WO2024093493A1 - 一种流域水风光资源联合随机模拟方法、装置 - Google Patents

一种流域水风光资源联合随机模拟方法、装置 Download PDF

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WO2024093493A1
WO2024093493A1 PCT/CN2023/116176 CN2023116176W WO2024093493A1 WO 2024093493 A1 WO2024093493 A1 WO 2024093493A1 CN 2023116176 W CN2023116176 W CN 2023116176W WO 2024093493 A1 WO2024093493 A1 WO 2024093493A1
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reservoir
runoff
longtou
inflow
wind
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PCT/CN2023/116176
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English (en)
French (fr)
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张玮
黄康迪
刘瑞阔
张璐
李梦杰
余意
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中国长江三峡集团有限公司
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Publication of WO2024093493A1 publication Critical patent/WO2024093493A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

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  • the present invention relates to the technical field of multivariable stochastic simulation, and in particular to a method and device for combined stochastic simulation of water, wind and light resources in a river basin.
  • Wind and solar resources are affected by climate and meteorological factors and are characterized by volatility, intermittency and randomness.
  • fluctuating wind and solar power output connected to the power grid will increase the frequency regulation and peak regulation pressure of the power grid and affect the safe operation of the power grid.
  • Hydropower energy has a fast regulation speed and storable energy, which can smooth out the fluctuations of wind and solar power connected to the power system and effectively solve the problem of accommodating large-scale wind and solar power centralized access to the grid.
  • the previously frequently fluctuating output curve can be improved to a relatively stable output through the complementary dispatch of water, wind and solar power, which is of great significance to ensuring the safe and stable operation of the power grid.
  • an embodiment of the present invention provides a method and device for joint random simulation of water, wind and light resources in a river basin, so as to solve the technical problem in the prior art that multi-variable and spatiotemporal dual-scale random simulation of water, wind and light resources within the river basin cannot be linked.
  • an embodiment of the present invention provides a method for joint random simulation of water, wind and solar resources in a watershed, the method comprising: obtaining a long series of historical data on watershed runoff and a long series of historical data on wind and solar output, the long series of historical data on watershed runoff including a long series of historical data on inflow runoff of the Longtou Reservoir and a long series of historical data on interval runoff of any adjacent reservoir; establishing a joint distribution function of inflow runoff of the Longtou Reservoir based on the long series of historical data on inflow runoff of the Longtou Reservoir, and generating a sequence of simulated sample values of inflow runoff of the Longtou Reservoir based on the joint distribution function of inflow runoff of the Longtou Reservoir; establishing a three-dimensional joint distribution function of reservoir runoff based on the long series of historical data on inflow runoff of the Longtou Reservoir and the long series of historical data on interval runoff; based on the long
  • the water, wind and solar power output simulation sample sequence values on the left and right banks of each reservoir are generated by using the water, wind and solar power resources joint distribution function based on the data and the historical long series data of wind and solar power output; based on the simulated sample value sequence of inflow runoff of the Longtou Reservoir or the simulated sample sequence values of inflow runoff of other downstream reservoirs, the water, wind and solar power output simulation sample sequence values on the left and right banks of each reservoir are generated by using the water, wind and solar power resources joint distribution function.
  • the long series of historical data on wind and solar power output include a long series of historical data on wind power output on the left and right banks of each reservoir and a long series of historical data on photovoltaic output on the left and right banks of each reservoir; after obtaining the long series of historical data on river basin runoff and the long series of historical data on wind and solar power output, the method also includes: standardizing the long series of historical data on river basin runoff, the long series of historical data on wind power output on the left and right banks of each reservoir and the long series of historical data on photovoltaic output on the left and right banks of each reservoir to obtain standardized long series of historical data on river basin runoff, standardized long series of historical data on wind power output on the left and right banks of each reservoir and standardized long series of historical data on photovoltaic output on the left and right banks of each reservoir; performing pairwise correlation analysis on the standardized long series of historical data on river basin runoff, the standardized long series of historical data on wind power output on the
  • a joint distribution function of the inflow runoff of the Longtou Reservoir is established based on the long series of historical data of the inflow runoff of the Longtou Reservoir, including: calculating the correlation coefficient of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the long series of historical data of the inflow runoff of the Longtou Reservoir; establishing the marginal distribution function of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the correlation coefficient of the inflow runoff of the Longtou Reservoir; and jointly processing the marginal distribution functions of the inflow runoff of the Longtou Reservoir in any adjacent time periods to obtain the joint distribution function of the inflow runoff of the Longtou Reservoir.
  • a sequence of simulated sample values of inflow runoff into the Longtou Reservoir is generated based on the joint distribution function of the inflow runoff into the Longtou Reservoir, including: generating a first random number corresponding to a current time period and a second random number corresponding to an adjacent time period adjacent to the current time period based on the joint distribution function of the inflow runoff into the Longtou Reservoir; generating a first sample value of simulated inflow runoff into the Longtou Reservoir corresponding to the current time period based on the first random number and the marginal distribution function of the inflow runoff into the Longtou Reservoir; generating a second sample value of simulated inflow runoff into the Longtou Reservoir corresponding to the adjacent time period based on the simulated sample value of the inflow runoff into the Longtou Reservoir, the second random number and the marginal distribution function of the inflow runoff into the Longtou Reservoir; and determining
  • a three-dimensional joint distribution function of reservoir runoff is established, including: calculating the spatiotemporal comprehensive condition correlation coefficient based on the long series of historical data of inflow runoff of the Longtou Reservoir and the long series of historical data of interval runoff; establishing the regional distribution function of any adjacent reservoirs in the adjacent time period based on the spatiotemporal comprehensive condition correlation coefficient; The marginal distribution function of the interval runoff of any adjacent reservoirs is jointly processed to obtain the three-dimensional joint distribution function of the reservoir runoff.
  • a joint distribution function of water, wind and solar resources is established based on the long series of historical data of watershed runoff and the long series of historical data of wind and solar output, including: based on the long series of historical data of wind and solar output, after fitting and optimization processing, obtaining the wind power output marginal distribution function and the photovoltaic output marginal distribution function in adjacent time periods on the left and right banks of each reservoir; based on the wind power output marginal distribution function on the left and right banks of each reservoir and the photovoltaic output marginal distribution function on the left and right banks of each reservoir, determining the joint distribution function of water, wind and solar resources.
  • the water, wind and solar resources joint distribution function is used to generate the wind and solar output simulation sample sequence values on the left and right banks of each reservoir, including: based on the water, wind and solar resources joint distribution function, generating a third random number corresponding to the current time period and a fourth random number corresponding to the adjacent time period; based on the water, wind and solar resources joint distribution function, the third random number and the Longtou Reservoir inflow simulation sample value sequence, generating the first wind and solar output simulation sample value on the left and right banks of the Longtou Reservoir corresponding to the adjacent time period; based on the water, wind and solar resources joint distribution function and the fourth random number, generating the second wind and solar output simulation sample value on the left and right banks of the Longtou Reservoir corresponding to the current time period; based on the first wind and solar output simulation sample value on the
  • an embodiment of the present invention provides a random simulation device for a watershed water, wind and solar resources.
  • the random simulation device for a watershed water, wind and solar resources comprises: an acquisition module for acquiring a long series of historical data on watershed runoff and a long series of historical data on wind and solar output, wherein the long series of historical data on watershed runoff comprises a long series of historical data on inflow runoff of the Longtou Reservoir and a long series of historical data on interval runoff of any adjacent reservoir; a first generation module for establishing a joint distribution function of inflow runoff of the Longtou Reservoir based on the long series of historical data on inflow runoff of the Longtou Reservoir, and generating a sequence of simulated sample values of inflow runoff of the Longtou Reservoir based on the joint distribution function of inflow runoff of the Longtou Reservoir; a first establishment module for generating a sequence of simulated sample values of inflow runoff of the Longtou Reservoir based
  • a first generation module is used to generate a three-dimensional joint distribution function of reservoir runoff based on the series data of the simulated runoff inflow of the Longtou Reservoir and the long series data of the historical runoff of the interval; a second generation module is used to generate a three-dimensional joint distribution function of the reservoir runoff simulation sample sequence values of the simulated runoff inflow of other downstream reservoirs by using the three-dimensional joint distribution function of the reservoir runoff; a second establishment module is used to establish a joint distribution function of water, wind and solar resources based on the long series data of the historical runoff of the basin and the long series data of the historical wind and solar output; a third generation module is used to generate a wind and solar output simulation sample sequence values on the left and right banks of each reservoir based on the Longtou Reservoir runoff simulation sample value sequence or the simulated runoff inflow sample sequence values of the other downstream reservoirs, using the joint distribution function of water, wind and solar resources.
