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

Abstract

The invention provides a power grid power flow optimization method based on an accident chain in extreme weather and a related device, and the method comprises the steps: recognizing a high-risk branch according to the basic data of a power grid, and bringing the high-risk branch into an initial fault set; and carrying out accident chain search by taking the high-risk branch as a starting point, determining an overload branch, and calculating a risk value of each accident chain. The accident chain with the risk value exceeding the threshold value is screened out, power grid power flow optimization is carried out, and the target is to minimize the sum of the generator output adjustment cost and the accident chain risk cost. And through optimization solution, a generator output adjustment scheme is obtained, and whether the optimized power grid still has a high-risk accident chain is verified. If so, re-optimizing; otherwise, determining a final scheme. According to the method, a high-risk branch in extreme weather is used as an initial cut-off branch, all possible accident chains are screened, power flow optimization is carried out by considering the risk cost of the accident chains, meanwhile, after verification is passed, it is guaranteed that no other serious accident chains are generated in the optimized power grid, and auxiliary support is provided for power grid dispatching personnel.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system safety constraint optimization dispatching, and specifically relates to a power grid flow optimization method based on accident chain under extreme weather conditions and related devices. Background Art

[0002] In recent years, extreme weather has occurred frequently around the world, and major power outages caused by extreme weather have occurred from time to time. According to relevant power research, most of the major power outages are caused by cascading failures, specifically, local small disturbances such as generator and transmission line failures trigger a series of power grid component failures through network connections, causing huge economic losses and adverse social impacts.

[0003] In extreme weather, affected by meteorological environmental factors such as wind speed and ice thickness, the probability of failure of some transmission lines increases significantly. If these high-risk lines are also vulnerable lines that cause system cascading failures, they are likely to cause major power outages after they are disconnected. Therefore, it is of great significance to quickly and accurately locate the evolution path of cascading failures and formulate targeted preventive control measures before cascading failures, in order to ensure the safe operation of the power system and reduce the losses caused by power outages.

[0004] In the current research on cascading failures, in terms of accident chain search, the branch with the largest risk index is usually selected as the lower-level disconnecting 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 them start from the perspective of relay protection and give prevention and control plans with the goal of maximizing the safety margin of the power grid, but they cannot reflect the risk consequences of cascading failures on the power grid. Summary of the invention

[0005] In order to reduce the risk of high-risk line faults in power grids causing cascading failures and leading to major power outages under extreme weather conditions, and in response to the problems currently existing in the prevention and control of cascading failures, the present invention provides a power grid flow optimization method and related devices based on accident chains under extreme weather conditions, taking high-risk branches under extreme weather conditions as initial disconnected branches, screening all possible accident chains, and optimizing the power grid flow with the goal of minimizing the sum of the adjustment cost of the generator output and the risk cost of the accident chain, minimizing the risk consequences caused by cascading failures as much as possible, and at the same time, through post-verification, ensuring that the power grid after flow optimization will not have other serious accident chains, providing auxiliary support for power grid dispatchers.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, this aspect provides a method for optimizing power flow of a power grid based on a fault chain under extreme weather conditions, comprising the following steps:

[0008] According to the basic data of the target power grid, the high-risk branches of the target power grid under extreme weather conditions are screened and included in the initial fault set;

[0009] A high-risk branch in the initial fault concentration is used as the initial disconnected branch of the corresponding fault chain to search for the fault chain. When the stop condition is reached, the search is stopped, and the overloaded branches obtained by the search are used as the lower disconnected branches of the corresponding fault chain in turn, so as to obtain the fault chain corresponding to the high-risk branch;

[0010] Based on the DC power flow, the minimum load shedding value of the power grid to ensure the safety of the line power flow under the fault path of the accident chain is calculated, and the product of the probability of the accident chain and the minimum load shedding value is taken as the risk value of the accident chain;

[0011] Perform an accident chain search on each high-risk branch in the initial fault set and calculate the risk value of each accident chain found. Filter the accident chains whose risk value exceeds the set threshold and include them in the accident chain set.

