Trusted capacity analysis method and device for power generation resources

Abstract

The invention discloses a credible capacity analysis method and device for power generation resources. The method comprises the following steps: acquiring load data and power generation resource data of a power system; wherein the power generation resource data comprises installed capacity of various types of power generation resources, existing total capacity of a system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data; according to the load data, the power generation resource data and the Monte Carlo method, determining an expected reliability index of insufficient electric quantity of the power system; determining the credible capacity of each type of power generation resources according to the dichotomy and the power shortage expected reliability index of the power system; determining a source load correlation coefficient of each power generation resource according to the load data and the power generation resource data; determining the credible capacity of each power generation resource according to the source load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource; and regulating and controlling the power system based on the credible capacity of each power generation resource.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a reliable capacity analysis method and device for power generation resources. Background Art

[0002] Credible capacity maps the actual capacity support capabilities of different power generation resources. In the power system, the capacity market improves the fairness and autonomy of different power generation resources in providing system capacity support through centralized optimization and marginal clearing, and guides the investment of power generation resources through market-oriented means. However, in a power system with a high proportion of renewable energy, traditional units have a more stable and reliable capacity support capability than new energy, and can achieve the same capacity support effect with less capacity, effectively reducing system capacity costs and ensuring system capacity adequacy. However, the measurement of the credible capacity of power generation resources is not only closely related to system reliability, but also greatly affected by source-load correlation. Especially for renewable energy, when finely evaluating the credible capacity of renewable energy power generation resources, reliability alone cannot fully characterize the credible capacity of different power generation resources. Therefore, it is urgent to fully consider the impact of reliability and source-load correlation on credible capacity, improve the accuracy and comprehensiveness of credible capacity analysis of different power generation resources, and thus improve the operational reliability of the power system. Summary of the invention

[0003] The embodiments of the present invention provide a method and device for analyzing the trusted capacity of power generation resources, which can comprehensively consider reliability and source-load correlation, so that the trusted capacity analysis results of each power generation resource are more comprehensive and accurate, thereby enabling the power system to have better operating stability when being regulated based on the trusted capacity analysis results, thereby improving the reliability of the power system.

[0004] An embodiment of the present invention provides a reliable capacity analysis method for power generation resources, comprising:

[0005] Obtaining load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, the total existing capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data;

[0006] Determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power;

[0007] Determine the credible capacity of each type of power generation resource based on the dichotomy method and the expected reliability index of power shortage in the power system;

[0008] Determine the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data;

[0009] Determine the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource;

[0010] The power system is regulated based on the trusted capacity of each power generation resource.

[0011] Further, determining the expected reliability index of power shortage of the power system according to the load data, the power generation resource data and the Monte Carlo method includes:

[0012] A wind power output characteristic model is constructed according to the wind power output data, and the wind power at each moment is determined according to the constructed wind power output characteristic model; wherein the wind power output data includes: a wind rotor swept area, a wind turbine power coefficient, an air density and a wind speed at each moment;

[0013] A photovoltaic output characteristic model is constructed according to the photovoltaic output data, and the photovoltaic power at each moment is determined according to the constructed photovoltaic output characteristic model; wherein the photovoltaic output data includes: conversion efficiency of photovoltaic cells, area of ​​photovoltaic panels, temperature of the working environment of photovoltaic panels and sunshine intensity at each moment;

[0014] The expected reliability index of power shortage of the power system is determined according to the wind power at each moment, the photovoltaic power at each moment, the load data, the power generation resource data and the Monte Carlo method.

[0015] Furthermore, the wind power output characteristic model is specifically:

[0016] P W，j =C p ρAV j 3 / 2;

[0017] Among them, P W，j is the wind power at time j; C p is the fan power coefficient; ρ is the air density; A is the swept area of ​​the wind wheel; V j is the wind speed at time j.

