Static security checking method and device for electric power spot market, and medium

Abstract

The embodiment of the invention provides a static security checking method and device for an electric power spot market and a medium, and the method comprises the steps: determining the semi-invariant of each order of the active power of each branch in an electric power system in a ground state mode and a breaking distribution factor of the breaking of any branch to another branch according to the clearing result of the electric power spot market; determining a probability distribution function of the active power of the other branch after any branch is cut off according to the semi-invariant of each order of the active power of each branch and the cut-off distribution factor of the cut-off of any branch to the other branch; and according to the probability distribution function, screening out a serious fault corresponding to branch circuit breaking so as to analyze the serious fault. By means of the method, through an efficient fault screening mode, the influence on spot market operation caused by the fact that massive loads participate in power uncertainty can be effectively considered, calculation time is greatly shortened by filtering out a large number of harmless faults, and therefore the requirement for online operation of the electric power spot market is met.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a static safety verification method, equipment and medium for a power spot market. Background Art

[0002] In order to ensure the reliability of the power system during actual operation, it is not only necessary to operate stably under normal conditions, but also to have a certain ability to resist accidents. Different accidents have different impacts on the system. Some minor accidents have little impact on the system, but some failures may cause serious consequences. Static safety analysis can analyze the system for anticipated accidents and warn of accidents that may cause threats such as overload of electrical equipment, thereby identifying serious accidents that pose a greater threat to the system.

[0003] In order to ensure the feasibility of the spot market clearing results, it is also necessary to conduct a static safety analysis of the spot market clearing calculation results. At present, most of the static safety analysis of power grids is based on the proposed framework of power system security assessment and security control, and conducts deterministic static safety analysis. An important premise of the deterministic static safety analysis method is that the node power injection is determined and unchanged. With the continuous advancement of the power spot market reform, this premise will become increasingly difficult to apply. Compared with the generation side resources, the observability and controllability of the load side resources are poor. When massive load side resources participate in the spot market in various ways, due to factors such as the autonomy of electricity consumption behavior and the uncertainty of demand response effects, the actual power injection of the load node may be different from the market clearing results, and the system operation is subject to great uncertainty. If the static safety analysis continues to be performed in a deterministic manner, there may be a large deviation between the calculation results and the actual system operation.

[0004] Deterministic static safety analysis methods can quickly screen faults based on compensation methods, local factor table corrections, and other methods, filtering out most of the "harmless" expected faults, and only retaining a small number of expected faults that may have a greater impact on system operation for further detailed analysis. Therefore, its calculation time is generally controllable and can meet the system's online operation requirements. However, the calculation of uncertain static safety analysis methods is larger than that of deterministic methods, and there is a lack of efficient fault screening methods. Therefore, uncertain static safety analysis methods take a long time to calculate and are mainly used in offline simulation analysis. There is currently no example of online application of probabilistic static safety analysis methods. Summary of the invention

[0005] The present invention provides a static safety verification method, device and medium for an electricity spot market, which realizes static safety analysis of an electricity spot market with uncertainty in node power injection, and greatly shortens calculation time by filtering out a large number of "harmless" faults.

[0006] In a first aspect, an embodiment of the present invention provides a static security verification method for a power spot market, comprising:

[0007] According to the clearing results of the power spot market, the semi-invariants of each order of active power of each branch in the power system under the base state mode and the interruption distribution factor of any branch on another branch are determined;

[0008] Determine the probability distribution function of the active power of the other branch after any branch is disconnected according to the semi-invariants of the active power of each branch and the disconnection distribution factor of any branch disconnected on the other branch;

[0009] According to the probability distribution function, the serious faults corresponding to the branch circuit breaking are screened out to analyze the serious faults.

[0010] In a second aspect, an embodiment of the present invention further provides an electronic device, including:

[0011] at least one processor; and

[0012] a memory communicatively connected to the at least one processor; wherein,

[0013] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the static security verification method for the electricity spot market as described in the embodiment of the first aspect.

[0014] In a third aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the static security verification method for the electricity spot market as described in the embodiment of the first aspect.

[0015] The embodiment of the present invention provides a static safety verification method, device and medium for the power spot market, the method comprising: determining the semi-invariants of the active power of each branch in the power system in the base state mode and the breaking distribution factor of any branch opening to another branch according to the clearing result of the power spot market; determining the probability distribution function of the active power of another branch after any branch is broken according to the semi-invariants of the active power of each branch and the breaking distribution factor of any branch opening to another branch; screening out the serious faults corresponding to the branch breaking according to the probability distribution function, so as to analyze the serious faults. The above technical scheme, for the static safety verification of the power spot market with massive load resources, can further determine the probability distribution function of the active power of another branch after any branch is broken by determining the semi-invariants of the active power of each branch in the base state mode and the breaking distribution factor of any branch opening to another branch, thereby filtering out serious faults. Through efficient fault screening methods, the impact of massive load participation power uncertainty on the spot market operation can be effectively taken into account, and by filtering out a large number of "harmless" faults, the calculation time can be greatly shortened. The overall calculation time can meet the needs of online operation of the electricity spot market.

