Methods, devices and electronic equipment for identifying power flow congestion in power systems

Abstract

This application provides a method, apparatus, and electronic device for identifying power flow congestion in a power system. The method includes: acquiring a first auxiliary variable of the power system's generating units, a second auxiliary variable of the power system's node loads, a third auxiliary variable of the power system's transmission lines, and a fourth auxiliary variable of the power system's grid stability profile; determining a combination of constraints based on at least a first, second, third, and fourth constraint condition; constructing an objective function based on the first, second, third, and fourth auxiliary variables; determining the minimum value of the objective function while satisfying the constraint condition combination; determining the location of power flow congestion in the power system based on the minimum value of the objective function; and issuing an early warning. This application solves the problem in existing technologies that cannot completely identify the location of power flow congestion in a power system.

Description

Technical Field

[0001] This application relates to the field of power flow congestion judgment technology, and more specifically, to a method for identifying power flow congestion in a power system, a device for identifying power flow congestion in a power system, a computer-readable storage medium, and an electronic device. Background Technology

[0002] Power flow congestion is typically caused by factors such as inadequate power grid structure, equipment capacity limitations, and uneven load distribution. In power systems, power flow distribution is influenced by various factors, including generator output, load demand, network structure, and control strategies. When these factors change, the power flow distribution also changes, potentially leading to overload on certain lines or sections, i.e., power flow congestion.

[0003] In the current development and operation of the power grid, the scale of the synchronous power grid is constantly expanding. However, the development of the power generation, grid, and load sides of the actual large power grid exhibits a significant imbalance. Firstly, the large-scale grid connection of new energy sources under the current new power system construction environment has led to significant changes on the power grid source side, gradually shifting from a traditional synchronous power source-dominated structure to one dominated by new energy sources. Secondly, the integration of distributed new energy sources, energy storage, and electric vehicles into the distribution network, coupled with the susceptibility of new energy generation to significant weather variations, results in substantial randomness and volatility. This, combined with the inherent uncertainty of load forecasting, further alters the characteristics of the power grid load side. Furthermore, the power grid, as the medium for power transmission between sources and loads, often struggles to adapt to changes in source-load conditions due to its long construction investment period and high costs. On the other hand, load fluctuations and the uncertainty of new energy generation frequently lead to exceeding safety and stability limits, posing a significant challenge to the safe and stable operation of the power grid. Finally, since the new round of power system reform, my country's power market construction has progressed steadily and orderly, achieving remarkable results. The Southern Power Spot Market has entered the continuous trial operation phase. In order to ensure the safe and stable operation of the power grid under the spot market environment, it is necessary to consider the power flow safety constraints of the power grid lines and sections and reserve a certain margin during the market clearing process. Once the power flow of the lines and sections exceeds the limit and causes grid congestion during the market clearing process, it will seriously affect the nodal electricity price at the clearing point.

[0004] While numerous studies have explored the mechanisms, management, and scenario selection of grid congestion, and some have considered grid congestion constraints for grid planning and risk analysis, no detailed mathematical model or method has yet been proposed for identifying power flow congestion at specific grid lines and sections. Furthermore, it is impossible to completely identify all potential power flow congestion scenarios at all grid lines and sections. Simultaneously, with the large-scale integration of renewable energy and the widespread adoption of distributed generation technologies, grid power flow distribution will become more complex and variable, placing increasingly higher demands on power flow congestion identification technology. Moreover, considering the vast number of lines and sections in actual power grids, identifying all potentially congested lines and sections presents enormous computational challenges, resulting in a massive number of variables in the optimization scheduling model, making large-scale solutions to such problems extremely difficult.

[0005] Therefore, a method for identifying power grid flow congestion is needed. Summary of the Invention

[0006] The main objective of this application is to provide a method, device, computer-readable storage medium, and electronic device for identifying power flow congestion in a power system, so as to at least solve the problem that the prior art cannot completely identify the location of power flow congestion in a power system.

[0007] To achieve the above objectives, according to one aspect of this application, a method for identifying power flow congestion in a power system is provided, comprising: acquiring a first auxiliary variable of the generating units of the power system, acquiring a second auxiliary variable of the node load of the power system, acquiring a third auxiliary variable of the transmission lines of the power system, and acquiring a fourth auxiliary variable of the grid stability section of the power system, wherein the first auxiliary variable is a variable representing the upper and lower limits of the output power constraints of the generating units, the second auxiliary variable is a variable representing the variation range constraints of the node load, the third auxiliary variable is a variable representing the upper and lower limits of the transmission capacity constraints of the transmission lines, and the fourth auxiliary variable is a variable representing the... The variables constrained by the power grid stability section are: obtaining the first constraint condition corresponding to the generator set, the second constraint condition of the node load, the third constraint condition of the transmission line, and the fourth constraint condition of the power grid stability section; determining the constraint condition combination based at least on the first, second, third, and fourth constraint conditions; constructing an objective function based on the first, second, third, and fourth auxiliary variables; determining the minimum value of the objective function when the constraint condition combination is satisfied; determining the location of power flow congestion in the power system based on the minimum value of the objective function, and issuing an early warning.

[0008] Optionally, constructing the objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable includes: constructing the objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable as follows: Where N represents the total number of nodes in the power system, L represents the total number of lines in the power system, and S represents the total number of stable grid sections in the power system. This represents the first auxiliary variable corresponding to the upper limit constraint on the output power of the generator set. This represents the first auxiliary variable corresponding to the lower limit constraint of the generator set's output power. The second auxiliary variable represents the upper limit constraint of the node load. The second auxiliary variable represents the lower limit constraint of the node load. The third auxiliary variable represents the upper limit constraint on the transmission capacity of the transmission line. The third auxiliary variable represents the lower limit constraint of the transmission capacity of the transmission line. The fourth auxiliary variable represents the upper limit constraint of the power grid stability section. The fourth auxiliary variable represents the lower limit constraint of the power grid stability section.

[0009] Optionally, obtaining the first constraint condition corresponding to the generator set and the second constraint condition of the node load includes: obtaining the active power, maximum output power, and minimum output power of each node of the generator set, and determining the first constraint condition as follows: Among them, g n Let m represent the active power of the generator set at node n, where m ∈ G. n This represents the set of generators connected at node n. This represents the maximum output power of generator set m located at node n. u represents the minimum output power of the generator set m located at node n. m Indicate the operating status of the generator set; obtain the upper and lower load limits of each node in the power system, and determine the second constraint condition as follows: Where, d n This represents the active load at node n. This represents the upper limit of the load at node n. This is the lower limit of the load at node n.