[0018] Furthermore, the photovoltaic output characteristic model is specifically:

[0019] P PV，j =ηSS PV，j [1-0.05(t0+25)];

[0020] Among them, P PV，j is the photovoltaic power at time j; η is the conversion efficiency of the photovoltaic cell; S is the area of ​​the photovoltaic panel; t0 is the temperature of the working environment of the photovoltaic panel; S PV，j is the sunshine intensity at time j.

[0021] Further, determining the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data includes:

[0022] Determine the correlation between the output of each power generation resource and the load curve according to the load data and the power generation resource data, and determine the first source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the load curve;

[0023] Determine the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource according to the power generation resource data, and determine the second source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource;

[0024] Determine the output correlation of different power generation resources according to the power generation resource data, and determine the third source-load correlation subdivision coefficient according to the output correlation of different power generation resources;

[0025] The source-load correlation coefficient of each power generation resource is determined according to the first source-load correlation subdivision coefficient, the second source-load correlation subdivision coefficient and the third source-load correlation subdivision coefficient.

[0026] Furthermore, the first source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the load curve, including:

[0027] The first source-load correlation subdivision coefficient is determined by the following formula:

[0028]

[0029]

[0030]

[0031] in, is the first source-load correlation subdivision coefficient; is the correlation between the kth generation resource in the Kth type of generation resource and the system load at time t; is the difference between the predicted output of the kth generation resource in the Kth generation resource category and the system load at time t; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; L t is the load forecast of the system at time t; N k is the total number of power generation resources; ρ is the resolution coefficient.

[0032] Furthermore, the second source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource, including:

[0033] The second source-load correlation subdivision coefficient is determined by the following formula:

[0034]

[0035] in, is the second source-load correlation subdivision coefficient; It is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t.

[0036] Furthermore, the third source-load correlation subdivision coefficient is determined according to the output correlation of different power generation resources, including:

[0037] The third source-load correlation subdivision coefficient is determined by the following formula:

[0038]

[0039]

[0040]

[0041] in, is the third source-charge correlation subdivision coefficient; K，k is the correlation between the kth power generation resource in the Kth type of power generation resource and other power generation resources of the same type; γ K，k，g is the correlation between the kth power generation resource and the gth power generation resource in the Kth type of power generation resources; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; is the predicted average output of the kth generation resource in the Kth generation resource category at time t; is the predicted average output of the g-th power generation resource among the K-th power generation resources at time t; It is the predicted output of the gth power generation resource among the Kth type of power generation resources at time t.

[0042] Furthermore, before determining the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource, it also includes: obtaining the plant power consumption rate, maintenance time ratio and equivalent forced outage rate of each power generation resource;

[0043] Determining the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource includes:

[0044] The credible capacity of each power generation resource is determined based on the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource, the installed capacity of each type of power generation resource, the plant power consumption rate of each power generation resource, the maintenance time ratio and the equivalent forced outage rate.

[0045] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0046] An embodiment of the present invention provides a reliable capacity analysis device for power generation resources, including: a data acquisition module, a reliability index determination module, a reliable capacity determination module for various types of power generation resources, a reliable capacity determination module for a single power generation resource, and a control module;

[0047] The data acquisition module is used to acquire load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, existing total capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data;

[0048] The reliability index determination module is used to determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power;

[0049] The module for determining the credible capacity of various types of power generation resources is used to determine the credible capacity of various types of power generation resources according to the dichotomy method and the expected reliability index of power shortage of the power system;

[0050] The single power generation resource trusted capacity determination module is used to determine the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data; determine the trusted capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the trusted capacity of each type of power generation resource and the installed capacity of each type of power generation resource;

[0051] The control module is used to control the power system based on the trusted capacity of each power generation resource.

