Flexible resource cluster reliable response instruction decomposition method and system

Abstract

The invention discloses a flexible resource cluster reliable response instruction decomposition method and system, and the method comprises the steps: obtaining the historical response data of each flexible resource, and calculating the previous response deviation rate of each flexible resource; obtaining a first moment and a second moment of the flexibility resources according to the response deviation ratio, and further calculating a mean value and a variance of the response deviation ratio of each flexibility resource; constructing a flexibility resource cluster response reliability evaluation index by taking the mean value and variance of each flexibility resource response deviation rate as a basis; optimizing by taking the maximum response reliability evaluation index of the flexibility resource cluster as a target function, and constructing a reliability regulation and control optimization model of the flexibility resource cluster; and equivalently converting the constructed flexible resource cluster reliable regulation and control optimization model into a quadratic programming model based on the chebyshev inequality inference, thereby realizing the reliable regulation and control of the flexible resource cluster.

Description

Technical Field

[0001] The present invention relates to the field of reliable control of flexible resources, and in particular to a method and system for reliably responding to instructions of a flexible resource cluster. Background Art

[0003] Compared with the regulation of traditional large-scale units, the direct regulation of incentive-based flexibility resources is affected by factors such as subjective user preferences and objective technical conditions. There are uncertainty deviations in the response process. The deviations accumulate disorderly as the scale of resources expands, which will result in a regulation effect far less than expected at a high cost. Therefore, the documents on flexibility resource management issued by various places require settlement based on the response effect, that is, if the actual response is lower than the margin, it is regarded as a response failure, and the part exceeding the margin will not be settled. However, this post-evaluation method is only applicable to the application scenario of "supply exceeds demand". In the process of dynamic management and optimization of large-scale flexibility resources, it is necessary to actively deal with the uncertainty of flexibility resource response, rather than passively control through post-evaluation and evaluation. Summary of the invention

[0004] The present invention provides a method and system for reliably responding to instructions of a flexible resource cluster, which are used to solve the problem of reliable regulation of the flexible resource cluster.

[0005] In a first aspect, the present invention provides a method for reliably responding to instructions of a flexible resource cluster, comprising:

[0006] S1: Obtain the historical response data of each flexibility resource and calculate the response deviation rate of each flexibility resource; obtain the first-order moment information and second-order moment information of the flexibility resource according to the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource;

[0007] S2: Based on the mean and variance of the response deviation rate of each flexible resource and the central limit theorem, a flexible resource cluster response reliability evaluation index is constructed;

[0008] S3: Optimize the flexibility resource cluster response reliability evaluation index maximization as the objective function and build a flexibility resource cluster reliable control optimization model;

[0009] S4: Aiming at the scenario of reliable regulation of large-scale flexible resources, based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable regulation optimization model is equivalently converted into a quadratic programming model, so as to achieve reliable regulation of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the reliability evaluation index of the flexible resource cluster response with the goal of minimizing the variance of the flexible resource cluster response.

[0010] Furthermore, the calculation formula for the response deviation rate of each flexibility resource is:

[0011]

[0012] Wherein, i is the number index of the flexibility resource; k is the response event sequence index of the flexibility resource; is the actual response capacity of flexibility resource i in its kth response event; is the target response capacity of flexibility resource i in its kth response event; δ i，k is the response deviation rate of resource i in its kth response event.

[0013] Furthermore, the calculation process of the first-order moment information and the second-order moment information of the flexibility resource i is:

[0014]

[0015]

[0016] Among them, K i is the total number of historical response events of flexibility resource i; is the first-order moment information of the response deviation rate of flexibility resource i; is the second-order moment information of the response deviation rate of flexible resource i; x i is the response deviation rate of flexible resource i, which is a random variable with unknown exact distribution; δ i，k is the response deviation rate of resource i in its kth response event.

[0017] Furthermore, the calculation formula of the mean and variance of the flexibility resource response deviation rate is:

[0018]

[0019]

[0020] Among them, μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i.