[0010] Optionally, obtaining the third constraint condition of the transmission line and the fourth constraint condition of the power grid stability section includes: obtaining the upper limit value of the active power transmission power of the transmission line, and determining the third constraint condition as follows: Where, d n G represents the active load at node n. n f represents the active power of the generator set at node n, N represents the total number of nodes in the power system, and f l max This indicates the upper limit of the active power transmission power of the transmission line. This represents the power transfer distribution factor of node n to line l under the network structure w of the power system, with node s as the slack node; the upper and lower limits of the power grid stability section are obtained, and the fourth constraint condition is determined as follows. Where, x s This refers to the relevant variables involved in the power grid stability section s, and these relevant variables include at least the output power of the generator set. This is the upper limit of the stable cross section s of the power grid. This is the lower limit of the power grid stability section s.

[0011] Optionally, determining a combination of constraints based at least on the first constraint, the second constraint, the third constraint, and the fourth constraint includes: determining a fifth constraint as... Where N represents the total number of nodes in the power system, g n d represents the active power output of the generator set at node n. n The active load at node n is represented; the sixth constraint is determined based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable; the combination of the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, and the sixth constraint is determined as the constraint combination.

[0012] Optionally, determining the location of power flow congestion in the power system based on the minimum value of the objective function includes: determining an auxiliary variable with a value of 0 based on the minimum value of the objective function, wherein the auxiliary variable includes the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable; and determining that the location corresponding to the auxiliary variable with a value of 0 is where power flow congestion occurs.

[0013] Optionally, determining the minimum value of the objective function under the condition that the combination of constraints is satisfied includes: using the Benders decomposition method to determine the main problem and multiple subproblems of the objective function; iteratively solving the main problem and multiple subproblems until the combination of constraints is satisfied to obtain the minimum value of the objective function.

[0014] According to another aspect of this application, a power flow congestion identification device for a power system is provided, comprising: an acquisition unit, configured to acquire a first auxiliary variable of the power system's generating units, a second auxiliary variable of the power system's node load, a third auxiliary variable of the power system's transmission lines, and a fourth auxiliary variable of the power system's grid stability profile, wherein the first auxiliary variable is a variable representing the upper and lower limits of the generating units' output power constraints, the second auxiliary variable is a variable representing the range constraints of the node load variations, the third auxiliary variable is a variable representing the upper and lower limits of the transmission line's transmission capacity constraints, and the fourth auxiliary variable represents the grid stability profile. The system includes: a constraint variable determination unit, configured to obtain the first constraint condition corresponding to the generator set, the second constraint condition of the node load, the third constraint condition of the transmission line, and the fourth constraint condition of the power grid stability section, and determine the constraint condition combination based at least on the first, second, third, and fourth constraint conditions; and an early warning unit, configured to construct an objective function based on the first, second, third, and fourth auxiliary variables, determine the minimum value of the objective function when the constraint condition combination is satisfied, determine the location of power flow congestion in the power system based on the minimum value of the objective function, and issue an early warning.

[0015] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform any of the power flow congestion identification methods of the power system described above.

[0016] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for identifying power flow congestion in any of the aforementioned power systems.

[0017] By applying the technical solution of this application, the first auxiliary variable of the power system's generator units, the second auxiliary variable of the power system's node loads, the third auxiliary variable of the power system's transmission lines, and the fourth auxiliary variable of the power system's grid stability section are obtained. The first constraint condition corresponding to the generator units, the second constraint condition of the node loads, the third constraint condition of the transmission lines, and the fourth constraint condition of the grid stability section are also obtained. At least the first, second, third, and fourth constraint conditions are used to determine the combination of constraint conditions. An objective function is constructed based on the first, second, third, and fourth auxiliary variables. Under the condition of satisfying the combination of constraint conditions, the minimum value of the objective function is determined. The location of power flow congestion in the power system is determined based on the minimum value of the objective function, and an early warning is issued. Compared with existing methods that cannot completely identify the location of power flow congestion, this application can establish an objective function based on the auxiliary variables corresponding to the constraints of each part of the power grid and solve it under the constraint conditions, thus accurately and completely determining the location of power flow congestion. Therefore, it can solve the problem of existing technologies that cannot completely identify the location of power flow congestion, achieving the effect of completely identifying the location of power flow congestion and issuing timely early warnings. Attached Figure Description

[0018] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a mobile terminal for performing a power flow congestion identification method according to an embodiment of this application is shown.

[0020] Figure 2 A flowchart illustrating a power flow congestion identification method for a power system according to an embodiment of this application is shown.

[0021] Figure 3 The illustration shows a flowchart of a specific power flow congestion identification method provided by an embodiment of this application;

[0022] Figure 4 A schematic diagram of the structure of a Benders decomposition method provided in an embodiment of this application is shown;

[0023] Figure 5 A schematic diagram of a node system wiring diagram for an application of a power flow congestion identification method provided by an embodiment of this application is shown.

[0024] Figure 6 This illustration shows a schematic diagram of a blocked circuit after the first iteration solution provided by an embodiment of this application;

[0025] Figure 7 This illustration shows a schematic diagram of a blocked circuit after the second iteration solution provided by an embodiment of this application;

[0026] Figure 8 This illustration shows a schematic diagram of a blocked circuit after the third iteration of the solution provided in an embodiment of this application;

[0027] Figure 9 A structural block diagram of a power flow obstruction identification device provided in an embodiment of this application is shown.

[0028] The above figures include the following reference numerals:

[0029] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0034] Power flow congestion: In power systems, power flow distribution is affected by various factors such as generator output, load demand, network structure, and control strategies. When these factors change, the power flow distribution also changes, which may lead to overload on certain lines or sections, i.e., power flow congestion.

[0035] As described in the background section, existing methods for identifying power flow congestion cannot completely identify the location of the congestion. To address this problem, embodiments of this application provide a method for identifying power flow congestion, a device for identifying power flow congestion, a computer-readable storage medium, and an electronic device.

[0036] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0037] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a power system power flow congestion identification method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0038] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the power flow congestion identification method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] This embodiment provides a method for identifying power flow congestion in a power system that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0040] Figure 2 This is a flowchart of a power flow congestion identification method according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0041] Step S201: Obtain the first auxiliary variable of the generator set of the power system, the second auxiliary variable of the node load of the power system, the third auxiliary variable of the transmission line of the power system, and the fourth auxiliary variable of the grid stability section of the power system. The first auxiliary variable is the variable corresponding to the upper and lower limit constraints of the output power of the generator set, the second auxiliary variable is the variable corresponding to the variation range constraint of the node load, the third auxiliary variable is the variable corresponding to the upper and lower limit constraints of the transmission capacity of the transmission line, and the fourth auxiliary variable is the variable corresponding to the grid stability section constraint.