  • an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the first aspect and the first aspect of the embodiment of the present invention.
  • an embodiment of the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the method for joint random simulation of water, wind and light resources in a watershed as described in the first aspect of the embodiment of the present invention and any one of the first aspects.
  • the method for joint random simulation of water, wind and solar resources in a watershed determines the sequence of simulated sample values of the inflow runoff of the Longtou Reservoir, the sequence of simulated sample values of the inflow runoff of other downstream reservoirs, and the sequence of simulated sample values of wind and solar output through the joint distribution function of the inflow runoff of the Longtou Reservoir, the three-dimensional joint distribution function of the reservoir runoff, and the joint distribution function of water, wind and solar resources.
  • step-by-step simulation is performed through the dimensionality reduction idea of "runoff first, then wind and solar, upstream first, then downstream", providing an efficient and highly operational way for the rapid generation of water, wind and solar resource samples at the watershed level.
  • FIG1 is a flow chart of a method for joint random simulation of water, wind and light resources in a river basin provided according to an embodiment of the present invention
  • FIG2 is a schematic diagram of a wind-solar-hydropower station in a river basin provided according to an embodiment of the present invention
  • FIG. 3 is another flow chart of a method for joint random simulation of water, wind and light resources in a river basin provided according to an embodiment of the present invention
  • FIG. 4 is a structural block diagram of a combined random simulation device for water, wind and light resources in a river basin provided according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention.
  • FIG6 is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention.
  • An embodiment of the present invention provides a method for joint random simulation of water, wind and light resources in a river basin. As shown in FIG1 , the method comprises the following steps:
  • Step 101 Obtain a long series of historical data on watershed runoff and a long series of historical data on wind and solar power output.
  • the long series of historical data of basin runoff can include the long series of historical data of runoff inflow into Longtou Reservoir Q1 and any corresponding
  • the long series of historical data of interval runoff near the reservoir, ⁇ Q k are shown in equations (1) and (2) respectively:
  • Step 102 establishing a joint distribution function of the inflow into the Longtou Reservoir based on the long series of historical inflow runoff data of the Longtou Reservoir, and generating a sequence of simulated sample values of the inflow into the Longtou Reservoir based on the joint distribution function of the inflow runoff of the Longtou Reservoir.
  • the joint distribution function of the inflow runoff into the Longtou Reservoir represents the relationship between the marginal distribution functions of the inflow runoff into the Longtou Reservoir corresponding to adjacent moments.
  • a two-dimensional Copula method is used to establish a joint distribution function of the inflow runoff into the Longtou Reservoir taking into account time correlation, and this function is used to generate the corresponding Longtou Reservoir inflow runoff simulation sample value sequence.
  • Step 103 Based on the long series of historical data on inflow into the Longtou Reservoir and the long series of historical data on interval runoff, a three-dimensional joint distribution function of reservoir runoff is established.
  • the three-dimensional joint distribution function of reservoir runoff represents the relationship between the inflow runoff of Longtou Reservoir and the runoff of each adjacent reservoir downstream.
  • a three-dimensional Copula is used to establish a three-dimensional joint distribution function of reservoir runoff considering temporal and spatial correlation.
  • Step 104 Based on the Longtou Reservoir inflow simulation sample value sequence, the reservoir runoff three-dimensional joint distribution function is used to generate the inflow simulation sample sequence values of other downstream reservoirs.
  • the corresponding inflow simulation sample sequence values of other downstream reservoirs can be generated.
  • Step 105 Establish a watershed runoff history long series data and a wind and solar output history long series data. Joint distribution function of wind and solar resources.
  • the joint distribution function of water, wind and solar resources can characterize the relationship between the runoff and wind and solar output of each adjacent reservoir area downstream, or the relationship between the inflow of Longtou Reservoir and the wind and solar output.
  • Step 106 Based on the Longtou Reservoir inflow simulation sample value sequence or the inflow simulation sample sequence values of other downstream reservoirs, and utilizing the water, wind, and solar resources joint distribution function, generate the wind and solar output simulation sample sequence values on the left and right banks of each reservoir.
  • the corresponding wind and solar output simulation sample sequence values are generated using the simulated sample sequence values of the inflow runoff of different reservoirs, including:
  • the water, wind and solar resources joint distribution function can be used, and the simulated sample value sequence of wind and solar output on the left and right banks can be generated by combining the simulated sample value sequence of runoff into Longtou Reservoir.
  • the joint distribution function of water, wind and solar resources can be used, and the simulated sample sequence values of inflow runoff from other downstream reservoirs can be combined to generate the simulated sample sequence values of wind and solar output on the left and right banks.
  • the method for joint random simulation of water, wind and solar resources in a watershed determines the sequence of simulated sample values of the inflow runoff of the Longtou Reservoir, the sequence of simulated sample values of the inflow runoff of other downstream reservoirs, and the sequence of simulated sample values of wind and solar output through the joint distribution function of the inflow runoff of the Longtou Reservoir, the three-dimensional joint distribution function of the reservoir runoff, and the joint distribution function of water, wind and solar resources.
  • step-by-step simulation is performed through the dimensionality reduction idea of "runoff first, then wind and solar, upstream first, then downstream", providing an efficient and highly operational way for the rapid generation of water, wind and solar resource samples at the watershed level.
  • the long series of historical data on wind and solar power output may include the long series of historical data on wind power output on the left and right banks of each reservoir and the long series of historical data on photovoltaic output on the left and right banks of each reservoir.
  • the reservoir wind power output historical long series data may include the reservoir left bank wind power output historical long series data NWL k and the reservoir right bank wind power output historical long series data NWR k , as shown in equations (3) and (4) respectively:
  • the long series of historical data on the photovoltaic output of the reservoir may include the long series of historical data on the photovoltaic output of the left bank of the reservoir NPL k and the long series of historical data on the photovoltaic output of the right bank of the reservoir NPR k , as shown in equations (5) and (6) respectively:
  • the method also includes: standardizing the long series of historical data of the watershed runoff, the long series of historical data of wind power output on the left and right banks of each reservoir, and the long series of historical data of photovoltaic output on the left and right banks of each reservoir to obtain standardized long series of historical data of watershed runoff, standardized long series of historical data of wind power output on the left and right banks of each reservoir, and standardized long series of historical data of photovoltaic output on the left and right banks of each reservoir; performing pairwise correlation analysis on the standardized long series of historical data of watershed runoff, standardized long series of historical data of wind power output on the left and right banks of each reservoir, and standardized long series of historical data of photovoltaic output on the left and right banks of each reservoir to obtain time and space correlation results.
  • a t represents the sample value of the original data (the long series of historical data of watershed runoff, the long series of historical data of wind power output of reservoirs, and the long series of historical data of photovoltaic output of reservoirs);
  • variables may include: inflow runoff of each reservoir, left bank/right bank wind power output, left bank/right bank photovoltaic output.
  • the complementarity between the two energy sources is analyzed based on the Spearman rank correlation coefficient: if the Spearman rank correlation coefficient is negative, it means that there is direct complementarity between the two energy sources; if the Spearman rank correlation coefficient is positive, it means that there is synergistic complementarity between the two energy sources.
  • a t-test method is used to perform a hypothesis test on whether the two energy sources are truly complementary. If the test p-value is less than the significance level, it is considered that the two energy sources meet the complementarity requirements (that is, there is direct complementarity or synergistic complementarity between the two energy sources), and steps 105 and 106 can be used to perform random simulation of wind and solar power output; otherwise, it is considered that there is no spatial correlation between the two energy sources, and the two-dimensional Copula method considering time correlation in step 102 can be used to perform univariate random simulation of wind power output or photovoltaic output.
  • a joint distribution function of the inflow runoff of the Longtou Reservoir is established based on the long series of historical data of the inflow runoff of the Longtou Reservoir, including: calculating the correlation coefficient of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the long series of historical data of the inflow runoff of the Longtou Reservoir; establishing the marginal distribution function of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the correlation coefficient of the inflow runoff of the Longtou Reservoir; and jointly processing the marginal distribution functions of the inflow runoff of the Longtou Reservoir in any adjacent time periods to obtain the joint distribution function of the inflow runoff of the Longtou Reservoir.
  • the Longtou water flow rate of any adjacent time period t and t-1 is calculated.
  • It represents the historical multi-year average of the runoff into Longtou Reservoir at the t period; It represents the historical multi-year average of the inflow into Longtou Reservoir in the t-1 period.
  • C( ⁇ ) represents the Copula link function
  • ⁇ t represents the parameter to be estimated in the Copula function, which can be calculated according to the Kendall- ⁇ method (i.e. )) is calculated by reverse solution
  • x t-1 ) represents the conditional distribution when the value x t-1 of the previous period is known
  • x t-1 represents the inflow runoff of Longtou Reservoir in the t-1th period
  • x t represents the inflow runoff of Longtou Reservoir in the tth period.
