Black-start capability evaluation method and device, equipment, storage medium and program product

Abstract

The invention relates to the technical field of black start, and discloses a black start capability evaluation method, device and equipment, a storage medium and a program product, and the black start capability evaluation method comprises the steps: carrying out the correlation analysis of each influence factor and generated power of a wind storage system, and obtaining a plurality of analysis results; screening out a plurality of target factors from the plurality of influence factors, and determining probability distribution of the plurality of target factors; performing random sampling of preset times on the target factor sample set to obtain a preset number of random samples as configuration parameters; according to the method, the probability distribution of various influence factors of the generation power is analyzed, the generation power of the wind storage system is obtained, the generation power of the wind storage system is obtained, a black-start simulation test is conducted on the wind storage system through the configuration parameters, the preset number of generation powers are obtained, and the black-start capacity of the wind storage system is evaluated according to the preset number of generation powers. And the black-start capability of the wind storage system is evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of black start, and in particular to a black start capability evaluation method, device, equipment, storage medium and program product. Background Art

[0002] In recent years, with the access of a large amount of electricity generated by renewable energy to the power grid, the ability of the power grid to maintain its original power generation status has declined, and the power system faces a greater risk of power outages. Black start is the first step to restore power supply after a power outage. Black start is the process of starting power generation in the power system with self-starting capabilities (such as hydropower generators in hydropower plants, gas turbines with black start functions, etc.) without external power support after a power outage, thereby driving the generator sets without self-starting capabilities, gradually expanding the scope of power system recovery, and finally realizing the process of restoring power supply to the entire power system.

[0003] In order to restore power supply as quickly as possible, more reliable black start power sources are needed. To improve the startup capability of the power system, the wind storage power generation system can be used as a black start power source. In order to ensure the stability of the black start, it is necessary to evaluate the black start capability of the wind storage system.

[0004] In the related art, a method for evaluating the black start capability is to determine an evaluation score of the black start capability based on a preset evaluation index. However, this method is greatly influenced by subjective factors, resulting in an inaccurate result of the black start capability evaluation. Summary of the invention

[0005] In view of this, the present invention provides a black start capability assessment method, apparatus, device, storage medium and program product to solve the problem that the result of black start capability assessment is not accurate enough.

[0006] In a first aspect, the present invention provides a black start capability assessment method, comprising: obtaining multiple influencing factors of the power generation of a wind-storage system, performing correlation analysis on each influencing factor and the power generation, and obtaining multiple analysis results; wherein the multiple analysis results are correlation relationships between the multiple influencing factors and the power generation; based on the multiple analysis results, screening out multiple target factors from the multiple influencing factors, and determining the probability distribution of the multiple target factors; performing random sampling a preset number of times on a set of target factor samples that conform to the probability distribution of the multiple target factors, and obtaining a preset number of random samples, and using the preset number of random samples as configuration parameters; performing a black start simulation test on the wind-storage system using the configuration parameters, and obtaining a preset amount of power generation, and evaluating the black start capability of the wind-storage system based on the preset amount of power generation.

[0007] The present invention obtains multiple influencing factors of the power generation of the wind storage system, performs correlation analysis on each influencing factor and the power generation, and obtains multiple analysis results representing the correlation relationship between the multiple influencing factors and the power generation. The present invention clarifies the correlation relationship between the multiple influencing factors and the power generation based on the multiple analysis results, thereby facilitating screening out multiple target factors with a strong correlation relationship with the power generation from the multiple analysis results. The present invention screens out multiple target factors with a strong correlation relationship with the power generation, and then analyzes the multiple target factors, which helps to improve the accuracy of the analysis of the multiple target factors. The present invention determines the probability distribution of multiple target factors, performs random sampling for a preset number of times on a target factor sample set that meets the probability distribution of multiple target factors, obtains a preset number of random samples, and uses the preset number of random samples as configuration parameters. The present invention samples from a target factor sample set that meets the probability distribution of multiple target factors, and can take many complex and interrelated target factors into consideration, so that the configuration parameters finally obtained comprehensively reflect the result of the joint action of multiple target factors in the actual scenario, and are more in line with the actual operating environment and conditions of the wind storage system. The present invention performs random sampling for a preset number of times on a target factor sample set that meets the probability distribution of multiple target factors, and then performs simulation experiments to determine the configuration parameters, which can simulate uncertainty well, so that the configuration parameters are more adaptable when facing various uncertain changes in reality, and use the configuration parameters to perform a black start simulation test on the wind storage system to obtain a preset amount of generated power. According to the preset amount of generated power, the black start capability of the wind storage system is evaluated, thereby improving the comprehensiveness and accuracy of the evaluation of the black start capability of the wind storage system.