[0042] Specifically, to facilitate the identification of power flow congestion, the constraints of various parts of the power system are represented by auxiliary variables, including generator units, node loads, transmission lines, etc. Each of these auxiliary variables is either 0 or 1, meaning its value is either 0 or 1. In addition, stable sections of the power grid are added, i.e., stable sections existing within the power grid. Thus, an objective function is established using these 0 or 1 variables. Solving this objective function determines whether the corresponding constraints are effective, thereby identifying the power flow congestion status of congested lines and sections.

[0043] Step S202: Obtain the first constraint condition corresponding to the generator set, obtain the second constraint condition of the node load, obtain the third constraint condition of the transmission line, and obtain the fourth constraint condition of the power grid stability section, and determine the constraint condition combination based at least on the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition.

[0044] Specifically, after obtaining the aforementioned auxiliary variables and establishing the objective function, it is necessary to obtain the corresponding constraints, and then solve the objective function under these constraints to obtain a solution suitable for the power system. Each component of the power system, including generator units, node loads, transmission lines, and grid stability sections, corresponds to a constraint, and at least one constraint combination is formed based on these constraints. In practical applications, the constraint combination can also include multiple different constraints.

[0045] Step S203: Construct an objective function based on the first, second, third, and fourth auxiliary variables. Under the condition of satisfying the above combination of constraints, determine the minimum value of the objective function. Based on the minimum value of the objective function, determine the location of power flow blockage in the power system and issue an early warning.

[0046] Specifically, the sum of the above auxiliary variables is taken as the objective function, and the solution is obtained with the objective function as the minimum value under the above combination of constraints. Then, the position of the auxiliary variable corresponding to 0 or 1 in the objective function can be used to determine whether power flow blockage has occurred.

[0047] This embodiment obtains the first auxiliary variable of the power system's generator sets, the second auxiliary variable of the power system's node loads, the third auxiliary variable of the power system's transmission lines, and the fourth auxiliary variable of the power system's grid stability section. It also obtains the first constraint condition corresponding to the generator sets, the second constraint condition of the node loads, the third constraint condition of the transmission lines, and the fourth constraint condition of the grid stability section. At least the first, second, third, and fourth constraint conditions are used to determine the combination of constraint conditions. An objective function is constructed based on the first, second, third, and fourth auxiliary variables. Under the condition of satisfying the constraint combination, the minimum value of the objective function is determined. The location of power flow congestion in the power system is determined based on the minimum value of the objective function, and an early warning is issued. Compared with existing methods that cannot completely identify the location of power flow congestion, this application can establish an objective function based on the auxiliary variables corresponding to the constraints of each part of the power grid and solve it under the constraint conditions, thus accurately and completely determining the location of power flow congestion. Therefore, it can solve the problem of existing technologies that cannot completely identify the location of power flow congestion, achieving the effect of completely identifying the location of power flow congestion and issuing timely early warnings.

[0048] In specific implementation, step S203 above, which constructs the objective function based on the first, second, third, and fourth auxiliary variables, can be achieved through the following steps: Constructing the objective function based on the first, second, third, and fourth auxiliary variables... Where N represents the total number of nodes in the aforementioned power system, L represents the total number of lines in the aforementioned power system, and S represents the total number of stable sections of the aforementioned power grid in the aforementioned power system. This represents the first auxiliary variable corresponding to the upper limit constraint on the output power of the aforementioned generator set. This represents the first auxiliary variable corresponding to the lower limit constraint of the output power of the aforementioned generator set. The second auxiliary variable mentioned above represents the upper limit constraint on the load of the aforementioned nodes. The second auxiliary variable mentioned above represents the lower limit constraint of the above node load. This represents the third auxiliary variable corresponding to the upper limit constraint on the transmission capacity of the aforementioned transmission lines. This represents the third auxiliary variable corresponding to the lower limit constraint of the transmission capacity of the aforementioned transmission lines. This represents the fourth auxiliary variable corresponding to the upper limit constraint of the aforementioned power grid stability section. This represents the fourth auxiliary variable corresponding to the lower limit constraint of the aforementioned power grid stability section. The method constructs the objective function through the above steps, allowing power flow congestion to be determined based on each auxiliary variable.

[0049] Specifically, all of the above auxiliary variables are 0-1 variables, that is, the value of the above objective function is actually an integer, for example, it may be 3. This indicates that three auxiliary variables have a value of 1, and the values ​​of the other auxiliary variables are all 0. The auxiliary variables with a value of 0 indicate that the constraint has taken effect, indicating that power flow blockage has occurred at this position.

[0050] In some optional implementations, the above step S202, which obtains the first constraint condition corresponding to the generator set and the second constraint condition of the node load, can be achieved through the following steps: obtaining the active power, maximum output power, and minimum output power of each node of the generator set, and determining the first constraint condition as... Among them, g n Let m represent the active power of the generator set at node n, where m ∈ G. n Let n represent the set of generators connected to node n. This represents the maximum output power of generator set m located at node n. u represents the minimum output power of the generator set m located at node n. m Indicate the operating status of the aforementioned generator sets; obtain the upper and lower load limits of each node in the aforementioned power system, and determine the aforementioned second constraint condition as follows: Where, d n This represents the active load at node n mentioned above. This represents the upper limit of the load at node n mentioned above. This represents the lower limit of the load at node n. This method determines the constraints on generator sets and node loads through the above steps, thus constraining both the generator sets and node loads.

[0051] In practical implementation, the output power of the generator set is also called the output force. The first constraint is the upper and lower limit constraint of the generator set output force, and the second constraint is the range constraint of the nodal load variation. m It is a 0-1 variable, representing the operating status of the unit. It is 1 if it is running and 0 if it is shut down.

[0052] In some alternative real-time methods, the above step S202, which obtains the third constraint condition of the transmission line and the fourth constraint condition of the power grid stability section, can be achieved through the following steps: obtaining the upper limit of the active power transmission power of the transmission line, and determining the third constraint condition as... Where, d n G represents the active load at node n. n f represents the active power of the generator set at node n, N represents the total number of nodes in the power system, and f l maxThis indicates the upper limit of the active power transmission capacity of the aforementioned transmission lines. This represents the power transfer distribution factor of node n to line l under the network structure w of the aforementioned power system, with node s as the slack node; the upper and lower limits of the aforementioned power grid stability section are obtained, and the aforementioned fourth constraint condition is determined as follows. Where, x s This refers to the relevant variables involved in the aforementioned power grid stability section s, and these relevant variables include at least the output power of the aforementioned generator units. This is the upper limit of the aforementioned power grid stability section s. This represents the lower limit of the aforementioned power grid stability section s. This method constrains the transmission capacity of transmission lines and the power grid stability section through the above steps. This allows for solution under these constraints, yielding a solution more applicable to the actual situation of the power system, thus more accurately identifying power flow congestion.