[0016] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a static safety verification method for a power spot market provided in Embodiment 1 of the present invention;

[0019] Figure 2 A schematic flow chart of another static safety verification method for a power spot market provided in Embodiment 2 of the present invention;

[0020] Figure 3 A schematic diagram of the structure of a static safety verification device for a power spot market provided in Embodiment 3 of the present invention;

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any other variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Embodiment 1

[0025] Figure 1 The present invention provides a flow chart of a static safety verification method for a power spot market, which is applicable to the case where a large number of load resources participate in the power spot market for static safety analysis. The method can be executed by a static safety verification device for the power spot market, which can be implemented in the form of hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the static security verification method for the power spot market provided in the first embodiment may specifically include the following steps:

[0026] S101. According to the clearing results of the electricity spot market, determine the semi-invariants of each order of the active power of each branch in the power system under the base state mode and the interruption distribution factor of any branch on another branch.

[0027] The application scenario of this embodiment can be described as: performing an uncertain static safety analysis of the power spot market under the participation of massive loads. It should be noted that in order to improve the calculation efficiency, when performing a static safety analysis on the power system, a strict N-1 test requires a simulation of all expected faults being disconnected one by one, and the safety of the power system is evaluated according to the obtained branch power flow distribution, which has a large amount of calculation, wherein N represents the number of branches. However, with the continuous and rapid development of power construction, the scale of the power system is getting larger and larger. Although the computing power of computers is also constantly improving, the use of strict N-1 tests takes too long and can no longer meet the needs of online operation of the power system. In fact, for large-scale interconnected power grids, except for individual branches, the disconnection of most single branches in the power system has little effect on the overall safety of the power system, and will neither cause a large-scale transfer of power flow nor cause serious over-limit of other branches in the system. Moreover, with the increasing strengthening of the power grid structure and the gradual improvement of the degree of interconnection of the power grid, the impact of the disconnection of a single branch in the power grid on the system will also become smaller and smaller. Therefore, it is necessary to screen or sort the faults according to the possibility of other equipment overload caused by the opening of each branch, and then only check the opening of the branch with more serious consequences. Through reasonable and effective screening, the amount of calculation can be significantly reduced while ensuring the accuracy of the calculation results.

[0028] For example, in order to ensure that the power system is safe after each branch is disconnected, if there is sufficient time, a detailed analysis can be performed after each branch is disconnected. However, when the number of branches is large, such as 1,000 branches, 1,000 branch disconnections are required. This method is inefficient and requires a large amount of calculation, and is not suitable for online applications. In this embodiment, a serious fault filtering method is adopted. First, 100 branches that may cause serious consequences are selected from the 1,000 branches, and then the disconnection of these 100 branches is included in the detailed calculation. This embodiment mainly describes how to screen out serious faults.

[0029] In this embodiment, in order to first screen out serious faults that cause overload of other equipment after branch disconnection, it is necessary to determine the semi-invariants of the active power of each branch in the power system under the base state mode and the disconnection distribution factor of any branch disconnection on another branch based on the clearing results of the electricity spot market.

[0030] Continuing from the above description, the clearing result of the power spot market includes the node injection active power and reactive power of each node in the power system. By performing deterministic power flow calculation on the power system, the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch can be determined. According to the sensitivity matrix of each node voltage to the node injection power and the branch power flow, the sensitivity matrix of the active power of each branch to the node injection power is determined. According to the sensitivity matrix of the active power of each branch to the node injection power, the breaking distribution factor of any branch breaking to another branch is determined.

[0031] It should be noted that the node injection power includes the load and the unit output, and the unit is divided into conventional units and new energy units. Considering that the randomness analysis is to be done in this embodiment, and the conventional units are not random, and the loads and new energy units are random, therefore, this step analyzes the loads and new energy units. First, according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result, the center distance of each order of active power of each node is determined. Secondly, by converting the center distance of each order of active power into the semi-invariant of each order of active power of each node. Finally, according to the semi-invariant of each order of active power of each node and the sensitivity matrix of the active power of each node to the node injection power, the semi-invariant of each order of active power of each branch in the power system under the base state mode is determined. Among them, the semi-invariant is a digital feature of a random variable, which has the effect of simplifying probability calculation.

[0032] This step is equivalent to determining the semi-invariants of the active power of each branch in the power system when all branches are not disconnected, and the disconnection distribution factor of any branch disconnected on another branch.

[0033] S102. Determine the probability distribution function of the active power of the other branch after any branch is disconnected according to the semi-invariants of the active power of each branch and the disconnection distribution factor of any branch on the other branch.

[0034] In this embodiment, the semi-invariants of the active power of each branch in the base state are determined, that is, the semi-invariants of the active power of each branch when none of the branches are disconnected. Branch disconnection is to superimpose some changes on the base state. After any branch is disconnected, the semi-invariants of the active power of each branch in the base state and the disconnection distribution factor of the branch disconnection on another branch can be substituted into the set formula to obtain the semi-invariants of the active power of the other branch after the branch is disconnected. That is, the semi-invariants of the active power of each branch after each branch is disconnected can be calculated.

[0035] Taking into account that determining the various order semi-invariants of the active power of the other branch after any branch is disconnected is only for simplifying the calculation, its essence is to determine the probability distribution function of the active power of the other branch after any branch is disconnected. Therefore, in this embodiment, based on the set series expansion method, the various order semi-invariants of the active power of the other branch after any branch is disconnected are processed to determine the probability distribution function of the active power of the other branch after any branch is disconnected.