[0052] The following beneficial effects are achieved by implementing the present invention:

[0053] The present invention provides a reliable capacity analysis method and device for power generation resources. The analysis method obtains load data and power generation resource data of the power system, and determines the expected reliability index of power shortage of the power system based on the obtained data combined with the Monte Carlo method, and then determines the reliable capacity of each type of power generation resource in combination with the dichotomy method; then determines the source-load correlation coefficient of each power generation resource based on the obtained data, and determines the reliable capacity of each power generation resource in combination with the source-load correlation coefficient of each power generation resource, the reliable capacity of each type of power generation resource and the installed capacity of each type of power generation resource, and then regulates the power system based on the reliable capacity of each power generation resource. After determining the overall reliable capacity of each type of power generation resource under the classification by determining the reliability index, considering the source-load correlation, combining the source-load correlation coefficient of each power generation resource, further refine and determine the reliable capacity of a single power generation resource, and comprehensively consider the reliability and source-load correlation, so that the reliable capacity analysis results of each power generation resource are more comprehensive and accurate, so that the power system can have better operating stability when it is regulated based on the reliable capacity analysis results, and improve the reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a reliable capacity analysis method of power generation resources provided by an embodiment of the present invention.

[0055] Figure 2 It is a structural schematic diagram of a reliable capacity analysis device for power generation resources provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 creative work are within the scope of protection of the present invention.

[0057] like Figure 1 As shown, a reliable capacity analysis method of power generation resources provided by an embodiment of the present invention includes:

[0058] Step S1: Obtain load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, existing total capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data;

[0059] Step S2: determining the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power;

[0060] Step S3: determining the credible capacity of each type of power generation resource according to the dichotomy method and the expected reliability index of power shortage of the power system;

[0061] Step S4: determining the source-load correlation coefficient of each power generation resource according to the load data and power generation resource data;

[0062] Step S5: determining the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource;

[0063] Step S6: regulating the power system based on the reliable capacity of each power generation resource.

[0064] For step S1, in the power system, the power generation resources mainly include wind turbines, photovoltaic units, thermal power units and nuclear power units, and their corresponding power generation resource types are wind power, photovoltaic, thermal power and nuclear power. Before analyzing the credible capacity of each power generation resource in the power system, it is necessary to obtain the load data and power generation resource data in the power system. The power generation resource data mainly includes the installed capacity of each type of power generation resource in the power system, the existing total capacity of the power system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data.

[0065] For step S2, based on the data obtained in step S1, the expected reliability index of power shortage of the power system is calculated in combination with the Monte Carlo method. This step starts from the perspective of the type of power generation resources, that is, the same type of power generation resources are regarded as a whole, and the credible capacity of different types of power generation resources is analyzed based on the equivalent reliable capacity measurement method.

[0066] In a preferred embodiment, determining the expected reliability index of power shortage of the power system according to the load data, the power generation resource data and the Monte Carlo method includes:

[0067] A wind power output characteristic model is constructed according to the wind power output data, and the wind power at each moment is determined according to the constructed wind power output characteristic model; wherein the wind power output data includes: a wind rotor swept area, a wind turbine power coefficient, an air density and a wind speed at each moment;

[0068] A photovoltaic output characteristic model is constructed according to the photovoltaic output data, and the photovoltaic power at each moment is determined according to the constructed photovoltaic output characteristic model; wherein the photovoltaic output data includes: conversion efficiency of photovoltaic cells, area of ​​photovoltaic panels, temperature of the working environment of photovoltaic panels and sunshine intensity at each moment;

[0069] The expected reliability index of power shortage of the power system is determined according to the wind power at each moment, the photovoltaic power at each moment, the load data, the power generation resource data and the Monte Carlo method.

[0070] Specifically, for different types of power generation resources, traditional units such as thermal power and nuclear power are relatively stable, and their output is close to the rated power after deducting factors such as planned maintenance and forced outages. However, the output of new energy sources such as wind power and photovoltaic power generation has greater uncertainty. In addition to considering factors such as planned maintenance and forced outages like traditional units, it is also necessary to combine the output forecast results for analysis.

[0071] Therefore, before analysis, it is necessary to build a wind power output characteristic model and a photovoltaic output characteristic model based on the wind power output data and the photovoltaic output data.