[0008] In an optional embodiment, based on multiple analysis results, multiple target factors are screened out from multiple influencing factors, including: screening multiple analysis results according to a preset threshold to obtain multiple target analysis results, and determining multiple target factors that correspond one to one to the multiple target analysis results; wherein the preset threshold is a preset threshold for judging the degree of correlation of multiple analysis results, and the multiple target analysis results are analysis results among the multiple analysis results whose degree of correlation is greater than the preset threshold.

[0009] In an optional embodiment, multiple analysis results are screened according to a preset threshold to obtain multiple target analysis results, including: comparing the multiple analysis results with the preset threshold respectively, retaining the analysis results greater than the preset threshold and using them as the target analysis results; and discarding the analysis results less than or equal to the preset threshold.

[0010] In an optional implementation, determining the probability distribution of multiple target factors includes: acquiring multiple historical data corresponding to the multiple target factors respectively, performing probability distribution analysis on the multiple historical data, and obtaining the probability distribution of the multiple target factors.

[0011] In an optional embodiment, a black start simulation test is performed on the wind storage system using configuration parameters to obtain a preset amount of power generation, including: obtaining a black start simulation model of the wind storage system, setting the configuration parameters into the black start simulation model, and controlling the black start simulation model to perform a black start simulation test to obtain a preset amount of power generation.

[0012] In an optional embodiment, the black start capability of the wind storage system is evaluated based on a preset amount of generated power, including: dividing the sum of the preset amounts of generated power by the preset amount to obtain an average generated power; summing the squares of the differences between each generated power and the average generated power to obtain a summed result; multiplying the difference between the preset amount and the preset value by the preset amount to obtain a product result; taking the square root of the quotient of the summation result and the product result to obtain an error result; subtracting the error result from the average generated power to obtain a target lower limit value of generated power, and adding the error result to the average generated power to obtain a target upper limit value of generated power; obtaining a target generated power range based on the target upper limit value of generated power and the target lower limit value of generated power, and evaluating the black start capability of the wind storage system based on the target generated power range.

[0013] Through multi-step calculations, the present invention can comprehensively consider all aspects of power generation data and avoid the one-sidedness of single indicator evaluation. The present invention evaluates the black start capability of the wind storage system according to the target power generation range, and can intuitively understand the power generation guarantee range of the wind storage system during the black start process, so as to determine whether it can meet actual needs, thereby comprehensively evaluating the black start capability of the wind storage system.

[0014] In a second aspect, the present invention provides a black start capability assessment device, comprising: a correlation analysis module, used to obtain multiple influencing factors of the power generation of a wind storage system, and perform correlation analysis on each influencing factor and the power generation to obtain multiple analysis results; wherein the multiple analysis results are the correlation relationship between the multiple influencing factors and the power generation; a probability distribution analysis module, used to screen out multiple target factors from the multiple influencing factors according to the multiple analysis results, and determine the probability distribution of the multiple target factors; a random sampling module, used to perform a preset number of random samplings on a target factor sample set that meets the probability distribution of the multiple target factors, to obtain a preset number of random samples, and use the preset number of random samples as configuration parameters; a black start capability assessment module, used to perform a black start simulation test on the wind storage system using the configuration parameters, to obtain a preset amount of power generation, and to assess the black start capability of the wind storage system according to the preset amount of power generation.

[0015] In a third aspect, the present invention provides a computer 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 black start capability assessment method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the black start capability assessment method of the first aspect or any corresponding embodiment thereof.

[0017] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, where the computer instructions are used to enable a computer to execute the black start capability assessment method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a schematic flow chart of a black start capability evaluation method according to an embodiment of the present invention;

[0020] Figure 2 is a flow chart of another black start capability evaluation method according to an embodiment of the present invention;

[0021] Figure 3 is a flow chart of another black start capability evaluation method according to an embodiment of the present invention;

[0022] Figure 4 is a structural block diagram of a black start capability evaluation device according to an embodiment of the present invention;

[0023] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] In recent years, with the access of a large amount of electricity generated by renewable energy to the power grid, the ability of the power grid to maintain its original power generation status has declined, and the power system faces a greater risk of power outages. Black start is the first step to restore power supply after a power outage. Black start is the process of starting power generation in the power system with self-starting capabilities (such as hydropower generators in hydropower plants, gas turbines with black start functions, etc.) without external power support after a power outage, thereby driving the generator sets without self-starting capabilities, gradually expanding the scope of power system recovery, and finally realizing the process of restoring power supply to the entire power system.