[0036] S103. According to the probability distribution function, the serious faults corresponding to the branch circuit breaking are screened out to analyze the serious faults.

[0037] In this step, severe fault screening is performed based on the calculation results of the base state flow. Specifically, the power upper limit of the branch can be pre-set, and exceeding the upper limit is considered to have an impact on the branch. When a branch is disconnected, there is a probability distribution function of the active power of any other branch after the branch is disconnected. Based on the probability distribution function of the active power, the probability that the active power of any other branch is greater than the set branch power can be determined. If it is calculated that when a branch is disconnected, the probability that the active power of another branch is greater than the set branch power is greater than the set probability threshold, it is determined that the branch disconnection has an impact on the other branch, and the fault is considered to be a severe fault and needs to be included in detailed analysis and calculation. If it is calculated that when a branch is disconnected, the probability that the active power of another branch is greater than the set branch power is less than or equal to the set probability threshold, it is determined that the branch disconnection will not have an impact on the other branch.

[0038] It should be noted that the screened severe faults are further analyzed and calculated in detail. The analysis of severe faults can be calculated based on the traditional probabilistic power flow analysis method, which will not be described in detail here.

[0039] The embodiment of the present invention provides a static safety verification method for the power spot market. For the static safety verification of the power spot market with massive load resources, by determining the semi-invariants of each order of the active power of each branch in the base state mode and the distribution factor of the disconnection of any branch on the other branch, the probability distribution function of the active power of the other branch after any branch is disconnected can be further determined, thereby filtering out serious faults. Through an efficient fault screening method, the impact of the power uncertainty of massive loads participating in the spot market operation can be effectively taken into account, and by filtering out a large number of "harmless" faults, the calculation time is greatly shortened, and the overall calculation time can meet the needs of online operation of the power spot market.

[0040] Embodiment 2

[0041] Figure 2A flow chart of another static safety verification method for the electricity spot market provided in Embodiment 2 of the present invention. This embodiment is a further optimization of the above-mentioned embodiment. In this embodiment, "determine the distribution factor of the opening of any branch on another branch according to the clearing result of the electricity spot market" is further limited and optimized, and "determine the various order semi-invariants of the active power of each branch in the power system under the base state mode according to the clearing result of the electricity spot market" is further limited and optimized, and "determine the probability distribution function of the active power of another branch after any branch is opened according to the various order semi-invariants of the active power of each of the branches and the opening distribution factor of any branch on another branch" is limited and optimized, and further "screen out the serious faults corresponding to the branch opening according to the probability distribution function, so as to analyze the serious faults" is limited and optimized.

[0042] like Figure 2 As shown, this embodiment 2 provides a static security verification method for the power spot market, which specifically includes the following steps:

[0043] S201. Determine, based on the clearing result, the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch.

[0044] In this embodiment, the clearing result of the power spot market includes the node injection of active power and reactive power to each node in the power system. According to the clearing result, a deterministic power flow calculation is performed on the power system to determine the base state solution of the node state variables of each node in the base state mode. According to the base state solution, the Jacobian matrix and the branch power flow of each branch are determined. According to the Jacobian matrix, the sensitivity matrix of each node voltage to the node injection power is determined.

[0045] As a specific implementation method, according to the clearing result, the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch are determined, including:

[0046] a1) Based on the clearing results, the deterministic power flow calculation is performed on the power system to determine the base state solution of the node state variables of each node under the base state mode.

[0047] In this embodiment, according to the clearing results of the electricity spot market, the Newton method is used to perform deterministic power flow calculation on the power system to solve the voltage amplitude of each node under the reference state. and phase angle The above formula is the standard equation for power system flow calculation:

[0048] ,

[0049] In the formula, , are the active power and reactive power injected into node i respectively, , is the voltage amplitude of nodes i and j, is the voltage phase angle difference between nodes i and j, , is the conductance and susceptance between nodes i and j. For the sake of simplicity in describing the process in subsequent steps, the above is abbreviated as:

[0050] , where W is the node power injection vector, including the node injected active power and reactive power, and X is the node state variable, including the node voltage amplitude and phase angle.

[0051] b1) Based on the base state solution, determine the Jacobian matrix and the branch power flow of each branch.

[0052] In this embodiment, according to the base state solution of the node state variable X calculated in the above steps, , get the Jacobian matrix and branch base flow , the Jacobian matrix can be understood as an intermediate matrix used to calculate the voltage amplitude and phase angle. Among them, The Jacobian matrix used in the last iteration of the power flow calculation in the above steps does not need to be recalculated.

[0053] In terms of branch power flow solution, according to the principle of electrical engineering, the branch power flow can be directly calculated if the voltage at both ends of the branch is known. For the sake of simplicity, the branch power flow Z calculation equation is simplified as: .

[0054] c1) According to the Jacobian matrix, determine the sensitivity matrix of each node voltage to the node injection power.

[0055] Among them, the node voltage to node injection power sensitivity matrix The solution formula is expressed as: , where is the Jacobian matrix.

[0056] The above technical solution concretizes the steps of determining the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch.

[0057] S202: Determine a sensitivity matrix of active power of each branch to the node injection power according to the sensitivity matrix of each node voltage to the node injection power and the power flow of each branch.

