A precise modeling method for renewable energy output suitable for risk assessment of transmission and power generation systems

Abstract

The present invention provides a method for accurately modeling the output of renewable energy suitable for risk assessment of power transmission and power generation systems, comprising: characterizing the seasonal timing characteristics of wind power generation based on a four-season Weibull distribution model, and constructing a probability distribution model for wind farm output power taking into account the wake effect and the influence of terrain height, obtaining the probability distribution of photovoltaic power using a kernel density parameter estimation method, and obtaining a joint probability distribution model reflecting the correlation between wind and solar power using Copula theory. Through this accurate modeling method for renewable energy, a renewable energy output power model suitable for power system risk assessment can be obtained. This model fully considers the timing and spatial distribution characteristics of renewable energy and the correlation between the outputs of different renewable energy sources. Compared with traditional renewable energy models, the factors considered are more comprehensive and more accurate. The risk indicators calculated using this model are also closer to reality.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for accurately modeling new energy output suitable for risk assessment of power transmission and generation systems. Background Art

[0002] Because renewable energy generation is subject to weather fluctuations and often exhibits strong randomness and volatility, its large-scale grid connection can severely impact transmission and power generation systems. Therefore, studying the impact of renewable energy integration on the risk level of the combined transmission and power generation system is crucial for promoting renewable energy development and ensuring the safe and stable operation of the power system.

[0003] Among existing risk assessment technologies for systems containing renewable energy, some focus on constructing wind farm output power models that account for topographical factors, but fail to consider the temporal characteristics of wind speed captured by wind turbines. Other technologies, when studying renewable energy output models, establish detailed output models that account for the temporal characteristics of renewable energy, but ignore the impact of terrain spatial distribution factors, including wake factors, on wind speed distribution. Some technologies, when performing risk assessment calculations for systems containing multiple renewable energy stations, consider the temporal and spatial characteristics of independent renewable energy generation, but fail to construct detailed correlations between the outputs of multiple renewable energy stations. Furthermore, while some technologies examine the correlation of renewable energy output, they fail to fully consider the temporal and spatial distribution characteristics of each renewable energy source.

[0004] Therefore, the new energy output models established by existing technologies are often not accurate enough and fail to fully consider the impact of various temporal and spatial factors on the combined output power of hybrid new energy, which in turn leads to inaccurate risk assessment calculation results. Summary of the Invention

[0005] In response to the above-mentioned shortcomings of the existing technology, the present invention proposes a precise modeling method for renewable energy output for risk assessment of power transmission and power generation systems. During the modeling process, the temporal characteristics and spatial distribution characteristics of the renewable energy output sequence and the correlation characteristics between different renewable energy power sequences are fully considered, thereby improving the accuracy of the risk assessment calculation results.

[0006] A method for accurately modeling renewable energy output suitable for risk assessment of power transmission and generation systems includes the following steps:

[0007] Step 1: Considering the time characteristics, a probability distribution function of the four-season Weibull distribution model is established to characterize the seasonal temporal sequence of wind speed;

[0008] Step 2: Considering the influence of the wake effect, a wind speed sequence is generated based on the probability distribution function of the four-season Weibull distribution model obtained in step 1, and then modified to obtain the actual captured wind speed of each wind turbine in the wind farm:

[0009] Step 3: Consider the influence of terrain height and correct the captured wind speed of wind turbines at different heights:

[0010] Step 4: After taking into account the wake effect and terrain height correction for each wind turbine in steps 2 and 3, the wind speed captured by each wind turbine i in the wind farm is obtained, and its ratio value r compared to the natural wind speed is calculated. i , multiply the wind speed probability distribution function F(v,j) by the proportional value r i The wind speed probability distribution function F of each wind turbine can be obtained wi (v,j);

[0011] Step 5: According to the following formula, the wind speed probability distribution function F of each wind turbine is wi (v, j) is converted into the power probability distribution function F of wind power wi (p w ,j):

[0012]

[0013] Where, v ci Indicates the cut-in wind speed, v co Indicates the cut-off wind speed, v r Indicates rated wind speed, P r represents the rated power, e and f represent the slope and intercept of the fitting line, and k is the wind speed order;

