An extreme weather based power grid flow optimization method and related device based on accident chain

Abstract

The application provides an extreme weather-based power grid power flow optimization method based on an accident chain and a related device, which comprises the following steps: identifying a high-risk branch according to power grid basic data, and including the high-risk branch in an initial fault set; taking the high-risk branch as a starting point, performing accident chain searching, determining an overload branch, and calculating a risk value of each accident chain; screening out an accident chain with a risk value exceeding a threshold value, performing power grid power flow optimization, and minimizing the sum of a generator output adjustment cost and an accident chain risk cost; obtaining a generator output adjustment scheme through optimization solving, and verifying whether the optimized power grid still has a high-risk accident chain; if yes, re-optimizing; otherwise, determining a final scheme. The application takes a high-risk branch under extreme weather as an initial opening branch, screens all possible accident chains, performs power flow optimization by considering an accident chain risk cost, simultaneously performs post-verification, ensures that the optimized power grid will not have other serious accident chains, and provides auxiliary support for power grid dispatchers.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system safety constraint optimization scheduling, and particularly relates to a power grid power flow optimization method based on accident chains under extreme weather and a related device. BACKGROUND

[0002] In recent years, extreme weather has occurred frequently worldwide, and large blackouts caused by extreme weather have occurred from time to time. According to relevant power research, most of the large blackouts are caused by cascading failures, which specifically means that local small disturbances such as generator and transmission line failures cause a series of power grid component failures through network connection, resulting in huge economic losses and adverse social impacts.

[0003] Under extreme weather, the failure probability of some transmission lines is significantly increased due to meteorological environmental factors such as wind speed and ice thickness. If these high-risk lines are also vulnerable lines that cause system cascading failures, their opening is likely to induce the occurrence of large blackout accidents. Therefore, it is of great significance to quickly and accurately locate the cascading failure development path and develop targeted prevention and control before the cascading failure to ensure the safe operation of the power system and reduce the loss of power failure.

[0004] At present, the research on cascading failures usually selects the branch with the largest risk index as the lower opening branch of the accident chain, but this may miss some accident chain paths with more serious consequences. In terms of accident chain prevention and control, most of the research is from the perspective of relay protection, and the prevention and control scheme is given to maximize the safety margin of the power grid, but it cannot reflect the risk consequences caused by cascading failures to the power grid. SUMMARY

[0005] To reduce the risk of cascading failures caused by high-risk line failures under extreme weather and lead to large blackout accidents, and to address the problems in the current cascading failure prevention and control, the application provides a power grid power flow optimization method based on accident chains under extreme weather and a related device, which takes the high-risk branch under extreme weather as the initial opening branch, screens all possible accident chains, and optimizes the power flow of the power grid to minimize the sum of the adjustment cost of generator output and the risk cost of the accident chain, thereby reducing the risk consequences caused by cascading failures as much as possible, and ensuring that no other serious accident chain occurs after the power flow optimization through post-validation, thereby providing auxiliary support for power grid dispatchers.

[0006] To achieve the above purpose, the technical solution provided by the application is as follows:

[0007] In a first aspect, the application provides a power grid power flow optimization method based on accident chains under extreme weather, which includes the following steps:

[0008] screening high-risk branches of the target power grid under extreme weather according to basic data of the target power grid, and adding the high-risk branches into an initial fault set;

[0009] performing accident chain search on a high-risk branch in the initial fault set as an initial opening branch of a corresponding accident chain, stopping the search when a stop condition is reached, and sequentially taking overload branches searched as lower-level opening branches of the corresponding accident chain, so as to obtain an accident chain corresponding to the high-risk branch;

[0010] calculating a minimum cut load value of the power grid guarantee line under a fault path of the accident chain based on direct current flow calculation, and taking a product of an accident chain occurrence probability and the minimum cut load value as a risk value of the accident chain;

[0011] performing accident chain search on each high-risk branch in the initial fault set and calculating risk values of each accident chain searched, screening accident chains with risk values exceeding a set threshold, and adding the accident chains into an accident chain set;

[0012] based on the obtained accident chain set, performing power flow optimization solving on the target power grid under extreme weather to obtain an optimized generator output adjustment scheme, with a sum of an adjustment cost of generator output and a risk cost of the accident chain as a target;

[0013] judging whether there is an accident chain with a risk value exceeding a set threshold generated in the target power grid optimized according to the generator output adjustment scheme, if yes, adding a new accident chain into the accident chain set and re-performing power flow optimization solving, and if no, taking the finally obtained generator output adjustment scheme as a power grid power flow optimization scheme.