[0058] In this embodiment, after the branch flow is determined, the partial derivative of the branch flow with respect to the node voltage variable can be calculated, and then the sensitivity matrix of the node voltage to the node injection power can be multiplied by the partial derivative of the branch flow with respect to the node voltage variable to obtain the sensitivity matrix of the branch active power to the node injection power.

[0059] Among them, the branch power to node injection power sensitivity matrix T, , where is the partial derivative of branch power flow with respect to node voltage variable, The node voltage to node injection power sensitivity matrix. It is understandable that when a branch is disconnected, the voltage amplitude and phase angle of the node will change. The voltage amplitude and phase angle of the node in the base state are determined above, but after a branch is disconnected, the calculated node voltage amplitude and phase angle are not the original values, and both will change. At this time, a change relationship between them is required, that is, the branch power to node injection power sensitivity matrix, which will be used for fault analysis later.

[0060] S203: Determine the disconnection distribution factor of any branch on another branch according to the sensitivity matrix of the active power of each branch to the node injection power.

[0061] In this embodiment, the breaking distribution factor is introduced into the uncertain stochastic power flow analysis, and the breaking distribution factor of any branch breaking on another branch can be calculated based on the sensitivity matrix of the branch active power to the node injection power. The formula can be expressed as:

[0062] , where the superscript ls is the node number of the first end of branch l, and le is the node number of the last end of branch l. is the active injection sensitivity of branch k to node ls, is the active injection sensitivity of branch k to node le, is the active power injection sensitivity of branch l to node ls, is the active injection sensitivity of branch l to node le. It should be clear that in this formula, when When it is equal to 1, it means that the disconnection of branch l will cause the power grid to be disconnected, and there is no need to proceed to the next step of detailed analysis.

[0063] S204. Determine the center distance of each order of active power of each node according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result.

[0064] It should be noted that the node injection power includes load and unit output, and the units are divided into conventional units and new energy units. Considering that randomness analysis is to be performed in this embodiment, and conventional units are not random, while loads and new energy units are random, this step analyzes loads and new energy units.

[0065] In this embodiment, the clearing result of the electricity spot market includes the active power value of the load of each node at different times and the active power value of the new energy unit. For each node, the sum of the active power value of the load on the node and the active power value of the new energy unit at each sampling moment can be calculated, and recorded as the active power value of the node. After calculating the active power value of the node at each sampling moment, the active power values ​​at each sampling moment can be added and then divided by the number of sampling points to obtain the average value of the random active power of the node, which is recorded as the expected value of active power. In this embodiment, for each node, the active power value and the expected value of active power of the node can be substituted into the set formula to obtain the center distance of each order of active power of the node.

[0066] As a specific implementation method, according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result, the active power center distance of each node is determined, including:

[0067] a2) For each node, determine the sum of the active power value of the node load and the active power value of the new energy unit at each sampling moment, and record it as the active power value of the node.

[0068] In this embodiment, the clearing result of the power spot market includes the active power value of the load of each node at different times and the active power value of the new energy unit. For each node, the sum of the active power value of the load on the node and the active power value of the new energy unit at each sampling time can be calculated and recorded as the active power value of the node.

[0069] The calculation formula can be expressed as: , where is the active power value at node i, is the active power value of the load on node i at time t, is the active power value of the new energy unit on node i at time t.

[0070] b2) Determine the expected value of active power based on the active power value of the node at each sampling moment.

[0071] In this embodiment, after calculating the active power value of the node at each sampling moment, the active power values ​​at each sampling moment can be added and then divided by the number of sampling points (i.e., the total number of samples) to obtain the average value of the random active power of the node, which is recorded as the expected value of active power. The calculation formula is expressed as:

[0072] , where is the expected value of active power of node i, is the active power value on node i, and T is the total number of samples in the statistical period.

[0073] c2) Determine the center distance of each order of active power of the node based on each active power value and the expected value of active power.

[0074] In this embodiment, the center distance of each order of active power of the node is used to measure the distribution characteristics of the active power of the node. For each node, the active power value and the expected value of active power of the node can be substituted into the setting formula to obtain the center distance of each order of active power of the node. The formula is as follows: , where is the r-order center distance of active power of node i, T is the total number of samples in the statistical period, is the active power value at node i, is the expected value of active power.

[0075] The above technical solution specifies the steps of determining the center distance of each order of active power of each node, and provides basic data for subsequent calculation of each order of semi-invariant.

[0076] S205. Determine semi-invariants of the active power of each node according to the center distances of each order of the active power of each node.

[0077] It should be clear that by converting the center distances of each order of active power into semi-invariants of each order, the excessive amount of calculation caused by convolution calculation can be avoided, thereby achieving the purpose of simplifying the amount of calculation. In this embodiment, the 1st to 8th order semi-invariants of the active power of each node are calculated using the center distances of each order, and the calculation formula is expressed as:

[0078]

[0079] In the formula, is the semi-invariant of the node active power, and the subscript is the order; is the center distance of active power of the node, and the subscript is the order; is the expected value of the node active power.

[0080] S206. Determine the semi-invariants of the active power of each branch in the power system in the base state according to the semi-invariants of the active power of each node and the sensitivity matrix of the active power of each branch to the node injection power.