  • a sequence of simulated sample values of inflow runoff into the Longtou Reservoir is generated based on the joint distribution function of the inflow runoff into the Longtou Reservoir, including: generating a first random number corresponding to a current time period and a second random number corresponding to an adjacent time period adjacent to the current time period based on the joint distribution function of the inflow runoff into the Longtou Reservoir; generating a first sample value of simulated inflow runoff into the Longtou Reservoir corresponding to the current time period based on the first random number and the marginal distribution function of the inflow runoff into the Longtou Reservoir; generating a second sample value of simulated inflow runoff into the Longtou Reservoir corresponding to the adjacent time period based on the simulated sample value of the inflow runoff into the Longtou Reservoir, the second random number and the marginal distribution function of the inflow runoff into the Longtou Reservoir; determining a sequence of
  • the simulated sample sequence values of the inflow runoff of Longtou Reservoir are generated by time period and year, including:
  • step 103 includes: calculating the spatial-temporal comprehensive condition correlation coefficient based on the long-term historical series data of the inflow runoff of the Longtou Reservoir and the long-term historical series data of the interval runoff; establishing the interval runoff marginal distribution function of any adjacent reservoirs in the adjacent time period based on the spatial-temporal comprehensive condition correlation coefficient; and jointly processing the interval runoff marginal distribution functions of any adjacent reservoirs to obtain the three-dimensional joint distribution function of the reservoir runoff.
  • the inflow of Longtou Reservoir is taken as the main variable and the interval runoff of the two adjacent reservoirs is taken as the subordinate variable.
  • the temporal and spatial comprehensive conditional correlation coefficient is calculated.
  • yt represents the interval runoff between any two adjacent reservoirs in the tth period
  • yt -1 represents the interval runoff between any two adjacent reservoirs in the t-1th period
  • It represents the spatial correlation coefficient between the interval runoff of two adjacent reservoirs in the tth period and the inflow runoff of the Longtou Reservoir in the tth period
  • It represents the temporal and spatial correlation coefficient between the interval runoff of two adjacent reservoirs in the t-1 period and the inflow runoff of Longtou Reservoir in the t period.
  • It represents the historical multi-year average of the interval runoff between the two adjacent reservoirs of reservoir k at the tth period; It represents the historical multi-year average of the interval runoff between the two adjacent reservoirs of reservoir k in the t-1 period.
  • y t-1 )) represents the Copula function of the conditional distribution F(x t
  • ⁇ t represents the parameter to be estimated in the Copula function, which can be solved according to the Kendall- ⁇ method and the correlation coefficient calculated by equations (12)-(15).
  • step 104 may refer to the above-mentioned process of generating a sequence of simulated runoff sample values for the Longtou Reservoir.
  • the simulated sample sequence values of runoff in each adjacent reservoir interval are generated from upstream to downstream by time period and year, including:
  • step 105 includes: based on the historical long series data of wind and solar power output, after fitting and optimization processing, obtaining the wind power output marginal distribution function and the photovoltaic output marginal distribution function in adjacent time periods on the left and right banks of each reservoir; based on the wind power output marginal distribution function on the left and right banks of each reservoir and the photovoltaic output marginal distribution function on the left and right banks of each reservoir, determining the joint distribution function of water, wind and solar resources.
  • the runoff into the reservoir is taken as the main variable
  • the wind power output or photovoltaic output on the left bank/right bank is taken as the subordinate variable
  • the stochastic simulation of wind and solar power output is carried out, including:
  • z t-1 )) represents the Copula function of the conditional distribution F(x t
  • z t-1 )) represents the Copula function of the conditional distribution F(x t
  • z t-1 ) under the other reservoir as the research object can be calculated by referring to equation (10);
  • ⁇ t represents the parameter to be estimated in the Copula function, which can be solved according to the Kendall- ⁇ method combined with the calculated Spearman rank correlation coefficient.
  • step 106 includes: based on the joint distribution function of the water, wind and solar resources, generating a third random number corresponding to the current time period and a fourth random number corresponding to the adjacent time period; based on the joint distribution function of the water, wind and solar resources, the third random number and the Longtou Reservoir inflow simulation sample value sequence, generating a first sample value of the wind and solar power simulation on the left and right banks of the Longtou Reservoir corresponding to the adjacent time period; based on the joint distribution function of the water, wind and solar resources and the fourth random number, generating a second sample value of the wind and solar power simulation on the left and right banks of the Longtou Reservoir corresponding to the current time period; based on the first sample value of the wind and solar power simulation on the left and right banks of the Longtou Reservoir and the second sample value of the wind and solar power simulation on the left and right banks of the Longtou Reservoir, determining the wind and solar power simulation sample sequence
  • simulated sample sequence values of wind power output or photovoltaic output on the left/right bank of each reservoir are generated from upstream to downstream, time period by time and year by year.
  • the left bank wind power output includes:
  • the above-mentioned embodiment of the present invention takes into account the spatiotemporal correlation of wind, solar and water multi-energy sources, and simulates the entire river basin system according to the cascade reservoir intervals respectively, so as to obtain random samples that conform to the actual situation of the river basin multi-energy system, thereby providing reliable data for the optimization scheduling model; decomposing the multivariate joint distribution into a combination of multiple two-dimensional Copulas can simplify the parameter estimation process compared to directly establishing a multi-dimensional nested Copula, effectively control the calculation cost, and is easy to implement in actual operation.
  • FIG2 a schematic diagram of a river basin wind, solar and hydropower station is shown in FIG2 ; further, a corresponding river basin water, wind and solar resources joint random simulation method is shown in FIG3 .
  • the embodiment of the present invention further provides a combined random simulation device for water, wind and light resources in a river basin, as shown in FIG4 , the device comprising:
  • Acquisition module 401 is used to obtain a long series of historical data on watershed runoff and a long series of historical data on wind and solar output.
  • the long series of historical data on watershed runoff includes a long series of historical data on inflow into the Longtou Reservoir and a long series of historical data on interval runoff of any adjacent reservoir. For details, see the relevant description of step 101 in the above method embodiment.
  • the first generation module 402 is used to establish a joint distribution function of the inflow runoff of the Longtou Reservoir based on the long series of historical inflow runoff data of the Longtou Reservoir, and to generate a sequence of simulated sample values of the inflow runoff of the Longtou Reservoir based on the joint distribution function of the inflow runoff of the Longtou Reservoir; for details, please refer to the relevant description of step 102 in the above method embodiment.
  • the first establishing module 403 is used to establish a three-dimensional joint distribution function of reservoir runoff based on the long series of historical data of inflow runoff of the Longtou Reservoir and the long series of historical data of interval runoff; for details, please refer to the relevant description of step 103 in the above method embodiment.
  • the second generation module 404 is used to generate the simulated runoff sample sequence values of other downstream reservoirs based on the Longtou Reservoir inlet runoff simulation sample value sequence and using the reservoir runoff three-dimensional joint distribution function; for details, please refer to the relevant description of step 104 in the above method embodiment.
  • the second establishing module 405 is used to establish a joint distribution function of water, wind and solar resources based on the long series of historical data on watershed runoff and the long series of historical data on wind and solar output; for details, please refer to the relevant description of step 105 in the above method embodiment.
  • the third generation module 406 is used to generate the wind and solar power simulation sample sequence values on the left and right banks of each reservoir based on the Longtou Reservoir inflow simulation sample value sequence or the inflow simulation sample sequence values of other downstream reservoirs and using the water, wind and solar resources joint distribution function; for details, please refer to the relevant description of step 106 in the above method embodiment.
  • the random simulation device for the joint simulation of water, wind and solar resources in the basin determines the sequence of simulated sample values of the inflow runoff of the Longtou Reservoir, the sequence of simulated sample values of the inflow runoff of other downstream reservoirs, and the sequence of simulated sample values of wind and solar output through the joint distribution function of the inflow runoff of the Longtou Reservoir, the three-dimensional joint distribution function of the reservoir runoff, and the joint distribution function of water, wind and solar resources.
  • step-by-step simulation is performed through the dimensionality reduction idea of "runoff first, then wind and solar, upstream first, then downstream", providing an efficient and highly operational way for the rapid generation of basin-level water, wind and solar resource samples.