[0014] Step 6: Accumulate the power probability distribution function F of each wind turbine in the wind farm in four seasons wi (p w ,j) to get the wind farm power probability distribution function F w (p w ), the wind farm power probability distribution function F w (p w ) can be derived to obtain the wind farm power probability density function f w (p w );

[0015] Step 7: Establish the probability density function f of photovoltaic power pv (p pv ):

[0016]

[0017] Where h is the bandwidth, n is the total number of samples, K(·) is the kernel function, and p1, p2, ..., pn are the photovoltaic output powers p pv n samples of ;

[0018] Step 8: Based on the historical data of wind and solar power, a wind-solar joint distribution scatter plot is established, and the appropriate Copula function C that represents the correlation between wind power and photovoltaic power is selected according to the distribution characteristics of the scatter plot. α And calculate the parameter value α of the Copula function to obtain C that represents the correlation between wind power and photovoltaic power. α ;

[0019] Step 9: Consider the correlation between wind and solar power, and combine the wind farm power probability density function f obtained in step 6 w (p), the probability density function f of photovoltaic power obtained in step 7 pv (p pv ) and the C obtained in step 8 to characterize the correlation between wind power and photovoltaic power α , and establish the probability density function of wind-solar combined output probability as follows:

[0020] f(p w ,p pv )=c α (F w (p w ),F pv (p pv ))f w (p w )·f Y (p pv )

[0021] Where F w (p w ) and F pv (p pv ) are the probability distribution functions of wind and light power, respectively, and their probability density functions f pv (p pv ) and f pv (p pv ) points are obtained.

[0022] Furthermore, the method further includes step 10, obtaining the wind and light power sequences by an inverse sampling method based on the wind-solar combined output probability density function obtained in step 9.

[0023] Furthermore, the probability distribution function of the four-season Weibull distribution model in step 1 is as follows:

[0024]

[0025] Where j represents the jth season, c j is the scale parameter, k j is the shape parameter, and v is the actual wind speed.

[0026] Furthermore, the scale parameter c of the j-th seasonal Weibull model in step 1 is j and shape parameter kj The calculation process is as follows:

[0027] (1) Let F w (v, j) = U, where U is a random number uniformly distributed between [0, 1]. By finding its inverse function, we can get the wind speed expression as follows:

[0028]

[0029] (2) Take the logarithm twice on both sides of the above equation to perform linearization and obtain the following equation:

[0030] ln{-ln[1-F w (v,j)]}=k j lnv j -k j lnc j

[0031] (3) Let y=ln{-ln[1-F(v,j)]}, x=lnv, a=k j , b=-k j lnc j , and then write the above equation into the linearized equation form of y=ax+b.

[0032] (4) Estimate the values ​​of parameters a and b using the least squares method. The specific expressions are as follows:

[0033]

[0034] Where i represents the i-th wind speed value in the order of wind speed samples from small to large, x i represents the i-th wind speed sample, y i Represents the distribution function value corresponding to the i-th wind speed value. In actual estimation, it is replaced by the median rank. The expression of the median rank is as follows:

[0035]

[0036] (5) According to the parameters a and b, the Weibull parameter value c corresponding to the jth season is obtained j and k j .

[0037] Furthermore, in step 2, the following formula is used for correction to obtain the actual captured wind speed of each wind turbine in the wind farm:

[0038] v x =v0(1-d)

[0039] Where v x represents the captured wind speed of the rear exhaust fan, v0 represents the captured wind speed of the front exhaust fan, and d is the terrain correction parameter;

[0040] The specific expression of the terrain parameter d is as follows:

[0041]

[0042] Where c T represents the thrust coefficient, R represents the blade radius, X represents the fan spacing, and k represents the wake drop coefficient. The specific expression is as follows:

[0043] k=0.5(σ G +σ o ) / [u*ln(h / z)]

[0044] Where, σ G represents the turbulence variance, σ o represents the natural turbulence variance, h represents the turbine hub height, z represents the surface roughness coefficient, and u represents the average wind speed.

[0045] Furthermore, in step 3, the captured wind speed of wind turbines at different heights is corrected according to the following formula:

[0046] h2=h1(v1 / v2) 2

[0047] Where v1 represents the wind speed at height h1, and v2 represents the wind speed at height h2.