[0053] Specifically, constraints also need to be placed on the transmission capacity of the transmission lines and the stability profile of the power grid. These relevant variables can be weighted coefficients of the active power of multiple lines and / or generator output, etc. It should be noted that the above constraints on transmission lines can consider the scenario where some lines in the power grid are out of service, i.e., under different topologies w. They are different.

[0054] In some optional implementations, step S202, which determines the combination of constraints based at least on the first, second, third, and fourth constraints, can be achieved through the following step: determining the fifth constraint as... Where N represents the total number of nodes in the aforementioned power system, g n d represents the active power output of the generator set at node n above. n This represents the active load at node n; the sixth constraint is determined based on the first, second, third, and fourth auxiliary variables mentioned above; the combination of the first, second, third, fourth, fifth, and sixth constraints is determined as the constraint combination. This method determines the system power balance constraints and the constraints corresponding to each auxiliary variable through the above steps, thus providing a more comprehensive determination of the constraint conditions.

[0055] In the specific implementation process, the above constraints are combined to obtain a constraint combination, and the objective function is solved under this constraint combination. The sixth constraint, determined based on the first, second, third, and fourth auxiliary variables, is as follows:

[0056]

[0057]

[0058] Where M is a sufficiently large constant. This represents the positive auxiliary variable corresponding to the upper and lower limits of the unit's output constraints. This represents the positive auxiliary variable corresponding to the constraint on the range of node load variation. This represents the positive auxiliary variable corresponding to the upper and lower limits of line transmission capacity constraints. This represents the positive auxiliary variable corresponding to the grid safety and stability section constraints. Considering that some power sources in the actual power grid may be subject to certain stability requirements and must remain operational or shut down—for example, nuclear power units often bear the basic load of the grid and therefore operate at full output under normal circumstances; others may require certain units to be operational or shut down due to voltage instability or excessive short-circuit current in the actual power grid. To address this, the corresponding generator state 0-1 variable u can be set. m Setting the values ​​to 0 or 1 and not participating in the free optimization of the optimization model can, on the one hand, adapt to the actual operation of the power grid, and on the other hand, fix the values ​​of some variables so that they do not participate in the optimization, which can also improve the solution efficiency of the model.

[0059] In some optional implementations, step S203, which determines the location of power flow congestion in the power system based on the minimum value of the objective function, can be achieved through the following steps: determining auxiliary variables with a value of 0 based on the minimum value of the objective function, wherein the auxiliary variables include the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable; and determining that the location corresponding to the auxiliary variable with a value of 0 indicates power flow congestion. This method determines the location of power flow congestion through the above steps, thus accurately and conveniently determining the location of power flow congestion.

[0060] In the specific implementation process, identifying the phenomenon of power flow blockage at the line and section in the power grid refers to the situation where the results corresponding to the constraints of the above-mentioned transmission line and the constraints of the stable section of the power grid are equal (=). Therefore, if the optimization results corresponding to the above-mentioned constraints are equal, then the corresponding constraints in the corresponding constraints (5)-(8) must also be equal. So the auxiliary positive variables in the corresponding constraints (5)-(8) are equal. Then the corresponding value must be 0. At this time, the corresponding constraint (13)-(16) becomes and Since these are 0-1 variables, when the objective function minimizes their sum, The corresponding optimization result will necessarily be 0. Conversely, if the results corresponding to the constraints of the above transmission line and the constraints of the power grid stability section do not have the constraint equality (=), then the auxiliary positive variables in their corresponding constraint equations (5)-(8) will be zero. Therefore, the corresponding value must not be 0. At this time, the corresponding constraint equations (13)-(16) become Here, eps represents a positive real number that is greater than 0 but very close to 0. Since it is a 0-1 variable, therefore The corresponding optimization result will necessarily be 1. Therefore, based on the above analysis, it can be concluded that, according to the optimization result... Whether the value is 0 or not can identify whether the power flow constraint of the corresponding line or section is in effect and causing a blockage.

[0061] In some optional implementations, determining the minimum value of the objective function while satisfying the above combination of constraints includes: using the Benders decomposition method to determine the main problem and multiple subproblems of the objective function; iteratively solving the main problem and multiple subproblems until the above combination of constraints is satisfied, thereby obtaining the minimum value of the objective function. This method, through iterative solution, can accurately determine the minimum value of the objective function.

[0062] Specifically, to identify all possible congestion scenarios in the power grid's lines and sections, an iterative algorithm is used to solve the model and identify all possible congested lines and sections. Each time the model is optimized, lines or sections that may simultaneously become congested under a certain power grid operating mode are obtained. Then, by setting the corresponding 0-1 variable value to 1 to remove the constraint, the model is optimized again to obtain lines or sections that may simultaneously become congested under the next power grid operating mode. This process is repeated until all lines and sections that may cause congestion in the power grid are identified, at which point the entire model solution is complete. Considering the enormous number of lines and sections in a real power grid, identifying all possible congested lines and sections would present a huge computational challenge, resulting in a massive number of variables in the optimization scheduling model. Furthermore, since the identification model proposed in this invention simultaneously includes both 0-1 discrete variables and continuous variables, the optimization model becomes a mixed integer programming problem containing both discrete and continuous variables, making large-scale solutions extremely difficult. To address this, the invention further proposes an efficient model-solving algorithm based on the Benders decomposition method, thereby improving the adaptability of the proposed model to the identification of actual large power grids. To efficiently solve the transformed large-scale mixed-integer linear programming (MILP) model, this invention further proposes a method using the Benders decomposition method. The essence of the Benders decomposition method is to decompose a large-scale problem that only needs to be solved once into a series of smaller problems that are iteratively solved. The general form of the above MILP problem expressed as a matrix is:

[0063] minc T x+f T y

[0064]

[0065] In the formula: y represents the 0-1 variables in the model; x represents the continuous variables in the model; A and B are the correlation coefficients in the constraints; c and f are the correlation coefficients in the objective function.

[0066] When the 0-1 variable is fixed Then, a suboptimal solution to the original problem can be obtained, which is the upper bound of the objective function of the original problem. Further applying the duality principle, the subproblems corresponding to the Benders decomposition method are:

[0067]

[0068] In the formula: p is the introduced dual variable.

[0069] After solving the subproblem, constraints are added to the main problem based on the judgment results, and the new 0-1 variable y is obtained again. This process is repeated iteratively until convergence. The corresponding form of the main problem is:

[0070] minz′

[0071]

[0072] In the formula: i is the iteration number.

[0073] The main problem here is actually a relaxation of the original problem. After calculation, the objective function of the main problem is found to be better than that of the original problem, and therefore represents the lower bound of the original problem's objective function. As iteration progresses, the iteration stops when the difference between the upper and lower bounds becomes sufficiently small. In practical applications, convergence is often achieved quickly with relatively few constraints.