[0021] Furthermore, the construction process of the flexibility resource cluster response reliability evaluation index is as follows:

[0022] S21: Computing flexibility resource i receives control instruction R i After that, subject to random response deviation x i The actual response capacity X of the flexibility resource response deviation after the impact i :

[0023] X i =(1+x i )R i

[0024] S22: The response deviation of each flexible resource is independent of each other. Based on the central limit theorem, the sum of a large number of independent random variables with finite variance will be close to the Gaussian distribution. Therefore, the response capacity of the flexible resource cluster is:

[0025]

[0026] Combined with linear transformation

[0027]

[0028]

[0029] Where Y is the response capacity of the flexible resource cluster, which is obtained by summing up the response capacities of each flexible resource; N is the total number of flexible resources; μ(Y) is the mean value of the response capacity of the flexible resource cluster; σ 2 (Y) is the variance of the flexible resource cluster response capacity; μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i;

[0030] S23: To make the response of the flexible resource cluster as reliable as possible, the probability that the response capacity of the flexible resource is within the reliable response range is should be as large as possible, so that the probability that the response capacity of the selected flexible resource is within the reliable response range is the cluster reliability response index, as shown in the following formula:

[0031]

[0032] D l =(1-θ)D

[0033] D u =(1+θ)D

[0034] Among them, φ(y, μ(Y), σ 2 (Y)) is the probability density function of the response capacity Y of the flexible resource cluster. When the system regulation demand is D, D l is the lower bound of the reliable response of the flexible resource cluster; D u is the upper bound of the reliable response of the flexible resource cluster; θ is the system reliability control coefficient, and the dispatcher can use θ to control the risk of cluster response deviation.

[0035] The optimization is performed with the maximum reliability index of the constructed cluster response as the goal, and the objective function in S3 is:

[0036]

[0037] Among them, the decision variables are the control instructions of each flexibility resource

[0038] Furthermore, since considering the reliability of the flexible resource cluster response will inevitably lead to an increase in the control cost, it is necessary to control the control cost. For this purpose, it is necessary to obtain the basic control cost F * Therefore, the optimization model for reliable control of flexible resource clusters is constructed as

[0039]

[0040] Among them, λ i is the declared price of resource i under the quotation and quantity reporting mechanism; R i max is the declared capacity of resource i under the quotation and quantity reporting mechanism.

[0041] Furthermore, the constraints of the optimization model specifically include:

[0042] Linear transformation constraints

[0043]

[0044]

[0045] Cost control constraints

[0046]

[0047] Among them, 9 is the cost control coefficient, which can be set by the dispatcher according to actual conditions.

[0048] Capacity Boundary Constraints

[0049] 0≤R i ≤R i max .

[0050] Furthermore, the specific process of S4 is as follows:

[0051] Based on the inference of Chebyshev's inequality, minimizing the variance of the flexible resource cluster response is equivalent to maximizing the cluster response reliability. Obviously, the transformed model is a quadratic programming model:

[0052]

[0053] st:0≤Ri ≤R i max

[0054]

[0055]

[0056] Among them, z1 and z2 are slack variables of the expected response capacity of the flexible resource cluster to ensure that the model is easy to solve; M is the slack variable penalty to ensure that the cluster expectation after solution is as close to the system demand as possible.

[0057] In a second aspect, the present invention provides a flexible resource cluster reliable response instruction decomposition system, comprising:

[0058] Data acquisition and processing module: used to obtain the historical response data of each flexibility resource and calculate the response deviation rate of each flexibility resource; obtain the first-order moment and second-order moment of the flexibility resource according to the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource;

[0059] Flexible resource cluster response reliability evaluation index construction module: used to construct the flexible resource cluster response reliability evaluation index based on the central limit theorem based on the mean and variance of the response deviation rate of each flexible resource;

[0060] Flexible resource cluster reliable control optimization model construction module: used to optimize the flexibility resource cluster response reliability evaluation index as the objective function to build a flexible resource cluster reliable control optimization model;

[0061] Quadratic programming model module: It is used for the scenario of reliable control of large-scale flexible resources. Based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable control optimization model is equivalently converted into a quadratic programming model, so as to realize the reliable control of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the flexible resource cluster response reliability evaluation index with the goal of minimizing the response variance of the flexible resource cluster.

[0062] The present invention proposes a method and system for reliably responding to instructions of a flexible resource cluster, wherein the method uses historical sample data to statistically analyze the moment information of the independent response deviations of each flexible resource to describe its response uncertainty, thereby avoiding reliance on the accurate probability distribution of uncertainty and making full use of existing information. Aiming at large-scale flexible resource control, the established non-convex model is inferred based on the Chebyshev inequality and is equivalently converted into a quadratic programming problem, which greatly reduces the computational overhead and makes it applicable to large-scale flexible resource reliable control scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only 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.

[0064] Figure 1 It is a flow chart of a method for reliably responding to instructions of a flexible resource cluster provided by an embodiment of the present invention;

[0065] Figure 2 It is a schematic diagram of a flexible resource quotation and quantity control mechanism provided by an embodiment of the present invention;

[0066] Figure 3 It is a schematic diagram of reliability indicators of a flexible resource cluster provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0068] Example 1

[0069] like Figure 1 As shown, the present invention provides a method for reliably responding to instructions of a flexible resource cluster, comprising:

[0070] S1: Obtain the historical response data of each flexibility resource and calculate the response deviation rate of each flexibility resource; obtain the first-order moment and second-order moment of the flexibility resource based on the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource.

