CN117421871A - Offshore wind power potential evaluation method and device and computer equipment - Google Patents
Offshore wind power potential evaluation method and device and computer equipment Download PDFInfo
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Abstract
The application relates to a method and a device for evaluating potential of offshore wind power and computer equipment. The method comprises the following steps: constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea; acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data; and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan. By adopting the method, the evaluation accuracy of the potential of the offshore wind power can be improved.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a method and a device for evaluating potential of offshore wind power and computer equipment.
Background
With the increasing global energy demand and increasing concern for environmental sustainability, renewable energy sources such as wind energy are becoming increasingly important in the energy industry. Because the offshore wind resources are richer and the wind speed is more stable, the offshore wind power has great potential as an important development form of wind energy development in the future. The planning, design and operation of offshore wind power requires accurate wind resource assessment to ensure feasibility of investment and minimization of risk.
However, the existing evaluation method has defects in research time scale, coverage range and data precision, so that the offshore wind power evaluation cannot reach the expected precision.
Disclosure of Invention
Based on the above, it is necessary to provide an offshore wind power potential evaluation method, an offshore wind power potential evaluation device and a offshore wind power potential evaluation computer device capable of improving evaluation accuracy.
In a first aspect, the present application provides a method for offshore wind power potential assessment. The method comprises the following steps:
constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea;
acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data;
and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In one embodiment, constructing a WRF model of the nested structure, the obtaining the predicted wind speed data at sea includes:
acquiring actual wind speed data observed at sea;
acquiring an error between predicted wind speed data and actual wind speed data;
correcting the WRF model according to the error;
and obtaining predicted wind speed data according to the corrected WRF model.
In one embodiment, obtaining the full life cycle power generation of the fan according to the predicted wind speed data includes:
acquiring a power curve and wind speed distribution according to the predicted wind speed data;
acquiring annual average power according to the power curve and the wind speed distribution;
and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
In one embodiment, the method further comprises:
based on different wind speed distribution scenes, building WRF models corresponding to the different scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
In one embodiment, the method further comprises:
and (3) carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
In one embodiment, wind power resource potential assessment based on the full life cycle power generation of the wind turbine comprises:
acquiring annual average wind power generation capacity according to the predicted wind speed data;
acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods;
based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
In a second aspect, the present application provides an offshore wind power potential assessment device, the device comprising:
the weather data acquisition module is used for acquiring the predicted wind speed data at sea through a WRF model of a nested structure;
the generating capacity accounting module is used for acquiring the generating capacity of the full life cycle of the fan according to the predicted wind speed data;
and the potential evaluation module is used for evaluating the potential of the wind power resources based on the full life cycle power generation amount of the fan.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea;
acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data;
and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea;
acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data;
and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of:
constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea;
acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data;
and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
According to the offshore wind power potential evaluation method, the offshore wind power potential evaluation device and the computer equipment, a WRF model of a nested structure is constructed, and offshore predicted wind speed data is obtained; acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data; and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan. The WRF model with the nested structure achieves the purpose of reducing the scale, converts the large-scale and low-resolution marine meteorological data into small-scale and high-resolution meteorological data, and greatly improves the scale in time and space. Meanwhile, the WRF model is used as a model for meteorological simulation, a large amount of meteorological data can be generated, and the limitation that the traditional technical scheme relies on actual collected data for evaluation is broken through, so that a large-amount and high-precision data support is provided for subsequent wind power resource potential evaluation, and the accuracy of wind power resource potential evaluation is improved. In addition, compared with the traditional evaluation method which only quantifies the wind power potential of the area based on wind speed and wind energy formulas, the method acquires the generated energy of the whole life cycle of the fan on the basis of the predicted wind speed data, brings the influence of the fan technology into the evaluation of the wind power potential, and further improves the accuracy of the evaluation of the wind power resource potential.
Drawings
FIG. 1 is an application environment diagram of an offshore wind power potential assessment method in one embodiment;
FIG. 2 is a flow chart of a method for offshore wind power potential assessment in one embodiment;
FIG. 3 is a schematic diagram of a WRF model in one embodiment;
FIG. 4 is a block diagram of an offshore wind power potential assessment device in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The offshore wind power potential evaluation method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
Offshore wind power potential assessment is the basis of wind power project decisions. High accuracy wind resource assessment may help determine optimal wind turbine size, type, and layout to maximize capture of wind energy and increase power generation efficiency. Furthermore, offshore wind projects involve a huge investment, requiring long-term returns. Based on accurate potential assessment, investors and decision makers can better assess the economic feasibility of projects, formulate reasonable investment plans, and reduce risks of projects in planning, construction and operation stages. Government and energy planners can formulate more targeted policies and plans according to accurate assessment results, and promote the development and energy transformation of renewable energy sources. Therefore, how to improve the accuracy of offshore wind power potential assessment as much as possible remains to be studied by the skilled person.