[0074] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the power flow congestion identification method of this application will be described in detail below with reference to specific embodiments.

[0075] This embodiment relates to a specific method for identifying power flow congestion in a power system, such as... Figure 3 As shown, it includes the following steps:

[0076] Step S1: Begin;

[0077] Step S2: Input all parameters of the corresponding optimization model in the power grid that need to be identified;

[0078] Step S3: Program the algorithm and call the solver to solve the proposed optimization model;

[0079] Step S4: Does the optimization result contain 0-1 variables? The case where it is 0;

[0080] Step S5: If yes, output the 0-1 variable corresponding to the record. A result of 0 indicates a comprehensive identification of blockages in power grid lines and sections.

[0081] Step S6: Set the corresponding 0-1 variables Set the value to 1 and continue with step S3;

[0082] Step S7: If not, end.

[0083] Figure 4 This is a schematic diagram of a Benders decomposition method, such as... Figure 4 As shown, the problem includes the main problem and subproblems 1, 2, 3, and n, and is iterated repeatedly until the conditions are met.

[0084] This application also provides a schematic diagram of a node system wiring diagram for applying a power flow congestion identification method to a power system, such as... Figure 5 As shown, generator G1 is node 1 with a power output range of [100, 250] MW, generator G2 is node 2 with a power output range of [100, 250] MW, load d3 is node 3, and the transmission lines between node 1 and node 2 are L1 and L2, with maximum transmission line capacities of [100, 250] MW and

[250] MW respectively. The transmission line between node 1 and node 3 is L3, and the maximum capacity of the transmission line is [value missing]. The transmission line between node 2 and node 3 is L4, and the maximum capacity of the transmission line is [value missing]. Node 2 is also connected to load d2.

[0085] After programming, optimization calculations are performed by calling the CPLEX solver. The relative optimal solution convergence criterion OptCR of the CPLEX solver is set to 0 during the calculation. The solution process ends after three model iterations. The calculation time for each iteration is very small, approximately 0.2 seconds, which is almost negligible. The power flow congestion identification results for the power grid lines and sections corresponding to the above embodiment are as follows:

[0086] After the first iteration, lines L1 and L2 were identified as potentially congested. The corresponding power generation and load flow conditions are as follows: Figure 6 As shown: Generator G1 has a power output of 250MW, generator G2 has a power output of 0MW, load d3 is also 0MW, load d2 is 250MW, and the transmission lines f l1 =100MW, f l2 =100MW, f l3 =50MW, f l4 =50MW.

[0087] After the second iteration, line L3 was identified as the likely point of congestion. The corresponding power generation and load flow conditions are as follows: Figure 7 As shown: Generator G1 has a power output of 100MW, generator G2 has a power output of 0MW, load d3 is 100MW, load d2 is 0MW, and the transmission lines f l1 =20MW, f l2 =20MW, f l3 =60MW, f l4 =40MW.

[0088] After the third iteration, line L4 was identified as the likely point of congestion. The corresponding power generation and load flow conditions are as follows: Figure 8As shown: Generator G1 has a power output of 0 MW, generator G2 has a power output of 133.333 MW, load d3 is 133.333 MW, load d2 is 0 MW, and the transmission lines f l1 =26.667MW, f l2 =26.667MW, f l3 =53.333MW, f l4 =80MW.

[0089] Thus, through the identification of the above embodiments, it can be found that the method of this application can not only identify all possible congestion scenarios in the network, but also determine the power output under the corresponding congestion scenario and the level of node load that can be met. Once the predicted load exceeds the maximum load level of the identified node, it means that the power grid may be at risk of load loss, and it is necessary to prepare demand response or load switching plans in advance.

[0090] Conversely, if the current operating status, output, or load of the network is already determined, the values ​​of the corresponding variables can be fixed based on the model in the above method. Then, the calculation can quickly identify the lines and sections that may be blocked under the current operating mode of the power grid, thereby enabling corresponding scheduling, prevention, and control measures to be taken in advance to ensure the safe and stable operation of the power grid.

[0091] This application also provides a device for identifying power flow congestion in a power system. It should be noted that this device can be used to execute the power flow congestion identification method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0092] The following describes the power flow congestion identification device for power systems provided in the embodiments of this application.

[0093] Figure 9 This is a schematic diagram of a power flow congestion identification device according to an embodiment of this application. Figure 9 As shown, the device includes:

[0094] The acquisition unit 10 is used to acquire a first auxiliary variable of the generator set of the power system, a second auxiliary variable of the node load of the power system, a third auxiliary variable of the transmission line of the power system, and a fourth auxiliary variable of the grid stability section of the power system. The first auxiliary variable is a variable that represents the upper and lower limit constraints of the output power of the generator set, the second auxiliary variable is a variable that represents the variation range constraint of the node load, the third auxiliary variable is a variable that represents the upper and lower limit constraints of the transmission capacity of the transmission line, and the fourth auxiliary variable is a variable that represents the grid stability section constraint.

[0095] Specifically, to facilitate the identification of power flow congestion, the constraints of various parts of the power system are represented by auxiliary variables, including generator units, node loads, transmission lines, etc. Each of these auxiliary variables is either 0 or 1, meaning its value is either 0 or 1. In addition, stable sections of the power grid are added, i.e., stable sections existing within the power grid. Thus, an objective function is established using these 0 or 1 variables. Solving this objective function determines whether the corresponding constraints are effective, thereby identifying the power flow congestion status of congested lines and sections.

[0096] The determining unit 20 is used to obtain the first constraint condition corresponding to the generator set, the second constraint condition of the node load, the third constraint condition of the transmission line, and the fourth constraint condition of the power grid stability section, and to determine the constraint condition combination based at least on the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition.

[0097] Specifically, after obtaining the aforementioned auxiliary variables and establishing the objective function, it is necessary to obtain the corresponding constraints, and then solve the objective function under these constraints to obtain a solution suitable for the power system. Each component of the power system, including generator units, node loads, transmission lines, and grid stability sections, corresponds to a constraint, and at least one constraint combination is formed based on these constraints. In practical applications, the constraint combination can also include multiple different constraints.

[0098] The early warning unit 30 is used to construct an objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable, determine the minimum value of the objective function under the condition that the above-mentioned combination of constraints is satisfied, determine the location of power flow blockage in the power system based on the minimum value of the objective function, and issue an early warning.

[0099] Specifically, the sum of the above auxiliary variables is taken as the objective function, and the solution is obtained with the objective function as the minimum value under the above combination of constraints. Then, the position of the auxiliary variable corresponding to 0 or 1 in the objective function can be used to determine whether power flow blockage has occurred.