[0048] The present invention has the following beneficial effects: the present invention characterizes the seasonal time series characteristics of wind power generation based on the four-season Weibull distribution model, and takes into account the wake effect and the influence of terrain height to construct a probability distribution model for wind farm output power, uses the kernel density parameter estimation method to obtain the probability distribution of photovoltaic power, and uses the Copula theory to obtain a joint probability distribution model reflecting the correlation between wind and light. Through this new energy precision modeling method, a new energy output power model suitable for power system risk assessment can be obtained. This model fully considers the time series and spatial distribution characteristics of new energy and the correlation between different new energy outputs. Compared with traditional new energy models, the factors considered are more comprehensive and more accurate. The risk indicators calculated using this model are also closer to reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a fan arrangement diagram of an embodiment of the present invention;

[0050] Figure 2 This is the simulation result of Weibull wind speed in four seasons according to the embodiment of the present invention;

[0051] Figure 3 is the output power fitting result of the photovoltaic power station according to the embodiment of the present invention;

[0052] Figure 4This is a scatter plot of wind-solar joint distribution according to an embodiment of the present invention;

[0053] Figure 5 This is the fitting result of the wind-solar correlation Copula function in the embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] An embodiment of the present invention provides a method for accurately modeling renewable energy output applicable to risk assessment of power transmission and generation systems, comprising the following steps:

[0056] Step 1: Considering the time characteristics, establish the probability distribution function of the four-season Weibull distribution model to characterize the seasonal temporal sequence of wind speed:

[0057]

[0058] Where j represents the jth season, c j is the scale parameter, k j is the shape parameter, v is the actual wind speed;

[0059] The scale parameter c of the j-th seasonal Weibull model in step 1 is j and shape parameter k j The calculation process is as follows:

[0060] (1) Let F w (v, j) = U, where U is a random number uniformly distributed between [0, 1]. By finding its inverse function, we can get the wind speed expression as follows:

[0061]

[0062] (2) Take the logarithm twice on both sides of the above equation to perform linearization and obtain the following equation:

[0063] ln{-ln[1-F w (v,j)]}=k j lnv j -k j lnc j

[0064] (3) Let y=ln{-ln[1-F(v,j)]}, x=lnv, a=k j, b=-k j lnc j , and then write the above equation into the linearized equation form of y=ax+b.

[0065] (4) Estimate the values ​​of parameters a and b using the least squares method. The specific expressions are as follows:

[0066]

[0067] Where i represents the i-th wind speed value in the order of wind speed samples from small to large, x i represents the i-th wind speed sample, y i Represents the distribution function value corresponding to the i-th wind speed value. In actual estimation, it is replaced by the median rank. The expression of the median rank is as follows:

[0068]

[0069] (5) According to the parameters a and b, the Weibull parameter value c corresponding to the jth season is obtained j and k j .

[0070] Step 2: Considering the wake effect, a wind speed sequence is generated based on the probability distribution function of the four-season Weibull distribution model obtained in step 1. The sequence is then modified using the following formula to obtain the actual captured wind speed for each wind turbine in the wind farm:

[0071] v x =v0(1-d)

[0072] Where v x represents the captured wind speed of the rear exhaust fan, v0 represents the captured wind speed of the front exhaust fan, and d is the terrain correction parameter;

[0073] The specific expression of the terrain parameter d in step 2 is as follows:

[0074]

[0075] In the above formula, c T represents the thrust coefficient, R represents the blade radius, X represents the fan spacing, and k represents the wake drop coefficient. The specific expression is as follows:

[0076] k=0.5(σ G +σ o ) / [u*ln(h / z)]

[0077] In the above formula, σ G represents the turbulence variance, σ o represents the natural turbulence variance, h represents the turbine hub height, z represents the surface roughness coefficient, and u represents the average wind speed.

[0078] Step 3: Consider the influence of terrain height and correct the captured wind speed of wind turbines at different heights according to the following formula:

[0079] h2=h1(v1 / v2) 2

[0080] In the above formula, v1 represents the wind speed value at height h1, and v2 represents the wind speed value at height h2.

[0081] Step 4: After taking into account the wake effect and terrain height correction for each wind turbine in steps 2 and 3, the wind speed captured by each wind turbine i in the wind farm is obtained, and its ratio value r compared to the natural wind speed is calculated. i , multiply the wind speed probability distribution function F(v,j) by the proportional value r i The wind speed probability distribution function F of each wind turbine can be obtained wi (v,j).