[0072] To construct a wind power output characteristic model, we first need to determine the probability distribution function of wind speed. The actual wind speed is a continuous time series, which is greatly affected by external natural factors. It is generally believed that the wind speed can be effectively fitted using a simple two-parameter Weibull distribution. Set the wind speed series as:

[0073] V={V1,V1,...,V j ,...,V J};

[0074] Then the distribution function of the wind speed change in different intervals obeys the Weibull distribution:

[0075]

[0076] Where F(V) is the wind speed distribution function; V is the wind speed sequence; λ and k are two key parameters of Weibull distribution, λ is the scale parameter, and k is the shape parameter;

[0077] The probability density function is:

[0078]

[0079] The wind speed is converted into wind power, and the wind power output characteristic model is obtained. The wind power and wind speed show a cubic proportional relationship.

[0080] In a preferred embodiment, the wind power output characteristic model is specifically:

[0081]

[0082] Among them, P W,j is the wind power at time j; C p is the fan power coefficient; ρ is the air density; A is the swept area of ​​the wind wheel; V j is the wind speed at time j.

[0083] For the construction of the photovoltaic output characteristic model, the sunshine intensity is mainly considered. First, the probability distribution function of sunshine intensity needs to be constructed. The photovoltaic output is closely related to the sunshine intensity. It is generally believed that the uncertainty of sunshine intensity can be effectively described by the Beta distribution with a complete structure. The sunshine intensity sequence is set as:

[0084]

[0085] Then the distribution function of the change of sunshine intensity in different intervals obeys the Bate distribution is:

[0086]

[0087] Among them, F(S PV ) is the sunshine intensity distribution function; S PV is the sunshine intensity sequence; α and β are two key positive real number parameters of Beta distribution, B(α,β) is the Beta function, which is used for normalization to ensure that the total probability is 1. is an incomplete B function;

[0088] The probability density function is:

[0089]

[0090] Among them, Γ is the gamma function, which is specifically expressed as:

[0091]

[0092] Among them, t V-1 is a power function of the independent variable V and the duration t.

[0093] The photovoltaic power generation power is calculated based on the sunshine intensity, that is, the photovoltaic output characteristic model is obtained. The photovoltaic power generation power is directly proportional to the sunshine intensity.

[0094] In a preferred embodiment, the photovoltaic output characteristic model is specifically:

[0095] P PV,j =ηSS PV,j [1-0.05(t0+25)];

[0096] Among them, P PV,j is the photovoltaic power at time j; η is the conversion efficiency of the photovoltaic cell; S is the area of ​​the photovoltaic panel; t0 is the temperature of the working environment of the photovoltaic panel; S PV,j is the sunshine intensity at time j.

[0097] The reliability index of the power system is the basis for measuring the system capacity demand and the premise for calculating the reliable capacity of power generation resources. The reliability index of the power system often adopts the probability of power shortage, the expected value of power shortage day, the expected value of power shortage hour and the expected value of power shortage. In order to effectively measure the severity of power outage accidents caused by the shortage of power generation resources, the present invention selects the expected value of power shortage as the reliability index of the power system, and its calculation formula is specifically expressed as follows:

[0098]

[0099] Among them, R EENS L is the expected reliability index for insufficient power; a,d,t is the system load at the tth moment on the dth day of the target year; R a,d,t is the total capacity of power generation resources at the tth moment on the dth day of the target year; p(L a,d,t >R a,d,t ) is the probability that the system load is greater than the total capacity of power generation resources at the t-th moment on the d-th day of the target year.

[0100] Based on the above-mentioned method for calculating the expected reliability index of power shortage, the present invention combines the Monte Carlo simulation method to further optimize the calculation of the expected reliability index of power shortage in order to comprehensively consider the output coordination of different power generation resources at different time scales. The specific steps of using Monte Carlo simulation to calculate the expected reliability index of power shortage of the power system include:

[0101] Step S101: input the original data of power system power supply and load, and set the simulation time N in years, 1≤n≤N;

[0102] Step S201: using the Monte Carlo method to simulate the comprehensive wind power output sequence and photovoltaic output sequence in the nth year, and randomly extracting the time sequence operation states of thermal power, nuclear power, wind power and photovoltaic power generation;

[0103] Step S301: Analyze the system shortage of the power system under the current power structure, and calculate and accumulate the EENS index of the power system under this state by combining parameters such as the existing total capacity of the system and the available capacity of the system;

[0104] Step S401: Repeat steps S201 and S301 until n=N, and obtain the expected reliability index of power shortage of the power system.