  • the long series of historical data on wind and solar power output include a long series of historical data on wind power output on the left and right banks of each reservoir and a long series of historical data on photovoltaic output on the left and right banks of each reservoir;
  • the device also includes: a processing module, which is used to standardize the long series of historical data on watershed runoff, the long series of historical data on wind power output on the left and right banks of each reservoir and the long series of historical data on photovoltaic output on the left and right banks of each reservoir to obtain standardized long series of historical data on watershed runoff, standardized long series of historical data on wind power output on the left and right banks of each reservoir and standardized long series of historical data on photovoltaic output on the left and right banks of each reservoir; an analysis module, which is used to perform pairwise correlation analysis on the standardized long series of historical data on watershed runoff, the standardized long series of historical data on wind power output on the left and right banks of each reservoir and the standardized long series of historical
  • the first generation module includes: a first calculation submodule, used to calculate the correlation coefficient of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the long series of historical data of the inflow runoff of the Longtou Reservoir; a first establishment submodule, used to establish the marginal distribution function of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the correlation coefficient of the inflow runoff of the Longtou Reservoir; a first processing submodule, used to jointly process the marginal distribution functions of the inflow runoff of the Longtou Reservoir in any adjacent time periods to obtain the joint distribution function of the inflow runoff of the Longtou Reservoir.
  • a first calculation submodule used to calculate the correlation coefficient of the inflow runoff of the Longtou Reservoir in any adjacent time periods based on the long series of historical data of the inflow runoff of the Longtou Reservoir
  • a first establishment submodule used to establish the marginal distribution function of the inflow runoff of
  • the first generation module also includes: a first generation submodule, which is used to generate a first random number corresponding to the current time period and a second random number corresponding to an adjacent time period adjacent to the current time period based on the joint distribution function of the inflow runoff of the Longtou Reservoir; a second generation submodule, which is used to generate a first sample value of the inflow runoff simulation of the Longtou Reservoir corresponding to the current time period based on the first random number and the marginal distribution function of the inflow runoff of the Longtou Reservoir; a third generation submodule, which is used to generate a second sample value of the inflow runoff simulation of the Longtou Reservoir corresponding to the adjacent time period based on the simulated sample value of the inflow runoff of the Longtou Reservoir, the second random number and the marginal distribution function of the inflow runoff of the Longtou Reservoir; and a first determination submodule, which is used to determine the
  • the first establishment module includes: a second calculation submodule for calculating the spatiotemporal comprehensive
  • the second establishment submodule is used to establish the interval runoff marginal distribution function of any adjacent reservoirs in the adjacent time period based on the temporal and spatial comprehensive conditional correlation coefficient; the second processing submodule is used to jointly process the interval runoff marginal distribution function of any adjacent reservoirs to obtain the three-dimensional joint distribution function of the reservoir runoff.
  • the second establishment module includes: a third processing sub-module, which is used to obtain the wind power output marginal distribution function and the photovoltaic output marginal distribution function in adjacent time periods on the left and right banks of each reservoir based on the historical long series of wind and solar output data through fitting and optimization processing; a second determination sub-module, which is used to determine the joint distribution function of water, wind and solar resources based on the wind power output marginal distribution function on the left and right banks of each reservoir and the photovoltaic output marginal distribution function on the left and right banks of each reservoir.