[0026] In order to restore power supply as soon as possible, more reliable black start power sources are needed. In order to improve the starting capability of the power system, the wind storage power generation system can be used as a black start power source. The power generation capacity of the wind storage system directly determines the stability of the black start of the wind storage system. In order to ensure the stability of the black start, it is necessary to evaluate the black start capability of the wind storage system.

[0027] In the related art, the black start capability evaluation is realized by determining the input quantity in the black start capability evaluation process and then using methods such as fuzzy evaluation. It is also possible to calculate the evaluation score by integrating the objective weights according to the evaluation indicators and the evaluation indicator weights given by the technicians, so as to realize the evaluation of the black start capability. However, the subjective factors in this method have a great influence, resulting in the inaccurate result of the black start capability evaluation.

[0028] An embodiment of the present invention provides a black start capability assessment method, which obtains the power generation power of a wind-storage system by analyzing the probability distribution of various influencing factors of the power generation power, and assesses the black start capability of the wind-storage system, so as to achieve the effect of improving the assessment accuracy of the black start capability of the wind-storage system.

[0029] According to an embodiment of the present invention, an embodiment of a black start capability assessment method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] In this embodiment, a black start capability evaluation method is provided, which can be used for computer equipment. Figure 1is a flow chart of a black start capability evaluation method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0031] Step S101, obtaining multiple influencing factors of the power generation of the wind-storage system, performing correlation analysis on each influencing factor and the power generation, and obtaining multiple analysis results; wherein the multiple analysis results are correlation relationships between the multiple influencing factors and the power generation.

[0032] The wind storage system is an energy system that combines wind power generation with an energy storage system. Wind power generation is a power generation method that uses wind power to drive the windmill blades to rotate, and then increases the speed of rotation through a speed increaser to drive the generator to generate electricity. The energy storage system is a system for storing the electric energy generated by wind power. The power generation capacity of the wind storage system refers to the electric energy that the wind storage system can generate per unit time.

[0033] In some optional embodiments, the multiple influencing factors are multiple factors that affect the power generation power of the wind storage system, wherein the multiple influencing factors include wind speed f, atmospheric thermal stability r, wake loss h1, blocking effect loss h2, availability loss h3 and energy storage power B, etc.

[0034] In some optional implementations, correlation analysis is performed on each influencing factor and the generated power to obtain multiple analysis results, including: using Pearson correlation analysis to perform correlation analysis on each influencing factor and the generated power to obtain multiple analysis results.

[0035] Exemplarily, the formula for calculating the correlation between wind speed f and power generation power P is:

[0036]

[0037] Among them, R1 is the correlation between wind speed f and power generation P, that is, the analysis result of wind speed f and power generation P, f is wind speed, P is power generation, cov(f, P) is the covariance between wind speed f and power generation P, σ f is the standard deviation of wind speed f, σ P is the standard deviation of the generated power P.

[0038] The formula for calculating the correlation between atmospheric thermal stability r and power generation power P is:

[0039]

[0040] Among them, R2 is the correlation between atmospheric thermal stability r and power generation P, that is, the analysis result of atmospheric thermal stability r and power generation P, r is atmospheric thermal stability, P is power generation, cov(r, P) is the covariance between atmospheric thermal stability r and power generation P, σ r is the standard deviation of the atmospheric thermal stability r, σ P is the standard deviation of the generated power P.

[0041] The formula for calculating the correlation between wake loss h1 and power generation power P is:

[0042]

[0043] Among them, R3 is the correlation between the wake loss h1 and the power generation P, that is, the analysis result of the wake loss h1 and the power generation P, h1 is the wake loss, P is the power generation, cov(h1, P) is the covariance between the wake loss h1 and the power generation P, σ h1 is the standard deviation of the wake loss h1, σ P is the standard deviation of the generated power P.

[0044] The formula for calculating the correlation between the blocking effect loss h2 and the generated power P is:

[0045]

[0046] Among them, R4 is the correlation between the blocking effect loss h2 and the power generation P, that is, the analysis result of the blocking effect loss h2 and the power generation P, h2 is the blocking effect loss, P is the power generation, cov(h2, P) is the covariance between the blocking effect loss h2 and the power generation P, σ h2 is the standard deviation of the blocking effect loss h2, σ P is the standard deviation of the generated power P.