[0105] For step S3, based on the calculation results of the expected reliability index of power shortage of each power system, the installed capacity of the same type of power generation resources (K) is counted as the upper limit of the credible capacity analysis of this type of power generation resources, and the lower limit of the credible capacity analysis of this type of power generation resources is 0 (that is, the contribution degree of system capacity support of this type of power generation resources is 0). Introduce ideal power generation resources to replace the capacity of the K-th type of power generation resources (with the same upper and lower limits), and use the binary search to repeatedly iterate and update the upper and lower limits of the ideal power generation resources until the system power shortage expected reliability index formed by the ideal power generation resources and the above-calculated power shortage reliability index meet the iteration termination condition, and obtain the credible capacity of the K-th type of power generation resources. Its function expression is as follows:

[0106]

[0107] Among them, R ∑ is the total capacity of the system; L is the system load; is the installed capacity of the Kth type of power generation resources; D(R ∑ ,L) is the system capacity R ∑ The reliability index expression method of the system load is L.

[0108] For step S4, in a preferred embodiment, the source-load correlation coefficient of each power generation resource is determined according to the load data and the power generation resource data, including: determining the correlation between the output of each power generation resource and the load curve according to the load data and the power generation resource data, and determining a first source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the load curve; determining the correlation between the output of each power generation resource and the comprehensive output of various types of power generation resources according to the power generation resource data, and determining a second source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the comprehensive output of various types of power generation resources; determining the correlation between the outputs of different power generation resources according to the power generation resource data, and determining a third source-load correlation subdivision coefficient according to the correlation between the outputs of different power generation resources; determining the source-load correlation coefficient of each power generation resource according to the first source-load correlation subdivision coefficient, the second source-load correlation subdivision coefficient and the third source-load correlation subdivision coefficient.

[0109] Specifically, after determining the credible capacity of each type of power generation resource, it is necessary to further determine the credible capacity of each independent power generation resource of the same type. In the process of finely calculating the credible capacity of each independent power generation resource, the source-load correlation coefficient of the kth power generation resource in the Kth type of power generation resource is calculated from the correlation between the output of the kth power generation resource and the load curve. (i.e., the first source-load correlation subdivision coefficient mentioned above), the correlation between the output of the kth power generation resource and the comprehensive output of the Kth type of power generation resource (i.e. the second source-load correlation subdivision coefficient mentioned above) and the output correlation of different power generation resources in the Kth type of power generation resources (i.e. the third source-load correlation subdivision coefficient mentioned above) three aspects should be considered.

[0110] For the kth power generation resource output and load curve correlation, when the output curve of a power generation resource is closer to the load curve in each time period, it indicates that the power generation resource has a stronger ability to track load demand, and its corresponding source-load correlation subdivision coefficient should be larger. The first source-load correlation subdivision coefficient is calculated using the grey correlation analysis method. Specifically, in a preferred embodiment, the first source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the load curve, including:

[0111] The first source-load correlation subdivision coefficient is determined by the following formula:

[0112]

[0113]

[0114]

[0115] in, is the first source-load correlation subdivision coefficient; is the correlation between the kth generation resource in the Kth generation resource and the system load at time t, is the resolution coefficient, and is generally taken as 0.5; is the difference between the predicted output of the kth generation resource in the Kth generation resource category and the system load at time t; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; L t is the load forecast of the system at time t; N k is the total number of power generation resources; ρ is the resolution coefficient in the grey relational analysis method, which ranges from 0 to 1 and is usually 0.5.