[0012] Based on the obtained set of accident chains, the target power grid is optimized under extreme weather conditions to obtain an optimized generator output adjustment plan with the goal of minimizing the sum of the adjustment cost of the generator output and the risk cost of the accident chain.

[0013] Determine whether there is an accident chain with a risk value exceeding the set threshold in the target power grid after optimization according to the generator output adjustment plan. If so, the newly added accident chain is included in the accident chain set and the power flow optimization is re-solved. If not, the final generator output adjustment plan is used as the power grid power flow optimization plan.

[0014] Furthermore, for an accident chain L with v links, the probability of the accident chain occurring is Calculate according to the following formula:

[0015]

[0016] In the formula, is the probability of the initial breaking failure of the fault chain L; is the probability of occurrence of each link in the accident chain L;

[0017] The probability of failure of the 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 the probability of failure of transmission line l; is the power on transmission line l; is the transmission capacity limit of transmission line l; is the probability of protecting hidden failure; b is the limit multiple.

[0020] Furthermore, the minimum load shedding value for the power grid to ensure the safety of line power flow under the fault path of the fault chain based on the DC power flow calculation includes:

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

[0022] With the goal of minimizing the total load shedding of all nodes in the power grid to ensure the safety of line flow, the DC flow calculation is performed on the target power grid to obtain the minimum load shedding value caused by the occurrence of the fault chain.

[0023] Furthermore, when solving the power flow optimization of the target power grid under extreme weather conditions, a power flow optimization model is constructed for solution. The objective function of the power flow optimization model is to minimize the 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 the risk value of the accident chain. The power flow optimization model takes the product of the failure probabilities of each link in the accident chain, that is, the probability of occurrence of the accident chain as a variable, and participates in the calculation of the risk cost of the accident chain, thereby reducing the multiplication order of variables in the objective function.

[0024] Furthermore, the objective function of the power 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 adjustment amount of the output of the i-th generator node respectively; is the number of accident chains obtained by screening, and j is the accident chain index; is the risk value of the jth accident chain; b is the risk cost coefficient of the accident chain.

[0027] Furthermore, when calculating the probability of failure of transmission line l, the power on transmission line l also needs to consider the impact of the upper disconnected branch on the active power flow of the lower branch. For the link t of the fault chain L propagation, assuming that the upper 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, and then:

[0028]

[0029] In the formula, is the active power flow of branch km; M is a row vector whose kth element is 1, mth element is -1, and the rest are all 0. is the voltage phase angle change between node k and node m, B is the susceptance;

[0030] The active power flow increment of the lower branch can be obtained based on the change of the susceptance and voltage phase angle of branch km.

[0031] Furthermore, the stopping conditions include:

[0032] The grid is disconnected, the fault chain search reaches the maximum prediction depth, and a certain stage of the fault chain search does not cause the lower branch to overload.

[0033] In a second aspect, the present invention provides a power grid flow optimization device based on a fault chain under extreme weather conditions, comprising:

[0034] An initial fault set generation module is used to screen 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;

[0035] The fault chain search module is used to use a high-risk branch in the initial fault concentration as the initial disconnected branch of the corresponding fault chain to search the fault chain. When the stop condition is reached, the search is stopped, and the overloaded branches obtained by the search are used as the lower disconnected branches of the corresponding fault chain in turn, so as to obtain the fault chain corresponding to the high-risk branch;

[0036] A risk value calculation module is used to calculate the minimum load shedding value for the power grid to ensure the safety of line flow under the fault path of the accident chain based on the DC power flow, and take the product of the probability of occurrence of the accident chain and the minimum load shedding value as the risk value of the accident chain;

[0037] The accident chain set generation module is used to search for accident chains on each high-risk branch in the initial fault set and calculate the risk value of each accident chain found, and screen the accident chains whose risk values ​​exceed the set threshold and include them in the accident chain set;

[0038] The power flow optimization solution module is used to optimize the power flow of 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 adjustment cost of the generator output and the risk cost of the accident chain, and obtain an optimized generator output adjustment plan;

[0039] The power flow optimization judgment module is used to determine whether there is an accident chain with a risk value exceeding the set threshold in the target power grid after optimization according to the generator output adjustment plan. If so, the newly added accident chain will be included in the accident chain set and the power flow optimization solution will be re-performed. If not, the final generator output adjustment plan will be used as the power grid power flow optimization plan.