  • the third generation module includes: a fourth generation sub-module, used to generate a third random number corresponding to the current time period and a fourth random number corresponding to the adjacent time period based on the joint distribution function of the water, wind and solar resources; a fifth generation sub-module, used to generate a first sample value of wind and solar power simulation on the left and right banks of the Longtou Reservoir corresponding to the adjacent time period based on the joint distribution function of the water, wind and solar resources, the third random number and the Longtou Reservoir inflow simulation sample value sequence; a sixth generation sub-module, used to generate a second sample value of wind and solar power simulation on the left and right banks of the Longtou Reservoir corresponding to the current time period based on the joint distribution function of the water, wind and solar resources and the fourth random number; a third determination sub-module, used to determine the wind and solar output simulation sample sequence value on the left and right banks of the Longtou Reservoir based on the
  • the embodiment of the present invention also provides a storage medium, as shown in FIG5 , on which a computer program 501 is stored, and when the instruction is executed by the processor, the steps of the random simulation method for the combined water, wind and light resources of the river basin in the above embodiment are implemented.
  • the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.
  • the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD). State Drive, SSD) etc.; the storage medium may also include a combination of the above-mentioned types of memories.
  • An embodiment of the present invention further provides an electronic device, as shown in FIG6 , which may include a processor 61 and a memory 62 , wherein the processor 61 and the memory 62 may be connected via a bus or other means, with FIG6 taking the connection via a bus as an example.
  • the processor 61 may be a central processing unit (CPU).
  • the processor 61 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.
  • DSP digital signal processors
  • ASIC application-specific integrated circuits
  • FPGA field-programmable gate arrays
  • the memory 62 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the corresponding program instructions/modules in the embodiment of the present invention.
  • the processor 61 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 62, that is, to implement the combined random simulation method of watershed water, wind and light resources in the above method embodiment.
  • the memory 62 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device and at least one function; the data storage area may store data created by the processor 61, etc.
  • the memory 62 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 62 may optionally include a memory remotely arranged relative to the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the one or more modules are stored in the memory 62, and when executed by the processor 61, perform the combined random simulation method of water, wind and light resources in the river basin in the embodiments shown in Figures 1-3.

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Abstract

本发明公开了一种流域水风光资源联合随机模拟方法、装置，获取龙头水库入库径流历史长系列数据、任意相邻水库的区间径流历史长系列数据和风光出力历史长系列数据；基于龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并生成龙头水库入库径流模拟样本值序列；基于龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数，并结合龙头水库入库径流模拟样本值序列生成下游其他水库入库径流模拟样本序列值；基于流域径流历史长系列数据和风光出力历史长系列数据，建立水风光资源联合分布函数并生成风光出力模拟样本序列值，通过"先径流后风光、先上游后下游"进行逐级模拟。

Description

一种流域水风光资源联合随机模拟方法、装置 技术领域
本发明涉及多变量随机模拟技术领域，具体涉及一种流域水风光资源联合随机模拟方法、装置。
背景技术
水风光清洁能源的开发利用是应对气候变化、保障未来能源安全的有效应对措施。风光资源受气候和气象因素影响具有波动性、间歇性、随机性等特点，频繁波动的风光出力接入电网会加剧电网的调频、调峰压力，影响电网安全运行。水电能源调节速度快、能源可存储，能够平抑风光能源接入电力系统的波动影响，有效解决大规模风光电集中入网的消纳难题。通过水风光多能互补调度将之前频繁波动的出力曲线改善为较为平稳的输出，对于保障电网的安全稳定运行具有重要意义。
水风光多能互补优化调度方案设计、规则提取及风险分析等均需以长系列径流及风光出力资料为基础。利用实测资料序列开展水风光资源多变量随机模拟研究，模拟生成大量径流和风光出力序列，并使其最大程度地保持历史序列的统计特性，能够为调度方案风险分析提供丰富的输入数据。目前，现有相关技术虽然可以有效解决流域范围内兼顾时空相关性的径流随机模拟问题，或者小区域范围内风光出力的联合随机模拟问题，但无法实现流域范围内水风光资源的多变量、时空双尺度的联动随机模拟。
发明内容
有鉴于此，本发明实施例提供了涉及一种流域水风光资源联合随机模拟方法、装置，以解决现有技术中流域范围内水风光资源的多变量与时空双尺度不能联动随机模拟的技术问题。
本发明提出的技术方案如下：
第一方面，本发明实施例提供一种流域水风光资源联合随机模拟方法，该流域水风光资源联合随机模拟方法包括：获取流域径流历史长系列数据和风光出力历史长系列数据，所述流域径流长系列历史数据包括龙头水库入库径流历史长系列数据和任意相邻水库的区间径流历史长系列数据；基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列；基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数；基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值；基于所述流域径流历史长系列 数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数；基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值。
结合第一方面，在第一方面的一种可能的实现方式中，所述风光出力历史长系列数据包括各水库左右两岸的风电出力历史长系列数据和各水库左右两岸的光伏出力历史长系列数据；获取流域径流历史长系列数据和风光出力历史长系列数据之后，所述方法还包括：对所述流域径流历史长系列数据、所述各水库左右两岸的风电出力历史长系列数据和所述各水库左右两岸的光伏出力历史长系列数据进行标准化处理，得到标准化流域径流历史长系列数据、各水库左右两岸的标准化风电出力历史长系列数据和各水库左右两岸的标准化光伏出力历史长系列数据；对所述标准化流域径流历史长系列数据、所述各水库左右两岸的标准化风电出力历史长系列数据和所述各水库左右两岸的标准化光伏出力历史长系列数据进行两两相关性分析，得到时间与空间的相关性结果。
结合第一方面，在第一方面的另一种可能的实现方式中，基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，包括：基于所述龙头水库入库径流历史长系列数据，计算任意相邻时段的龙头水库入库径流相关系数；基于所述龙头水库入库径流相关系数，建立任意相邻时段的龙头水库入库径流的边缘分布函数；对所述任意相邻时段的龙头水库入库径流的边缘分布函数进行联合处理，得到所述龙头水库入库径流联合分布函数。
结合第一方面，在第一方面的又一种可能的实现方式中，基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列，包括：基于所述龙头水库入库径流联合分布函数，生成当前时段对应的第一随机数和与所述当前时段相邻的相邻时段对应的第二随机数；基于所述第一随机数和所述龙头水库入库径流的边缘分布函数，生成所述当前时段对应的龙头水库入库径流模拟第一样本值；基于所述龙头水库入库径流模拟样本值、所述第二随机数和所述龙头水库入库径流的边缘分布函数，生成所述相邻时段对应的龙头水库入库径流模拟第二样本值；基于所述龙头水库入库径流模拟第一样本值和所述龙头水库入库径流模拟第二样本值，确定所述龙头水库入库径流模拟样本值序列。