[0081] In this embodiment, the semi-invariant of the power of each branch in the base state (that is, no branch disconnection) is calculated, and the calculation formula is expressed as:

[0082] ,

[0083] ,

[0084] In the formula, is the rth order semi-invariant of the active power of branch l in the base state, is the active power injection sensitivity of branch l to node i, is the r-order semi-invariant of the active power of branch k in the base state, is the active power injection sensitivity of branch k to node i, is the active power injected by node i is an order semi-invariant, and N is the total number of nodes in the power system.

[0085] S207. According to the semi-invariants of the active power of each branch and the interruption distribution factor of any branch interruption on another branch, determine the semi-invariants of the active power of another branch after any branch is interrupted.

[0086] In this embodiment, the semi-invariants of the active power of each branch in the base state are determined, that is, the semi-invariants of the active power of each branch when none of the branches are disconnected. Branch disconnection is to superimpose some changes on the base state. After any branch is disconnected, the semi-invariants of the active power of each branch in the base state and the disconnection distribution factor of the branch disconnection on another branch can be substituted into the following formula to obtain the semi-invariants of the active power of the other branch after the branch is disconnected. That is, the semi-invariants of the active power of each branch after each branch is disconnected can be calculated.

[0087] Among them, the formula for calculating the semi-invariant of each order of active power of other branches when branch l is disconnected can be expressed as:

[0088] , where is the r-order semi-invariant of the active power of branch k after branch l is disconnected, is the rth-order semi-invariant of the active power of branch k in the base state, It is the disconnection distribution factor of branch k after branch l is disconnected.

[0089] S208. Based on the set series expansion method, the semi-invariants of each order of the active power of the other branch after any branch is disconnected are processed to determine the probability distribution function of the active power of the other branch after any branch is disconnected.

[0090] The set series expansion method may be a Gram-Charlier expansion series. The Gram-Charlier expansion series is used to describe the probability density function of a random variable, and the probability density function of the random variable is expanded into a set of series to achieve an approximate representation of the probability distribution.

[0091] In this embodiment, the Gram-Charlier expansion series is applied to calculate the probability distribution function of the active power of the branch from the semi-invariant of the active power of the branch. The process can be described as: first, the branch active power state variable is standardized based on the expected value and standard deviation of the active power of the other branch after any branch is disconnected; then, the coefficient of the expansion series is determined according to the semi-invariant and standard deviation of each order of the active power of the other branch after any branch is disconnected; based on the coefficient of the expansion series and the probability distribution function of the standard probability distribution, the probability distribution function of the active power of the other branch after any branch is disconnected is determined.

[0092] As a specific implementation method, based on a set series expansion method, the semi-invariants of each order of the active power of the other branch after any branch is disconnected are processed to determine the probability distribution function of the active power of the other branch after any branch is disconnected, including:

[0093] a3) Standardize the branch active power state variables based on the expected value and standard deviation of the active power of the other branch after any branch is disconnected.

[0094] In this embodiment, the Gram-Charlier expansion series is applied to calculate the probability distribution function of the active power of the branch from the semi-invariant of the active power of the branch. The calculation formula is as follows:

[0095] , where is a random variable Z The normalized probability distribution function is is the expected value of the random variable Z, is the standard deviation of the random variable Z. It should be noted that the random variable Z refers to the active power state variable of the other branch after any branch is disconnected.

[0096] b3) Determine the coefficients of the expansion series based on the semi-invariants and standard deviations of the active power of any branch after the other branch is disconnected.

[0097] In this embodiment, assuming that the coefficient of the expansion series is represented by A, the calculation formula of the coefficient A is expressed as:

[0098] ,

[0099] In the formula, is the standard deviation of the random variable Z, It is the r-order semi-invariant of the active power of branch k after branch l is disconnected.

[0100] c3) Based on the coefficients of the expansion series and the probability distribution function of the standard probability distribution, determine the probability distribution function of the active power of any branch after the other branch is disconnected.

[0101] Among them, the probability distribution function of the standard probability distribution The calculation formula is as follows:

[0102] , where is the probability cumulative distribution function of a standard normally distributed variable with expected value 0 and standard deviation 1.

[0103] in, The calculation method is as follows:

[0104] ,

[0105] The above technical solution specifies the steps of converting each order semi-invariant into a probability distribution function by using a series expansion method, which provides basic data for the subsequent screening of serious faults based on the probability distribution function of the active power of the branch.

[0106] S209. Determine the probability that the active power of each branch is greater than the set branch power according to the probability distribution function.

[0107] The set branch power can be specifically understood as the upper limit of the branch power, which can be set according to the actual situation. Specifically, when a branch is disconnected, there is a probability distribution function of the active power of any other branch after the branch is disconnected. Based on the probability distribution function of the active power, it can be determined that the probability that the active power of any other branch is greater than the set branch power.

[0108] S210. If the probability that the active power of a branch is greater than the set branch power is greater than a set probability threshold, it is determined that the disconnection of the branch corresponding to the probability greater than the set probability threshold has an impact on other branches and is treated as a serious fault to analyze the serious fault.

[0109] Among them, setting the probability threshold can be specifically understood as setting the probability upper limit, which can be set according to actual conditions. In this embodiment, if it is calculated that when a branch is disconnected, the probability that the active power of another branch is greater than the set branch power is greater than the set probability threshold, it is determined that the disconnection of the branch has an impact on the other branch, and the fault is considered to be a serious fault that needs to be included in detailed analysis and calculation.