[0014] Further, for an accident chain L with v links, an accident chain occurrence probability is calculated according to the following formula:

[0015]

[0016] In the formula, is a probability of occurrence of an initial opening fault of the accident chain L; is a probability of occurrence of each link of the accident chain L;

[0017] a probability of occurrence of a fault of a transmission line l corresponding to each link of the accident chain L is calculated according to the following formula:

[0018]

[0019] In the formula, is a probability of occurrence of a fault of the transmission line l; is power on the transmission line l; is a transmission capacity limit of the transmission line l; is a probability of protection hidden fault; and b is a limit multiple.

[0020] Further, the minimum cut load value of the power grid protection line flow safety under the fault path of the accident chain based on the direct current flow calculation includes:

[0021] cutting off all lines under the fault path of the accident chain from the target power grid;

[0022] performing direct current flow calculation on the target power grid to obtain the minimum cut load value caused by the accident chain.

[0023] Further, when performing flow optimization solving on the target power grid under extreme weather, the flow optimization model is constructed for solving, the objective function of the flow optimization model is the sum of the minimum adjustment cost of generator output and the risk cost of the accident chain, the risk cost of the accident chain is determined based on the accident chain risk value, and the flow optimization model multiplies the failure probability of each link of the accident chain, i.e. the accident chain occurrence probability, as a variable to participate in the risk cost calculation of the accident chain, thereby reducing the multiplication order of the variable in the objective function.

[0024] Further, the objective function of the flow optimization model is as follows:

[0025]

[0026] In the formula, is the number of generator nodes, i is the node index; , are the adjustment cost coefficient and the adjustment amount of the i-th generator node output, respectively; is the number of selected accident chains, j is the accident chain index; is the risk value of the j-th accident chain; b is the risk cost coefficient of the accident chain.

[0027] Further, when calculating the failure probability of the power transmission line l, the power on the power transmission line l also needs to consider the influence of the upper breaking branch breaking on the active power flow of the lower branch. For the link t of the accident chain L propagation, assuming that the upper breaking branch is km, the direct current method is used to quantify the influence of the breaking of the branch km on the flow of other branches, and then:

[0028]

[0029] In the formula, is the active power flow of the branch km; M is a row vector with the k-th element being 1, the m-th element being -1, and the rest being 0, is the voltage phase angle change amount of the node k and the node m, and B is the electric capacity;

[0030] According to the susceptance and voltage phase angle change of the branch km, the active power flow increment of the lower branch can be obtained.

[0031] Further, the stop condition comprises:

[0032] Any one of the following conditions: the power grid is split, the accident chain search reaches the maximum prediction depth, and a certain stage of the accident chain search does not cause the lower branch overload.

[0033] In a second aspect, the present application provides an extreme weather based power grid flow optimization device based on an accident chain, comprising:

[0034] An initial fault set generation module is configured to filter high-risk branches of a target power grid under extreme weather according to basic data of the target power grid, and include the high-risk branches in an initial fault set;

[0035] An accident chain search module is configured to perform accident chain search on a high-risk branch in the initial fault set as an initial opening branch of a corresponding accident chain, stop the search when a stop condition is reached, and sequentially take the overload branches obtained by the search as lower opening branches of the corresponding accident chain, so as to obtain the accident chain corresponding to the high-risk branch;

[0036] A risk value calculation module is configured to calculate the minimum cut load value of the power grid guarantee line flow safety under the fault path of the accident chain based on direct current flow, and take the product of the accident chain occurrence probability and the minimum cut load value as the risk value of the accident chain;

[0037] An accident chain set generation module is configured to perform accident chain search on each high-risk branch in the initial fault set, calculate the risk value of each accident chain obtained by the search, filter the accident chains with risk values exceeding a set threshold, and include the accident chains in an accident chain set;

[0038] A flow optimization solving module is configured to perform flow optimization solving of the target power grid under extreme weather based on the obtained accident chain set, so as to obtain an optimized generator output adjustment scheme, with the sum of the generator output adjustment cost and the risk cost of the accident chain being minimized as the target.