[0100] This embodiment obtains the first auxiliary variable of the power system's generator sets, the second auxiliary variable of the power system's node loads, the third auxiliary variable of the power system's transmission lines, and the fourth auxiliary variable of the power system's grid stability section. It also obtains the first constraint condition corresponding to the generator sets, the second constraint condition of the node loads, the third constraint condition of the transmission lines, and the fourth constraint condition of the grid stability section. At least the first, second, third, and fourth constraint conditions are used to determine the combination of constraint conditions. An objective function is constructed based on the first, second, third, and fourth auxiliary variables. Under the condition of satisfying the combination of constraint conditions, the minimum value of the objective function is determined. The location of power flow congestion in the power system is determined based on the minimum value of the objective function, and an early warning is issued. Compared with existing devices that cannot completely identify the location of power flow congestion, this application can establish an objective function based on the auxiliary variables corresponding to the constraints of each part of the power grid and solve it under the constraint conditions, thus accurately and completely determining the location of power flow congestion. Therefore, it can solve the problem of existing technologies that cannot completely identify the location of power flow congestion, achieving the effect of completely identifying the location of power flow congestion and issuing timely early warnings.

[0101] In specific implementation, the aforementioned early warning unit includes a construction module, used to construct the aforementioned objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable. Where N represents the total number of nodes in the aforementioned power system, L represents the total number of lines in the aforementioned power system, and S represents the total number of stable sections of the aforementioned power grid in the aforementioned power system. This represents the first auxiliary variable corresponding to the upper limit constraint on the output power of the aforementioned generator set. This represents the first auxiliary variable corresponding to the lower limit constraint of the output power of the aforementioned generator set. The second auxiliary variable mentioned above represents the upper limit constraint on the load of the aforementioned nodes. The second auxiliary variable mentioned above represents the lower limit constraint of the above node load. This represents the third auxiliary variable corresponding to the upper limit constraint on the transmission capacity of the aforementioned transmission lines. This represents the third auxiliary variable corresponding to the lower limit constraint of the transmission capacity of the aforementioned transmission lines. This represents the fourth auxiliary variable corresponding to the upper limit constraint of the aforementioned power grid stability section. This represents the fourth auxiliary variable corresponding to the lower limit constraint of the aforementioned power grid stability section. The device constructs the objective function through the above steps, allowing it to determine power flow congestion based on each auxiliary variable.

[0102] Specifically, all of the above auxiliary variables are 0-1 variables, that is, the value of the above objective function is actually an integer, for example, it may be 3. This indicates that three auxiliary variables have a value of 1, and the values ​​of the other auxiliary variables are all 0. The auxiliary variables with a value of 0 indicate that the constraint has taken effect, indicating that power flow blockage has occurred at this position.

[0103] In some optional implementations, the determining unit includes a first determining module, used to obtain the active power, maximum output power, and minimum output power of each node of the generator set, and to determine the first constraint condition as follows: Among them, g n Let m represent the active power of the generator set at node n, where m ∈ G. n Let n represent the set of generators connected to node n. This represents the maximum output power of generator set m located at node n. u represents the minimum output power of the generator set m located at node n. m Indicate the operating status of the aforementioned generator sets; obtain the upper and lower load limits of each node in the aforementioned power system, and determine the aforementioned second constraint condition as follows: Where, d n This represents the active load at node n mentioned above. This represents the upper limit of the load at node n mentioned above. This represents the lower limit of the load at node n. The device determines the constraints on the generator sets and node loads through the above steps, thus constraining the generator sets and node loads.

[0104] In practical implementation, the output power of the generator set is also called the output force. The first constraint is the upper and lower limit constraint of the generator set output force, and the second constraint is the range constraint of the nodal load variation. m It is a 0-1 variable, representing the operating status of the unit. It is 1 if it is running and 0 if it is shut down.

[0105] In some alternative real-time methods, the aforementioned determining unit further includes a second determining module and a third determining module. The second determining module is used to obtain the upper limit value of the active power transmission power of the aforementioned transmission line and determine the aforementioned third constraint condition as follows: Where, d n G represents the active load at node n. n f represents the active power of the generator set at node n, N represents the total number of nodes in the power system, and f lmax This indicates the upper limit of the active power transmission capacity of the aforementioned transmission lines. This represents the power transfer distribution factor of node n to line l under the network structure w of the aforementioned power system, with node s as the slack node; the third determining module is used to obtain the upper and lower limits of the aforementioned power grid stability section, and to determine the aforementioned fourth constraint condition. Where, x s This refers to the relevant variables involved in the aforementioned power grid stability section s, and these relevant variables include at least the output power of the aforementioned generator units. This is the upper limit of the aforementioned power grid stability section s. This is the lower limit of the aforementioned power grid stability section s. The device constrains the transmission capacity of the transmission line and the power grid stability section through the above steps. This allows for solution under these constraints, yielding a solution more applicable to the actual situation of the power system, thus more accurately identifying power flow congestion.

[0106] Specifically, constraints also need to be placed on the transmission capacity of the transmission lines and the stability profile of the power grid. These relevant variables can be weighted coefficients of the active power of multiple lines and / or generator output, etc. It should be noted that the above constraints on transmission lines can consider the scenario where some lines in the power grid are out of service, i.e., under different topologies w. They are different.

[0107] In some optional implementations, the previous determining unit further includes a fourth determining module and a fifth determining module: the fourth determining module is used to determine the fifth constraint condition as follows. Where N represents the total number of nodes in the aforementioned power system, g n d represents the active power output of the generator set at node n above. n The active load at node n is represented; the sixth constraint is determined based on the first, second, third, and fourth auxiliary variables; the fifth determining module is used to determine the combination of the first, second, third, fourth, fifth, and sixth constraints as the constraint combination. This device determines the system power balance constraints and the constraints corresponding to each auxiliary variable through the above steps, thus enabling a more comprehensive determination of the constraint conditions.

[0108] In the specific implementation process, the above constraints are combined to obtain a constraint combination, and the objective function is solved under the above constraint combination. The sixth constraint determined by the above first auxiliary variable, the above second auxiliary variable, the above third auxiliary variable and the above fourth auxiliary variable is: formula (1)-(20) in the above text.

[0109] Where M is a sufficiently large constant. This represents the positive auxiliary variable corresponding to the upper and lower limits of the unit's output constraints. This represents the positive auxiliary variable corresponding to the constraint on the range of node load variation. This represents the positive auxiliary variable corresponding to the upper and lower limits of line transmission capacity constraints. This represents the positive auxiliary variable corresponding to the grid safety and stability section constraints. Considering that some power sources in the actual power grid may be subject to certain stability requirements and must remain operational or shut down—for example, nuclear power units often bear the basic load of the grid and therefore operate at full output under normal circumstances; others may require certain units to be operational or shut down due to voltage instability or excessive short-circuit current in the actual power grid. To address this, the corresponding generator state 0-1 variable u can be set. m Setting the values ​​to 0 or 1 and not participating in the free optimization of the optimization model can, on the one hand, adapt to the actual operation of the power grid, and on the other hand, fix the values ​​of some variables so that they do not participate in the optimization, which can also improve the solution efficiency of the model.