结合第一方面，在第一方面的又一种可能的实现方式中，基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数，包括：基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，计算时空综合条件相关系数；基于所述时空综合条件相关系数，建立所述相邻时段内所述任意相邻水库的区 间径流边缘分布函数；对所述任意相邻水库的区间径流边缘分布函数进行联合处理，得到所述水库径流三维联合分布函数。
结合第一方面，在第一方面的又一种可能的实现方式中，基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数，包括：基于所述风光出力历史长系列数据，经过拟合优选处理，得到各水库左右两岸的相邻时段内的风电出力边缘分布函数和光伏出力边缘分布函数；基于各水库左右两岸的所述风电出力边缘分布函数和各水库左右两岸的所述光伏出力边缘分布函数，确定所述水风光资源联合分布函数。
结合第一方面，在第一方面的又一种可能的实现方式中，基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值，包括：基于所述水风光资源联合分布函数，生成所述当前时段对应的第三随机数和所述相邻时段对应的第四随机数；基于所述水风光资源联合分布函数、所述第三随机数和所述龙头水库入库径流模拟样本值序列，生成所述相邻时段对应的龙头水库左右两岸的风光出力模拟第一样本值；基于所述水风光资源联合分布函数和所述第四随机数，生成所述当前时段对应的龙头水库左右两岸的风光出力模拟第二样本值；基于所述龙头水库左右两岸的风光出力模拟第一样本值和所述龙头水库左右两岸的风光出力模拟第二样本值，确定所述龙头水库左右两岸的风光出力模拟样本序列值。
第二方面，本发明实施例提供一种流域水风光资源联合随机模拟装置，该流域水风光资源联合随机模拟装置包括：获取模块，用于获取流域径流历史长系列数据和风光出力历史长系列数据，所述流域径流长系列历史数据包括龙头水库入库径流历史长系列数据和任意相邻水库的区间径流历史长系列数据；第一生成模块，用于基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列；第一建立模块，用于基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数；第二生成模块，用于基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值；第二建立模块，用于基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数；第三生成模块，用于基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值。
第三方面，本发明实施例提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机指令，所述计算机指令用于使所述计算机执行如本发明实施例第一方面及第一方面 任一项所述的流域水风光资源联合随机模拟方法。
第四方面，本发明实施例提供一种电子设备，包括：存储器和处理器，所述存储器和所述处理器之间互相通信连接，所述存储器存储有计算机指令，所述处理器通过执行所述计算机指令，从而执行如本发明实施例第一方面及第一方面任一项所述的流域水风光资源联合随机模拟方法。
本发明提供的技术方案，具有如下效果：
本发明实施例提供的流域水风光资源联合随机模拟方法，分别通过龙头水库入库径流联合分布函数、水库径流三维联合分布函数和水风光资源联合分布函数确定龙头水库入库径流模拟样本值序列、下游其他水库入库径流模拟样本序列值和风光出力模拟样本序列值。通过对整个流域系统按照水库自上而下的顺序分别模拟，得到符合流域多能源系统实际情况的随机样本，为优化调度模型提供了可靠数据。因此，通过实施本发明，通过“先径流后风光、先上游后下游”的降维思路进行逐级模拟，为流域级水风光资源样本快速生成提供了一种高效且可操作性强的途径。
附图说明
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现有技术描述中所需要使用的附图作简单地介绍。
图1是根据本发明实施例提供的一种流域水风光资源联合随机模拟方法的一流程图；
图2是根据本发明实施例提供的流域风光水电站示意图；
图3是根据本发明实施例提供的一种流域水风光资源联合随机模拟方法的另一流程图；
图4是根据本发明实施例提供的一种流域水风光资源联合随机模拟装置的结构框图；
图5是根据本发明实施例提供的计算机可读存储介质的结构示意图；
图6是根据本发明实施例提供的一种电子设备的结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述。
本发明实施例提供一种流域水风光资源联合随机模拟方法，如图1所示，该方法包括如下步骤：
步骤101：获取流域径流历史长系列数据和风光出力历史长系列数据。
具体地，考虑流域内梯级水库共M座，收集流域径流和风光出力的历史长系列数据。
其中，流域径流长系列历史数据可以包括龙头水库入库径流历史长系列数据Q1和任意相 邻水库的区间径流历史长系列数据ΔQk，分别如关系式(1)和(2)所示：

式中：表示龙头水库在第j年第t时段的入库径流值；表示k水库与k+1水库在第j年第t时段的区间径流值；T表示年内总时段数(若时段取为月，T＝12；若时段取为旬，T＝36；若时段取为日，T＝365或T＝366)；Yr表示长系列历史数据的总年数；
步骤102：基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列。
其中，龙头水库入库径流联合分布函数表征相邻时刻对应的龙头水库入库径流边缘分布函数之间的关系。
具体地，采用二维Copula方法建立考虑时间相关性的龙头水库入库径流联合分布函数，并利用该函数生成对应的龙头水库入库径流模拟样本值序列。
步骤103：基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数。
其中，水库径流三维联合分布函数表征龙头水库入库径流与下游各相邻水库区间径流之间的关系。
具体地，采用三维Copula建立考虑时空相关性的水库径流三维联合分布函数。
步骤104：基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值。
具体地，利用龙头水库入库径流模拟样本值序列，经过水库径流三维联合分布函数处理，可以生成对应的下游其他水库入库径流模拟样本序列值。
步骤105：基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水 风光资源联合分布函数。
其中，水风光资源联合分布函数可以表征下游各相邻水库区间径流和风光出力之间的关系，或表征龙头水库入库径流和风光出力之间的关系。
步骤106：基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值。
具体地，对于不同的水库，利用不同水库的入库径流模拟样本序列值生成对应的风光出力模拟样本序列值，包括：
对于龙头水库，可以利用水风光资源联合分布函数，并结合龙头水库入库径流模拟样本值序列生成左右两岸的风光出力模拟样本序列值；
对于下游其他水库，可以利用水风光资源联合分布函数，并结合下游其他水库入库径流模拟样本序列值生成左右两岸的风光出力模拟样本序列值。
本发明实施例提供的流域水风光资源联合随机模拟方法，分别通过龙头水库入库径流联合分布函数、水库径流三维联合分布函数和水风光资源联合分布函数确定龙头水库入库径流模拟样本值序列、下游其他水库入库径流模拟样本序列值和风光出力模拟样本序列值。通过对整个流域系统按照水库自上而下的顺序分别模拟，得到符合流域多能源系统实际情况的随机样本，为优化调度模型提供了可靠数据。因此，通过实施本发明，通过“先径流后风光、先上游后下游”的降维思路进行逐级模拟，为流域级水风光资源样本快速生成提供了一种高效且可操作性强的途径。
作为本发明实施例一种可选的实施方式，风光出力历史长系列数据可以包括各水库左右两岸的风电出力历史长系列数据和各水库左右两岸的光伏出力历史长系列数据。
具体地，水库风电出力历史长系列数据可以包括水库左岸风电出力历史长系列数据NWLk和水库右岸风电出力历史长系列数据NWRk，分别如关系式(3)和(4)所示：

式中：表示k水库在第j年第t时段的左岸风电出力值；表示k水库在第j年第t时段的右岸风电出力值。
水库光伏出力历史长系列数据可以包括水库左岸光伏出力历史长系列数据NPLk和水库右岸光伏出力历史长系列数据NPRk，分别如关系式(5)和(6)所示：

式中：表示k水库在第j年第t时段的左岸光伏出力值；表示k水库在第j年第t时段的右岸光伏出力值。
进一步，步骤101之后，所述方法还包括：对所述流域径流历史长系列数据、所述各水库左右两岸的风电出力历史长系列数据和所述各水库左右两岸的光伏出力历史长系列数据进行标准化处理，得到标准化流域径流历史长系列数据、各水库左右两岸的标准化风电出力历史长系列数据和各水库左右两岸的标准化光伏出力历史长系列数据；对所述标准化流域径流历史长系列数据、所述各水库左右两岸的标准化风电出力历史长系列数据和所述各水库左右两岸的标准化光伏出力历史长系列数据进行两两相关性分析，得到时间与空间的相关性结果。
具体地，基于流域径流与风、光出力的历史数据，开展水风光资源的互补性分析。
首先，对流域径流和风光出力的长系列历史数据，根据最大-最小值标准化方法对数据 进行标准化处理，如关系式(7)所示：
式中：表示标准化后的变量样本值(标准化流域径流历史长系列数据、标准化水库风电出力历史长系列数据和标准化水库光伏出力历史长系列数据)；at表示原始数据样本值(流域径流历史长系列数据、水库风电出力历史长系列数据和水库光伏出力历史长系列数据)；
其次，基于标准化后的样本数据，分别计算每个水库k(k＝1，…，M)每个时段t(t＝1，…，T)的入库径流与左岸/右岸风电出力、入库径流与左岸/右岸光伏出力的Spearman秩相关系数，如关系式(8)所示：
式中：表示k水库在第t时段的两个标准化变量分别排序后成对的变量位置差，即其中，表示原始数据序列成对变量的排序位置编号；表示原始数据序列成对变量的排序位置编号。
进一步，变量可以包括：各个水库的入库径流、左岸/右岸风电出力、左岸/右岸光伏出力。
最后，根据Spearman秩相关系数进行两能源之间的互补性分析：若Spearman秩相关系数为负，则说明两能源之间存在直接互补性；若Spearman秩相关系数为正，则说明两能源之间存在协同互补性。
进一步，通过t检验法对两能源是否真正互补做假设检验，若检验p值小于显著性水平，则认为两能源满足互补性要求(即两能源之间存在直接互补性或协同互补性)，可以采用步骤105和步骤106进行风光出力随机模拟；否则，认为两能源之间不存在空间相关，可以采用步骤102中考虑时间相关性的二维Copula方法进行风电出力或光伏出力单变量随机模拟。
作为本发明实施例一种可选的实施方式，基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，包括：基于所述龙头水库入库径流历史长系列数据，计算任意相邻时段的龙头水库入库径流相关系数；基于所述龙头水库入库径流相关系数，建立任意相邻时段的龙头水库入库径流的边缘分布函数；对所述任意相邻时段的龙头水库入库径流的边缘分布函数进行联合处理，得到所述龙头水库入库径流联合分布函数。
具体地，基于龙头水库入库径流历史长系列数据，计算任一相邻时段t和t-1的龙头水 库入库径流相关系数并通过拟合优选方式得到任一相邻时段t和t-1的龙头水库入库径流的边缘分布函数：u＝F(xt-1)和v＝F(xt)。
其中，相关系数的具体计算表达式如关系式(9)所示：
式中：表示第t时段龙头水库入库径流的历史多年平均值；表示第t-1时段龙头水库入库径流的历史多年平均值。
进一步，通过阿基米德类Copula函数(包括：Gumbel、Clayton、Frank)将相邻时段的龙头水库入库径流的边缘分布函数进行联合，建立考虑时间相关性的龙头水库入库径流联合分布函数，表达式如关系式(10)所示：