[0039] A flow optimization judgment module is configured to judge whether there is an accident chain with a risk value exceeding a set threshold generated in the target power grid optimized according to the generator output adjustment scheme, if yes, include the new accident chain in the accident chain set and perform flow optimization solving again, and if no, take the finally obtained generator output adjustment scheme as the power grid flow optimization scheme.

[0040] In a third aspect, the present application provides a computer device, comprising a processor and a memory:

[0041] The memory is configured to store a computer program and send instructions of the computer program to the processor;

[0042] The processor executes the instructions of the computer program to perform the method for power grid power flow optimization based on accident chain under extreme weather according to the first aspect.

[0043] In a fourth aspect, the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for power grid power flow optimization based on accident chain under extreme weather according to the first aspect.

[0044] In summary, the present application provides a method for power grid power flow optimization based on accident chain under extreme weather and related devices, including identifying high-risk branches according to power grid basic data, and including the high-risk branches in the initial fault set. Then, taking the high-risk branches as the starting point, the accident chain search is performed to determine the overload branches, and the risk value of each accident chain is calculated. The accident chains with risk values exceeding the threshold value are screened out, and the power grid power flow optimization is performed, and the target is to minimize the sum of the generator output adjustment cost and the accident chain risk cost. Through optimization solution, the generator output adjustment scheme is obtained, and whether the optimized power grid still has a high-risk accident chain is verified. If yes, re-optimization is performed; otherwise, the final scheme is determined. The present application takes the high-risk branches under extreme weather as the initial opening branches, screens all possible accident chains, takes the sum of the generator output adjustment cost and the accident chain risk cost as the minimum as the target, performs the power flow optimization on the power grid, reduces the risk consequences caused by the cascading failures as much as possible, and guarantees that the power grid after the power flow optimization will not have other serious accident chains, thereby providing auxiliary support for the power grid dispatchers. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0046] Figure 1 A flowchart of a method for power grid power flow optimization based on accident chain under extreme weather provided by an embodiment of the present application is shown in the figure.

[0047] Figure 2 A schematic diagram of accident chain search provided by an embodiment of the present application is shown in the figure.

[0048] Figure 3 An IEEE39 node system (red for high-risk branches) schematic diagram provided by an embodiment of the present application is shown in the figure.

[0049] Figure 4 A schematic diagram of the search result of the accident chain provided by an embodiment of the present application is shown in the figure.

[0050] Figure 5 An accident chain diagram after power flow optimization provided by the embodiment of the present application;

[0051] Figure 6 A composition block diagram of an extreme weather based power grid power flow optimization device based on an accident chain provided by the embodiment of the present application;

[0052] Figure 7 A composition block diagram of a computer device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, characteristics and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the embodiments described below are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0054] The embodiment of the present application provides an extreme weather based power grid power flow optimization method based on an accident chain, including the following steps:

[0055] Step one: according to the basic data of the target power grid, high-risk branches of the target power grid under extreme weather are screened and included in the initial fault set.

[0056] It should be noted that this step provides basic data for subsequent analysis by loading power grid model, normal operation mode, meteorological information and other information.

[0057] According to the wind speed, line icing thickness and other meteorological information, the fault probability of each power transmission line is calculated, and the high-risk branches with fault probability exceeding a certain threshold are included in the initial fault set.

[0058] Step two: one high-risk branch in the initial fault set is taken as the initial opening branch of the corresponding accident chain for accident chain search, and the search is stopped when the stop condition is reached, and the overload branch obtained by the search is taken as the lower level opening branch of the corresponding accident chain in turn, so as to obtain the accident chain corresponding to the high-risk branch.

[0059] It should be noted that the direct current power flow calculation is a simplified power system power flow calculation method, which only considers active power and voltage phase angle, and ignores reactive power and voltage amplitude change. In the power flow calculation, the branch with current exceeding the rated value is the overload branch.

[0060] The step selects a high-risk branch in the initial fault set to search for an accident chain. The high-risk branch is taken as the initial opening branch of the accident chain, the branch is disconnected, the network parameters are modified, the DC power flow calculation is performed on the target power grid, all overload branches are taken as the lower-level opening branches of the accident chain in turn, the process is repeated, and the accident chain search is terminated when the stop condition is met. At this time, all accident chains starting from the high-risk branch are obtained.