[0047] The formula for calculating the correlation between the availability loss h3 and the generated power P is:

[0048]

[0049] Wherein, R5 is the correlation between the availability loss h3 and the power generation P, that is, the analysis result of the availability loss h3 and the power generation P, h3 is the availability loss, P is the power generation, cov(h3, P) is the covariance between the availability loss h3 and the power generation P, σ3 is the standard deviation of the availability loss h3, σ P is the standard deviation of the generated power P.

[0050] The formula for calculating the correlation between energy storage power B and power generation power P is:

[0051]

[0052] Among them, R6 is the correlation between the energy storage power B and the power generation power P, that is, the analysis result of the energy storage power B and the power generation power P, B is the energy storage power, P is the power generation power, cov(B, P) is the covariance between the energy storage power B and the power generation power P, σ B is the standard deviation of the energy storage power B, σ P is the standard deviation of the generated power P.

[0053] In some optional embodiments, when the correlation is greater than 0, it indicates that there is a positive correlation between the influencing factor and the generated power; when the correlation is less than or equal to 0, it indicates that there is a negative correlation between the influencing factor and the generated power; the closer the absolute value of the correlation is to 1, the stronger the correlation is; and the closer the absolute value of the correlation is to 0, the weaker the correlation is.

[0054] Step S102 , screening out multiple target factors from multiple influencing factors according to multiple analysis results, and determining probability distribution of the multiple target factors.

[0055] In some optional embodiments, based on multiple analysis results, multiple target factors are screened out from multiple influencing factors, including: screening multiple analysis results according to a preset threshold to obtain multiple target analysis results, and determining multiple target factors that correspond one to one to the multiple target analysis results; wherein the preset threshold is a preset threshold for judging the degree of correlation of multiple analysis results, and the multiple target analysis results are analysis results among the multiple analysis results whose degree of correlation is greater than the preset threshold.

[0056] In some optional embodiments, multiple analysis results are screened according to a preset threshold to obtain multiple target analysis results, including: comparing the multiple analysis results with the preset threshold respectively, retaining the analysis results greater than the preset threshold and using them as the target analysis results; and discarding the analysis results less than or equal to the preset threshold.

[0057] Among them, the preset threshold can be 0.5. When the analysis result, i.e. the correlation, is greater than 0.5, the analysis result will be retained. When the analysis result, i.e. the correlation, is less than or equal to 0.5, the analysis result will be discarded, and the multiple influencing factors corresponding to the retained multiple target analysis results will be used as multiple target factors.

[0058] Exemplarily, when the correlation between wind speed f and power generation is 0.9, wind speed f is retained and used as the target factor; when the correlation between atmospheric thermal stability r and power generation is 0.8, atmospheric thermal stability r is retained and used as the target factor; when the correlation between wake loss h1 and power generation is 0.8, wake loss h1 is retained and used as the target factor; when the correlation between blocking effect loss h2 and power generation is 0.7, blocking effect loss h2 is retained and used as the target factor; when the correlation between availability loss h3 and power generation is 0.8, availability loss h3 is retained and used as the target factor; when the correlation between energy storage power B and power generation is 0.6, energy storage power B is retained and used as the target factor.

[0059] In some optional implementations, a plurality of historical data corresponding to the plurality of target factors are obtained, and probability distribution analysis is performed on the plurality of historical data to obtain probability distribution of the plurality of target factors.

[0060] For example, the probability distribution of the target factor wind speed is usually represented by a Weibull function, and the Weibull function expression is:

[0061] f(x)=(k / λ)*(x / λ) (k-1) *e -(x / λ)^k ;

[0062] Among them, k is the shape parameter, λ is the scale parameter, and x is the probability distribution of wind speed.

[0063] In some optional implementations, historical wind speed data is selected, and the least squares method is used to fit discrete data for parameter identification to obtain shape parameters and scale parameters. The objective function of the least squares method is:

[0064]

[0065] Where E is the error, y n is the Weibull function expression, y m The historical wind speed data is obtained by iterative optimization to minimize the error E, thereby obtaining the shape parameter k and the scale parameter λ.

[0066] For the target factors, atmospheric thermal stability r, wake loss h1, blocking effect loss h2, and availability loss h3 can usually be expressed according to a uniform distribution in a fixed interval. The probability distribution function expression of atmospheric thermal stability r is:

[0067]

[0068] Among them, f1(r) is the probability distribution function of the atmospheric thermal stability, r is the atmospheric thermal stability, a1 is the minimum value of the uniform distribution, b1 is the maximum value of the uniform distribution, and else refers to other cases except a1 < r < b1, that is, the cases when r ≥ b1 and r ≤ a1.

