. The remote control terminal of the assessment system issues corresponding operation instructions through real-time monitoring by remote staff and switches the operation mode according to the needs of the task. The access unit of the assessment system summarizes the solutions after completing this refined assessment and connects them to the assessment system for assessment of the next city or assessment of data from different times in the same city. The manual interaction management unit of the assessment system is used to verify the identity of the manual personnel, start and stop the assessment system, and continuously optimize the interactive interface. S3. Evaluation system control: Existing wind energy resource assessment methods. Based on wind observation data from a 100m anemometer tower along the coast of Fujian Province, data collection and quality control were performed on 2-minute average wind speed and direction, 10-minute average wind speed and direction, maximum wind speed and direction, and extreme wind speed and direction. Based on wind resource assessment methods, parameters such as turbulence intensity and gust coefficient were calculated.

[0028] The assessment system controls the existing assessment technology synchronous update unit to record the existing publicly available offshore wind energy resource assessment methods and updates the continuously improving processes in real time. The assessment system controls the historical record backup storage unit to back up and store the data of each updated offshore wind energy resource assessment method, including recording the backup time, differences from the previous backup, and backup location. The assessment system controls the visualization data comparison unit to visualize the required offshore wind energy resource data through multiple sets of tables and marks the key cities to be searched. S4. The evaluation system's control unit for offshore wind energy resource distribution map data acquisition collects, integrates, and processes multi-source heterogeneous data. Based on the raw data, it extracts key indicators such as annual average wind speed, wind power density, wind direction frequency rose diagram, turbulence intensity, and wind shear index. The sea area is divided into high-resolution grid cells, and wind energy parameter values ​​are assigned to each cell to form a visual distribution map. The evaluation system's control unit for numerical simulation conducts numerical simulations based on multiple schemes related to wind elements, including cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long-wave and short-wave radiation, using a three-dimensional linear interpolation method. Numerical simulations using two-dimensional spatial downscaling methods were conducted to obtain a spatiotemporal resolution wind field dataset with arbitrary heights ranging from 10 to 300 meters above the near-sea surface in coastal and deep-sea areas of Fujian Province. The evaluation system controls the coastal data storage unit to record and continuously update the relevant coastal city data to be evaluated, and provides a retrieval window to provide the corresponding coastal data for the searched city. The offshore wind energy resource monitoring unit is used to provide accurate offshore wind energy resource data through real-time monitoring and data acquisition to support the dynamic evaluation of wind energy resources, optimize wind farm site selection, and predict wind energy production capacity. The assessment system controls the offshore wind energy resource estimation unit to assess wind energy resources and estimate their exploitability potential using existing data. By analyzing historical data and real-time monitoring data, it estimates the wind energy potential of a specific area, thereby providing a scientific basis for the planning, site selection and design of wind power projects. The assessment system controls the exploitability potential estimation unit to estimate the exploitability potential of wind energy resources using existing data and real-time data. S5, the evaluation system control parameter calculation unit calculates various wind parameters such as turbulence intensity and wind shear index, providing important scientific and technological support for the rational development of offshore wind power in Fujian Province and the long-term planning and layout of offshore wind farms. The evaluation system control auxiliary algorithm database acquires wind speed, wind direction, and meteorological data for prediction and analysis, and further supports key tasks such as wind farm site selection, power generation prediction, and resource assessment. The evaluation system control selects and activates corresponding algorithms, including statistical model algorithms, which establish a probability distribution model of wind speed through statistical analysis of historical wind energy data; and neural network algorithms, which can help with pattern recognition and prediction of wind energy resources and construct models for complex nonlinear data. The system employs several algorithms: a model to identify implicit patterns in wind energy resources; a remote sensing and geographic information system algorithm that combines remote sensing technology and GIS systems with satellite imagery, meteorological data, and terrain features to provide spatial data analysis for wind energy resource assessment; a big data analysis algorithm that integrates large amounts of data on wind speed, meteorology, oceanography, and the environment using big data analysis techniques, and utilizes data mining techniques to discover patterns in wind energy resources; and a system control parameter preset unit that presets multiple sets of calculation parameters, adding or deleting corresponding parameters as needed for each assessment. These parameters include wind speed, wind energy density, wind direction, turbulence intensity, wind shear index, air pressure, ocean waves, air density, ocean currents, sea surface temperature and humidity, and seasonal wind energy variations.