[0061] Step three: based on the DC power flow calculation, the minimum cut load value of the power grid security line is searched along the fault path of the accident chain, and the product of the accident chain occurrence probability and the minimum cut load value is taken as the risk value of the accident chain.

[0062] It should be noted that the fault path is the path along the accident chain from the initial opening branch to the lower-level other branch. The minimum cut load value is the minimum load amount that needs to be cut off to ensure the safe operation of the power grid.

[0063] The product of the accident chain occurrence probability and the minimum cut load value is taken as the risk value of the accident chain, which is used to evaluate the potential risk of the accident chain.

[0064] Step four: each high-risk branch in the initial fault set is searched for an accident chain, and the risk value of each accident chain searched is calculated. The accident chains with risk values exceeding the set threshold are screened and included in the accident chain set.

[0065] Step five: based on the obtained accident chain set, the sum of the adjustment cost of the generator output and the risk cost of the accident chain is minimized, and the power flow optimization solution of the target power grid under extreme weather is obtained. The optimized generator output adjustment scheme is obtained.

[0066] It should be noted that the generator output adjustment is to adjust the output of the generator according to the operating state of the power grid and the optimization target. The power flow optimization solution is to solve the optimal operating state of the power grid under extreme weather through mathematical optimization method.

[0067] The step minimizes the cost of generator output adjustment and the risk cost of the accident chain when performing power flow optimization solution.

[0068] Step six: determine whether there is an accident chain with a risk value exceeding the set threshold in the optimized target power grid according to the generator output adjustment scheme. If yes, the new accident chain is included in the accident chain set and the power flow optimization solution is performed again. If not, the final generator output adjustment scheme is taken as the power flow optimization scheme of the power grid.

[0069] It should be noted that after obtaining the preliminary optimization result, it is necessary to check again whether there are new high-risk accident chains in the power grid which are not considered. If there are, these newly discovered accident chains are added to the accident chain set, and the optimization process is re-executed. This cycle will continue until no new high-risk accident chains are found, and the final output is the power flow distribution scheme after multiple iterations of optimization.

[0070] The embodiment provides an accident chain-based power grid power flow optimization method under extreme weather. Unlike the traditional method which only selects the branch with the maximum risk index as the next level opening object, in the embodiment, all overloaded branches are taken as potential next level opening objects for further search in the accident chain search process. This means that even if the risk index of some branches is not the highest, if they can lead to more serious consequences in the future development, they are also considered, thereby avoiding omission of important accident chain paths.

[0071] In addition, the traditional preventive control scheme often focuses on improving the safety margin of the power grid, and ignores the actual economic loss and social impact caused by cascading failures. The embodiment introduces the risk value concept of the accident chain, combines the accident chain occurrence probability with the minimum load shedding value, and quantitatively evaluates the potential threat of each accident chain to the power grid. On this basis, optimization is solved with the minimum sum of generator output adjustment cost and accident chain risk cost as the target, and the balance between economic benefit and safety performance is realized.

[0072] Please refer to Figure 1 , Figure 1 is a flowchart of an accident chain-based power grid power flow optimization method under extreme weather. The following Figure 1 introduces other embodiments of the application.

[0073] In an embodiment of the application, without considering the influence of the external environment, when the line flow does not exceed the flow limit, the probability of transmission line fault trip is the implicit failure probability of the relay protection, and the value is close to 0. In the development and propagation process of the accident chain, the power grid dispatchers often take corresponding blocking measures, so the search depth of the accident chain will not exceed the maximum depth (usually 4). In the actual power grid, the accident chain triggered power grid splitting usually leads to the occurrence of a large-scale power failure. Therefore, the stop condition of the accident chain search is: 1) the power grid is split; 2) the accident chain search reaches the maximum prediction depth; 3) the accident chain search at a certain stage does not lead to the overload of the lower branch. When any condition is met, the accident chain search stops. The search process of the accident chain is as shown in Figure 2 .

[0074] In one embodiment of the present application, a method for calculating the probability of occurrence of an accident chain is provided.