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

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

[0042] The processor executes a power grid power flow optimization method based on a fault chain under extreme weather conditions as described in the first aspect according to the instructions of the computer program.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for optimizing power flow in an extreme weather environment based on a fault chain as in the first aspect.

[0044] In summary, the present invention provides a power grid flow optimization method and related devices based on accident chains under extreme weather, including identifying high-risk branches according to basic power grid data and incorporating them into the initial fault set. Then, starting from the high-risk branch, the accident chain search is performed to determine the overload branch, and the risk value of each accident chain is calculated. The accident chain with a risk value exceeding the threshold is screened out, and the power grid flow optimization is performed, with the goal of minimizing the sum of the generator output adjustment cost and the accident chain risk cost. Through optimization and solution, the generator output adjustment plan is obtained, and it is verified whether the optimized power grid still has a high-risk accident chain. If so, re-optimize; otherwise, determine the final plan. The present invention uses the high-risk branch under extreme weather as the initial disconnection branch, screens all possible accident chains, and optimizes the power grid with the goal of minimizing the sum of the generator output adjustment cost and the accident chain risk cost, and minimizes the risk consequences caused by chain failures. At the same time, through post-verification, it is ensured that the power grid after the flow optimization will not have other serious accident chains, providing auxiliary support for power grid dispatchers. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a flow chart of a method for optimizing power flow in an extreme weather environment based on a fault chain provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of an accident chain search provided by an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of an IEEE39 node system (red represents a high-risk branch) provided in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of search results of an accident chain provided by an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of a fault chain after power flow optimization provided by an embodiment of the present invention;

[0051] Figure 6 A block diagram of a power grid flow optimization device based on a fault chain under extreme weather conditions provided by an embodiment of the present invention;

[0052] Figure 7 A block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The embodiment of the present invention provides a method for optimizing power flow of a power grid based on a fault chain under extreme weather conditions, comprising the following steps:

[0055] Step 1: Screen the 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.

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

[0057] Based on meteorological information such as wind speed and line ice thickness, the failure probability of each transmission line is calculated, and high-risk branches with failure probabilities exceeding a certain threshold are included in the initial fault set.

[0058] Step 2: Use a high-risk branch in the initial fault concentration as the initial disconnected branch of the corresponding fault chain to search the fault chain. Stop the search when the stop condition is reached, and use the overloaded branches obtained in the search as the lower-level disconnected branches of the corresponding fault chain in turn, so as to obtain the fault chain corresponding to the high-risk branch.

[0059] It should be noted that DC power flow calculation is a simplified power system power flow calculation method that only considers active power and voltage phase angle, and ignores reactive power and voltage amplitude changes. In power flow calculation, a branch whose current exceeds the rated value is an overload branch.

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

[0061] Step 3: Based on the DC power flow, calculate the minimum load shedding value for the power grid to ensure the safety of the line power flow under the fault path of the accident chain, and take the product of the probability of the accident chain occurrence and the minimum load shedding value as the risk value of the accident chain.

[0062] It should be noted that the fault path is the path from the initial disconnected branch to other lower branches along the fault chain. The minimum load shedding value is the minimum load that needs to be removed to ensure the safe operation of the power grid.

[0063] In this step, the product of the probability of occurrence of the accident chain and the minimum load shedding value is used as the risk value of the accident chain to assess the potential risk of the accident chain.

[0064] Step 4: Perform an accident chain search on each high-risk branch in the initial fault set and calculate the risk value of each accident chain obtained by the search, screen the accident chains whose risk values ​​exceed the set threshold and include them in the accident chain set.

[0065] Step 5: Based on the obtained set of accident chains, with the goal of minimizing the sum of the adjustment cost of the generator output and the risk cost of the accident chain, the power flow optimization solution under extreme weather conditions is performed on the target power grid to obtain the optimized generator output adjustment plan.