式中：C(·)表示Copula联结函数；θt表示Copula函数中的待估参数，可以根据Kendall-τ法(即))进行反向求解计算得到；F(xt|xt-1)表示已知前一时段取值xt-1情况下的条件分布；xt-1表示第t-1时段的龙头水库入库径流；xt表示第t时段的龙头水库入库径流。
作为本发明实施例一种可选的实施方式，基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列，包括：基于所述龙头水库入库径流联合分布函数，生成当前时段对应的第一随机数和与所述当前时段相邻的相邻时段对应的第二随机数；基于所述第一随机数和所述龙头水库入库径流的边缘分布函数，生成所述当前时段对应的龙头水库入库径流模拟第一样本值；基于所述龙头水库入库径流模拟样本值、所述第二随机数和所述龙头水库入库径流的边缘分布函数，生成所述相邻时段对应的龙头水库入库径流模拟第二样本值；基于所述龙头水库入库径流模拟第一样本值和所述龙头水库入库径流模拟第二样本值，确定所述龙头水库入库径流模拟样本值序列。
具体地，根据所构建的考虑时间相关性的龙头水库入库径流联合分布函数，基于服从标准均匀分布的随机模拟器，逐时段、逐年份生成龙头水库入库径流的模拟样本序列值，包括：
(1)对于第一年的第一个时段(j＝1，t＝1)，生成服从标准均匀分布U(0，1)的随机数ε(第一随机数)，令初始时段的边缘分布F(x1)＝ε，并计算当前时段的模拟样本值：x1＝F-1(ε)，并将初始时段的模拟样本值记为
(2)对于第一年的其他时段(j＝1，2≤t≤T)，已知前一时段的模拟样本值和边缘 分布，生成服从标准均匀分布U(0，1)的随机数δ(第二随机数)，令条件分布F(xt|xt-1)＝δ，并计算当前时段的模拟样本值：并将本时段的模拟样本值记为最终得到第一年的龙头水库入库径流模拟样本值序列
(3)考虑相邻年份时段连续性，对于每一年份(2≤j≤Yr)，重复上述(2)，计算各年份各时段的龙头水库入库径流模拟样本序列，最终计算结果如关系式(11)所示：
作为本发明实施例一种可选的实施方式，步骤103，包括：基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，计算时空综合条件相关系数；基于所述时空综合条件相关系数，建立所述相邻时段内所述任意相邻水库的区间径流边缘分布函数；对所述任意相邻水库的区间径流边缘分布函数进行联合处理，得到所述水库径流三维联合分布函数。
具体地，以龙头水库入库径流为主变量、相邻两水库的区间径流为从变量，基于龙头水库入库径流的历史长系列数据、相邻两水库的区间径流的历史长系列数据，计算时空综合条件相关系数并通过拟合优选方式得到任一相邻时段各个相邻两水库的区间径流的边缘分布函数：w＝F(yt-1)和p＝F(yt)。
其中，时空综合条件相关系数的具体计算表达式如关系式(12)所示：
式中：yt表示第t时段的任一相邻两水库的区间径流；yt-1表示第t-1时段的任一相邻两水库的区间径流；表示第t时段的相邻两水库的区间径流与第t时段的龙头水库入库径流所形成的空间相关系数；表示第t时段与第t-1时段的相邻两水库的区间径流的时间相关系数；表示第t-1时段的相邻两水库的区间径流与第t时段的龙头水库入库径流所形成的跨时空间相关系数。
进一步，各项相关系数的计算表达式分别如关系式(13)、(14)和(15)所示：


式中：表示k水库在第t时段的相邻两水库的区间径流的历史多年平均值；表示k水库在第t-1时段的相邻两水库的区间径流的历史多年平均值。
进一步，以龙头水库入库径流为主变量、相邻两水库的区间径流为从变量，基于Copula函数和条件分布理论，建立考虑时空相关性的水库径流三维联合分布函数，表达式如关系式(16)所示：
式中：CXY(F(xt|yt-1)，F(yt|yt-1))表示条件分布F(xt|yt-1)和F(yt|yt-1)的Copula函数，可参考关系式(10)计算；λt表示Copula函数中的待估参数，可以根据Kendall-τ法和关系式(12)-(15)所计算的相关系数进行求解。
进一步，步骤104可以参考上述生成龙头水库入库径流模拟样本值序列的过程。
具体地，根据所构建的考虑时空相关性的流域径流联合分布，基于服从标准均匀分布的随机模拟器，逐时段、逐年份、自上游向下游生成各相邻水库区间径流的模拟样本序列值，包括：
(1)对于水库k和水库k+1的区间流域，针对第j年的第t时段，生成服从标准均匀分布U(0，1)的随机数ε′，令条件分布F(xt|yt-1)＝ε′，结合龙头水库模拟样本值xt，可以计算前一时段的相邻水库区间径流的模拟样本值：再生成服从标准均匀分布U(0，1)的随机数δ′，令条件分布F(yt|yt-1)＝δ′并计算本时段的区间径流模拟样本值记为
(2)若时段1≤t≤T，重复上述(1)，可以得到水库k和水库k+1的区间径流模拟样本序列值然后，考虑相邻年份时段连续性，若时段1≤j≤Yr，重复上述步骤，计算水库k和水库k+1在各年份各时段的区间径流模拟样本序列，如关系式(17)所示：
(3)重复(1)和(2)，计算所有相邻水库的区间径流模拟样本，直至k≥M，在不考虑水库调蓄作用影响下，按照如下关系式(18)计算除龙头水库外的其他水库入库径流的模拟样本序列值：
作为本发明实施例一种可选的实施方式，步骤105，包括：基于所述风光出力历史长系列数据，经过拟合优选处理，得到各水库左右两岸的相邻时段内的风电出力边缘分布函数和光伏出力边缘分布函数；基于各水库左右两岸的所述风电出力边缘分布函数和各水库左右两岸的所述光伏出力边缘分布函数，确定所述水风光资源联合分布函数。
具体地，以入库径流为主变量、左岸/右岸的风电出力或光伏出力为从变量，进行风光出力的随机模拟，包括：
(1)基于各水库从变量的历史长系列数据，通过拟合优选方式得到任一相邻时段从变量的边缘分布q＝F(zt-1)和g＝D(zt)；
(2)对于任一水库k，以水库入库径流为主变量、左岸/右岸的风电出力或光伏出力为从变量，基于Copula函数和条件分布理论，建立考虑互补性的水风光资源联合分布函数。以左岸风电出力为例，三维Copula联合分布函数计算表达式如关系式(19)和(20)所示：
龙头水库：
其他水库：
式中：CXY(F(xt|zt-1)，F(zt|zt-1))表示以龙头水库为研究对象下条件分布F(xt|zt-1)和F(zt|zt-1)的Copula函数，可参考关系式(10)计算；CYZ(F(yt|zt-1)，F(zt|zt-1))表示以其 他水库为研究对象下条件分布F(yt|zt-1)和F(zt|zt-1)的Copula函数，可参考关系式(10)计算；ζt表示Copula函数中的待估参数，可以根据Kendall-τ法，结合所计算的Spearman秩相关系数进行求解。
作为本发明实施例一种可选的实施方式，步骤106，包括：基于所述水风光资源联合分布函数，生成所述当前时段对应的第三随机数和所述相邻时段对应的第四随机数；基于所述水风光资源联合分布函数、所述第三随机数和所述龙头水库入库径流模拟样本值序列，生成所述相邻时段对应的龙头水库左右两岸的风光出力模拟第一样本值；基于所述水风光资源联合分布函数和所述第四随机数，生成所述当前时段对应的龙头水库左右两岸的风光出力模拟第二样本值；基于所述龙头水库左右两岸的风光出力模拟第一样本值和所述龙头水库左右两岸的风光出力模拟第二样本值，确定所述龙头水库左右两岸的风光出力模拟样本序列值。
具体地，根据所构建的考虑互补性的水风光资源联合分布，基于服从标准均匀分布的随机模拟器，逐时段、逐年份、自上游向下游生成各水库左岸/右岸的风电出力或光伏出力的模拟样本序列值。以左岸风电出力为例，包括：
(1)对于龙头水库，针对第j年的第t时段，生成服从标准均匀分布U(0，1)的随机数ε″(第三随机数)，令条件分布F(xt|zt-1)＝ε″结合龙头水库模拟样本值xt，可以计算前一时段的龙头水库左岸风电出力的模拟样本值：再生成服从标准均匀分布U(0，1)的随机数δ″，令条件分布F(zt|zt-1)＝δ″并计算本时段的龙头水库左岸风电出力的模拟样本值记为
(2)若时段1≤t≤T，重复上述(1)，可以得到龙头水库左岸风电出力的模拟样本序列然后，考虑相邻年份时段连续性，若时段1≤j≤Yr，重复上述步骤，计算龙头水库在各年份各时段的左岸风电出力模拟样本序列值；
(3)对于其他水库，令条件分布F(yt|zt-1)＝ε″，参考上述(1)和(2)计算其他水库在各年份各时段的左岸风电出力模拟样本序列，最终可得到各个水库的左岸风电出力模拟样本序列值，如关系式(21)所示：
进一步，右岸风电出力、左岸光伏出力、右岸光伏出力的模拟样本计算过程，与之类 似，此处不再赘述。
可选地，根据上述资源互补性分析结果，如果各级水库的入库径流与其左右两岸的风光出力不满足互补性要求，则对单独左岸/右岸风电出力或光伏出力进行单变量、考虑时间相关性的随机模拟，计算过程与龙头水库入库径流随机模拟过程类似，此处不再赘述。
本发明上述实施例考虑了风光水多能源的时空相关性，并对整个流域系统按照梯级水库区间分别模拟，能够得到符合流域多能源系统实际情况的随机样本，从而为优化调度模型提供可靠数据；将多变量联合分布分解为多个二维Copula的组合，相较于直接建立多维嵌套Copula能够简化参数估计过程，有效控制计算成本，实际操作中易于实现。
在一实例中，流域风光水电站示意图如图2所示；进一步，对应的流域水风光资源联合随机模拟方法如图3所示。
本发明实施例还提供一种流域水风光资源联合随机模拟装置，如图4所示，该装置包括：
获取模块401，用于获取流域径流历史长系列数据和风光出力历史长系列数据，所述流域径流长系列历史数据包括龙头水库入库径流历史长系列数据和任意相邻水库的区间径流历史长系列数据；详细内容参见上述方法实施例中步骤101的相关描述。
第一生成模块402，用于基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列；详细内容参见上述方法实施例中步骤102的相关描述。
第一建立模块403，用于基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数；详细内容参见上述方法实施例中步骤103的相关描述。
第二生成模块404，用于基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值；详细内容参见上述方法实施例中步骤104的相关描述。
第二建立模块405，用于基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数；详细内容参见上述方法实施例中步骤105的相关描述。
第三生成模块406，用于基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值；详细内容参见上述方法实施例中步骤106的相关描述。
本发明实施例提供的流域水风光资源联合随机模拟装置，分别通过龙头水库入库径流联合分布函数、水库径流三维联合分布函数和水风光资源联合分布函数确定龙头水库入库径流模拟样本值序列、下游其他水库入库径流模拟样本序列值和风光出力模拟样本序列值。通过对整个流域系统按照水库自上而下的顺序分别模拟，得到符合流域多能源系统实际情况的随机样本，为优化调度模型提供了可靠数据。因此，通过实施本发明，通过“先径流后风光、先上游后下游”的降维思路进行逐级模拟，为流域级水风光资源样本快速生成提供了一种高效且可操作性强的途径。
作为本发明实施例一种可选的实施方式，所述风光出力历史长系列数据包括各水库左右两岸的风电出力历史长系列数据和各水库左右两岸的光伏出力历史长系列数据；所述装置还包括：处理模块，用于对所述流域径流历史长系列数据、所述各水库左右两岸的风电出力历史长系列数据和所述各水库左右两岸的光伏出力历史长系列数据进行标准化处理，得到标准化流域径流历史长系列数据、各水库左右两岸的标准化风电出力历史长系列数据和各水库左右两岸的标准化光伏出力历史长系列数据；分析模块，用于对所述标准化流域径流历史长系列数据、所述各水库左右两岸的标准化风电出力历史长系列数据和所述各水库左右两岸的标准化光伏出力历史长系列数据进行两两相关性分析，得到时间与空间的相关性结果。