[0110] In some optional embodiments, the aforementioned early warning unit further includes a sixth determining module and a seventh determining module. The sixth determining module is used to determine an auxiliary variable with a value of 0 based on the minimum value of the objective function, wherein the auxiliary variable includes the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable. The seventh determining module is used to determine the location of power flow blockage corresponding to the auxiliary variable with a value of 0. This device determines the location of power flow blockage through the above steps, thus accurately and conveniently determining the location of power flow blockage.

[0111] In the specific implementation process, identifying the phenomenon of power flow blockage at the line and section in the power grid refers to the situation where the results corresponding to the constraints of the above-mentioned transmission line and the constraints of the stable section of the power grid are equal (=). Therefore, if the optimization results corresponding to the above-mentioned constraints are equal, then the corresponding constraints in the corresponding constraints (5)-(8) must also be equal. So the auxiliary positive variables in the corresponding constraints (5)-(8) are equal. Then the corresponding value must be 0. At this time, the corresponding constraint (13)-(16) becomes and Since these are 0-1 variables, when the objective function minimizes their sum, The corresponding optimization result will necessarily be 0. Conversely, if the results corresponding to the constraints of the above transmission line and the constraints of the power grid stability section do not have the constraint equality (=), then the auxiliary positive variables in their corresponding constraint equations (5)-(8) will be zero. Therefore, the corresponding value must not be 0. At this time, the corresponding constraint equations (13)-(16) become Here, eps represents a positive real number that is greater than 0 but very close to 0. Since it is a 0-1 variable, therefore The corresponding optimization result will necessarily be 1. Therefore, based on the above analysis, it can be concluded that, according to the optimization result... Whether the value is 0 or not can identify whether the power flow constraint of the corresponding line or section is in effect and causing a blockage.

[0112] In some optional implementations, the aforementioned early warning unit further includes an eighth determining module and a solving module. The Benders decomposition method is used to determine the main problem and multiple sub-problems of the objective function. The main problem and the multiple sub-problems are iteratively solved until the combination of constraints is satisfied, thus obtaining the minimum value of the objective function. This device can accurately determine the minimum value of the objective function through iterative solving.

[0113] Specifically, to identify all possible congestion scenarios in the power grid's lines and sections, an iterative algorithm is used to solve the model and identify all possible congested lines and sections. Each time the model is optimized, lines or sections that may simultaneously become congested under a certain power grid operating mode are obtained. Then, by setting the corresponding 0-1 variable value to 1 to remove the constraint, the model is optimized again to obtain lines or sections that may simultaneously become congested under the next power grid operating mode. This process is repeated until all lines and sections that may cause congestion in the power grid are identified, at which point the entire model solution is complete. Considering the enormous number of lines and sections in a real power grid, identifying all possible congested lines and sections would present a huge computational challenge, resulting in a massive number of variables in the optimization scheduling model. Furthermore, since the identification model proposed in this invention simultaneously includes both 0-1 discrete variables and continuous variables, the optimization model becomes a mixed integer programming problem containing both discrete and continuous variables, making large-scale solutions extremely difficult. To address this, the invention further proposes an efficient model-solving algorithm based on the Benders decomposition method, thereby improving the adaptability of the proposed model to the identification of actual large power grids. To efficiently solve the transformed large-scale mixed-integer linear programming (MILP) model, the invention further proposes a device for solving the model using the Benders decomposition method. The essence of the Benders decomposition method is to decompose a large-scale problem that only needs to be solved once into a series of smaller problems that are iteratively solved. The general form of the above MILP problem expressed as a matrix is ​​as follows:

[0114] minc Tx+f T y

[0115]

[0116] In the formula: y represents the 0-1 variables in the model; x represents the continuous variables in the model; A and B are the correlation coefficients in the constraints; c and f are the correlation coefficients in the objective function.

[0117] When the 0-1 variable is fixed Then, a suboptimal solution to the original problem can be obtained, which is the upper bound of the objective function of the original problem. Further applying the duality principle, the subproblems corresponding to the Benders decomposition method are:

[0118]

[0119] In the formula: p is the introduced dual variable.

[0120] After solving the subproblem, constraints are added to the main problem based on the judgment results, and the new 0-1 variable y is obtained again. This process is repeated iteratively until convergence. The corresponding form of the main problem is:

[0121] minz′

[0122]

[0123] In the formula: i is the iteration number.

[0124] The main problem here is actually a relaxation of the original problem. After calculation, the objective function of the main problem is found to be better than that of the original problem, and therefore represents the lower bound of the original problem's objective function. As iteration progresses, the iteration stops when the difference between the upper and lower bounds becomes sufficiently small. In practical applications, convergence is often achieved quickly with relatively few constraints.

[0125] The aforementioned power flow congestion identification device includes a processor and a memory. The acquisition unit, determination unit, and early warning unit are all stored as program units in the memory, and the processor executes these program units to achieve their respective functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0126] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can help identify the location of power flow congestion in the power system.

[0127] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0128] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0129] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0135] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0136] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0137] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0138] 1) The power flow congestion identification method of this application obtains the first auxiliary variable of the power system's generator units, the second auxiliary variable of the power system's node loads, the third auxiliary variable of the power system's transmission lines, and the fourth auxiliary variable of the power system's grid stability section. It also obtains the first constraint condition corresponding to the generator units, the second constraint condition of the node loads, the third constraint condition of the transmission lines, and the fourth constraint condition of the grid stability section, and determines the constraint condition combination based at least on the first, second, third, and fourth constraint conditions. An objective function is constructed based on the first, second, third, and fourth auxiliary variables. Under the condition of satisfying the constraint condition combination, the minimum value of the objective function is determined. The location of power flow congestion in the power system is determined based on the minimum value of the objective function, and an early warning is issued. Compared with existing methods that cannot completely identify the location of power flow congestion, this application can establish an objective function based on the auxiliary variables corresponding to the constraints of each part of the power grid and solve it under the constraint conditions, thus accurately and completely determining the location of power flow congestion. Therefore, it can solve the problem of existing methods that cannot completely identify the location of power flow congestion, achieving the effect of completely identifying the location of power flow congestion and issuing timely early warnings.