[0066] It should be noted that generator output adjustment is to adjust the generator output according to the grid operation status and optimization objectives. Power flow optimization solution is to solve the optimal operation status of the grid under extreme weather conditions through mathematical optimization methods.

[0067] In this step, the goal of solving the power flow optimization is to minimize the cost of generator output adjustment and the risk cost caused by the accident chain.

[0068] Step 6: Determine whether there is an accident chain with a risk value exceeding the set threshold in the target power grid after optimization according to the generator output adjustment plan. If so, the newly added accident chain is included in the accident chain set and the power flow optimization is re-solved. If not, the final generator output adjustment plan is used as the power grid power flow optimization plan.

[0069] It should be noted that after obtaining the preliminary optimization results, it is necessary to check again whether there are any new high-risk accident chains that have not been considered in the power grid. If there are, these newly discovered accident chains need to be 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 grid flow distribution plan after multiple iterations of optimization.

[0070] This embodiment provides a method for optimizing power flow based on fault chains under extreme weather conditions. Unlike the traditional method of only selecting the branch with the largest risk index as the next-level disconnection object, in the embodiment of the present invention, during the fault chain search process, all overloaded branches are further searched as potential next-level disconnection objects. This means that even if the current risk index of some branches is not the highest, if they may cause more serious consequences in the future development, they will also be taken into consideration, thereby avoiding the omission of important fault chain paths.

[0071] In addition, traditional preventive control schemes often focus on improving the safety margin of the power grid, while ignoring the actual economic losses and social impacts that may be caused by cascading failures. The embodiment of the present invention introduces the concept of risk value of the accident chain, combines the probability of the accident chain with the minimum load shedding value, and quantitatively evaluates the potential threat of each accident chain to the power grid. On this basis, the optimization solution is performed with the goal of minimizing the sum of the generator output adjustment cost and the accident chain risk cost, achieving a balance between economic benefits and safety performance.

[0072] See also Figure 1 , Figure 1 This is a flow chart of a method for optimizing power flow based on an accident chain in extreme weather conditions proposed in combination with the above-mentioned embodiments. Figure 1 Other embodiments of the present invention are introduced.

[0073] In one embodiment of the present invention, without considering the influence of the external environment, when the line current does not exceed its current limit, the probability of the transmission line fault tripping is the hidden fault probability of the relay protection, and its value is close to 0. In the process of the development and propagation of the accident chain, the power grid dispatching personnel often take corresponding blocking measures, so the search depth of the accident chain will not exceed the set maximum depth (usually 4). In the actual power grid, the accident chain triggering the power grid decoupling usually leads to a major power outage. Therefore, this article defines the stopping conditions for the accident chain search as: 1) the power grid is decoupled; 2) the accident chain search reaches the maximum predicted depth; 3) a certain stage of the accident chain search will not cause the lower-level branch to be overloaded. When any condition is met, the accident chain search stops. The accident chain search process is as follows: Figure 2 shown.

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

[0075] This embodiment uses a piecewise linear function to describe the close relationship between the failure probability of the transmission line and the line power, that is,

[0076] (1)

[0077] In the formula, is the probability of failure of transmission line l; is the power on transmission line l; is the transmission capacity limit of transmission line l; is the probability of hidden protection failure; b is the limit multiple, which is 1.4. It means that when the power of the transmission line exceeds 1.4 times the transmission capacity limit, the line will be cut off due to the action of the protection device, and the failure probability is 1 at this time.

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

[0079] (2)

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

[0081] In one embodiment of the present invention, the minimum load shedding value for ensuring line power flow safety of the power grid under the fault path of the fault chain calculated based on the DC power flow includes:

[0082] Cut off all lines under the fault path of the accident chain from the target power grid;

[0083] With the goal of minimizing the total load shedding of all nodes in the power grid to ensure the safety of line flow, the DC flow calculation is performed on the target power grid to obtain the minimum load shedding value caused by the occurrence of the fault chain.