Potential assessment of offshore wind power has several features:
first, the data demand is large. The influence factors of wind power generation include wind speed, wind speed distribution, air density, fan type and the like. Meanwhile, marine environmental factors such as ocean currents, ocean waves, salinity, temperature and the like have influence on the utilization of wind energy, so that a large amount of weather and ocean data such as wind speeds, ocean currents, ocean waves and the like are required for evaluation.
Secondly, the research time scale is long, the space range is large, and the data precision requirement is high. Taking wind speed as an example, there is a large difference in wind speed both in different times and spaces. On one hand, the daily scale, seasonal scale and annual scale of the wind speed can influence the potential and availability of power generation in different time periods, and on the other hand, the spatial variation of the wind speed can influence the spatial layout of the fan, so that meteorological data such as wind speed with higher resolution are the basis for developing evaluation.
Third, the evaluation model is complex. The uncertainty exists in the marine environment, the marine power generation potential evaluation needs to consider both a meteorological model and wind speed distribution from a natural perspective and the technology (such as a fan power curve) and layout of a wind turbine from a technical perspective, and the evaluation flow is complex.
Fourth, the difficulty of data downscaling is great. In order to acquire high spatial-temporal resolution data, long time series data are required for downscaling. Due to the complex weather and climate conditions at sea, the data downscaling is more considered factors, and has great challenges.
Corresponding to the potential evaluation characteristics of the offshore wind power, the existing evaluation method has the following defects: the number of evaluation is insufficient; study time scale, coverage and data accuracy are not sufficient; the evaluation model is simpler; climate and meteorological model are incomplete.
In view of the above problems, in one embodiment, as shown in fig. 2, there is provided an offshore wind power potential evaluation method, which is described by taking the application of the method to the terminal 102 in fig. 1 as an example, and includes the following steps:
step 202, constructing a WRF model of a nested structure, and acquiring offshore predicted wind speed data.
The WRF model is a mesoscale numerical weather forecast model capable of atmospheric simulation for research and business forecasting purposes. The present embodiment uses a WRF model to implement meteorological data downscaling based on fully compressible and non-hydrostatic euler equations.
As shown in fig. 3, the WRF model includes one primary mesh (D01) and three nested meshes (D02, D03, and D04) that interact unidirectionally. D01 is centered on the target area, grid points are 121×121, and horizontal resolution is 27km. The horizontal resolutions of D02, D03, and D04 are 9 km (121×121 grid points), 3 km (91×91 grid points), and 1km (91×91 grid points), respectively. The WRF model may substantially simulate local weather effects occurring around the wind farm. The lateral and boundary conditions of the model are driven by the re-analysis data set of the fifth generation analysis product ERA5 of the middle-term weather forecast center in europe, the spatial resolution is 27km, and the downloading period of the data set is 2012, 1 month, 1 day to 2022, 12 month, 31 days, which is the hourly wind speed data of ten years.
The problem that the spatial resolution is low or the time resolution is low exists in the currently available wind speed data, and the accuracy of the evaluation result in time and space is limited. E.g. ERA5 data has a higher temporal resolution (hours scale), a lower spatial resolution (27 km), whereas Global Wind Atlas data has a higher spatial resolution (250 m), but a lower temporal resolution (years of averaging). None of the above data alone is sufficient to accurately assess offshore wind power potential.
According to the embodiment, the wind speed data with high precision (1 km horizontal resolution) and long time sequence (ten years/hour) can be fitted through the WRF model, so that predicted wind speed data with good fitting effect and large data volume can be obtained, the change of wind energy resources can be simulated more accurately, and further more accurate offshore wind power potential evaluation results can be achieved. This helps to avoid investment risk and optimize resource allocation.
In this embodiment, parameters such as time series and horizontal resolution in the WRF model may be adjusted according to practical situations, and the above parameter setting is only one implementation, and is not limited to the model itself.
And 204, acquiring the full life cycle power generation amount of the fan according to the predicted wind speed data.