[0075] In this embodiment, a piecewise linear function is used to describe the relationship between the fault probability of a transmission line and the power on the line, i.e.

[0076] (1)

[0077] In the formula, is the fault probability of the transmission line l; is the power on the transmission line l; is the transmission capacity limit of the transmission line l; is the probability of hidden fault protection; b is the limit multiple, which is 1.4, i.e. when the power on the transmission line exceeds 1.4 times the transmission capacity limit, the line is disconnected due to the action of the protection device, at which time the fault probability is 1.

[0078] For an accident chain L with v links, the probability P of occurrence of the accident chain L is: L

[0079] (2)

[0080] In the formula, p l0 is the probability of occurrence of the initial open fault of the accident chain L; p l1 ~p lv is the probability of occurrence of each link of the accident chain.

[0081] In one embodiment of the present application, the minimum cut load value for ensuring the power flow safety of the power grid based on the fault path of the accident chain includes:

[0082] All lines in the fault path of the accident chain are cut from the target power grid;

[0083] The sum of the cut load of all nodes for ensuring the power flow safety of the power grid is minimized, and a direct current power flow calculation is performed on the target power grid to obtain the minimum cut load value caused by the occurrence of the accident chain.

[0084] Suppose that a certain accident chain has k line faults, and when the k lines are cut, the minimum cut load value for ensuring the power flow safety of the power grid is calculated based on the direct current power flow. The calculation of the minimum cut load value is introduced below by taking a specific objective function and constraint condition as an example. The objective function for calculating the minimum cut load value is

[0085] (3)

[0086] In the formula, n B is the number of nodes of the power grid; is the cut load of the i-th node. ​

[0087] The satisfied constraint conditions include:

[0088] Node load shedding constraint:

[0089] (4)

[0090] In the formula, S N is the node set of the power system; D i is the load size of node i.

[0091] Unit output constraint:

[0092] (5)

[0093] In the formula, S G is the generator node set of the power system; PG i is the output of the i-th generator node; PG i_min and PG i_max are the minimum and maximum technical outputs of the i-th generator node, respectively.

[0094] Line flow safety constraint:

[0095] (6)

[0096] In the formula, S L is the line set of the power system; P ij is the flow of line ij; P ijmax is the transmission capacity limit of line ij.

[0097] Node power balance constraint:

[0098] (7)

[0099] DC flow constraint:

[0100] (8)

[0101] In the formula, , are the power angle values of nodes i and j, respectively; x ij is the reactance of line ij.

[0102] The risk value R L of accident chain L is defined as:

[0103] (9)

[0104] In the formula, SL is the load shedding value caused by the occurrence of accident chain L.

[0105] In one embodiment of the present invention, when performing power flow optimization on the target power grid under extreme weather conditions, a power flow optimization model is constructed for the solution. The objective function of the power flow optimization model is to minimize the sum of the generator output adjustment cost and the risk cost of the accident chain. The risk cost of the accident chain is determined based on the risk value of the accident chain. The power flow optimization model uses the product of the failure probabilities of each link in the accident chain, i.e., the probability of the accident chain occurring, as a variable in the calculation of the risk cost of the accident chain, thereby reducing the order of the multiplication of variables in the objective function.

[0106] The established power flow optimization model's objective function involves the product of the failure probabilities of each link in the accident chain. Treating the failure probabilities of each link in the accident chain as variables, if the order of the multiplication of these variables is too high, the model becomes difficult to solve. Using intelligent algorithms such as particle swarm optimization or genetic algorithms often fails to yield a globally optimal solution. This embodiment treats the product of the failure probabilities of multiple links in the accident chain as a new variable, thereby reducing the order of the multiplication of variables in the objective function. Then, commercial solvers such as CPLEX and GUROBI can be used to solve the problem.

[0107] In a further embodiment of the present invention, a power flow optimization model is established based on the obtained set of accident chains, and its objective function is:

[0108] (10)

[0109] In the formula, n G The number of generator nodes; a i , These represent the adjustment cost coefficient and adjustment amount for the output of the i-th generator node, respectively; n L R is the number of incident chains obtained through screening; j is the risk value of the j-th accident chain; b is the risk cost coefficient of the accident chain.