[0116] For the calculation of the correlation between the output of the kth power generation resource and the comprehensive output of the Kth type of power generation resource, the greater the output of a single power generation resource, the greater the impact on the credible capacity analysis results of the same type of power generation resources, which is conducive to improving the upper limit of the evaluation results. Therefore, the greater the output of the power generation resource among the same type of power generation resources, the higher the source-load correlation subdivision coefficient should be.

[0117] In a preferred embodiment, the second source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource, including:

[0118] The second source-load correlation subdivision coefficient is determined by the following formula:

[0119]

[0120] in, is the second source-load correlation subdivision coefficient; It is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t.

[0121] For the correlation between the outputs of different power generation resources in the Kth type of power generation resources, the correlation between the outputs of different power generation resources in the same type of power generation resources will affect the power mutual assistance frequency of the power generation resources, and then affect the source-load correlation subdivision coefficient. Especially for renewable energy, the power complementary effect between different renewable energy sources is obvious, while traditional units such as thermal power are less affected by this factor. The greater the correlation of a single power generation resource, the closer the output of the power generation resource is to the remaining power generation resources of the same type, the lower the power complementary effect, and the corresponding source-load correlation coefficient should be smaller. The Pearson correlation coefficient method is introduced to calculate the correlation between different power generation resources in the same type of power generation resources. Specifically, in a preferred embodiment, the third source-load correlation subdivision coefficient is determined according to the correlation of the outputs of different power generation resources, including:

[0122] The third source-load correlation subdivision coefficient is determined by the following formula:

[0123]

[0124]

[0125]

[0126] in, is the third source-charge correlation subdivision coefficient; K,k is the correlation between the kth power generation resource in the Kth type of power generation resource and other power generation resources of the same type; γ K,k,g is the correlation between the kth power generation resource and the gth power generation resource in the Kth type of power generation resources; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; is the predicted average output of the kth generation resource in the Kth generation resource category at time t; is the predicted average output of the g-th power generation resource among the K-th power generation resources at time t; It is the predicted output of the gth power generation resource among the Kth type of power generation resources at time t.

[0127] After determining the first source-load correlation subdivision coefficient, the second source-load correlation subdivision coefficient and the third source-load correlation subdivision coefficient, the above subdivision coefficients are weighted by the entropy weight method, and the source-load correlation coefficient of the kth power generation resource in the Kth type of power generation resource is obtained as follows:

[0128]

[0129] Among them, θ1, θ2 and θ3 are subdivision coefficients respectively. and The proportionality coefficient calculated by the entropy weight method.

[0130] For step S5, after determining the source-load correlation coefficient of each power generation resource, in a preferred embodiment, before determining the credible capacity of each power generation resource based on the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource, it also includes: obtaining the plant power consumption rate, maintenance time ratio and equivalent forced outage rate of each power generation resource; the determination of the credible capacity of each power generation resource based on the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource includes: determining the credible capacity of each power generation resource based on the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource, the installed capacity of each type of power generation resource, the plant power consumption rate, maintenance time ratio and equivalent forced outage rate of each power generation resource.

[0131] Specifically, the capacity credibility factor of different types of power generation resources is first calculated; the capacity credibility factor is defined as: the ratio of the credible capacity of any power generation resource that meets the reliability requirements of the power system in the target year to its own installed capacity, and its corresponding calculation expression is as follows:

[0132]

[0133] Among them, ξ K is the capacity credibility factor of the Kth generation resource type; is the credible capacity of the kth generation resource type; is the installed capacity of the Kth generation resource type.