And combining the predicted wind speed data with the running parameters of the fan to obtain the full life cycle power generation amount of the fan. The full life cycle generated energy refers to all generated energy of the fan from service to retirement. The operational parameters of the wind turbine include the service life of the wind turbine, the size of the wind turbine, the type of wind turbine, etc.
When the potential of the wind power resources is evaluated, the influence of the fan technology is considered besides wind speed related meteorological data, so that the potential is evaluated more comprehensively and accurately.
And 206, evaluating the potential of wind power resources based on the total life cycle power generation amount of the fan.
And setting a target area of the research object based on a geographic information technology, and calculating the full life cycle power generation capacity of the fan in the target area according to the downscaled predicted wind speed data. The larger the full life cycle generated energy of the fan is, the larger the potential of wind power resources is.
Based on accurate wind power potential assessment, more reasonable project planning can be supported, including size selection, layout design, grid connection, and the like of wind turbines, which will improve the power generation efficiency and overall performance of the wind farm.
The WRF model with the nested structure achieves the purpose of reducing the scale, converts the large-scale and low-resolution marine meteorological data into small-scale and high-resolution meteorological data, and greatly improves the scale in time and space. Meanwhile, the WRF model is used as a model for meteorological simulation, a large amount of meteorological data can be generated, and the limitation that the traditional technical scheme relies on actual collected data for evaluation is broken through, so that a large-amount and high-precision data support is provided for subsequent wind power resource potential evaluation, and the accuracy of wind power resource potential evaluation is improved. In addition, compared with the traditional evaluation method which only quantifies the wind power potential of the area based on wind speed and wind energy formulas, the method acquires the generated energy of the whole life cycle of the fan on the basis of the predicted wind speed data, brings the influence of the fan technology into the evaluation of the wind power potential, and further improves the accuracy of the evaluation of the wind power resource potential.
In one embodiment, step 202 includes: acquiring actual wind speed data observed at sea; acquiring an error between predicted wind speed data and actual wind speed data; correcting the WRF model according to the error; and obtaining predicted wind speed data according to the corrected WRF model.
And calculating the error between the actual wind speed data and the predicted wind speed data to obtain the deviation between the WRF model and the actual marine meteorological conditions when the marine meteorological conditions are simulated. If the error is larger, the simulation effect of the WRF model is poor, and the WRF model needs to be corrected until the error is within an acceptable range.
The simulation of the WRF model to the meteorological conditions comprises various meteorological factors such as wind speed, temperature, air pressure, wind direction and the like, so that the WRF model can better simulate the marine meteorological conditions, and parameters of the various meteorological factors need to be adjusted simultaneously when the WRF model is corrected.
In acquiring the Error between the predicted wind speed data and the actual wind speed data, it can be obtained by calculating an average absolute Error (MAE, mean Absolute Error), an average Error (ME, mean Error), or the like.
Where MAE is a predictive tool that can provide clear information about the deviation between predicted and actual values, ME can give an assessment of the overall prediction giving an underor overestimated level. MAE and ME are expressed as respectively，/>. Where i denotes a time point, n denotes the total number of time points, and f and ob denote predicted wind speed data and actual wind speed data, respectively.
In one embodiment, step 204 includes: acquiring a power curve and wind speed distribution according to the predicted wind speed data; acquiring annual average power according to the power curve and the wind speed distribution; and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
The calculation formula of the full life cycle power generation amount of the fan is as follows:
；
where LE represents the full life cycle power generation (kWh) of the fan; AF represents the annual proportion of use, which means the proportion of time actually put into use, except that the fan is shut down for maintenance and other reasons, and can take a value of 0.97; y represents the general service life (year) of the fan and can take a value of 20;represents annual average power (kW).
The method is obtained through calculation of a power curve and wind speed distribution, and the calculation formula is as follows:
；
wherein Pw (u) represents the fitted fan power curve; u represents the cut-in wind speed to the cut-out wind speed, the value is related to the type of the fan, if the type of the fan is 3-22.5m/s, the value of u is 3-22.5, the lower limit a of integration is 3, and the upper limit b of integration is 22.5; f (u) represents the probability of occurrence of different wind speeds u, i.e. wind speed distribution.
A common wind speed distribution is, for example, the weibull distribution, which is expressed as follows:
；
wherein,representing wind speed; k is a shape parameter, c is a scale parameter (m/s), and fitting can be performed according to high-precision long-time series offshore wind speed data to obtain pixel-by-pixel parameter distribution.