[0139] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying power flow congestion in a power system, characterized in that, include: The system obtains a first auxiliary variable for the generator sets of the power system, a second auxiliary variable for the node load of the power system, a third auxiliary variable for the transmission lines of the power system, and a fourth auxiliary variable for the grid stability section of the power system. The first auxiliary variable is a variable representing the upper and lower limits of the output power of the generator sets, the second auxiliary variable is a variable representing the range of variation of the node load, the third auxiliary variable is a variable representing the upper and lower limits of the transmission capacity of the transmission lines, and the fourth auxiliary variable is a variable representing the grid stability section constraint. Obtain the first constraint condition corresponding to the generator set, obtain the second constraint condition of the node load, obtain the third constraint condition of the transmission line, and obtain the fourth constraint condition of the grid stability section, and determine the combination of constraint conditions based at least on the first constraint condition, the second constraint condition, the third constraint condition, and the fourth constraint condition; A target function is constructed based on the first, second, third, and fourth auxiliary variables. Under the condition that the given combination of constraints is satisfied, the minimum value of the target function is determined. Based on the minimum value of the target function, the location of power flow congestion in the power system is determined, and an early warning is issued. Constructing a target function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable includes: constructing the target function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable. Where N represents the total number of nodes in the power system, L represents the total number of lines in the power system, and S represents the total number of stable grid sections in the power system. This represents the first auxiliary variable corresponding to the upper limit constraint on the output power of the generator set. This represents the first auxiliary variable corresponding to the lower limit constraint of the generator set's output power. The second auxiliary variable represents the upper limit constraint of the node load. The second auxiliary variable represents the lower limit constraint of the node load. The third auxiliary variable represents the upper limit constraint on the transmission capacity of the transmission line. The third auxiliary variable represents the lower limit constraint of the transmission capacity of the transmission line. The fourth auxiliary variable represents the upper limit constraint of the power grid stability section. The fourth auxiliary variable represents the lower limit constraint of the power grid stability section.

2. The identification method according to claim 1, characterized in that, Obtain the first constraint condition corresponding to the generator set, and obtain the second constraint condition of the node load, including: Obtain the active power, maximum output power, and minimum output power of each node of the generator set, and determine the first constraint condition as follows. , where g n This represents the active power of the generator set at node n. This represents the set of generators connected at node n. This represents the maximum output power of generator set m located at node n. This represents the minimum output power of the generator set m located at node n. This indicates the operating status of the generator set; Obtain the upper and lower load limits of each node in the power system, and determine the second constraint condition as follows. , where d n This represents the active load at node n. This represents the upper limit of the load at node n. This is the lower limit of the load at node n.

3. The identification method according to claim 1, characterized in that, Obtain the third constraint condition of the transmission line and the fourth constraint condition of the power grid stability section, including: Obtain the upper limit of the active power transmission power of the transmission line, and determine the third constraint condition as follows. , where d n G represents the active load at node n. n This represents the active power of the generator set at node n, and N represents the total number of nodes in the power system. This indicates the upper limit of the active power transmission power of the transmission line. The term represents the power transfer distribution factor of node n to line l under the network structure w of the power system, with node s as the balancing node. Obtain the upper and lower limits of the power grid stability section, and determine the fourth constraint condition as follows. ,in, This refers to the relevant variables involved in the power grid stability section s, and these relevant variables include at least the output power of the generator set. This is the upper limit of the stable cross section s of the power grid. This is the lower limit of the power grid stability section s.

4. The identification method according to claim 1, characterized in that, Determining a combination of constraints based at least on the first constraint, the second constraint, the third constraint, and the fourth constraint includes: The fifth constraint is determined as follows: Where N represents the total number of nodes in the power system, g n d represents the active power output of the generator set at node n. n This represents the active load at node n; The sixth constraint is determined based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable; The combination of the first constraint, the second constraint, the third constraint, the fourth constraint, the fifth constraint, and the sixth constraint is determined as the constraint combination.

5. The identification method according to claim 1, characterized in that, Determining the location of power flow congestion in the power system based on the minimum value of the objective function includes: Based on the minimum value of the objective function, auxiliary variables with a value of 0 are determined, wherein the auxiliary variables include the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable; A power flow blockage occurs at the location corresponding to the auxiliary variable whose value is determined to be 0.

6. The identification method according to claim 1, characterized in that, Determining the minimum value of the objective function while satisfying the aforementioned combination of constraints includes: The Benders decomposition method is used to determine the main problem and multiple subproblems of the objective function; The main problem and multiple sub-problems are solved iteratively until the combination of constraints is satisfied, thereby obtaining the minimum value of the objective function.

7. A device for identifying power flow congestion in a power system, characterized in that, include: The acquisition unit is used to acquire a first auxiliary variable of the generator set of the power system, a second auxiliary variable of the node load of the power system, a third auxiliary variable of the transmission line of the power system, and a fourth auxiliary variable of the grid stability section of the power system. The first auxiliary variable is a variable representing the upper and lower limit constraints of the output power of the generator set, the second auxiliary variable is a variable representing the variation range constraint of the node load, the third auxiliary variable is a variable representing the upper and lower limit constraints of the transmission capacity of the transmission line, and the fourth auxiliary variable is a variable representing the grid stability section constraint. The determining unit is used to obtain the first constraint condition corresponding to the generator set, the second constraint condition of the node load, the third constraint condition of the transmission line, and the fourth constraint condition of the power grid stability section, and to determine the combination of constraint conditions based at least on the first constraint condition, the second constraint condition, the third constraint condition, and the fourth constraint condition; The early warning unit is used to construct an objective function based on the first, second, third, and fourth auxiliary variables, determine the minimum value of the objective function under the condition that the combination of constraints is satisfied, determine the location of power flow congestion in the power system based on the minimum value of the objective function, and issue an early warning. The early warning unit includes an early warning module, used to construct an objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable, including: constructing the objective function based on the first auxiliary variable, the second auxiliary variable, the third auxiliary variable, and the fourth auxiliary variable as follows: Where N represents the total number of nodes in the power system, L represents the total number of lines in the power system, and S represents the total number of stable grid sections in the power system. This represents the first auxiliary variable corresponding to the upper limit constraint on the output power of the generator set. This represents the first auxiliary variable corresponding to the lower limit constraint of the generator set's output power. The second auxiliary variable represents the upper limit constraint of the node load. The second auxiliary variable represents the lower limit constraint of the node load. The third auxiliary variable represents the upper limit constraint on the transmission capacity of the transmission line. The third auxiliary variable represents the lower limit constraint of the transmission capacity of the transmission line. The fourth auxiliary variable represents the upper limit constraint of the power grid stability section. The fourth auxiliary variable represents the lower limit constraint of the power grid stability section.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the power flow congestion identification method of any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for identifying power flow congestion in a power system according to any one of claims 1 to 6.

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