[0110] The constraints that must be satisfied can be:

[0111] Power balance constraints:

[0112] (11)

[0113] Generator output adjustment constraints:

[0114] (12)

[0115] Line power flow safety constraints:

[0116] (13)

[0117] In the formula, PTDF is the power transfer distribution factor matrix of the power grid; P is the injected power matrix; For generator output adjustment matrix; Fmax This is the power flow capacity matrix for the lines.

[0118] In one embodiment of the present invention, when calculating the probability of a fault occurring in transmission line l, the power on transmission line l also needs to consider the impact of the opening of the upstream branch on the active power flow of the downstream branch. For the link t( of the fault chain L propagation)... Assuming the upstream disconnected branch is km, the DC method is used to quantify the impact of the disconnection of branch km on the power flow of other branches. That is, when branch km is not disconnected, the power grid operation satisfies:

[0119] (14)

[0120] When the branch road km is disconnected, the following conditions are met:

[0121] (15)

[0122] By omitting the terms resulting from the product of two increments, we obtain:

[0123] (16)

[0124] By combining equations (14) and (16), we can obtain:

[0125] (17)

[0126] Further conversion yields:

[0127] (18)

[0128] In the formula, P km Let M be the active power flow of branch km; M is a row vector where the k-th element is 1, the m-th element is -1, and the rest are all 0.

[0129] According to equation (18), after the branch km is disconnected at link t in the fault chain L, the increment of the active power flow of the lower branch can be expressed as a linear function of the active power flow of the branch km. Then, according to equation (1), the fault probability of the lower branch can be mapped.

[0130] To verify the rationality of the method proposed in this invention, a simulation was performed on the IEEE 39-node system. Branches 1-2, 2-3, and 4-5 were assumed to be high-risk branches under extreme disaster conditions. Figure 3 As shown in Table 1, the active power limit of the branch is defined as 0.9 times the power flow capacity, the prediction depth of the fault chain is dmax=3, and the generator adjustment cost coefficient and output range are shown in Table 1. The risk cost coefficient is b=100 / MW, and the fault probability of the initial disconnected branch is set to 1. After establishing the model, Gurobi is called to solve it.

[0131] Table 1. Information on the IEEE 39-Node System

[0132]

[0133] Through fault chain search, disconnecting branches 2-3 and 4-5 will not cause overload on other branches, so no fault chain will be generated. Disconnecting branch 1-2 may trigger 4 fault chains: [1-2, 2-3, 26-27], [1-2, 2-3, 25-26], [1-2, 2-3, 17-18, 26-27], and [1-2, 2-3, 17-18, 25-26]. The fault chain search results are as follows: Figure 4 As shown in the figure, the results show that the fault chains [1-2, 2-3, 25-26] will not cause load shedding, and the risk value of the fault chains [1-2, 2-3, 17-18, 25-26] is much smaller than that of the remaining two chains. Therefore, the fault chains [1-2, 2-3, 26-27] and [1-2, 2-3, 17-18, 26-27] are included in the fault chain set for power flow optimization of the power system.

[0134] The results of the power flow optimization are shown in Table 2. The optimized accident chain is as follows: Figure 5 As shown, after adjusting the power output of the grid generators, the expected load shedding of the fault chain decreased from 11.835MW before optimization to 0.670MW, and the risk value of the cascading failure fault chain was significantly reduced. Furthermore, post-validation showed that no other fault chains were generated after the power flow optimization, and this optimization scheme can be directly output to provide auxiliary support for dispatchers.

[0135] Table 2. Power Flow Optimization Results

[0136]

[0137] Based on the same inventive concept, this application also provides an extreme weather-based fault chain-based power flow optimization device for implementing the above-mentioned extreme weather-based fault chain-based power flow optimization method. The solution provided by this device is similar to the solution described in the above-described method. Therefore, the specific limitations in the embodiments of the extreme weather-based fault chain-based power flow optimization device provided below can be found in the limitations of the extreme weather-based fault chain-based power flow optimization method described above, and will not be repeated here.

[0138] Please see Figure 6 This invention provides a power flow optimization device based on fault chains under extreme weather conditions, comprising:

[0139] The initial fault set generation module is used to filter high-risk branches of the target power grid under extreme weather conditions based on the basic data of the target power grid and include them in the initial fault set;

[0140] The accident chain search module is used to search for the accident chain by taking a high-risk branch in the initial fault set as the initial disconnected branch of the corresponding accident chain. When the stopping condition is met, the search stops and the overloaded branches obtained by the search are used as the lower disconnected branches of the corresponding accident chain in turn, thereby obtaining the accident chain corresponding to the high-risk branch.