The current wind energy potential evaluation is only based on wind speed and wind energy formula quantization area wind energy potential, and influences of fan technology are ignored. Meanwhile, partial evaluation uses a simple capacity factor to evaluate regional wind power generation potential, and the space and time conditions are not considered enough. According to the embodiment, the evaluation model is optimized by calculating the full life cycle power generation amount of the fan, and the accuracy of the evaluation result is improved.
In one embodiment, the method further comprises: based on different wind speed distribution scenes, building WRF models corresponding to the different scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
There are many models of wind speed distribution, such as gamma distribution, lognormal distribution, rayleigh distribution, etc. Taking the change of wind speed distribution into consideration, a WRF model of different scenes can be constructed according to different wind speed distributions, and then the steps 202 to 204 are repeated to obtain the wind power resource potential evaluation under the corresponding scenes.
In an actual scene, a WRF model in a corresponding scene is called, so that the wind power resource potential evaluation in the scene can be obtained, and the evaluation result is more accurate and reliable.
In one embodiment, the method further comprises: and (3) carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
And when the WRF model is built each time, the Monte Carlo method can be adopted to verify the WRF model. The Monte Carlo method comprises the following steps:
1. and (5) confirming the verification index. And determining indexes output by the WRF model as verification indexes, such as temperature, wind speed and the like. In this embodiment, the wind speed is the WRF model output is the predicted wind speed data.
2. A monte carlo method is used to introduce random errors into the WRF model output. These errors should be similar to those in the actual observed data. Random errors may be generated based on error characteristics of the observed data, such as normal distribution.
3. Random errors are added to the original WRF model output, generating a plurality of different simulation results. Each simulation result contains random errors.
4. For each simulation result, it is compared with the corresponding observed data. The differences between the model and the observed data, such as root mean square error, correlation coefficient, etc., are calculated.
5. And outputting a set of comparison results of the WRF model and the observed data, and calculating a confidence interval. This may be accomplished by calculating statistics of mean, standard deviation, etc. The confidence interval represents a confidence range for the accuracy of the model results.
In one embodiment, the WRF model output may be visualized with the observation data and confidence intervals by plotting, in order to better understand the model performance.
In addition, in step 202, a correction step for the WRF model is included, and the above-mentioned monte carlo method may be used for correction. At this time, the WRF model output is predicted wind speed data, and the observed data is actual wind speed data. After introducing random errors, comparing the simulation result with the observed data, judging the deviation between the simulation result and the observed data according to the comparison result, and if the deviation is large, correcting the WRF model, namely adjusting parameters of the WRF model, and further optimizing the WRF model.
The accuracy of the WRF model is verified by the Monte Carlo method, so that the simulation effect of the WRF model on the marine meteorological conditions can be guaranteed, the accuracy of the fitting data of the WRF model is further guaranteed, and the accuracy of subsequent wind power resource evaluation is further guaranteed.
In one embodiment, step 206 includes: acquiring annual average wind power generation capacity according to the predicted wind speed data; acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods; based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
In some offshore wind power potential assessment schemes, only short-term or specific time scale data is of interest, and factors such as long-term, seasonal and annual changes also have an effect on the feasibility of the project. Studies have shown that climate change is leading to a change in offshore wind resources, which has an impact on future power generation potential. Current potential evaluations may not adequately account for this effect.
The WRF model obtains wind speed data of different time under different longitudes and latitudes, and extracts wind speed data of different time periods, so that annual average wind power generation capacity, seasonal change and annual change indexes can be calculated correspondingly.
The annual average wind power generation capacity calculation can be determined through the ratio of the total life cycle power generation capacity of the fan to the service life of the fan. The seasonal variation can be obtained by acquiring the predicted wind speed data of each season and calculating the corresponding generated energy respectively. The annual change can be obtained by acquiring the predicted wind speed data of the period, and respectively calculating the corresponding generated energy, wherein the period can be two years to eight years.
In other embodiments, other time scales may also be selected for evaluation, such as chronological changes (more than ten years), month changes, and so forth.
According to the method, the meteorological data of the high-precision long-time sequence are established through the high-precision long-time sequence offshore wind power potential evaluation calculation method. Through power evaluation of the wind generating set, a wind power generation capacity accounting model is established and is applied to wind energy development in a target area. The high-precision long-time-sequence offshore wind power potential evaluation calculation method is beneficial to more accurately evaluating the offshore wind power potential, and the feasibility and benefit of wind power development projects are improved.