作为本发明实施例一种可选的实施方式，所述第一生成模块包括：第一计算子模块，用于基于所述龙头水库入库径流历史长系列数据，计算任意相邻时段的龙头水库入库径流相关系数；第一建立子模块，用于基于所述龙头水库入库径流相关系数，建立任意相邻时段的龙头水库入库径流的边缘分布函数；第一处理子模块，用于对所述任意相邻时段的龙头水库入库径流的边缘分布函数进行联合处理，得到所述龙头水库入库径流联合分布函数。
作为本发明实施例一种可选的实施方式，所述第一生成模块还包括：第一生成子模块，用于基于所述龙头水库入库径流联合分布函数，生成当前时段对应的第一随机数和与所述当前时段相邻的相邻时段对应的第二随机数；第二生成子模块，用于基于所述第一随机数和所述龙头水库入库径流的边缘分布函数，生成所述当前时段对应的龙头水库入库径流模拟第一样本值；第三生成子模块，用于基于所述龙头水库入库径流模拟样本值、所述第二随机数和所述龙头水库入库径流的边缘分布函数，生成所述相邻时段对应的龙头水库入库径流模拟第二样本值；第一确定子模块，用于基于所述龙头水库入库径流模拟第一样本值和所述龙头水库入库径流模拟第二样本值，确定所述龙头水库入库径流模拟样本值序列。
作为本发明实施例一种可选的实施方式，所述第一建立模块包括：第二计算子模块，用于基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，计算时空综 合条件相关系数；第二建立子模块，用于基于所述时空综合条件相关系数，建立所述相邻时段内所述任意相邻水库的区间径流边缘分布函数；第二处理子模块，用于对所述任意相邻水库的区间径流边缘分布函数进行联合处理，得到所述水库径流三维联合分布函数。
作为本发明实施例一种可选的实施方式，所述第二建立模块包括：第三处理子模块，用于基于所述风光出力历史长系列数据，经过拟合优选处理，得到各水库左右两岸的相邻时段内的风电出力边缘分布函数和光伏出力边缘分布函数；第二确定子模块，用于基于各水库左右两岸的所述风电出力边缘分布函数和各水库左右两岸的所述光伏出力边缘分布函数，确定所述水风光资源联合分布函数。
作为本发明实施例一种可选的实施方式，所述第三生成模块包括：第四生成子模块，用于基于所述水风光资源联合分布函数，生成所述当前时段对应的第三随机数和所述相邻时段对应的第四随机数；第五生成子模块，用于基于所述水风光资源联合分布函数、所述第三随机数和所述龙头水库入库径流模拟样本值序列，生成所述相邻时段对应的龙头水库左右两岸的风光出力模拟第一样本值；第六生成子模块，用于基于所述水风光资源联合分布函数和所述第四随机数，生成所述当前时段对应的龙头水库左右两岸的风光出力模拟第二样本值；第三确定子模块，用于基于所述龙头水库左右两岸的风光出力模拟第一样本值和所述龙头水库左右两岸的风光出力模拟第二样本值，确定所述龙头水库左右两岸的风光出力模拟样本序列值。
本发明实施例提供的流域水风光资源联合随机模拟装置的功能描述详细参见上述实施例中流域水风光资源联合随机模拟方法描述。
本发明实施例还提供一种存储介质，如图5所示，其上存储有计算机程序501，该指令被处理器执行时实现上述实施例中流域水风光资源联合随机模拟方法的步骤。其中，存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)、随机存储记忆体(Random Access Memory，RAM)、快闪存储器(Flash Memory)、硬盘(Hard Disk Drive，HDD)或固态硬盘(Solid-State Drive，SSD)等；所述存储介质还可以包括上述种类的存储器的组合。
本领域技术人员可以理解，实现上述实施例方法中的全部或部分流程，是可以通过计算机程序来指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，所述存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory，ROM)、随机存储记忆体(Random Access Memory，RAM)、快闪存储器(Flash Memory)、硬盘(Hard Disk Drive，HDD)或固态硬盘(Solid- State Drive，SSD)等；所述存储介质还可以包括上述种类的存储器的组合。
本发明实施例还提供了一种电子设备，如图6所示，该电子设备可以包括处理器61和存储器62，其中处理器61和存储器62可以通过总线或者其他方式连接，图6中以通过总线连接为例。
处理器61可以为中央处理器(Central Processing Unit，CPU)。处理器61还可以为其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等芯片，或者上述各类芯片的组合。
存储器62作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本发明实施例中的对应的程序指令/模块。处理器61通过运行存储在存储器62中的非暂态软件程序、指令以及模块，从而执行处理器的各种功能应用以及数据处理，即实现上述方法实施例中的流域水风光资源联合随机模拟方法。
存储器62可以包括存储程序区和存储数据区，其中，存储程序区可存储操作装置、至少一个功能所需要的应用程序；存储数据区可存储处理器61所创建的数据等。此外，存储器62可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器62可选包括相对于处理器61远程设置的存储器，这些远程存储器可以通过网络连接至处理器61。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述一个或者多个模块存储在所述存储器62中，当被所述处理器61执行时，执行如图1-3所示实施例中的流域水风光资源联合随机模拟方法。
上述电子设备具体细节可以对应参阅图1至图3所示的实施例中对应的相关描述和效果进行理解，此处不再赘述。

Claims (8)

  1. 一种流域水风光资源联合随机模拟方法，其特征在于，所述方法包括：
    获取流域径流历史长系列数据和风光出力历史长系列数据，所述流域径流长系列历史数据包括龙头水库入库径流历史长系列数据和任意相邻水库的区间径流历史长系列数据；
    基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列；
    基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数；
    基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值；
    基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数；
    基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值。
  2. 根据权利要求1所述的方法，其特征在于，所述风光出力历史长系列数据包括各水库左右两岸的风电出力历史长系列数据和各水库左右两岸的光伏出力历史长系列数据；获取流域径流历史长系列数据和风光出力历史长系列数据之后，所述方法还包括：
    对所述流域径流历史长系列数据、所述各水库左右两岸的风电出力历史长系列数据和所述各水库左右两岸的光伏出力历史长系列数据进行标准化处理，得到标准化流域径流历史长系列数据、各水库左右两岸的标准化风电出力历史长系列数据和各水库左右两岸的标准化光伏出力历史长系列数据；
    对所述标准化流域径流历史长系列数据、所述各水库左右两岸的标准化风电出力历史长系列数据和所述各水库左右两岸的标准化光伏出力历史长系列数据进行两两相关性分析，得到时间与空间的相关性结果。
  3. 根据权利要求2所述的方法，其特征在于，基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，包括：
    基于所述龙头水库入库径流历史长系列数据，计算任意相邻时段的龙头水库入库径流相关系数；
    基于所述龙头水库入库径流相关系数，建立任意相邻时段的龙头水库入库径流的边缘分布函数；
    对所述任意相邻时段的龙头水库入库径流的边缘分布函数进行联合处理，得到所述龙头 水库入库径流联合分布函数。
  4. 根据权利要求2所述的方法，其特征在于，基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列，包括：
    基于所述龙头水库入库径流联合分布函数，生成当前时段对应的第一随机数和与所述当前时段相邻的相邻时段对应的第二随机数；
    基于所述第一随机数和所述龙头水库入库径流的边缘分布函数，生成所述当前时段对应的龙头水库入库径流模拟第一样本值；
    基于所述龙头水库入库径流模拟样本值、所述第二随机数和所述龙头水库入库径流的边缘分布函数，生成所述相邻时段对应的龙头水库入库径流模拟第二样本值；
    基于所述龙头水库入库径流模拟第一样本值和所述龙头水库入库径流模拟第二样本值，确定所述龙头水库入库径流模拟样本值序列。
  5. 根据权利要求3所述的方法，其特征在于，基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数，包括：
    基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，计算时空综合条件相关系数；
    基于所述时空综合条件相关系数，建立所述相邻时段内所述任意相邻水库的区间径流边缘分布函数；
    对所述任意相邻水库的区间径流边缘分布函数进行联合处理，得到所述水库径流三维联合分布函数。
  6. 根据权利要求4所述的方法，其特征在于，基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数，包括：
    基于所述风光出力历史长系列数据，经过拟合优选处理，得到各水库左右两岸的相邻时段内的风电出力边缘分布函数和光伏出力边缘分布函数；
    基于各水库左右两岸的所述风电出力边缘分布函数和各水库左右两岸的所述光伏出力边缘分布函数，确定所述水风光资源联合分布函数。
  7. 根据权利要求6所述的方法，其特征在于，基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值，包括：
    基于所述水风光资源联合分布函数，生成所述当前时段对应的第三随机数和所述相邻时段对应的第四随机数；
    基于所述水风光资源联合分布函数、所述第三随机数和所述龙头水库入库径流模拟样本值序列，生成所述相邻时段对应的龙头水库左右两岸的风光出力模拟第一样本值；
    基于所述水风光资源联合分布函数和所述第四随机数，生成所述当前时段对应的龙头水库左右两岸的风光出力模拟第二样本值；
    基于所述龙头水库左右两岸的风光出力模拟第一样本值和所述龙头水库左右两岸的风光出力模拟第二样本值，确定所述龙头水库左右两岸的风光出力模拟样本序列值。
  8. 一种流域水风光资源联合随机模拟装置，其特征在于，所述装置包括：
    获取模块，用于获取流域径流历史长系列数据和风光出力历史长系列数据，所述流域径流长系列历史数据包括龙头水库入库径流历史长系列数据和任意相邻水库的区间径流历史长系列数据；
    第一生成模块，用于基于所述龙头水库入库径流历史长系列数据建立龙头水库入库径流联合分布函数，并基于所述龙头水库入库径流联合分布函数生成龙头水库入库径流模拟样本值序列；
    第一建立模块，用于基于所述龙头水库入库径流历史长系列数据和所述区间径流历史长系列数据，建立水库径流三维联合分布函数；
    第二生成模块，用于基于所述龙头水库入库径流模拟样本值序列，利用所述水库径流三维联合分布函数生成下游其他水库入库径流模拟样本序列值；
    第二建立模块，用于基于所述流域径流历史长系列数据和所述风光出力历史长系列数据，建立水风光资源联合分布函数；
    第三生成模块，用于基于所述龙头水库入库径流模拟样本值序列或所述下游其他水库入库径流模拟样本序列值，利用所述水风光资源联合分布函数，生成各水库左右两岸的风光出力模拟样本序列值。
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