[0141] The risk value calculation module is used to calculate the minimum load shedding value for ensuring power flow safety of the power grid under the fault path of the accident chain based on DC power flow, and to use the product of the probability of the accident chain occurrence and the minimum load shedding value as the risk value of the accident chain.

[0142] The accident chain set generation module is used to search for accident chains for each high-risk branch in the initial fault set and calculate the risk value of each accident chain obtained by the search. Accident chains with risk values ​​exceeding the set threshold are filtered and included in the accident chain set.

[0143] The power flow optimization solution module is used to perform power flow optimization on the target power grid under extreme weather conditions, based on the obtained set of accident chains, with the goal of minimizing the sum of the generator output adjustment cost and the risk cost of the accident chains, and to obtain an optimized generator output adjustment scheme.

[0144] The power flow optimization judgment module is used to determine whether there is a fault chain with a risk value exceeding a set threshold after the target power grid is optimized according to the generator output adjustment scheme. If so, the newly added fault chain is included in the fault chain set and the power flow optimization solution is performed again. If not, the final generator output adjustment scheme is used as the power flow optimization scheme.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0146] Reference Figure 7The present invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it implements the power flow optimization method based on fault chains under extreme weather conditions as described in any of the above methods.

[0147] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 7 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0148] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0149] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0150] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the power flow optimization method based on fault chains under extreme weather conditions as described in any of the above methods.

[0151] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0152] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0153] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0154] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for power grid flow optimization based on accident chain under extreme weather, characterized in that, The method comprises the following steps: According to the basic data of the target power grid, high-risk branches of the target power grid under extreme weather are screened and included in an initial fault set; An accident chain search is performed on one high-risk branch in the initial fault set, taking the high-risk branch as an initial opening branch of a corresponding accident chain, and the search is stopped when a stop condition is reached, and an overload branch searched is taken as a lower-level opening branch of the corresponding accident chain in sequence, so as to obtain an accident chain corresponding to the high-risk branch; A minimum cut load value of a power grid protection line under a fault path of the accident chain is calculated based on direct current flow, and a product of an accident chain occurrence probability and the minimum cut load value is taken as a risk value of the accident chain; Each high-risk branch in the initial fault set is subjected to an accident chain search, and a risk value of each accident chain searched is calculated, and the accident chains whose risk values exceed a set threshold are screened and included in an accident chain set; Based on the obtained accident chain set, a power flow optimization under extreme weather is solved for the target power grid, taking a sum of an adjustment cost of generator output and a risk cost of the accident chain as a minimum target, to obtain an optimized generator output adjustment scheme; It is judged whether the target power grid optimized according to the generator output adjustment scheme has an accident chain whose risk value exceeds the set threshold, if yes, the new accident chain is included in the accident chain set and the power flow optimization is solved again, and if no, the finally obtained generator output adjustment scheme is taken as a power grid power flow optimization scheme; For a chain L of v links, the probability of occurrence of the chain L is is calculated according to the following formula: ； wherein is the probability of the initial opening fault of the accident chain L; is the probability of the occurrence of each link of the accident chain L; The probability that each link of the accident chain L has a fault of the transmission line l is calculated according to the following formula: ； In the formula, The probability of a fault occurring in transmission line l; The power on transmission line l; This represents the transmission capacity limit of transmission line l; To protect against the probability of latent faults; b is the limit multiple.

2. The method for power grid flow optimization based on accident chain in extreme weather according to claim 1, characterized in that, The minimum cut load value of the power grid protection line under the fault path of the accident chain is calculated based on the direct current flow, which comprises: All lines under the fault path of the accident chain are cut from the target power grid; The direct current flow of the target power grid is calculated, taking the sum of cut load amounts of all nodes of the power grid protection line under the flow safety as a minimum target, to obtain the minimum cut load value caused by the accident chain.