Based on the evaluation result of high accuracy, the following effects can be brought:
1. more rational project planning: accurate wind power potential assessment may support more rational project planning including wind turbine sizing, layout design, grid connection, and the like. This will increase the power generation efficiency and overall performance of the wind farm.
2. Fine wind farm layout: based on the high-precision evaluation result, the layout of wind turbines in the wind power plant can be optimized, wind energy can be captured to the greatest extent, the mutual influence in the array is reduced, and the generated energy is improved.
3. Investment decision support: reliable wind power potential assessment helps investors make informed decisions to determine whether to invest in offshore wind projects and to make reasonable investment plans.
4. Grid planning and stability: accurate potential evaluation is helpful for optimizing power grid connection and energy transmission, ensures that electric energy generated by wind power can be stably integrated into a power grid, and improves the stability of the power grid.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an offshore wind power potential evaluation device for realizing the above-mentioned offshore wind power potential evaluation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitations in the embodiments of the device for evaluating the potential of offshore wind power provided below may be referred to as the limitations of the method for evaluating the potential of offshore wind power hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 4, there is provided an offshore wind power potential assessment apparatus comprising: a meteorological data acquisition module 402, a power generation accounting module 404, and a potential assessment module 406, wherein:
the meteorological data acquisition module 402 is configured to acquire predicted wind speed data at sea through a WRF model of a nested structure.
And the generating capacity accounting module 404 is used for acquiring the generating capacity of the full life cycle of the fan according to the predicted wind speed data.
The potential evaluation module 406 is configured to evaluate a potential of the wind power resource based on the full life cycle power generation of the wind turbine.
Wherein, the meteorological data acquisition module 402 is further used for acquiring actual wind speed data observed at sea; acquiring an error between predicted wind speed data and actual wind speed data; correcting the WRF model according to the error; and obtaining predicted wind speed data according to the corrected WRF model.
The generating capacity accounting module 404 is further configured to obtain a power curve and wind speed distribution according to the predicted wind speed data; acquiring annual average power according to the power curve and the wind speed distribution; and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
The potential evaluation module 406 is further configured to obtain annual average wind power generation according to the predicted wind speed data; acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods; based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
In addition, the offshore wind power potential evaluation device can also construct WRF models corresponding to different scenes based on different wind speed distribution scenes, and perform corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
The offshore wind power potential evaluation device can also simulate the WRF model for a plurality of times by adopting a Monte Carlo method, verify the confidence interval of the predicted wind speed data obtained according to the WRF model, and confirm the accuracy of the WRF model.
The various modules in the above-described offshore wind power potential assessment device may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In the embodiment, an evaluation device consisting of 3 modules including a meteorological data acquisition module 402, a generated energy accounting module 404 and a potential evaluation module 406 is established, potential evaluation analysis is performed on offshore wind power project development of a target area, and high-precision potential evaluation is realized. Meanwhile, the meteorological data is subjected to downscaling simulation by adopting multi-layer nesting, high spatial resolution and time resolution, the simulation effect is evaluated by using an error analysis method, the accuracy of the model is improved, and the optimal configuration of the model is realized.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing weather-related data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of offshore wind power potential assessment.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of: constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea; acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data; and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In one embodiment, the processor when executing the computer program further performs the steps of: constructing a WRF model of the nested structure, and acquiring the predicted wind speed data at sea comprises the following steps: acquiring actual wind speed data observed at sea; acquiring an error between predicted wind speed data and actual wind speed data; correcting the WRF model according to the error; and obtaining predicted wind speed data according to the corrected WRF model.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a power curve and wind speed distribution according to the predicted wind speed data; acquiring annual average power according to the power curve and the wind speed distribution; and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
In one embodiment, the processor when executing the computer program further performs the steps of: based on different wind speed distribution scenes, building WRF models corresponding to the different scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
In one embodiment, the processor when executing the computer program further performs the steps of: and (3) carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring annual average wind power generation capacity according to the predicted wind speed data; acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods; based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of: constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea; acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data; and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing a WRF model of the nested structure, and acquiring the predicted wind speed data at sea comprises the following steps: acquiring actual wind speed data observed at sea; acquiring an error between predicted wind speed data and actual wind speed data; correcting the WRF model according to the error; and obtaining predicted wind speed data according to the corrected WRF model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a power curve and wind speed distribution according to the predicted wind speed data; acquiring annual average power according to the power curve and the wind speed distribution; and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
In one embodiment, the computer program when executed by the processor further performs the steps of: based on different wind speed distribution scenes, building WRF models corresponding to the different scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
In one embodiment, the computer program when executed by the processor further performs the steps of: and (3) carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring annual average wind power generation capacity according to the predicted wind speed data; acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods; based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
In one embodiment, a computer program product is provided comprising a computer program which, when executed by a processor, performs the steps of: constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea; acquiring the full life cycle power generation capacity of the fan according to the predicted wind speed data; and carrying out wind power resource potential evaluation based on the full life cycle power generation amount of the fan.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing a WRF model of the nested structure, and acquiring the predicted wind speed data at sea comprises the following steps: acquiring actual wind speed data observed at sea; acquiring an error between predicted wind speed data and actual wind speed data; correcting the WRF model according to the error; and obtaining predicted wind speed data according to the corrected WRF model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a power curve and wind speed distribution according to the predicted wind speed data; acquiring annual average power according to the power curve and the wind speed distribution; and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
In one embodiment, the computer program when executed by the processor further performs the steps of: based on different wind speed distribution scenes, building WRF models corresponding to the different scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of the different scenes.