[0082] Step 5: According to the following formula, the wind speed probability distribution function F of each wind turbine is wi (v, j) is converted into the power probability distribution function F of wind power wi (p w ,j):

[0083]

[0084] In the above formula, v ci Indicates the cut-in wind speed, v co Indicates the cut-off wind speed, v r Indicates rated wind speed, P r represents the rated power, e and f represent the slope and intercept of the fitting line, and k is the wind speed order, which is generally 1 to 3.

[0085] Step 6: Accumulate the power probability distribution function F of each wind turbine in the wind farm in four seasons wi (p w ,j) to get the wind farm power probability distribution function F w (p w ), the wind farm power probability distribution function F w (p w ) can be derived to obtain the wind farm power probability density function f w (p w ).

[0086] Step 7: Establish the probability density function f of photovoltaic power according to the following formula: pv (p pv ):

[0087]

[0088] In the above formula, h represents bandwidth, n represents the total number of samples, K(·) represents kernel function, p1, p2, ..., pn are photovoltaic output power p pv n samples of ;

[0089] Step 8: Based on the historical data of wind and solar power, a wind-solar joint distribution scatter plot is established, and the appropriate Copula function C that represents the correlation between wind power and photovoltaic power is selected according to the distribution characteristics of the scatter plot. α And calculate the parameter value α of the Copula function to obtain C that represents the correlation between wind power and photovoltaic power. α ;

[0090] Step 9: Consider the correlation between wind and solar power, and combine the wind farm power probability density function f obtained in step 6 w (p), the probability density function f of photovoltaic power obtained in step 7 pv (p pv ) and the C obtained in step 8 to characterize the correlation between wind power and photovoltaic power α , and establish the probability density function of wind-solar combined output probability as follows:

[0091] f(p w ,p pv )=c α (F w (p w ),F pv (p pv ))f w (p w )·f Y (p pv )

[0092] In the above formula, F w (p w ) and F pv (p pv ) are the probability distribution functions of wind and optical power, respectively, which can be obtained by integrating their probability density functions.

[0093] Step 10: Based on the wind-solar combined output probability density function obtained in step 9, the wind and solar power sequences are obtained by the inverse sampling method.

[0094] The following is an example: (1) An example analysis is performed based on the 2020 wind farm and photovoltaic power station data of a certain place. The wind farm contains 100 wind turbines, each with an installed capacity of 1.5kW, and the total rated capacity of the wind farm is 150kW. The distribution characteristics of the wind turbines in the wind farm are as follows: Figure 1 As shown, the ground height of the first row of the 10th row of wind turbines is higher than that of the wind turbines in the same row, at a height of 15m, while the heights of the remaining wind turbines are 1m.

[0095] First, the four-season Weibull model of wind speed is fitted based on the historical wind speed data of the wind farm. The evaluation results of Weibull parameters in each season are shown in Table 1.

[0096] Table 1 Evaluation results of Weibull parameters for wind speed in four seasons

[0097]

[0098] Based on the above four-season Weibull model, wind speed samples are sampled for one month respectively. The simulation results are as follows Figure 2 shown.

[0099] (2) The model obtained in step 1 is modified to obtain the actual wind speed captured by each wind turbine within the wind farm, taking into account the influence of terrain. The results are shown in Table 2 below, where the wind speed ratio is the wind speed captured by the wind turbine at the current location / the wind speed captured by the first row of wind turbines. The probability distribution function of wind speed is converted into the probability distribution function of wind power.

[0100] Table 2 Considering the influence of terrain on wind speed captured by wind turbine

[0101]

[0102]

[0103] (3) Establish the probability density function of photovoltaic power.

[0104] Based on the historical data of photovoltaic power stations, the kernel function parameter estimation method is used to fit the photovoltaic probability distribution model, in which the bandwidth is selected as 0.1627. The photovoltaic power station output power probability distribution histogram and kernel density results are shown as follows: Figure 3 shown.

[0105] (4) According to the scatter plot of wind-solar joint distribution, select the appropriate Copula function to characterize the correlation between wind power and photovoltaic power.

[0106] Based on the historical data of wind and solar power, a scatter plot of the joint distribution of wind and solar power in four seasons is established to observe the correlation characteristics of wind and solar power, such as Figure 4 As shown. Clayton Copula is used to fit the wind-solar correlation. Based on the historical wind-solar data, the parameter value of the Clayton Copula function is estimated, and the calculated result is α=0.4591. Its image is shown as follows Figure 5 shown.