3. The method for power grid flow optimization based on accident chain in extreme weather according to claim 1, characterized in that, When the power flow optimization under extreme weather is solved for the target power grid, the optimization is solved by constructing a power flow optimization model, a target function of the power flow optimization model is a sum of the adjustment cost of the generator output and the risk cost of the accident chain, the risk cost of the accident chain is determined based on an accident chain risk value, and the product of the fault occurrence probability of each link of the accident chain, i.e., the accident chain occurrence probability, is taken as a variable and is involved in the risk cost calculation of the accident chain, so as to reduce the multiplication order of the variable in the target function.

4. The method for power flow optimization in an electrical grid based on a chain of accidents in extreme weather according to claim 3, characterized in that, The target function of the power flow optimization model is as follows: ； In the formula, is the number of generator nodes, i is the node index; , is the adjustment cost coefficient of the i th generator node output, and is the adjustment amount; is the number of accident chains screened out, and j is the accident chain index; is the risk value of the j th accident chain; b is a risk cost coefficient of the accident chain.

5. The method for power flow optimization based on accident chain in extreme weather according to claim 1, characterized in that, When the probability that the transmission line l has a fault is calculated, the power on the transmission line l also needs to consider the influence of the active power flow of the lower-level branch after the opening of the upper-level opening branch, for the link t of the accident chain L, assuming that the upper-level opening branch is km, the direct current method is used to quantify the influence of the opening of the branch km on the power flow of other branches, and the following formula is obtained: ； In the formula, is the active power flow of branch km; M is a row vector whose kth element is 1, whose mth element is -1, and whose other elements are all 0, is the voltage phase angle change amount of node k and node m, and B is a susceptance. According to the susceptance of the branch km and the voltage phase angle change amount, the active power flow increment of the lower-level branch can be obtained.

6. The method for power grid flow optimization based on accident chain in extreme weather according to claim 1, characterized in that, The stop condition comprises any one of: a power grid splitting, an accident chain search reaching a maximum predicted depth, and a certain stage of the accident chain search not leading to an overload of a lower-level branch.

7. An apparatus for power flow optimization of power grid based on accident chain under extreme weather, characterized in that, comprise: an initial fault set generation module configured to filter high-risk branches of a target power grid under extreme weather from basic data of the target power grid and include the high-risk branches in an initial fault set; an accident chain search module configured to perform accident chain search on a high-risk branch in the initial fault set as an initial opening branch of a corresponding accident chain, stop the search when a stop condition is reached, and sequentially take an overload branch obtained by the search as a lower-level opening branch of the corresponding accident chain, so as to obtain an accident chain corresponding to the high-risk branch; a risk value calculation module configured to calculate a minimum cut load value of a power grid guarantee line flow safety under a fault path of the accident chain based on direct current flow, and take a product of an accident chain occurrence probability and the minimum cut load value as a risk value of the accident chain; an accident chain set generation module configured to perform accident chain search on each high-risk branch in the initial fault set, calculate risk values of each accident chain obtained by the search, filter the accident chains whose risk values exceed a set threshold, and include the accident chains in an accident chain set; a power flow optimization solution module configured to perform power flow optimization solution on the target power grid under extreme weather based on the obtained accident chain set, so as to obtain an optimized generator output adjustment scheme, with a sum of a generator output adjustment cost and a risk cost of the accident chain as a target; a power flow optimization judgment module configured to judge whether the target power grid after optimization according to the generator output adjustment scheme has an accident chain whose risk value exceeds the set threshold, if yes, include a newly added accident chain in the accident chain set and perform power flow optimization solution again, and if no, take the finally obtained generator output adjustment scheme as a power grid power flow optimization scheme; For a chain L of v links, the probability of occurrence of the chain L is is calculated according to the following formula: ； wherein is the probability of the initial opening fault of the accident chain L; is the probability of the occurrence of each link of the accident chain L; a probability that a transmission line l corresponding to each link of an accident chain L fails is calculated according to the following formula: ； In the formula, The probability of a fault occurring in transmission line l; The power on transmission line l; This represents the transmission capacity limit of transmission line l; To protect against the probability of latent faults; b is the limit multiple.

8. A computer device, comprising: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes the instructions of the computer program to perform the power grid power flow optimization method under extreme weather based on an accident chain according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the power grid power flow optimization method under extreme weather based on an accident chain according to any one of claims 1-6.

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