[0069] The expression of the probability distribution function of the wake loss h1 is:

[0070]

[0071] Among them, f2(h1) is the probability distribution function of the wake loss, h1 is the wake loss, a2 is the minimum value of the uniform distribution, b2 is the maximum value of the uniform distribution, and else refers to other cases except a2 < h1 < b2, that is, the cases when h1 ≥ b2 and h1 ≤ a2.

[0072] The expression of the probability distribution function of the blocking effect loss h2 is:

[0073]

[0074] Among them, f3(h2) is the probability distribution function of the blocking effect loss, h2 is the availability loss, a3 is the minimum value of the uniform distribution, b3 is the maximum value of the uniform distribution, and else refers to other cases except a3 < h2 < b3, that is, the cases when h2 ≥ b3 and h2 ≤ a3.

[0075] The expression of the probability distribution function of the availability loss h3 is:

[0076]

[0077] Among them, f4(h3) is the probability distribution function of the availability loss, h3 is the availability loss, a4 is the minimum value of the uniform distribution, b4 is the maximum value of the uniform distribution, and else refers to other cases except a4 < h3 < b4, that is, the cases when h3 ≥ b4 and h2 ≤ a4.

[0078] For the target factor, the energy storage power B usually satisfies the 0-1 distribution. The probability of outputting full power is p, and the probability of not outputting full power is 1 - p. The expectation and variance are set according to experience. The expression of the probability distribution function of the energy storage power B is:

[0079] f5(B) = p B (1 - p) 1-B ;

[0080] Among them, f5(B) is the probability distribution function of the energy storage power B, p is the probability of outputting full power, 1 - p is the probability of not outputting full power, and B is 0 or 1.

[0081] Step S103 , performing random sampling for a preset number of times on a target factor sample set that meets the probability distribution of multiple target factors, obtaining a preset number of random samples, and using the preset number of random samples as configuration parameters.

[0082] Among them, the preset number of times can be 10,000 times. Through 10,000 Monte Carlo random simulation tests, 10,000 random samplings are performed on the target factor sample set that meets the probability distribution of multiple target factors to obtain 10,000 random samples. The random samples are target factor samples, that is, 1 Monte Carlo random simulation test will randomly obtain 1 group of wind speed f, atmospheric thermal stability r, wake loss h1, blocking effect loss h2, availability loss h3 and energy storage power B. 10,000 Monte Carlo random simulation tests obtain 10,000 groups of wind speed f, atmospheric thermal stability r, wake loss h1, blocking effect loss h2, availability loss h3 and energy storage power B, that is, 10,000 groups of configuration parameters.

[0083] Step S104, using the configuration parameters to perform a black start simulation test on the wind storage system to obtain a preset amount of generated power, and based on the preset amount of generated power, evaluating the black start capability of the wind storage system.

[0084] In some optional embodiments, a black start simulation test is performed on the wind storage system using configuration parameters to obtain a preset amount of power generation, including: obtaining a black start simulation model of the wind storage system, setting the configuration parameters into the black start simulation model, and controlling the black start simulation model to perform a black start simulation test to obtain a preset amount of power generation.

[0085] In some optional implementations, the black start capability of the wind storage system is the size of the power generation of the wind storage system. Therefore, the black start capability of the wind storage system can be evaluated based on a preset amount of power generation.

[0086] The black start capability assessment method provided in this embodiment obtains multiple influencing factors of the power generation of the wind storage system, performs correlation analysis on each influencing factor and the power generation, and obtains multiple analysis results representing the correlation relationship between the multiple influencing factors and the power generation. The present invention clarifies the correlation relationship between the multiple influencing factors and the power generation based on the multiple analysis results, thereby facilitating the screening of multiple target factors with a strong correlation with the power generation from the multiple analysis results. The present invention screens out multiple target factors with a strong correlation with the power generation, and then analyzes the multiple target factors, which helps to improve the accuracy of the analysis of the multiple target factors. The present invention determines the probability distribution of multiple target factors, performs random sampling for a preset number of times on a target factor sample set that meets the probability distribution of multiple target factors, obtains a preset number of random samples, and uses the preset number of random samples as configuration parameters. The present invention samples from a target factor sample set that meets the probability distribution of multiple target factors, and can take many complex and interrelated target factors into consideration, so that the configuration parameters finally obtained comprehensively reflect the result of the joint action of multiple target factors in the actual scenario, and are more in line with the actual operating environment and conditions of the wind storage system. The present invention performs random sampling for a preset number of times on a target factor sample set that meets the probability distribution of multiple target factors, and then performs simulation experiments to determine the configuration parameters, which can simulate uncertainty well, so that the configuration parameters are more adaptable when facing various uncertain changes in reality, and use the configuration parameters to perform a black start simulation test on the wind storage system to obtain a preset amount of generated power. According to the preset amount of generated power, the black start capability of the wind storage system is evaluated, thereby improving the comprehensiveness and accuracy of the evaluation of the black start capability of the wind storage system.