[0107] Combining the probability distribution functions of wind power and photovoltaic power and the Copula function that characterizes their correlation, a probability distribution model of wind-solar combined output is established.

[0108] (5) Evaluate the impact of new energy precision models on system risk indicators.

[0109] Risk assessment calculations were performed on the IEEE-RTS79 system. At node 21, different types of renewable energy stations were connected according to the following eight scenarios, and system risk index calculations were performed to compare the impact of the proposed renewable energy precision model on the system risk index results.

[0110] The 6 scenarios include:

[0111] 1) Scenario A: No connection to any new energy plants or stations;

[0112] 2) Scenario B: Connect to a wind farm that uses a year-round Weibull distribution model and does not consider the impact of terrain distribution.

[0113] 3) Scenario C: Connect to a wind farm that uses a four-season Weibull distribution model and does not consider the impact of terrain distribution.

[0114] 4) Scenario D: Connect to a wind farm that uses a four-season Weibull distribution model and takes into account the influence of terrain distribution.

[0115] 5) Scenario E: Connecting one photovoltaic power station and one wind farm, considering the timing and spatial characteristics, but not the correlation between wind and solar power.

[0116] Scenario F: Connecting one PV plant and one wind farm, using the refined model proposed in this paper, taking into account wind speed seasonality, the spatial characteristics of the wind farm, and the correlation between wind and solar power. The risk indicator assessment results for the six scenarios are shown in Table 3.

[0117] Table 3 Risk assessment results of six new energy access scenarios

[0118]

[0119] Comparing the risk indicators of scenario A with those of other scenarios, it can be seen that the system operation risk is reduced after the new energy station is connected; comparing scenario B with scenario C, it can be seen that after considering seasonal factors, the system risk index is smaller, indicating that the seasonal timing factors of new energy have a certain impact on the risk level of the system; comparing scenario D with scenario C, after considering the spatial distribution factors of wind farms, the system risk index becomes larger. The reason is that the spatial distribution makes the actual wind speed captured by the wind turbine lower than the natural wind speed, and the actual power of the wind farm is reduced. Therefore, the spatial characteristics have an impact on the system risk level; comparing scenarios E and F, it can be seen that after considering the correlation between different energy sources, the system risk index has decreased. This is because the complementary characteristics of new energy make the energy output more stable and the system more reliable.

[0120] A comprehensive comparison of the system risk indicators under scenario F, which takes into account the precise model of new energy, and the risk indicators under the other four scenarios B to E, which do not consider comprehensive factors, shows that the precise modeling of new energy will have an impact on the system risk indicators, and the results obtained are close to the theoretical analysis results and the actual operation level.

[0121] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for accurately modeling renewable energy output suitable for risk assessment of power transmission and generation systems, characterized by: The steps include: Step 1: Considering the time characteristics, a probability distribution function of the four-season Weibull distribution model is established to characterize the seasonal temporal sequence of wind speed; Step 2: Considering the influence of the wake effect, a wind speed sequence is generated based on the probability distribution function of the four-season Weibull distribution model obtained in step 1, and then modified to obtain the actual captured wind speed of each wind turbine in the wind farm: Step 3: Consider the influence of terrain height and correct the captured wind speed of wind turbines at different heights: Step 4: After the wind speed of each wind turbine is corrected by considering the wake effect and terrain height in steps 2 and 3 respectively, the wind speed captured by each wind turbine i in the wind farm is obtained, and its ratio value r compared to the natural wind speed is calculated. i , multiply the wind speed probability distribution function F(v,j) by the proportional value r i The wind speed probability distribution function F of each wind turbine can be obtained wi (v,j); Step 5: According to the following formula, the wind speed probability distribution function F of each wind turbine is wi (v, j) is converted into the power probability distribution function F of wind power wi (p w ,j): Where, v ci Indicates the cut-in wind speed, v co Indicates the cut-off wind speed, v r Indicates rated wind speed, P r represents the rated power, e and f represent the slope and intercept of the fitting line, and k is the wind speed order; Step 6: Accumulate the power probability distribution function F of each wind turbine in the wind farm in four seasons wi (p w ,j) to get the wind farm power probability distribution function F w (p w ), the wind farm power probability distribution function F w (p w ) can be derived to obtain the wind farm power probability density function f w (p w ); Step 7: Establish the probability density function f of photovoltaic power pv (p pv ): Where h is the bandwidth, n is the total number of samples, K(·) is the kernel function, and p1, p2, ..., pn are the photovoltaic output powers p pv n samples of ; Step 8: Based on the historical data of wind and solar power, a wind-solar joint distribution scatter plot is established, and the appropriate Copula function C that represents the correlation between wind power and photovoltaic power is selected according to the distribution characteristics of the scatter plot. α And calculate the parameter value α of the Copula function to obtain C α ; Step 9: Consider the correlation between wind and solar power, and combine the wind farm power probability density function f obtained in step 6 w (p), the probability density function f of photovoltaic power obtained in step 7 pv (p pv ) and the C obtained in step 8 to characterize the correlation between wind power and photovoltaic power α , and establish the probability density function of wind-solar combined output probability as follows: f(p w ,p pv )=c α (F w (p w ),F pv (p pv ))·f w (p w )·f Y (p pv ) Where F w (p w ) and F pv (p pv ) are the probability distribution functions of wind and light power, respectively, and their probability density functions f pv (p pv ) and f pv (p pv ) points are obtained.

