Medium-and-long-term prediction model for wave energy resources

Abstract

The invention belongs to the field of marine new energy assessment, development and utilization, and relates to a medium and long-term prediction model for wave energy resources. Firstly, CMIP data, satellite and buoy observation data and water depth data are collected, then sea wave modes WW3 and SWAN are selected and installed, the effectiveness of the sea wave modes is fully checked, and proper mode nesting, parameterization and boundary condition schemes are explored. Then CMIP data is utilized to drive a sea wave mode, sea wave data in the next decades are constructed, and then middle and long term estimation of wave energy in the next decades is developed, including spatial and temporal distribution characteristics of resources in the next decades, inter-monthly / seasonal / inter-annual / inter-year change rules and the like. Scientific basis and decision support can be provided for site selection design and middle and long-term planning of projects such as wave power generation and sea water desalination.

Description

Technical Field

[0001] The invention belongs to the field of marine new energy assessment and development and utilization, and relates to a medium- and long-term prediction model for wave energy resources. Background Art

[0002] Wave energy has many advantages such as large energy, all-weather, and wide distribution, and can be applied to wave power generation, seawater desalination, hydrogen production, etc. The rational development of wave energy resources is beneficial to alleviate resource crises, protect marine ecology, enhance the survival capacity of offshore strategic fulcrums, improve the quality of life of island residents, promote island tourism and deep-sea development, and provide strong power guarantees, with broad prospects.

[0003] Mastering resource characteristics is a prerequisite for achieving orderly and efficient development and utilization of wave energy. Predecessors have made a lot of accumulation and contributions to the research on wave energy resources in many sea areas around the world, but they are constrained by the shortage of marine data and high theoretical requirements, which is not conducive to the industrialization and scale of wave energy. Predecessors have done a lot of work and contributions in the analysis of climate characteristics and short-term forecasts of wave energy, which can provide important references for the site selection and commercial operation of resource development, but research on medium- and long-term predictions of wave energy is extremely scarce. In addition, there is currently a wealth of research on medium- and long-term predictions of meteorological and oceanographic elements, but research on medium- and long-term predictions of wave energy is scarce.

[0004] From the above analysis, the inventors realized that there are very few studies on the medium- and long-term prediction of wave energy. The few existing studies on the medium- and long-term prediction of wave energy mainly focus on the changing trend of energy flux density, and do not comprehensively consider the future spatial and temporal distribution and medium- and long-term changes of a series of key wave energy indicators concerned with resource development, the future wave energy grade zoning, and the characteristics of future catastrophic wave factors that are closely concerned with environmental safety and security. These are urgently needed when formulating medium-, medium- and long-term development plans for projects such as wave power generation and seawater desalination. Summary of the invention

[0005] To solve the above problems, the present invention provides a medium- and long-term prediction model for wave energy resources. The present invention uses a combination of numerical simulation and theoretical analysis to create a medium- and long-term prediction technology for wave energy resources, providing a scientific basis and decision-making support for the site selection design and medium- and long-term planning of projects such as wave power generation and seawater desalination.

[0006] The technical solution of the present invention is as follows:

[0007] A mid- to long-term prediction model for wave energy resources first collects CMIP (Coupled Model Intercomparison Program) data, satellite and buoy observation data, and water depth data, and then selects and installs the wave model WW3 (WAVEWATCH-III, the third generation of wave observation) and SWAN (Simulating WAves Nearshore, nearshore wave simulation), and fully tests the effectiveness of the wave model, and explores suitable model nesting, parameterization, and boundary condition schemes. Then, the CMIP data is used to drive the wave model, construct wave data for the next few decades, and then carry out mid- to long-term predictions of wave energy for the next few decades, including the spatiotemporal distribution characteristics of resources in the next few decades, and the monthly / seasonal / annual / decadal changes. The steps include:

[0008] The first step is to independently produce and generate future ocean wave data:

[0009] (1.1) Data collection and preprocessing: The collected data include: CMIP data, satellite and buoy observation data, and water depth data. CMIP data is used as the driving field of the wave model, satellite and buoy observation data is used to verify the validity of the model simulation data, and water depth data is used to calculate the wave energy flux density;

[0010] (1.2) Model selection: Select the currently advanced third-generation wave models WW3 and SWAN. WW3 is usually used in the ocean; SWAN is usually used in coastal areas, lakes and river estuaries;

[0011] (1.3) Model installation and debugging: Use parallel computing methods to install and debug the wave models WW3 and SWAN;

[0012] (1.4) Simulation scheme setting: select a series of historical typhoon waves, cold air waves, and low sea conditions, use the wave model to carry out wave simulation, use satellite and buoy observation data to verify the accuracy of the wave model simulation data, adjust the parameterization scheme and boundary conditions of the wave model within the range specified in the wave model guidance manual, and emphasize the consideration of the impact of offshore swells when regional nesting;

[0013] (1.5) Generation of future wave data: Using the parameterization scheme and boundary conditions set in (1.4), the CMIP data are used to drive the wave model and calculate the wave field for the next few decades.