[0110] S211. If the probability that the active power of a branch is greater than the set branch power is less than or equal to the set probability threshold, it is determined that the disconnection of the branch corresponding to the probability less than or equal to the set probability threshold has no effect on other branches.

[0111] In this embodiment, if it is calculated that when a branch is disconnected, the probability that the active power of another branch is greater than the set branch power is less than or equal to the set probability threshold, it is determined that the disconnection of the branch will not affect the other branch.

[0112] Exemplarily, assuming that branch 1 is disconnected, it is necessary to analyze whether the disconnection of branch 1 has an impact on any other branch such as branch i, branch j, branch k, etc. For example, when analyzing whether the disconnection of branch 1 has an impact on branch i, it is necessary to calculate the probability that the active power of branch i is greater than the set branch power based on the probability distribution function of the active power of branch i after branch 1 is disconnected. Assuming that the probability that the calculated active power of branch i is greater than the set branch power is greater than the set probability threshold, it is considered that the disconnection of branch 1 has an impact on branch i, which is a serious fault. Then continue to analyze the impact of the disconnection of branch 1 on branch j. Assuming that the calculated active power of branch j is greater than the set branch power and the power is less than the set probability threshold, it is considered that the disconnection of branch 1 will not have an impact on branch j. Then continue to analyze the impact of the disconnection of branch 1 on other branches, until it is analyzed whether the disconnection of branch 1 has an impact on any other branch, thereby determining the serious faults contained therein, and then perform detailed analysis and calculation on the serious faults.

[0113] The above technical scheme specifies the steps of determining the breaking distribution factor of any branch breaking on another branch, the steps of determining the semi-invariants of each order of the active power of each branch in the power system under the base state mode, the steps of determining the probability distribution function of the active power of another branch after any branch breaking, and the steps of screening out the serious faults corresponding to the branch breaking according to the probability distribution function, and realizes the application of the breaking distribution factor of any branch breaking on another branch to the static safety analysis of uncertainty, and determines the probability distribution function of another branch after any branch breaking. Based on the probability distribution function, the impact of any branch breaking on another branch can be evaluated, thereby screening out serious faults. Through an efficient fault screening scheme, the impact of massive load participation power uncertainty on the spot market operation can be effectively taken into account, and by filtering out a large number of "harmless" faults, the calculation time can be greatly shortened, and the overall calculation time can meet the needs of online operation of the power spot market.

[0114] In order to more clearly illustrate the effect achieved by the static safety verification method of the power spot market provided by the embodiment of the present invention. The method provided by this embodiment is used to perform uncertainty static safety analysis on the IEEE118 standard example, assuming that 12, 25, 49, and 100 of the power generation nodes are wind power access points, and the other generators are conventional units. The active output of wind power obeys the normal distribution , is the expected value of wind power active power. The system is subjected to branch N-1 disconnection analysis. When calculating, the over-limit probability of 5% is used as the screening threshold, and the static safety verification method proposed in the present invention is used to perform N-1 disconnection analysis. Table 1 is the statistical results of severe fault screening during the N-1 disconnection process. As shown in Table 1, when the number of disconnected branches is 8, the number of over-limit branches is 37, and the fault screening over-limit rate calculated by the method provided in this embodiment is 100%. When the number of disconnected branches is 36, the number of over-limit branches is 54, and the fault screening over-limit rate calculated by the method provided in this embodiment is 11.72%, which will not be described one by one here. 10 severe faults can be screened out by this method.

[0115] Table 1

[0116]

[0117] In order to verify the accuracy of the method proposed in this paper, the branches in the system are disconnected one by one, and the semi-invariant probability power flow algorithm is used to perform detailed analysis and calculation, and the branch over-limit situation under each expected fault is statistically analyzed. Table 2 shows the branch over-limit statistics determined by detailed analysis using the semi-invariant probability power flow algorithm. The detailed statistical results are as follows:

[0118] Table 2

[0119]

[0120] From the statistical results in Table 1 and Table 2, it can be found that among all the expected faults, there are 3 branches that will cause other branches in the system to exceed the limit with a high probability after being disconnected, namely branches 8, 38, and 97. Using the fault screening method proposed in this embodiment, a total of 6 expected serious fault sets are screened out (the disconnection of branches 96 and 107 will cause multiple branches to exceed the limit), which completely include these 3 actual faults, and the fault capture rate reaches 100%.

[0121] Calculation speed is the primary requirement for static safety analysis. In order to better compare the calculation performance of the static safety verification method proposed in the present invention, IEEE118 nodes, IEEE300 node systems and a certain power grid dispatching system model (1079 nodes) are used here for probabilistic static safety analysis. The different calculation times when the fault screening strategy of this scheme is adopted and when the fault screening strategy of this scheme is not adopted are respectively counted. Table 3 shows the different calculation times when the fault screening strategy is adopted and when the fault screening strategy is not adopted. As shown in Table 3, the statistical results are as follows:

[0122] Table 3

[0123]

[0124] It can be seen from the statistical results in Table 3 that the probabilistic static safety analysis method proposed in the present invention greatly improves the computational efficiency of static safety analysis of the system due to the adoption of an effective fault screening method. In terms of overall computational speed, the probabilistic static safety analysis method proposed in the present invention can meet the needs of online operation.