[0084] Assume that there are k line faults in a certain fault chain. When these k lines are cut off, the minimum load shedding value of the power grid to ensure the safety of the line flow is calculated based on the DC flow. The calculation of the minimum load shedding value is introduced below with a specific objective function and constraint conditions as an example. The objective function for calculating the minimum load shedding value is:

[0085] (3)

[0086] Where n B is the number of nodes in the power grid; is the load shedding amount of the i-th node.

[0087] The constraints that must be met are:

[0088] Node load shedding constraints:

[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 constraints:

[0092] (5)

[0093] In the formula, S G is the set of generator nodes in 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 constraints:

[0095] (6)

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

[0097] Node power balance constraints:

[0098] (7)

[0099] DC power flow constraints:

[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 of the accident chain L L Defined as:

[0103] (9)

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

[0105] In one embodiment of the present invention, when solving the power flow optimization of the target power grid under extreme weather conditions, the solution is obtained by constructing a power flow optimization model. The objective function of the power flow optimization model is to minimize the 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 the risk value of the accident chain. The power flow optimization model multiplies the probability of failure of each link in the accident chain, that is, the probability of occurrence of the accident chain as a variable, and participates in the calculation of the risk cost of the accident chain, thereby reducing the order of multiplication of variables in the objective function.

[0106] The objective function of the established power flow optimization model contains the product of the failure probabilities of each link in the accident chain. The failure probabilities of each link in the accident chain are regarded as variables. If the order of multiplication of variables is too large, the model is difficult to solve. If intelligent algorithms such as particle swarm and genetic algorithms are used for solving, it is usually difficult to obtain the global optimal solution. In this embodiment, the product of the failure probabilities of multiple links in the accident chain is regarded as a new variable, thereby reducing the order of multiplication of variables in the objective function, and then commercial solvers such as cplex and gurobi can be called for solving.

[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] Where n G is the number of generator nodes; a i , are the adjustment cost coefficient and adjustment amount of the output of the i-th generator node respectively; n L is the number of accident chains screened; R j is the risk value of the jth accident chain; b is the risk cost coefficient of the accident chain.

[0110] The constraints that can be satisfied are:

[0111] Power balance constraints:

[0112] (11)

[0113] Generator output adjustment constraints:

[0114] (12)

[0115] Line flow safety constraints:

[0116] (13)

[0117] Where PTDF is the power transfer distribution factor matrix of the power grid; P is the injected power matrix; F is the generator output adjustment matrix;max is the line power flow capacity matrix.

[0118] In one embodiment of the present invention, when calculating the probability of a transmission line l failing, the power on the transmission line l also needs to consider the impact of the upper disconnected branch on the lower branch active power flow after the upper disconnected branch is disconnected. ), assuming that the disconnected branch of the upper level 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 km is disconnected, the following conditions are met:

[0121] (15)

[0122] Ignoring the term of multiplying two increments, we get:

[0123] (16)

[0124] Combining equations (14) and (16), we can get:

[0125] (17)

[0126] Further conversion yields:

[0127] (18)

[0128] Where P km is the active power flow of branch km; M is a row vector whose kth element is 1, mth element is -1, and the rest are all 0.

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

[0130] In order to verify the rationality of the method proposed in this invention, a simulation example is carried out in the IEEE 39-node system, assuming that branches 1-2, 2-3, and 4-5 are high-risk branches under extreme disasters. Figure 3 The branch active limit is defined as 0.9 times the flow capacity, the predicted depth of the accident chain is dmax=3, the generator adjustment cost coefficient and output range are shown in Table 1, the risk cost coefficient b=100 / MW, the initial failure probability of the disconnected branch is set to 1, and Gurobi is called to solve after the model is established.

[0131] Table 1. IEEE39 node system information

[0132]

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

[0134] The results of power flow optimization are shown in Table 2. The optimized accident chain is as follows: Figure 5 As shown in the figure. After the power grid generator output is adjusted, the expected load shedding of the accident chain is reduced from 11.835MW before optimization to 0.670MW, and the risk value of the cascading failure accident chain is significantly reduced. At the same time, through post-verification, no other accident chains are generated after the power flow optimization. The optimization plan 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, the embodiment of the present application also provides a device for optimizing power flow based on an accident chain under extreme weather conditions, which is used to implement the above-mentioned method for optimizing power flow based on an accident chain under extreme weather conditions. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiment of the device for optimizing power flow based on an accident chain under extreme weather conditions provided below can be referred to the limitations of the method for optimizing power flow based on an accident chain under extreme weather conditions above, and will not be repeated here.