[0134] Since the credible capacity differences of the same type of power generation resources are relatively small, the credible capacity of each independent power generation resource of the same type of power generation resources can be determined based on the credible factors of the capacity of the same type of power generation resources calculated above, combined with the installed capacity of the power generation resources, the plant power consumption rate, the maintenance time ratio, the equivalent forced outage rate and the source-load correlation coefficient. The corresponding calculation formula is as follows:

[0135]

[0136] ψ K，k =δ K，k (1-ρ K，k )(1-ε K，k )(1-γ K，k );

[0137] Among them, ψ K，k is the correction coefficient of the kth power generation resource in the Kth type of power generation resource; δ K，kis the source-load correlation coefficient of the kth generation resource in the Kth type of generation resource; ρ K，k is the power consumption rate of the kth power generation resource in the Kth type of power generation resources; ε K，k is the maintenance time ratio of the kth generation resource in the Kth generation resource; γ K，k is the equivalent forced outage rate of the kth generation resource in the Kth generation resource category; is the credible capacity of the kth generation resource; is the installed capacity of the kth power generation resource.

[0138] For step S6, after the reliable capacity of each power generation resource is determined, the power system is regulated based on the corresponding reliable capacity of each power generation resource to avoid the problem of excess capacity of power generation resources in the power system and improve the reliability of the power system.

[0139] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0140] like Figure 2 As shown, an embodiment of the present invention provides a reliable capacity analysis device for power generation resources, including: a data acquisition module, a reliability index determination module, a reliable capacity determination module for various types of power generation resources, a reliable capacity determination module for a single power generation resource, and a control module;

[0141] The data acquisition module is used to acquire load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, existing total capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data;

[0142] The reliability index determination module is used to determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power;

[0143] The module for determining the credible capacity of various types of power generation resources is used to determine the credible capacity of various types of power generation resources according to the dichotomy method and the expected reliability index of power shortage of the power system;

[0144] The single power generation resource trusted capacity determination module is used to determine the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data; determine the trusted capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the trusted capacity of each type of power generation resource and the installed capacity of each type of power generation resource;

[0145] The control module is used to control the power system based on the trusted capacity of each power generation resource.

[0146] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0147] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0148] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A reliable capacity analysis method for power generation resources, characterized in that: include: Obtaining load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, existing total capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data; Determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power; Determine the credible capacity of each type of power generation resource based on the dichotomy method and the expected reliability index of power shortage in the power system; Determine the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data; Determine the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource; The power system is regulated based on the trusted capacity of each power generation resource.

2. A reliable capacity analysis method for power generation resources according to claim 1, characterized in that: Determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method, including: A wind power output characteristic model is constructed according to the wind power output data, and the wind power at each moment is determined according to the constructed wind power output characteristic model; wherein the wind power output data includes: a wind rotor swept area, a wind turbine power coefficient, an air density and a wind speed at each moment; A photovoltaic output characteristic model is constructed according to the photovoltaic output data, and the photovoltaic power at each moment is determined according to the constructed photovoltaic output characteristic model; wherein the photovoltaic output data includes: conversion efficiency of photovoltaic cells, area of ​​photovoltaic panels, temperature of the working environment of photovoltaic panels and sunshine intensity at each moment; The expected reliability index of power shortage of the power system is determined according to the wind power at each moment, the photovoltaic power at each moment, the load data, the power generation resource data and the Monte Carlo method.

3. A reliable capacity analysis method for power generation resources as claimed in claim 2, characterized in that: The wind power output characteristic model is specifically: P W,j =C p ρAV j 3 / 2; Among them, P W,j is the wind power at time j; C p is the fan power coefficient; ρ is the air density; A is the swept area of ​​the wind wheel; V j is the wind speed at time j.

4. A reliable capacity analysis method for power generation resources as claimed in claim 2, characterized in that: The photovoltaic output characteristic model is specifically: P PV,j =ηSS PV,j [1-0.05(t0+25)]; Among them, P PV,j is the photovoltaic power at time j; η is the conversion efficiency of the photovoltaic cell; S is the area of ​​the photovoltaic panel; t0 is the temperature of the working environment of the photovoltaic panel; S PV,j is the sunshine intensity at time j.