[0029] Specifically, in practical applications, multiple offshore wind energy resource distribution mapping modules are used in conjunction with offshore wind energy resource monitoring modules, wind energy resource meteorological disaster risk assessment design parameter calculation modules, existing assessment method control ports, and manual interactive management terminals. These multiple offshore wind energy resource distribution mapping modules are located in different geographical locations. This invention, through the establishment of an assessment system, forms a high spatiotemporal resolution wind field dataset for coastal to deep-sea areas of Fujian Province, with a spatial resolution of 1km×1km and a temporal resolution of 1h, covering any height layer from 10-300m. Hourly mapping of 1km×1km at any height from 10-300m is also provided. A refined wind resource assessment map fills the gap in wind fields from the coastal areas of Fujian Province to the deep sea (including the Taiwan Strait). A refined assessment system for offshore wind resources in Fujian Province has been developed, enabling refined assessment of offshore wind energy resources at different altitudes and prediction of exploitable potential based on multi-source meteorological data. It is currently in operational use. This invention is based on numerical simulations of multiple schemes related to wind elements, including cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long and short wave radiation, in the WRF numerical model. A spatiotemporal resolution wind field dataset with arbitrary height between 10-300m near the near-sea surface in the coastal areas of Fujian Province to the deep sea is obtained by using three-dimensional linear interpolation and two-dimensional spatial downscaling methods. Based on the model products obtained by this method, as well as multi-source meteorological data such as meteorological stations and reanalysis data, a refined assessment system for offshore wind resources in Fujian Province has been developed. This system enables a refined assessment of offshore wind energy resources and development potential in Fujian Province, including the automatic drawing of offshore wind energy resource distribution maps, real-time monitoring and forecasting of changes in offshore wind energy resources, and calculation of various wind parameters such as turbulence intensity and wind shear index. This provides important scientific and technological support for the rational development of offshore wind power generation and the long-term planning and layout of offshore wind farms in Fujian Province. Currently, this method and system have been applied in meteorological services such as the development and utilization of offshore wind energy resources and the tropical cyclone risk assessment of offshore wind farms in our province. It manages, visualizes, and stores the refined assessment data and corresponding analysis results of offshore wind energy resources from coastal to deep-sea areas, which helps to realize the refined assessment and management of offshore wind energy resources from coastal to deep-sea areas through Internet cloud management and control, and improves the intelligent level of refined assessment and management of offshore wind energy resources from coastal to deep-sea areas.

[0030] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0031] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, equipment, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0032] In the embodiments provided in this application, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or equipment, and may be electrical, mechanical, or other forms.

[0033] Furthermore, it should be noted that the combination of the various technical features in this case is not limited to the combination methods described in the claims of this case or the combination methods described in the specific embodiments. All technical features described in this case can be freely combined or combined in any way, unless they contradict each other.