[0071] Specifically, the calculation formula for the response deviation rate of each flexibility resource is:

[0072]

[0073] Wherein, i is the number index of the flexibility resource; k is the response event sequence index of the flexibility resource; is the actual response capacity of flexibility resource i in its kth response event; is the target response capacity of flexibility resource i in its kth response event; δ i,k is the response deviation rate of resource i in its kth response event.

[0074] The calculation process of the first-order moment information and the second-order moment information of the flexibility resource i is as follows:

[0075]

[0076]

[0077] Among them, K i is the total number of historical response events of flexibility resource i; is the first-order moment information of the response deviation rate of flexibility resource i; is the second-order moment information of the response deviation rate of flexible resource i; x i is the response deviation rate of flexibility resource i, which is a random variable with unknown exact distribution.

[0078] The calculation formula of the mean and variance of the flexibility resource response deviation rate is:

[0079]

[0080]

[0081] Among them, μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i.

[0082] S2: Based on the mean and variance of the response deviation rate of each flexible resource and the central limit theorem, a flexible resource cluster response reliability evaluation index is constructed.

[0083] Specifically, the construction process of the flexibility resource cluster response reliability evaluation index is as follows:

[0084] Computational flexibility resource i receives control instruction R i After that, subject to random response deviation x i The actual response capacity X of the flexibility resource response deviation after the impact i :

[0085] X i =(1+x i )R i

[0086] The response deviation of each flexible resource is independent of each other. Based on the central limit theorem, the sum of a large number of independent random variables with finite variance will be close to the Gaussian distribution. Therefore, the response capacity of the flexible resource cluster is:

[0087]

[0088]

[0089]

[0090] Where Y is the response capacity of the flexible resource cluster, which is obtained by summing up the response capacities of each flexible resource; N is the total number of flexible resources; μ(Y) is the mean value of the response capacity of the flexible resource cluster; σ 2 (Y) is the variance of the flexible resource cluster response capacity; μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i;

[0091] In order to make the response of the flexible resource cluster as reliable as possible, the probability that the response capacity of the flexible resource is within the reliable response range is should be as large as possible, so that Figure 3 As shown in Figure 2, the probability that the response capacity of the selected flexible resource is within the reliable response range is is the cluster reliability response index, as shown in the following formula:

[0092]

[0093] D l =(1-θ)D

[0094] D u =(1+θ)D

[0095] Among them, φ(y, μ(Y), σ 2 (Y)) is the probability density function of the response capacity Y of the flexible resource cluster. When the system regulation demand is D, D l is the lower bound of the reliable response of the flexible resource cluster; D u is the upper bound of the reliable response of the flexible resource cluster; θ is the system reliability control coefficient, and the dispatcher can use θ to control the risk of cluster response deviation.

[0096] S3: The maximum evaluation index of the flexibility resource cluster response reliability is optimized as the objective function to build a flexible resource cluster reliable control optimization model.

[0097] Specifically, the optimization is performed with the maximum constructed cluster response reliability index as the goal, and the objective function in S3 is:

[0098]

[0099] Among them, the decision variables are the control instructions of each flexibility resource

[0100] Since considering the flexibility of resource cluster response reliability will inevitably lead to an increase in control costs, it is necessary to control the control costs. To this end, it is necessary to obtain the basic control cost F * Therefore, the optimization model for reliable control of flexible resource clusters is constructed as

[0101]

[0102] Among them, λ i is the declared price of resource i under the quotation and quantity reporting mechanism; R i max is the declared capacity of resource i under the quotation and quantity reporting mechanism; i is the reported unit price of flexible resource i under the quotation and quantity reporting mechanism; R i is the flexibility resource i receiving the control instruction; μ(x i ) is the mean of the response deviation rate of flexible resource i; N is the total number of flexible resources; D is the system regulation demand.

[0103] Furthermore, the constraints of the optimization model specifically include:

[0104] Linear transformation constraints

[0105]

[0106]

[0107] Cost control constraints

[0108]

[0109] Among them, θ is the cost control coefficient, which can be set by the dispatcher according to the actual situation.

[0110] Capacity Boundary Constraints

[0111] 0≤R i ≤R i max .