5. A reliable capacity analysis method for power generation resources according to claim 1, characterized in that: Determining the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data includes: Determine the correlation between the output of each power generation resource and the load curve according to the load data and the power generation resource data, and determine the first source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the load curve; Determine the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource according to the power generation resource data, and determine the second source-load correlation subdivision coefficient according to the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource; Determine the output correlation of different power generation resources according to the power generation resource data, and determine the third source-load correlation subdivision coefficient according to the output correlation of different power generation resources; The source-load correlation coefficient of each power generation resource is determined according to the first source-load correlation subdivision coefficient, the second source-load correlation subdivision coefficient and the third source-load correlation subdivision coefficient.

6. A reliable capacity analysis method for power generation resources as claimed in claim 5, characterized in that: The first source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the load curve, including: The first source-load correlation subdivision coefficient is determined by the following formula: in, is the first source-load correlation subdivision coefficient; is the correlation between the kth generation resource in the Kth type of generation resource and the system load at time t; is the difference between the predicted output of the kth generation resource in the Kth generation resource category and the system load at time t; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; L t is the load forecast of the system at time t; N k is the total number of power generation resources; ρ is the resolution coefficient.

7. A reliable capacity analysis method for power generation resources as claimed in claim 6, characterized in that: The second source-load correlation subdivision coefficient is determined according to the correlation between the output of each power generation resource and the comprehensive output of each type of power generation resource, including: The second source-load correlation subdivision coefficient is determined by the following formula: in, is the second source-load correlation subdivision coefficient; It is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t.

8. A reliable capacity analysis method for power generation resources as claimed in claim 7, characterized in that: The third source-load correlation subdivision coefficient is determined according to the output correlation of different power generation resources, including: The third source-load correlation subdivision coefficient is determined by the following formula: in, is the third source-charge correlation subdivision coefficient; K,k is the correlation between the kth power generation resource in the Kth type of power generation resource and other power generation resources of the same type; γ K,k,g is the correlation between the kth power generation resource and the gth power generation resource in the Kth type of power generation resources; is the predicted output of the kth power generation resource in the Kth type of power generation resource at time t; is the predicted average output of the kth generation resource in the Kth generation resource category at time t; is the predicted average output of the g-th power generation resource among the K-th power generation resources at time t; It is the predicted output of the gth power generation resource among the Kth type of power generation resources at time t.

9. A reliable capacity analysis method for power generation resources according to claim 1, characterized in that: Before determining the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource, it also includes: obtaining the plant power consumption rate, maintenance time ratio and equivalent forced outage rate of each power generation resource; Determining the credible capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource and the installed capacity of each type of power generation resource includes: The credible capacity of each power generation resource is determined based on the source-load correlation coefficient of each power generation resource, the credible capacity of each type of power generation resource, the installed capacity of each type of power generation resource, the plant power consumption rate of each power generation resource, the maintenance time ratio and the equivalent forced outage rate.

10. A reliable capacity analysis device for power generation resources, characterized in that: include: Data acquisition module, reliability index determination module, various power generation resource credible capacity determination module, single power generation resource credible capacity determination module and control module; The data acquisition module is used to acquire load data and power generation resource data of the power system; wherein the power generation resource data includes: installed capacity of various types of power generation resources, existing total capacity of the system, wind power output data, photovoltaic output data, thermal power output data and nuclear power output data; The reliability index determination module is used to determine the expected reliability index of power shortage of the power system according to the load data, power generation resource data and Monte Carlo method; wherein the types of power generation resources include: wind power, photovoltaic power, thermal power and nuclear power; The module for determining the credible capacity of various types of power generation resources is used to determine the credible capacity of various types of power generation resources according to the dichotomy method and the expected reliability index of power shortage of the power system; The single power generation resource trusted capacity determination module is used to determine the source-load correlation coefficient of each power generation resource according to the load data and the power generation resource data; determine the trusted capacity of each power generation resource according to the source-load correlation coefficient of each power generation resource, the trusted capacity of each type of power generation resource and the installed capacity of each type of power generation resource; The control module is used to control the power system based on the trusted capacity of each power generation resource.

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