[0034] It should be noted that the above examples are merely specific embodiments of the present invention, and the present invention is obviously not limited to the above embodiments, with many similar variations. All modifications that can be directly derived or conceived by those skilled in the art from the content disclosed in this invention should fall within the protection scope of this invention.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A refined assessment method for wind energy resources from coastal to deep-sea areas, characterized in that, The specific steps include the following: S1. Construct a multi-source gradient wind measurement database of the Fujian Provincial National Meteorological Observatory, coastal automatic weather stations, marine buoy stations, reanalysis data, and offshore wind measurement towers. Integrate the existing wind energy resource assessment methods and the numerical model WRF simulation technology to construct a refined assessment method for coastal to deep-sea wind energy resources based on numerical model WRF simulation. S2. Select a set of high-resolution reanalysis data as the initial field, select USGS geographic information base data and gtopo elevation data in the WRF model, and use multiple schemes covering cloud microphysics, near-surface layer, boundary layer, cumulus convection, and long and short wave radiation to carry out numerical simulation. S3. Using the wind measurement data from the multi-source wind tower database constructed in S1, the effects of different model schemes were verified and the optimal combination of model schemes was selected. Numerical simulation was carried out using three-dimensional linear interpolation and two-dimensional spatial downscaling methods to develop the wind resource characteristics of any height layer between 10-300m in coastal and deep-sea areas. S4. The sea area of ​​Fujian Province is divided into northern, central and southern sea areas. Using daily maximum wind speed data from national meteorological observation stations, coastal island automatic weather stations and marine buoy stations, tropical cyclones that have historically affected different sea areas of Fujian are selected to construct long-term maximum wind speed sequences from national meteorological observation stations and more than 250 regional automatic weather stations and marine buoy stations during the influence of tropical cyclones. Correlation analysis is used to determine the long-term representative stations of automatic weather stations in each region. Then, using ratio correction, correlation analysis and other methods, the maximum wind speed sequences during the influence of tropical cyclones from more than 250 regional automatic weather stations and marine buoy stations from the coast to the deep sea are constructed. The two correction methods are fully compared and analyzed to determine the optimal correction method and construct the optimal long-term daily maximum wind speed sequences of regional automatic weather stations and marine buoy stations. S5. Calculate the WTGS exceedance probability and threshold of wind speeds at different levels during the influence of tropical cyclones, determine the areas of wind speeds available for wind resource development, warning wind speeds, and defense wind speeds, and draw a risk zoning map of wind farm development and utilization from the coast to the deep sea.

2. The refined assessment method for coastal to deep-sea wind energy resources according to claim 1, characterized in that: The reanalysis data in step S2 includes 6-hourly data of the ERA5 ground field and upper-air field at a resolution of 0.25° × 0.25°.

3. The refined assessment method for coastal to deep-sea wind energy resources according to claim 1, characterized in that: The hourly standard for the height-level spatiotemporal data in step S3 is 1km × 1km.

4. The refined assessment method for coastal to deep-sea wind energy resources according to claim 1, characterized in that: The wind energy resource assessment method in step S1 includes the following specific steps: S101. Based on wind observation data from a 100m anemometer tower in the coastal area, collect and control the quality of data for 2-minute average wind speed and direction, 10-minute average wind speed and direction, maximum wind speed and direction, and extreme wind speed and direction. S102. Based on wind resource assessment methods, calculate turbulence intensity and gust coefficient.

5. A refined assessment system for coastal to deep-sea wind energy resources, used to implement the refined assessment method for coastal to deep-sea wind energy resources as described in any one of claims 1-4, characterized in that: The assessment system includes a multi-source fusion data spatiotemporal distribution characteristic analysis module for wind energy resources, a marine wind energy resource monitoring module, a marine wind energy resource numerical model simulation assessment result module, a wind energy resource meteorological disaster risk assessment design parameter calculation module, a marine wind energy resource distribution map drawing module, an existing assessment method control port, and a manual interaction management terminal. The existing assessment method control port and the manual interaction management terminal are bidirectionally connected. The output of the marine wind energy resource distribution map drawing module is connected to the input of the marine wind energy resource monitoring module. The output of the marine wind energy resource monitoring module is connected to the input of the wind energy resource meteorological disaster risk assessment design parameter calculation module. The output of the wind energy resource meteorological disaster risk assessment design parameter calculation module is connected to the input of the manual interaction management terminal.

6. The refined assessment system for coastal to deep-sea wind energy resources according to claim 5, characterized in that: The offshore wind energy resource distribution map drawing module includes an offshore wind energy resource distribution map data acquisition unit, a numerical simulation unit, and a coastal related data storage unit. The output end of the coastal related data storage unit is communicatively connected to the input end of the offshore wind energy resource distribution map data acquisition unit, and the output end of the offshore wind energy resource distribution map data acquisition unit is communicatively connected to the input end of the numerical simulation unit. The offshore wind energy resource distribution map data acquisition unit is used to collect, integrate and process multi-source heterogeneous data, and automatically calculate key indicators such as annual average wind speed, wind power density, wind direction frequency rose diagram, turbulence intensity and wind shear index for any time period based on the raw data. The numerical simulation unit is used to perform numerical simulations based on multiple schemes in the numerical model WRF, which cover cloud microphysics, near-surface layer, boundary layer, cumulus convection, long and short wave radiation and wind elements. The coastal relevant data storage unit is used to record and continuously update the relevant coastal city data that needs to be evaluated. The multi-source fusion data wind energy resource spatiotemporal distribution feature analysis module is integrated into the offshore wind energy resource distribution map drawing module. The multi-source fusion data wind energy resource spatiotemporal distribution feature analysis module is used to provide the necessary algorithmic technical support for resource acquisition and image drawing.