[0112] S4: Aiming at the scenario of reliable regulation of large-scale flexible resources, based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable regulation optimization model is equivalently converted into a quadratic programming model, so as to achieve reliable regulation of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the reliability evaluation index of the flexible resource cluster response with the goal of minimizing the variance of the flexible resource cluster response.

[0113] Specifically, based on Chebyshev's inequality, it can be seen that for any random variable Z and any k>0, when the standard deviation σ becomes smaller, the probability that the random variable deviates from the mean μ by more than kσ will decrease, that is, the value of the random variable will be more concentrated near the mean:

[0114]

[0115] That is, when μ∈[a,b], as the variance decreases, the probability that Z is within this interval will increase because the smaller variance makes it less likely that Z will deviate from the mean.

[0116] Therefore, minimizing the variance of the flexible resource cluster response is equivalent to maximizing the cluster response reliability. Obviously, the converted model is a quadratic programming model:

[0117]

[0118] st:0≤R i ≤R i max

[0119]

[0120]

[0121] Among them, z1 and z2 are used as slack variables of the cluster response capacity expectation to ensure that the model is easy to solve, and M is the slack variable penalty to ensure that the cluster expectation after solution is as close to the system demand as possible.

[0122] When implementing it, Figure 2 As shown, the upper-level power grid sends the flexibility resource regulation demand required by the system to the active distribution network. The active distribution network organizes similar interactive mechanisms such as demand-side response. After the flexibility resource users quote the quantity, the active distribution network determines the winning quantity of each resource regulation according to the method described in this embodiment, and sends it to the terminal flexibility resource users in the form of regulation instructions, which they respond to autonomously.

[0123] Example 2

[0124] This embodiment provides a flexible resource cluster reliable response instruction decomposition system, including:

[0125] Data acquisition and processing module: used to obtain the historical response data of each flexibility resource and calculate the response deviation rate of each flexibility resource; obtain the first-order moment and second-order moment of the flexibility resource according to the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource;

[0126] Flexible resource cluster response reliability evaluation index construction module: used to construct the flexible resource cluster response reliability evaluation index based on the central limit theorem based on the mean and variance of the response deviation rate of each flexible resource;

[0127] Flexible resource cluster reliable control optimization model construction module: used to optimize the flexibility resource cluster response reliability evaluation index as the objective function to build a flexible resource cluster reliable control optimization model;

[0128] Quadratic programming model module: It is used for the scenario of reliable control of large-scale flexible resources. Based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable control optimization model is equivalently converted into a quadratic programming model, so as to realize the reliable control of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the flexible resource cluster response reliability evaluation index with the goal of minimizing the response variance of the flexible resource cluster.

[0129] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0130] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0131] It should be understood that in the embodiments of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the type of device.

[0132] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as a hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the controller. Furthermore, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0133] Based on this understanding, the technical solution of the present invention, in essence or in other words, the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

Claims

1. A method for reliably responding to instructions of a flexible resource cluster, characterized in that: include: S1: Obtain the historical response data of each flexibility resource and calculate the response deviation rate of each flexibility resource; obtain the first-order moment information and second-order moment information of the flexibility resource according to the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource; S2: Based on the mean and variance of the response deviation rate of each flexible resource and the central limit theorem, a flexible resource cluster response reliability evaluation index is constructed; S3: Optimize the flexibility resource cluster response reliability evaluation index maximization as the objective function and build a flexibility resource cluster reliable control optimization model; S4: Aiming at the scenario of reliable regulation of large-scale flexible resources, based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable regulation optimization model is equivalently converted into a quadratic programming model, so as to achieve reliable regulation of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the reliability evaluation index of the flexible resource cluster response with the goal of minimizing the variance of the flexible resource cluster response.

2. The flexible resource cluster reliable response instruction decomposition method according to claim 1 is characterized in that: The calculation formula for the response deviation rate of each flexibility resource is: Wherein, i is the number index of the flexibility resource; k is the response event sequence index of the flexibility resource; is the actual response capacity of flexibility resource i in its kth response event; is the target response capacity of flexibility resource i in its kth response event; δ i,k is the response deviation rate of resource i in its kth response event.

3. The method for reliably responding to instructions of a flexible resource cluster according to claim 1, characterized in that: The calculation formulas for the first-order moment information and the second-order moment information of the flexibility resource are: in, K i is the total number of historical response events of flexibility resource i; is the first-order moment information of the response deviation rate of flexibility resource i; is the second-order moment information of the response deviation rate of flexible resource i; x i is the response deviation rate of flexible resource i; δ i,k is the response deviation rate of resource i in its kth response event.