2. The method for accurately modeling renewable energy output for risk assessment of power transmission and generation systems according to claim 1, characterized in that: The method further includes step 10, obtaining the wind and light power sequences by an inverse sampling method based on the wind-solar combined output probability density function obtained in step 9.

3. The method for accurately modeling renewable energy output for risk assessment of power transmission and generation systems according to claim 1, characterized in that: The probability distribution function of the four-season Weibull distribution model described in step 1 is as follows: Where j represents the jth season, c j is the scale parameter, k j is the shape parameter, and v is the actual wind speed.

4. The method for accurately modeling renewable energy output for risk assessment of power transmission and generation systems according to claim 3, characterized in that: The scale parameter c of the j-th seasonal Weibull model in step 1 is j and shape parameter k j The calculation process is as follows: (1) Let F w (v, j) = U, where U is a random number uniformly distributed between [0, 1]. By finding its inverse function, we can get the wind speed expression as follows: (2) Take the logarithm twice on both sides of the above equation to perform linearization and obtain the following equation: ln{-ln[1-F w (v,j)]}=k j lnv j -k j lnc j (3) Let y=ln{-ln[1-F(v,j)]}, x=lnv, a=k j , b=-k j lnc j , and then write the above equation into the linearized equation form of y=ax+b; (4) Estimate the values ​​of parameters a and b using the least squares method. The specific expressions are as follows: Where i represents the i-th wind speed value in the order of wind speed samples from small to large, x i represents the i-th wind speed sample, y i Represents the distribution function value corresponding to the i-th wind speed value. In actual estimation, it is replaced by the median rank. The expression of the median rank is as follows: (5) According to the parameters a and b, the Weibull parameter value c corresponding to the jth season is obtained j and k j .

5. The method for accurately modeling renewable energy output for risk assessment of power transmission and generation systems according to claim 1, characterized in that: In step 2, the following formula is used for correction to obtain the actual captured wind speed of each wind turbine in the wind farm: v x =v0(1-d) Where v x represents the captured wind speed of the rear exhaust fan, v0 represents the captured wind speed of the front exhaust fan, and d is the terrain correction parameter; The specific expression of the terrain parameter d is as follows: Where c T represents the thrust coefficient, R represents the blade radius, X represents the fan spacing, and k represents the wake drop coefficient. The specific expression is as follows: k=0.5(σ G +σ o ) / [u*ln(h / z)] Where σ G represents the turbulence variance, σ o represents the natural turbulence variance, h represents the turbine hub height, z represents the surface roughness coefficient, and u represents the average wind speed.

6. The method for accurately modeling renewable energy output for risk assessment of power transmission and generation systems according to claim 1, characterized in that: In step 3, the captured wind speed of wind turbines at different heights is corrected according to the following formula: <h2 style=";text-align:left;direction:ltr">h2 = h1(v1 / v2)<h2 style=";text-align:left;direction:ltr"> 2 Where v1 represents the wind speed at height h1, and v2 represents the wind speed at height h2.

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