[0125] Embodiment 3

[0126] Figure 3 The present invention provides a schematic diagram of the structure of a static safety verification device for a power spot market provided in the third embodiment of the present invention. The device can be applied to the situation where a large number of load resources participate in the power spot market for static safety analysis. The static safety verification device for the power spot market can be configured in an electronic device, such as Figure 3 As shown, the device includes: a distribution factor determination module 31, a function determination module 32 and a fault screening module 33; wherein,

[0127] The distribution factor determination module 31 is used to determine the semi-invariants of the active power of each branch in the power system in the base state mode and the breaking distribution factor of any branch breaking on another branch according to the clearing result of the power spot market;

[0128] The function determination module 32 is used to determine the probability distribution function of the active power of the other branch after any branch is disconnected according to the semi-invariants of the active power of each branch and the disconnection distribution factor of any branch disconnected on the other branch;

[0129] The fault screening module 33 is used to screen out the serious faults corresponding to the branch circuit breaking according to the probability distribution function, so as to analyze the serious faults.

[0130] The above technical solution realizes the static safety verification of the power spot market with massive load resources. By determining the semi-invariants of each order of the active power of each branch in the base state and the distribution factor of the opening of any branch on the other branch, the probability distribution function of the active power of the other branch after any branch is opened can be further determined, thereby filtering out serious faults. Through efficient fault screening methods, the impact of the power uncertainty of massive loads on the spot market operation can be effectively taken into account, and by filtering out a large number of "harmless" faults, the calculation time can be greatly shortened, and the overall calculation time can meet the needs of online operation of the power spot market.

[0131] Optionally, the distribution factor determination module 31 includes:

[0132] A node sensitivity determination unit, used to determine the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch according to the clearing result;

[0133] A branch sensitivity determination unit, used to determine the sensitivity matrix of active power of each branch to the node injection power according to the sensitivity matrix of each node voltage to the node injection power and the power flow of each branch;

[0134] According to the sensitivity matrix of active power of each branch to node injection power, the breaking distribution factor of any branch on another branch is determined.

[0135] Optionally, the node sensitivity determination unit is specifically configured to:

[0136] According to the clearing results, the power system is subjected to deterministic power flow calculation to determine the base state solution of the node state variables of each node in the base state mode;

[0137] According to the basic state solution, determine the Jacobian matrix and the branch power flow of each branch;

[0138] According to the Jacobian matrix, the sensitivity matrix of each node voltage to the node injection power is determined.

[0139] Optionally, the distribution factor determination module 31 includes:

[0140] The center distance determination unit is used to determine the center distances of each order of active power of each node according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result;

[0141] A first determining unit, configured to determine semi-invariants of each order of active power of each node according to center distances of each order of active power of each node;

[0142] The second determination unit is used to determine the semi-invariants of the active power of each branch in the power system in the base state mode according to the semi-invariants of the active power of each node and the sensitivity matrix of the active power of each branch to the node injection power.

[0143] Optionally, the center distance determination unit is specifically used to:

[0144] For each node, determine the sum of the active power value of the load on the node and the active power value of the new energy unit at each sampling time, and record it as the active power value of the node;

[0145] Determine the expected value of active power according to the active power value of the node at each sampling moment;

[0146] According to each active power value and the expected value of active power, the center distance of each order of active power of the node is determined.

[0147] Optionally, the function determination module 32 includes:

[0148] A breaking semi-invariant determination unit, used to determine the semi-invariants of the active power of each branch after any branch is broken according to the semi-invariants of each order of the active power of each branch and the breaking distribution factor of any branch breaking on another branch;

[0149] The distribution function determination unit is used to process the semi-invariants of each order of the active power of the other branch after any branch is disconnected based on the set series expansion method, and determine the probability distribution function of the active power of the other branch after any branch is disconnected.

[0150] Optionally, the distribution function determination unit is specifically configured to:

[0151] The branch active power state variables are standardized based on the expected value and standard deviation of the active power of the other branch after any branch is disconnected;

[0152] Determine the coefficients of the expansion series according to the semi-invariants and standard deviations of the active power of any branch after the other branch is disconnected;

[0153] According to the coefficient of the expansion series and the probability distribution function of the standard probability distribution, the probability distribution function of the active power of any branch after the other branch is disconnected is determined.

[0154] The fault screening module 33 is specifically used for:

[0155] According to the probability distribution function, determine the probability that the active power of each branch is greater than the set branch power;

[0156] If the probability that the active power of a branch is greater than the set branch power is greater than the set probability threshold, it is determined that the disconnection of the branch corresponding to the probability greater than the set probability threshold has an impact on other branches and is regarded as a serious fault, so as to analyze the serious fault;

[0157] If the probability that the active power of a branch is greater than the set branch power is less than or equal to the set probability threshold, then it is determined that disconnecting the branch corresponding to the probability threshold that is less than or equal to the set probability threshold has no effect on other branches.

[0158] The static safety verification device for the electricity spot market provided in the embodiment of the present invention can execute the static safety verification method for the electricity spot market provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0159] Embodiment 4

[0160] Figure 4 A schematic diagram of the structure of an electronic device provided for Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0161] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0162] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0163] The processor 41 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a static security verification method for a spot electricity market.