4. The method for reliably responding to instructions of a flexible resource cluster according to claim 3, characterized in that: The calculation formula of the mean and variance of the flexibility resource response deviation rate is: Among them, μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i.

5. The method for reliably responding to instructions of a flexible resource cluster according to claim 1, characterized in that: The construction process of the flexibility resource cluster response reliability evaluation index is as follows: S21: Computing flexibility resource i receives control instruction R i After that, subject to random response deviation x i The actual response capacity X of the flexibility resource response deviation after the impact i : X i =(1+x i )R i S22: Based on the central limit theorem, the response capacity of the flexible resource cluster is calculated as: Where Y is the response capacity of the flexible resource cluster; N is the total number of flexible resources; μ(Y) is the mean value of the response capacity of the flexible resource cluster; σ 2 (Y) is the variance of the flexible resource cluster response capacity; μ(x i ) is the mean value of the response deviation rate of flexible resource i; σ 2 (x i ) is the variance of the response deviation rate of flexibility resource i; S23: The probability that the response capacity of the selected flexible resource is within the reliable response range Cluster reliability response indicators: D l =(1-θ)D D u =(1+θ)D Among them, φ(y,μ(Y),σ 2 (Y)) is the probability density function of the response capacity Y of the flexible resource cluster. When the system regulation demand is D, D l is the lower bound of the reliable response of the flexible resource cluster; D u is the upper bound of the reliable response of the flexible resource cluster; θ is the system reliability control coefficient.

6. The flexible resource cluster reliable response instruction decomposition method according to claim 1, characterized in that: The flexible resource cluster reliable control optimization model constructed is Among them, F * To control costs; i is the declared price of flexible resource i under the quotation and quantity reporting mechanism; R i max is the declared capacity of flexible resource i under the quotation and quantity reporting mechanism; R i is the flexibility resource i receiving the control instruction; μ(x i ) is the mean of the response deviation rate of flexible resource i; N is the total number of flexible resources; D is the system regulation demand.

7. The method for reliably responding to instructions of a flexible resource cluster according to claim 6, characterized in that: The constraints of the optimization model specifically include: Linear transformation constraints Where μ(Y) is the mean value of the flexible resource cluster response capacity; σ 2 (Y) is the variance of the response capacity of the flexibility resource cluster; Cost control constraints Among them, θ is the cost control coefficient; Capacity Boundary Constraints 0≤R i ≤R i max 。 8. The method for reliably responding to instructions of a flexible resource cluster according to claim 1, characterized in that: The specific process of S4 is as follows: Based on the inference of Chebyshev's inequality, minimizing the variance of the flexible resource cluster response is equivalent to maximizing the cluster response reliability. The transformed model is a quadratic programming model: s.t:0≤R i ≤R i max Among them, σ 2 (Y) is the variance of the response capacity of the flexible resource cluster; N is the total number of flexible resources; σ 2 (x i ) is the variance of the response deviation rate of flexible resource i; R i is the flexible resource i receiving the control instruction; M is the slack variable penalty; z1, z2 are both slack variables of the cluster response capacity expectation; R i max is the declared capacity of flexible resource i under the quotation and quotation mechanism; i is the declared price of flexible resource i under the quotation and quantity reporting mechanism; μ(x i ) is the mean value of the response deviation rate of flexible resource i; θ is the cost control coefficient; F * To control costs; i is the declared price of flexible resource i under the quotation and quantity reporting mechanism; D is the system regulation demand.

9. A flexible resource cluster reliable response instruction decomposition system, characterized in that: include: Data acquisition and processing module: used to obtain the historical response data of each flexibility resource and calculate the historical response deviation rate of each flexibility resource; obtain the first-order moment and second-order moment of the flexibility resource according to the response deviation rate, and then calculate the mean and variance of the response deviation rate of each flexibility resource; Flexible resource cluster response reliability evaluation index construction module: used to construct the flexible resource cluster response reliability evaluation index based on the central limit theorem based on the mean and variance of the response deviation rate of each flexible resource; Flexible resource cluster reliable control optimization model construction module: used to optimize the flexibility resource cluster response reliability evaluation index as the objective function, and build a flexible resource cluster reliable control optimization model; Quadratic programming model module: It is used for the scenario of reliable control of large-scale flexible resources. Based on the inference of Chebyshev's inequality, the constructed flexible resource cluster reliable control optimization model is equivalently converted into a quadratic programming model, so as to realize the reliable control of the flexible resource cluster; wherein, the inference of the Chebyshev's inequality is equivalent to maximizing the flexible resource cluster response reliability evaluation index with the goal of minimizing the response variance of the flexible resource cluster.

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