[0014] In the second step, the future wave data calculated by the wave model is combined with the water depth topography data to calculate the future wave energy flow density of the long-term series. The calculation method is as follows:

[0015] In shallow water (d / λ<1 / 20):

[0016]

[0017] In deep water (d / λ≥1 / 2):

[0018]

[0019] In the case of medium water depth (1 / 20≤d / λ<1 / 2):

[0020]

[0021] Where, WPD is the energy flux density (unit: kW / m), H s is the effective wave height (unit: m), T e is the wave period (unit: s), d is the water depth (unit: m), λ is the wavelength (unit: m), ρ is the density of seawater, g is the acceleration due to gravity, and k is the wave number.

[0022] The third step is to analyze the spatiotemporal distribution characteristics of wave energy in the future. Based on the future wave energy flux density data, the spatiotemporal distribution characteristics of wave energy flux density in the next few decades are calculated and analyzed, including: (1) the spatial distribution of wave energy flux density and its seasonal, interannual, and interdecadal variation patterns; (2) the characteristics of wave energy flux density from important sea areas or islands and reefs.

[0023] The fourth step is the wave energy resource reserves. Based on the future wave energy flow density data, the wave energy resource reserves in the next few decades are calculated and analyzed. The calculation method is as follows:

[0024]

[0025] Among them, E PT is the total resource reserves, is the annual average wave energy flow density, H = 365d × 24h = 8760h.

[0026] The fifth step is to use linear regression, Thiessen polygons and other methods to calculate and analyze the long-term change trend of wave energy flow density; the changing trend of wave energy in the next few decades will provide a scientific basis for the medium and long-term planning of resource development.

[0027] The sixth step is to use climate statistical analysis methods to obtain the frequency of wave levels in the next few decades, especially the frequency of big waves, and calculate the spatial distribution characteristics of the big wave frequency and the monthly / seasonal / annual / decadal variation patterns; the spatiotemporal distribution characteristics of disastrous waves in the next few decades will provide a reference for wave energy development, disaster prevention and mitigation, etc.

[0028] Beneficial effects of the present invention:

[0029] (1) The present invention can demonstrate the spatiotemporal distribution characteristics of wave energy flux density in the next few years or even decades, providing a scientific basis for the mid- and long-term planning of resource development.

[0030] (2) The present invention can demonstrate the changing trend of wave energy flux density in the next few decades (including overall trend, regional and seasonal differences in trend), and can provide decision support for the mid- and long-term planning of wave energy development.

[0031] (3) The present invention can show the spatiotemporal distribution characteristics of disastrous ocean waves in different decades in the future, mainly including: wave grade frequency, especially the frequency of large waves, and calculate the spatial distribution characteristics of the large wave frequency, and the monthly / seasonal / annual / decadal variation patterns, providing a reference for wave energy development, disaster prevention and mitigation, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is the technical roadmap for analyzing the spatiotemporal characteristics of future wave energy flow density of the present invention. DETAILED DESCRIPTION

[0033] In order to make the content of the present invention more clearly understood, the present invention is further described in detail by taking the mid- to long-term prediction of wave energy in the northwest Pacific Ocean in the next 20 years as an example.

[0034] The first step is to collect CMIP5 wind field data as the driving field of the wave model. The time series is selected from 2025-2044. The data comes from the IPCC (Intergovernmental Panel on Climate Change). Satellite and buoy observation data from countries and regions around the northwest Pacific are collected to verify the validity of the model simulation data. Etopo1 water depth data is collected to calculate the wave energy flow density.

[0035] The second step is to select a series of historical typhoon waves and cold air waves, use the wave model WW3 and SWAN to carry out wave simulation, use satellite, buoy and other observation data to verify the accuracy of simulated wave data, adjust the parameterization scheme and boundary conditions of the model within the range specified in the model guidance manual, and emphasize the consideration of the influence of offshore swells when regional nesting. The test is mainly carried out by calculating the correlation coefficient (CC), root mean square error (RMSE), bias, and mean absolute error (MAE). The calculation method is as follows:

[0036]

[0037] The root mean square error (RMSE) is defined as the root mean square of the difference between the simulated value and the observed value.

[0038]

[0039] Bias is defined as the difference between the mean of the simulated data and the mean of the observed data.

[0040]

[0041] The mean absolute error (MAE) is defined as

[0042]

[0043] In formula (4) to formula (7), x i represents the observed data, y i represents the simulated data, and are the means of the observed data and the simulated data respectively, and N is the total sample size.