[0164] In some embodiments, the static security verification method for the electricity spot market may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the static security verification method for the electricity spot market described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the static security verification method for the electricity spot market in any other appropriate manner (e.g., by means of firmware).

[0165] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0167] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0168] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0169] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0170] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0171] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the static security verification method for the electricity spot market provided in any embodiment of the present invention.

[0172] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code for the operation of the present disclosure, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0173] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0174] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A static security verification method for a power spot market, characterized in that: include: According to the clearing results of the power spot market, the semi-invariants of each order of active power of each branch in the power system under the base state mode and the interruption distribution factor of any branch on another branch are determined; Determine the probability distribution function of the active power of the other branch after any branch is disconnected according to the semi-invariants of the active power of each branch and the disconnection distribution factor of any branch disconnected on the other branch; According to the probability distribution function, the serious faults corresponding to the branch circuit breaking are screened out to analyze the serious faults.

2. The static security verification method for the power spot market according to claim 1 is characterized in that: The above-mentioned method of determining the breaking distribution factor of any branch circuit to another branch circuit according to the clearing result of the electricity spot market includes: Determine, according to the clearing result, a sensitivity matrix of the node voltage of each node in the power system to the node injection power and a branch power flow of each branch; Determine the sensitivity matrix of the active power of each branch to the node injection power according to the sensitivity matrix of each node voltage to the node injection power and the power flow of each branch; According to the sensitivity matrix of the active power of each branch to the node injection power, the breaking distribution factor of any branch breaking on another branch is determined.

3. The static security verification method for the electricity spot market according to claim 2 is characterized in that: Determining the sensitivity matrix of the node voltage of each node in the power system to the node injection power and the branch power flow of each branch according to the clearing result of the power spot market includes: According to the clearing result, a deterministic power flow calculation is performed on the power system to determine a base state solution of a node state variable of each node in a base state mode; Determine the Jacobian matrix and the branch power flow of each branch according to the base state solution; According to the Jacobian matrix, a sensitivity matrix of each node voltage to the node injection power is determined.

4. The static security verification method for the electricity spot market according to claim 2 is characterized in that: The method of determining the semi-invariants of the active power of each branch in the power system in the base state mode according to the clearing result of the power spot market includes: Determine the center distances of each order of active power of each node according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result; Determining semi-invariants of the active power of each node according to the center distances of each order of the active power of each node; According to the semi-invariants of the active power of each node and the sensitivity matrix of the active power of each branch to the node injection power, the semi-invariants of the active power of each branch in the power system in the base state are determined.

5. The static safety verification method for the electricity spot market according to claim 4 is characterized in that: Determining the center distances of each order of active power of each node according to the active power value of the load on each node and the active power value of the new energy unit in the clearing result includes: For each node, determine the sum of the active power value of the load on the node and the active power value of the new energy unit at each sampling time, and record it as the active power value of the node; Determining an expected value of active power according to the active power value of the node at each sampling moment; According to each of the active power values ​​and the expected active power value, the center distances of each order of active power of the node are determined.

6. The static security verification method for the electricity spot market according to claim 1 is characterized in that: The method of determining the probability distribution function of the active power of another branch after any branch is disconnected according to the semi-invariants of the active power of each branch and the disconnection distribution factor of any branch disconnected on another branch comprises: Determine the semi-invariants of the active power of the other branch after any branch is disconnected according to the semi-invariants of the active power of the branches and the disconnection distribution factor of any branch disconnected on the other branch; Based on the set series expansion method, the semi-invariants of each order of the active power of the other branch after any branch is disconnected are processed to determine the probability distribution function of the active power of the other branch after any branch is disconnected.

7. The static security verification method for the electricity spot market according to claim 6 is characterized in that: The set series expansion method is used to process the semi-invariants of each order of the active power of the other branch after any branch is disconnected, and determine the probability distribution function of the active power of the other branch after any branch is disconnected, including: The branch active power state variables are standardized based on the expected value and standard deviation of the active power of the other branch after any branch is disconnected; Determine the coefficient of the expansion series according to the semi-invariants of each order and the standard deviation of the active power of the other branch after any branch is disconnected; According to the coefficient of the expansion series and the probability distribution function of the standard probability distribution, the probability distribution function of the active power of any branch after the other branch is disconnected is determined.

8. The static security verification method for the electricity spot market according to claim 1, characterized in that: The step of screening out the serious faults corresponding to the branch circuit breaking according to the probability distribution function to analyze the serious faults includes: Determining, according to the probability distribution function, the probability that the active power of each branch is greater than the set branch power; If the probability that the active power of the branch is greater than the set branch power is greater than the set probability threshold, it is determined that the disconnection of the branch corresponding to the probability greater than the set probability threshold has an impact on other branches and is regarded as a serious fault, so as to analyze the serious fault; If the probability that the active power of the branch is greater than the set branch power is less than or equal to the set probability threshold, it is determined that the disconnection of the branch corresponding to the probability less than or equal to the set probability threshold has no effect on other branches.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the static security verification method for the electricity spot market as described in any one of claims 1-8.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the static security verification method for the electricity spot market as described in any one of claims 1-8.

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