In one embodiment, the computer program when executed by the processor further performs the steps of: and (3) carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring annual average wind power generation capacity according to the predicted wind speed data; acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods; based on the total life cycle power generation of the fan, the annual average wind power generation, seasonal change index and annual change index are combined to comprehensively evaluate the potential of wind power resources.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method for offshore wind power potential assessment, the method comprising:
constructing a WRF model of a nested structure, and acquiring predicted wind speed data at sea;
acquiring the full life cycle generating capacity of the fan according to the predicted wind speed data;
and carrying out wind power resource potential evaluation based on the full life cycle generated energy of the fan.
2. The method of claim 1, wherein constructing a WRF model of the nested structure, obtaining predicted wind speed data at sea comprises:
acquiring actual wind speed data observed at sea;
acquiring an error between the predicted wind speed data and the actual wind speed data;
correcting the WRF model according to the error;
and acquiring the predicted wind speed data according to the corrected WRF model.
3. The method of claim 1, wherein the obtaining the full life cycle power generation of the wind turbine from the predicted wind speed data comprises:
acquiring a power curve and wind speed distribution according to the predicted wind speed data;
acquiring annual average power according to the power curve and the wind speed distribution;
and according to the annual average power, the annual use ratio and the service life of the fan are combined, and the full life cycle power generation capacity of the fan is obtained.
4. The method according to claim 1, wherein the method further comprises:
and constructing WRF models corresponding to different scenes based on different wind speed distribution scenes, and carrying out corresponding wind power resource potential evaluation based on the WRF models of different scenes.
5. The method according to any one of claims 1 to 4, further comprising:
and carrying out multiple simulation on the WRF model by adopting a Monte Carlo method, verifying a confidence interval of the predicted wind speed data obtained according to the WRF model, and confirming the accuracy of the WRF model.
6. The method of claim 1, wherein the wind power resource potential assessment based on the full life cycle power generation of the wind turbine comprises:
acquiring annual average wind power generation capacity according to the predicted wind speed data;
acquiring seasonal variation indexes and annual variation indexes according to the predicted wind speed data of different time periods;
and comprehensively evaluating the potential of the wind power resources based on the full life cycle generated energy of the fan by combining the annual average wind power generated energy, seasonal change indexes and annual change indexes.
7. An offshore wind power potential assessment device, the device comprising:
the weather data acquisition module is used for acquiring the predicted wind speed data at sea through a WRF model of a nested structure;
the generating capacity accounting module is used for acquiring the generating capacity of the full life cycle of the fan according to the predicted wind speed data;
and the potential evaluation module is used for evaluating the potential of the wind power resources based on the full life cycle generated energy of the fan.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN117951422A (en) * | 2024-03-26 | 2024-04-30 | 中国电建集团华东勘测设计研究院有限公司 | Real-time collection method and system for offshore wind power energy data |
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| CN117951422A (en) * | 2024-03-26 | 2024-04-30 | 中国电建集团华东勘测设计研究院有限公司 | Real-time collection method and system for offshore wind power energy data |
| CN117951422B (en) * | 2024-03-26 | 2024-06-07 | 中国电建集团华东勘测设计研究院有限公司 | Real-time collection method and system for offshore wind power energy data |
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