[0138] See also Figure 6 The present invention provides a power grid flow optimization device based on a fault chain under extreme weather conditions, comprising:

[0139] An initial fault set generation module is used to screen 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 fault chain search module is used to use a high-risk branch in the initial fault concentration as the initial disconnected branch of the corresponding fault chain to search the fault chain. When the stop condition is reached, the search is stopped, and the overloaded branches obtained by the search are used as the lower disconnected branches of the corresponding fault chain in turn, so as to obtain the fault chain corresponding to the high-risk branch;

[0141] A risk value calculation module is used to calculate the minimum load shedding value for the power grid to ensure the safety of line flow under the fault path of the accident chain based on the DC power flow, and take the product of the probability of occurrence of the accident chain 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 on each high-risk branch in the initial fault set and calculate the risk value of each accident chain found, and screen the accident chains whose risk values ​​exceed the set threshold and include them in the accident chain set;

[0143] The power flow optimization solution module is used to optimize the power flow of 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 adjustment cost of the generator output and the risk cost of the accident chain, and obtain an optimized generator output adjustment plan;

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

[0145] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0146] Reference Figure 7An embodiment of the 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 grid flow optimization method based on the fault chain under extreme weather conditions as described in any of the above methods.

[0147] The computer device may be a desktop computer, a notebook, a PDA, a cloud server or other computing device. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 7 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0148] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0149] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may include both an internal storage unit and an external storage device of the computer device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been output or is to be output.

[0150] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing power grid flow based on a fault chain under extreme weather conditions as described in any one of the above methods is implemented.

[0151] In this embodiment, if the integrated unit is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0152] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0153] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0154] In the embodiments disclosed in the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid flow optimization method based on fault chain under extreme weather conditions, characterized in that: The steps include: Screen high-risk branches of the target power grid under extreme weather conditions based on basic data of the target power grid, and include them in the initial fault set; Taking one of the high-risk branches in the initial fault set as the initial disconnected branch of the corresponding fault chain to search for the fault chain, stopping the search when the stop condition is reached, and taking the overloaded branches obtained by the search as the lower disconnected branches of the corresponding fault chain in sequence, thereby obtaining the fault chain corresponding to the high-risk branch; Based on the DC power flow, the minimum load shedding value for the power grid to ensure the safety of the line power flow under the fault path of the fault chain is calculated, and the product of the probability of occurrence of the fault chain and the minimum load shedding value is used as the risk value of the fault chain; Performing an accident chain search on each of the high-risk branches in the initial fault set and calculating the risk value of each of the accident chains obtained by the search, screening the accident chains whose risk values ​​exceed a set threshold and incorporating them into the accident chain set; Based on the obtained set of fault chains, with the goal of minimizing the sum of the adjustment cost of the generator output and the risk cost of the fault chain, the power flow optimization solution under extreme weather conditions is performed on the target power grid to obtain an optimized generator output adjustment plan; Determine whether the target power grid after optimization according to the generator output adjustment plan has an accident chain with a risk value exceeding the set threshold. If so, include the newly added accident chain in the accident chain set and re-perform power flow optimization solution. If not, use the generator output adjustment plan finally obtained as the power grid power flow optimization plan.

2. The power grid flow optimization method based on the fault chain under extreme weather conditions according to claim 1 is characterized in that: For an accident chain L with v links, the probability of occurrence of the accident chain is Calculate according to the following formula: In the formula, is the probability of the initial breaking failure of the fault chain L; is the probability of occurrence of each link in the accident chain L; The probability of failure of the transmission line l corresponding to each link of the accident chain L is calculated according to the following formula: In the formula, is the probability of failure of transmission line l; is the power on transmission line l; is the transmission capacity limit of transmission line l; is the probability of protecting hidden failure; b is the limit multiple.