[0087] In this embodiment, a black start capability evaluation method is provided, which can be used for computer equipment. Figure 2 FIG. 4 is a flow chart of another black start capability evaluation method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0088] Step S201, obtaining multiple influencing factors of the power generation of the wind-storage system, and performing correlation analysis on each influencing factor and the power generation to obtain multiple analysis results; wherein the multiple analysis results are the correlation relationships between the multiple influencing factors and the power generation. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0089] Step S202 , screening out multiple target factors from multiple influencing factors according to multiple analysis results, and determining probability distribution of the multiple target factors.

[0090] Specifically, the above step S202 includes:

[0091] Step S2021, screen multiple analysis results according to a preset threshold to obtain multiple target analysis results, and determine multiple target factors that correspond one to one to the multiple target analysis results; wherein the preset threshold is a preset threshold for judging the degree of correlation of the multiple analysis results, and the multiple target analysis results are analysis results among the multiple analysis results whose degree of correlation is greater than the preset threshold.

[0092] Step S2022, obtaining a plurality of historical data corresponding to the plurality of target factors respectively, performing probability distribution analysis on the plurality of historical data, and obtaining probability distribution of the plurality of target factors.

[0093] In some optional implementations, the above step S2021 includes:

[0094] Step a1, compare multiple analysis results with preset thresholds respectively, retain analysis results greater than the preset thresholds and use them as target analysis results; and discard analysis results less than or equal to the preset thresholds.

[0095] Step S203, perform a preset number of random sampling on the target factor sample set that meets the probability distribution of multiple target factors to obtain a preset number of random samples, and use the preset number of random samples as configuration parameters. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0096] Step S204, using the configuration parameters to perform a black start simulation test on the wind storage system to obtain a preset amount of generated power, and based on the preset amount of generated power, evaluating the black start capability of the wind storage system.

[0097] Specifically, the above step S204 includes:

[0098] Step S2041, obtaining a black start simulation model of the wind energy storage system, setting configuration parameters into the black start simulation model, controlling the black start simulation model to perform a black start simulation test, and obtaining a preset amount of generated power.

[0099] Step S2042, divide the sum of the preset number of generated powers by the preset number to obtain the average generated power; sum the squares of the differences between each generated power and the average generated power to obtain a sum result; multiply the difference between the preset number and the preset value by the preset number to obtain a product result; calculate the square root of the quotient of the sum result and the product result to obtain an error result.

[0100] Step S2043, subtract the error result from the average power generation value to obtain the target power generation lower limit value, and add the error result to the average power generation value to obtain the target power generation upper limit value.

[0101] Step S2044, obtain the target power generation range according to the target power generation upper limit and the target power generation lower limit, and evaluate the black start capability of the wind-storage system according to the target power generation range.

[0102] The formula for calculating the average power generation value is:

[0103]

[0104] in, is the average power generation value, ∑x i is the sum of the power generated by the preset number, n is the preset number, x i is the i-th generated power.

[0105] In some optional implementations, the calculation formula of the error result is:

[0106]

[0107] Among them, u is the error result, x i is the i-th power generation, is the average power generation value, and n is the preset number.

[0108] In some optional implementations, the target power generation upper limit is: The target power generation lower limit is:

[0109] The black start capability assessment method provided in this embodiment can comprehensively consider all aspects of the power generation data through multi-step calculations, avoiding the one-sidedness of single indicator assessment. The present invention assesses the black start capability of the wind storage system according to the target power generation range, and can intuitively understand the power generation guarantee range of the wind storage system during the black start process, so as to determine whether it can meet actual needs, thereby comprehensively assessing the black start capability of the wind storage system.

[0110] In this embodiment, a black start capability evaluation method is provided, which can be used for computer equipment. Figure 3 FIG. 4 is a flow chart of another black start capability evaluation method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0111] Step S301, determining multiple influencing factors of the power generation of the wind-storage system, and using Pearson correlation analysis to determine the correlation between the multiple influencing factors and the power generation of the wind-storage system.

[0112] Among them, the multiple influencing factors are multiple factors that affect the power generation power of the wind storage system, wherein the multiple influencing factors include wind speed f, atmospheric thermal stability r, wake loss h1, blocking effect loss h2, availability loss h3 and energy storage power B, etc.