7. The refined assessment system for coastal to deep-sea wind energy resources according to claim 5, characterized in that: The offshore wind energy resource monitoring module includes an offshore wind energy resource monitoring unit, an offshore wind energy resource assessment unit, and a developable potential estimation unit. The output end of the offshore wind energy resource monitoring unit is communicatively connected to the input end of the offshore wind energy resource assessment unit, and both the offshore wind energy resource monitoring unit and the offshore wind energy resource assessment unit are bidirectionally communicatively connected to the developable potential estimation unit. The offshore wind energy resource monitoring unit is used to provide accurate offshore wind energy resource data through real-time monitoring and data acquisition to support the dynamic assessment capability of wind energy resources. The offshore wind energy resource assessment unit is used to assess wind energy resources using existing and real-time data. The exploitable potential estimation unit is used to estimate the exploitable potential of wind energy resources using existing data and real-time data. The numerical model simulation and evaluation result module for offshore wind energy resources is integrated into the offshore wind energy resource monitoring module. The numerical model simulation and evaluation result module for offshore wind energy resources is used to make the final verification of the prediction and estimation results during the operation of the offshore wind energy resource prediction unit, and is processed through manual intervention.

8. The refined assessment system for coastal to deep-sea wind energy resources according to claim 5, characterized in that: The wind energy resource meteorological disaster risk assessment design parameter calculation module includes a parameter calculation unit, an auxiliary algorithm database, and a parameter preset unit. The output end of the parameter preset unit is communicatively connected to the input end of the auxiliary algorithm database, and the output end of the auxiliary algorithm database is communicatively connected to the input end of the parameter calculation unit. The parameter calculation unit is used to calculate various wind parameters such as turbulence intensity and wind shear index; The auxiliary algorithm database is used to obtain and analyze wind speed, wind direction and meteorological data, and further support key tasks such as wind farm site selection, power generation prediction and resource assessment. The parameter preset unit is used to preset multiple sets of calculation parameters, and to add or delete corresponding parameters as needed for each evaluation.

9. The refined assessment system for coastal to deep-sea wind energy resources according to claim 5, characterized in that: The control port of the existing evaluation method includes an existing evaluation technology synchronization update unit, a historical record backup storage unit, and a visualization data comparison unit. The output of the evaluation technology synchronization update unit is communicatively connected to the input of the historical record backup storage unit, and the output of the historical record backup storage unit is communicatively connected to the input of the visualization data comparison unit. The existing assessment technology synchronization update unit is used to record existing publicly available offshore wind energy resource assessment methods; The historical record backup storage unit is used to back up and store the data of the offshore wind energy resource assessment method for each update; The visualization data comparison unit is used to visualize the required offshore wind energy resource data through multiple sets of tables.

10. The refined assessment system for coastal to deep-sea wind energy resources according to claim 5, characterized in that: The human interaction management terminal includes a mobile control terminal, a remote control terminal, an access unit, and a human interaction management unit. The mobile control terminal and the remote control terminal are both bidirectionally connected to the human interaction management unit, and the access unit is bidirectionally connected to the human interaction management unit. The mobile control terminal is used for manual handheld devices to work while moving, which improves the convenience of controlling the device; The remote control terminal is used to issue corresponding operation instructions through real-time monitoring by remote staff. The access unit is used to summarize the solutions after this refined evaluation and connect them to the evaluation system. The human interaction management unit is used to verify the human identity and to start or stop the evaluation system.

Images