3. The power grid flow optimization method based on fault chain under extreme weather conditions according to claim 1 is characterized in that: The minimum load shedding value for ensuring the safety of line power flow under the fault path of the fault chain calculated based on the DC power flow includes: Cutting off all lines under the fault path of the fault chain from the target power grid; With the goal of minimizing the sum of load shedding amounts of all nodes of the power grid to ensure line power flow safety, DC power flow calculation is performed on the target power grid to obtain the minimum load shedding value caused by the occurrence of the fault chain.

4. The power grid flow optimization method based on the fault chain under extreme weather conditions according to claim 2 is characterized in that: When solving the power flow optimization of 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 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 the risk value of the accident chain. The power flow optimization model multiplies the probability of failure of each link in the accident chain, that is, the probability of occurrence of the accident chain as a variable, and participates in the risk cost calculation of the accident chain, thereby reducing the multiplication order of variables in the objective function.

5. The power grid flow optimization method based on the fault chain under extreme weather conditions according to claim 4 is characterized in that: The objective function of the power flow optimization model is as follows: In the formula, is the number of generator nodes, i is the node index; , are the adjustment cost coefficient and adjustment amount of the output of the i-th generator node respectively; is the number of accident chains obtained by screening, and j is the accident chain index; is the risk value of the jth accident chain; b is the risk cost coefficient of the accident chain.

6. The method for optimizing power flow of power grid based on fault chain under extreme weather conditions according to claim 2, characterized in that: When calculating the probability of failure of transmission line l, the power on transmission line l also needs to consider the impact of the upper disconnected branch on the active power flow of the lower branch. For the link t of the fault chain L propagation, assuming that the upper 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, then: In the formula, is the active power flow of branch km; M is a row vector whose kth element is 1, mth element is -1, and the rest are all 0. is the voltage phase angle change between node k and node m, B is the susceptance; The active power flow increment of the lower branch can be obtained according to the susceptance of the branch km and the voltage phase angle change.

7. The power grid flow optimization method based on fault chain under extreme weather conditions according to claim 1 is characterized in that: The stop conditions include: The grid is disconnected, the fault chain search reaches the maximum prediction depth, and a certain stage of the fault chain search does not cause the lower branch to overload.

8. A power grid flow optimization device based on fault chain under extreme weather conditions, characterized in that: include: An initial fault set generation module, used to screen high-risk branches of the target power grid under extreme weather conditions according to basic data of the target power grid, and include them in the initial fault set; An accident chain search module is used to use one of the high-risk branches in the initial fault set as the initial disconnected branch of the corresponding accident chain to perform an accident chain search, stop the search when the stop condition is reached, and use the overload branches obtained by the search as the lower disconnected branches of the corresponding accident chain in sequence, so as to obtain the accident chain corresponding to the high-risk branch; A risk value calculation module is used to calculate the minimum load shedding value of the power grid to ensure the safety of line flow under the fault path of the accident chain based on the DC power flow, and take the product of the probability of occurrence of the accident chain and the minimum load shedding value as the risk value of the accident chain; An accident chain set generation module is used to search for an accident chain for each of the high-risk branches in the initial fault set and calculate the risk value of each of the accident chains obtained by the search, and to screen the accident chains whose risk values ​​exceed a set threshold and include them in the accident chain set; A power flow optimization solution module is used to optimize the power flow under extreme weather conditions for the target power grid based on the obtained set of fault chains, with the goal of minimizing the sum of the adjustment cost of the generator output and the risk cost of the fault chain, to obtain an optimized generator output adjustment plan; The power flow optimization judgment module is used to judge whether the target power grid after optimization according to the generator output adjustment plan has an accident chain with a risk value exceeding the set threshold. If so, the newly added accident chain is included in the accident chain set and the power flow optimization solution is re-performed. If not, the generator output adjustment plan finally obtained is used as the power grid power flow optimization plan.

9. A computer device, characterized in that: 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 a power grid flow optimization method based on a fault chain under extreme weather conditions as described in any one of claims 1-7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing power flow of a power grid based on a fault chain under extreme weather conditions as described in any one of claims 1 to 7 is implemented.

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