[0113] Step S302: If the correlation between the influencing factor and the power generation of the wind energy storage system is greater than a preset threshold, the correlation is considered strong and the influencing factor is further analyzed. If the correlation between the influencing factor is less than the preset threshold, the correlation is considered weak and the influencing factor is discarded and no further analysis is performed.

[0114] Among them, the sensitivity threshold can be 0.5. When the correlation is greater than 0.5, the influencing factor is retained. When the correlation is less than or equal to 0.5, the influencing factor is discarded, and the retained multiple influencing factors are used as multiple target factors to continue to be analyzed.

[0115] Step S303, determining the probability distribution of the multiple target factors to be analyzed.

[0116] Among them, the probability distribution of the target factor wind speed is usually expressed by the Weibull function; the target factors atmospheric thermal stability r, wake loss h1, blocking effect loss h2, and availability loss h3 can usually be expressed according to a uniform distribution in a fixed interval; the target factor energy storage power B usually satisfies a 0-1 distribution.

[0117] Step S304, conduct a Monte Carlo random simulation test on the target factor sample set that conforms to the probability distribution, set the results of the Monte Carlo random simulation test as parameters in the wind-storage system black start simulation model, carry out simulation, and obtain the power generation capacity of the wind-storage system.

[0118] Step S305: Evaluate the black start capability of the wind energy storage system according to the generated power.

[0119] The black start capability assessment method provided in this embodiment performs uncertainty analysis on the power generation capacity of the wind storage system based on the characteristics of the black start of the wind storage system. In the black start assessment, the uncertainty is comprehensively considered, thereby improving the comprehensiveness of the black start capability assessment of the wind storage system.

[0120] In this embodiment, a black start capability assessment device is also provided, which is used to implement the above embodiments and preferred implementation modes, and the descriptions that have been made are not repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0121] This embodiment provides a black start capability evaluation device, such as Figure 4As shown, including:

[0122] The correlation analysis module 401 is used to obtain multiple influencing factors of the power generation of the wind-storage system, and perform correlation analysis on each influencing factor and the power generation to obtain multiple analysis results; wherein the multiple analysis results are the correlation relationship between the multiple influencing factors and the power generation.

[0123] The probability distribution analysis module 402 is used to select multiple target factors from multiple influencing factors according to multiple analysis results, and determine the probability distribution of the multiple target factors.

[0124] The random sampling module 403 is used to perform a preset number of random samplings on a target factor sample set that meets the probability distribution of multiple target factors, obtain a preset number of random samples, and use the preset number of random samples as configuration parameters.

[0125] The black start capability evaluation module 404 is used to perform a black start simulation test on the wind storage system using configuration parameters to obtain a preset amount of generated power, and evaluate the black start capability of the wind storage system based on the preset amount of generated power.

[0126] In some optional implementations, the probability distribution analysis module 402 includes:

[0127] The target factor determination unit is used to screen multiple analysis results according to a preset threshold value, obtain multiple target analysis results, and determine multiple target factors corresponding to the multiple target analysis results; wherein the preset threshold value is a preset threshold value for judging the degree of correlation of the multiple analysis results, and the multiple target analysis results are analysis results among the multiple analysis results whose degree of correlation is greater than the preset threshold value.

[0128] The probability distribution analysis unit is used to obtain a plurality of historical data corresponding to a plurality of target factors, respectively, and perform probability distribution analysis on the plurality of historical data to obtain the probability distribution of the plurality of target factors.

[0129] Specifically, the target factor determination unit includes:

[0130] The comparison subunit is used to compare multiple analysis results with preset thresholds respectively, retain the analysis results greater than the preset threshold and use them as target analysis results; and discard the analysis results less than or equal to the preset threshold.

[0131] In some optional implementations, the black start capability assessment module 404 includes:

[0132] The simulation test unit is used to obtain a black start simulation model of the wind storage system, set configuration parameters into the black start simulation model, control the black start simulation model to perform a black start simulation test, and obtain a preset amount of generated power.

[0133] The calculation unit is used to divide the sum of the preset number of generated powers by the preset number to obtain the average value of the generated powers; sum the squares of the differences between each generated power and the average value of the generated powers to obtain a sum result; multiply the difference between the preset number and the preset value by the preset number to obtain a product result; and calculate the square root of the quotient of the sum result and the product result to obtain an error result.

[0134] The upper and lower limit determination unit is used to subtract the error result from the average power generation value to obtain the target power generation lower limit value, and add the error result to the average power generation value to obtain the target power generation upper limit value.