[0044] The third step is to produce the wave data for the northwest Pacific in the next 20 years. The wave model is driven by CMIP5 data to calculate the wave field in the northwest Pacific in the next 20 years. The data spatial range is: 0°~70°N, 90°~180°E, the spatial resolution is set to 1°×1° (the resolution can be adjusted according to actual needs), the time series is: January 2025-December 2044, and the time resolution is set to 3 hours (the resolution can be adjusted according to actual needs).

[0045] The fourth step is to use satellite, buoy and other observation data to verify the accuracy of the simulated wave data. A series of typhoons, strong cold air and other processes are randomly selected to verify the accuracy of the simulated wave data by calculating CC, RMSE, Bias and MAE.

[0046] The fifth step is to use the future wave data calculated by the wave model and the water depth topography data to calculate the future wave energy flux density covering the northwest Pacific Ocean from 2025 to 2044. The spatial range of the wave energy flux density data is: 0°~70°N, 90°~180°E, the spatial resolution is 1°×1°, and the time series is: January 2025-December 2044, with a time resolution of 3 hours.

[0047] Step 6: The future spatiotemporal distribution characteristics of wave energy in the Northwest Pacific. Based on the future wave energy flux density data, the spatiotemporal distribution characteristics of the wave energy flux density in the Northwest Pacific in the next 20 years are calculated and analyzed, including: (1) the spatial distribution of wave energy flux density, seasonal / interannual / interdecadal variation patterns; (2) the characteristics of wave energy flux density from important sea areas or islands and reefs.

[0048] Step 7: Wave energy resource reserves: Based on the future wave energy flux density data, calculate and analyze the wave energy resource reserves of the northwest Pacific Ocean in the next 20 years.

[0049] The eighth step is to use linear regression, Thiessen polygons and other methods to calculate and analyze the changing trend of wave energy in the northwest Pacific in the next 20 years.

[0050] The ninth step is to use climate statistical analysis methods to obtain the frequency of wave levels in the northwest Pacific in the next 20 years, especially the frequency of large waves, and calculate the spatial distribution characteristics of the large wave frequency and the monthly / seasonal / annual / decadal variation patterns.

Claims

1. A mid- to long-term prediction model for wave energy resources, characterized in that: Here are the steps: The first step is to independently produce and generate future ocean wave data: (1.1) Data collection and preprocessing: The collected data include: CMIP data, satellite and buoy observation data, and water depth data; CMIP data is used as the driving field of the wave model, satellite and buoy observation data is used to verify the validity of the model simulation data, and water depth data is used to calculate the wave energy flow density; (1.2) Mode selection: Select the third-generation wave modes WW3 and SWAN; WW3 is used in the ocean; SWAN is used in coastal, lake and estuary areas; (1.3) Model installation and debugging: Use parallel computing methods to install and debug the wave models WW3 and SWAN; (1.4) Simulation scheme setting: select a series of historical typhoon waves, cold air waves, and low sea conditions, use the wave model to carry out wave simulation, use satellite and buoy observation data to verify the accuracy of the wave model simulation data, adjust the parameterization scheme and boundary conditions of the wave model within the range specified in the wave model guidance manual, and emphasize the consideration of the impact of offshore swells when regional nesting; (1.5) Generation of future wave data: Using the parameterization scheme and boundary conditions set in (1.4), the CMIP data are used to drive the wave model and calculate the wave field for the next few decades; In the second step, the future wave data calculated by the wave model is combined with the water depth topography data to calculate the future wave energy flow density of the long-term series. The calculation method is as follows: In shallow water, that is, d / λ<1 / 20: In deep water, that is, d / λ≥1 / 2: In the case of medium water depth, that is, 1 / 20≤d / λ<1 / 2: Where WPD is the energy flux density, H s is the effective wave height, T e is the wave period, d is the water depth (unit: m), λ is the wavelength, ρ is the density of seawater, g is the acceleration of gravity, and k is the wave number; The third step is to analyze the spatial and temporal distribution characteristics of wave energy in the future. Based on the future wave energy flux density data, the spatial and temporal distribution characteristics of wave energy flux density in the next few decades are calculated and analyzed, including: (1) the spatial distribution of wave energy flux density and its seasonal, interannual and interdecadal variation patterns; (2) the characteristics of wave energy flux density from important sea areas or islands and reefs; The fourth step is the wave energy resource reserves. Based on the future wave energy flow density data, the wave energy resource reserves in the next few decades are calculated and analyzed. The calculation method is as follows: Among them, E PT is the total resource reserves, is the annual average wave energy flux density, H = 365d × 24h = 8760h; The fifth step is to use linear regression, Thiessen polygon and other methods to calculate and analyze the long-term trend of wave energy flow density; The sixth step is to use climate statistical analysis methods to obtain the frequency of wave levels in the next few decades, especially the frequency of big waves, and calculate the spatial distribution characteristics of the frequency of big waves and the monthly / seasonal / annual / decadal changes.

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