[0135] The black start capability evaluation unit is used to obtain a target power generation range according to a target power generation upper limit value and a target power generation lower limit value, and to evaluate the black start capability of the wind storage system according to the target power generation range.

[0136] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0137] The black start capability evaluation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0138] The embodiment of the present invention also provides a computer device having the above Figure 4 The black start capability evaluation device shown.

[0139] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5A processor 10 is taken as an example.

[0140] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0141] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0142] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device 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.

[0143] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0144] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0145] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0146] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0147] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A black start capability evaluation method, characterized in that: The method comprises: Acquire multiple influencing factors of the power generation of the wind-storage system, perform correlation analysis on each influencing factor and the power generation, and obtain multiple analysis results; wherein the multiple analysis results are correlation relationships between the multiple influencing factors and the power generation; According to the multiple analysis results, screening out multiple target factors from the multiple influencing factors, and determining probability distribution of the multiple target factors; Performing random sampling for a preset number of times on a target factor sample set that conforms to the probability distribution of the multiple target factors to obtain a preset number of random samples, and using the preset number of random samples as configuration parameters; The configuration parameters are used to perform a black start simulation test on the wind storage system to obtain the preset amount of generated power, and the black start capability of the wind storage system is evaluated based on the preset amount of generated power.

2. The method according to claim 1, characterized in that The method of screening out multiple target factors from the multiple influencing factors according to the multiple analysis results includes: The multiple analysis results are screened according to a preset threshold to obtain multiple target analysis results, and multiple target factors corresponding to the multiple target analysis results are determined; wherein the preset threshold is a preset threshold for judging the degree of correlation of the multiple analysis results, and the multiple target analysis results are analysis results among the multiple analysis results whose degree of correlation is greater than the preset threshold.

3. The method according to claim 2, characterized in that The screening of the multiple analysis results according to the preset threshold to obtain multiple target analysis results includes: The multiple analysis results are compared with the preset threshold respectively, and the analysis results greater than the preset threshold are retained and used as target analysis results; and the analysis results less than or equal to the preset threshold are discarded.

4. The method according to any one of claims 1 to 3, characterized in that Determining the probability distribution of the multiple target factors includes: A plurality of historical data corresponding to the plurality of target factors are obtained, and probability distribution analysis is performed on the plurality of historical data to obtain probability distribution of the plurality of target factors.

5. The method according to any one of claims 1 to 3, characterized in that The step of performing a black start simulation test on the wind-storage system using the configuration parameters to obtain the preset amount of generated power includes: A black start simulation model of the wind-storage system is obtained, the configuration parameters are set in the black start simulation model, and the black start simulation model is controlled to perform a black start simulation test to obtain the preset amount of generated power.

6. The method according to any one of claims 1 to 3, characterized in that The step of evaluating the black start capability of the wind-storage system according to the preset amount of generated power includes: Dividing the sum of the preset number of generated powers by the preset number to obtain an average value of the generated power; summing the squares of the differences between each generated power and the average value of the generated power to obtain a summation result; The difference between the preset value and the preset number is multiplied by the preset number to obtain a product result; Taking the square root of the quotient of the sum result and the product result to obtain an error result; Subtract the error result from the average power generation value to obtain a target power generation lower limit value, and add the error result to the average power generation value to obtain a target power generation upper limit value; According to the target power generation power upper limit value and the target power generation power lower limit value, a target power generation power range is obtained, and the black start capability of the wind storage system is evaluated according to the target power generation power range.

7. A black start capability evaluation device, characterized in that: The device comprises: A correlation analysis module, used to obtain multiple influencing factors of the power generation of the wind-storage system, and to perform correlation analysis on each influencing factor and the power generation to obtain multiple analysis results; wherein the multiple analysis results are correlation relationships between the multiple influencing factors and the power generation; A probability distribution analysis module, configured to screen out a plurality of target factors from the plurality of influencing factors according to the plurality of analysis results, and determine the probability distribution of the plurality of target factors; A random sampling module, used for performing a preset number of random samplings on a target factor sample set that conforms to the probability distribution of the multiple target factors, obtaining a preset number of random samples, and using the preset number of random samples as configuration parameters; The black start capability evaluation module is used to perform a black start simulation test on the wind storage system using the configuration parameters to obtain the preset amount of generated power, and evaluate the black start capability of the wind storage system based on the preset amount of generated power.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the black start capability assessment method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the black start capability assessment method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the black start capability assessment method according to any one of claims 1 to 6.

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